On 2020-07-06 18:41:52, user Fatnot wrote:
Unlikely that just zinc supplementation would work,,,a zinc ionophore is also required.. We also have the report from Dr Zelensky, in Rockland County, NY, who treated hundreds with<br /> a combo including zinc and HCQ, resulting that few required hospitalization. The report is anecdotal...but another term for a set of anecdotes is of course DATA And with data and analysis, one can draw conclusions and confidence intervals.
On 2020-08-12 08:06:03, user Marek Kochanczyk wrote:
The source code to analyze data and generate figures as well as updated figures are available at GitHub: https://github.com/kochancz...
On 2020-08-12 12:37:37, user Marc Imbert wrote:
It is worth to not that this study has more cormobity and symptomes for the group treated with HCQ and AZT. All patient not treated has a mild desease while about only 63% in the group treated. Further one should use a healthy scientific scepticims regarding hasting conlusions based on studies at the late stage of the desease. In particular with the description of the evolution of the disease which is now known,
.
On 2020-07-10 18:25:07, user Joanna Spencer-Segal wrote:
The authors speculate in the discussion that "It is also possible there is an effect via mineralocorticoid receptor binding in the context of SARS-CoV-2 induced dysregulation of the renin-angiotensin system." It is not clear what this means, but dexamethasone has minimal activity at the mineralocorticoid receptor, which distinguishes it from the other corticosteroids often used in critically ill patients (methylprednisolone, hydrocortisone). More clarification of what they mean regarding "mineralocorticoid effect" and rationale about why dexamethasone was chosen for this study would be welcome.
On 2020-07-13 11:08:47, user Andrew D'Silva wrote:
In any infection IgM responses converting to IgG responses fall over time and rise when there is a secondary antigen exposure. Why do these findings suggest that there is loss of immunity with declining neutralising antibody levels? Surely the questions are: what happens after secondary antigen exposure? Do the neutralising antibody levels rise again? Do they protect from developing the same clinical disease again? Do they affect severity of disease after second exposure?
On 2020-07-15 13:15:39, user E Y wrote:
Something is wrong here, the IHME projected 2020 total US death is about 250000, that's 0.08% of the US population, how can that cause 1% of reduction of life expectancy of US population?
On 2020-08-21 19:06:14, user Heather Gray wrote:
I'm a layman but I want to express my appreciation for exploring the issue from this angle
On 2020-08-25 09:01:02, user Tjabbe wrote:
Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.
If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.
On 2020-08-30 11:33:10, user Martijn Weterings wrote:
One problem with those S(E)IR compartmental models is that they always assume/pretend that a virus is spread homogeneously among a well mixed population. According to such models, the chance that someone in a small village in the South infects somebody else, is the same chance for anyone. The same for somebody in the North as somebody in their immediate family or other people in close neighborhood.
Such compartments are obviously not realistic for modeling an entire country. More suitable are networked S(E)IR' or spatial S(E)IR models. In such models, the virus spreads more like an ink blot.
Due to the local saturation, growth rates are already decreasing early on. Models that do not incorporate local saturation will 'compensate' (in order to get the same early deflection) by either reducing R0, or the (effective) population, or the reporting factor (upscaling the number of infected). If you try to fit a simple compartment SIR model to real data, then you will get unrealistic epidemiological parameters.
What they are doing in this article, dividing the population into layers with different rates of infection, is effectively shrinking the population that is 'reached' by the virus.
So this effectively makes the population smaller, but the question is whether it is the right way to shrink the population? Instead of a parameter in a mechanistic model, it might better be regarded as a parameter in an empirical model. It is an extra variable to ensure that the unsuitable simple SEIR model corresponds somewhat better with the measurements.
In reality, there are several effects that cause the observed epidemiological curves to deviate from the simple models (Besides heterogeneity, the use of local distribution in spatial or networked S(E)IR models, instead of global homogeneous compartments, is another important one).
By only including only a single effect in fitting, you get that all other effects are absorbed by that one effect. The result is an unrealistic estimate of the epidemiological parameters, which will not be suitable for extrapolation (for example calculating the 'herd immunity' percentage).
It is to be expected that this model, with only the heterogeneity incorporated, will likely underestimate the percentage to reach herd immunity. This is because it is overestimating the effect to compensate for the lack of other non-incorporated effects (and spatial models will be able to model the same deflection of the curves, but with less reduction of the herd immunity).
The above is a severe systematical problem, which will result in a bias towards smaller herd immunity percentages.
In addition: The fit with the curve is strongly determined by an interaction of the population size and the factor between the reported infections and actual infections (in a simple S(E)IR model, the two have the same effect). Such correlation between the two parameters will cause great inaccuracy.
And these are considerations that do not yet mention the problems with measurements of the epidemiological curve. For instance, the inaccuracies in reporting are not easily solved with a single (constant) reporting fraction. In order to estimate epidemiological parameters we need more direct experimental data (e.g. detailed information about contact tracing). From those we can deduce more directly the variations in infection rates and estimate the potential impact on herd immunity. Just fitting a model to the curve is a bad idea.
On 2020-09-23 07:52:50, user Subhajit Biswas wrote:
Pleased to see other scientists are supporting with further evidences, the trend we had observed and reported as early as April 2020.
Based on non-overlap of dengue and COVID-19 global severity maps and evidences of SARS-CoV-2 serological cross-reactions with dengue, we proposed that immunization of susceptible populations in Europe, North America and Asia (China, Iran) with available live-attenuated dengue vaccines, may cue the anti-viral immune response to thwart COVID-19.
https://www.preprints.org/m...
Our publications in this area to support our proposition:<br /> 1) COVID-19 Virus Infection and Transmission are Observably Less in Highly Dengue-Endemic Countries: Is Pre-Exposure to Dengue Virus Protective Against COVID-19 Severity and Mortality? Will the Reverse Scenario Be True?
Clinical and Experimental Investigations, Volume 1(2): 2-5.<br /> https://www.sciencereposito...
Nath, H., Mallick, A., Roy, S., Sukla, S., & Biswas, S. (2020, June 19). Computational modelling predicts that Dengue virus antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?. https://doi.org/10.31219/os...
This one in medRxiv!
Now, other scientists are observing the same trend in Brazil! Exciting!
See recent publication below and news coverage
1.https://www.medrxiv.org/content/10....
Amazing! Nature has its own ways of controlling parasite aggression! Antigenic correlation between a flavivirus and a coronavirus was unprecedented.
Existing and licensed dengue vaccines could be tested in SARS CoV2 animal models and tried in dengue non-endemic countries.
Use in dengue-endemic countries may be problematic as such vaccination can elicit antibody-dependent enhancement of subsequent dengue infections.
On 2020-08-09 17:02:50, user David Leidner wrote:
Article says all data fully available, but no link to the data is provided.
On 2020-08-09 21:12:25, user Cynac wrote:
The results appear to show a significant relationship between menopause and diagnosis of Covid-19 by your algorithm. There is no significant association with positive Covid test ("proven" Covid) or severe disease.<br /> The significant symptom associations do include fever, but not cough or even the anosmia. Whereas "skipping meals" is a highly significant association.<br /> This brings the major possibility that it is your algorithm for diagnosing the disease that best relates to menopause, perhaps by some quirky inclusions.<br /> There must also be some difficulties in allowing for age etc. When the influences of these factors themselves are not precisely defined.<br /> This study is clearly worthwhile, and of interest. But the way the abstract will be viewed in the media might be an over-simplification.
On 2020-08-13 00:49:59, user Jesse Baker wrote:
Regarding a passage in this MedRxiv post (July 21, paragraph 3 with citation to reference #15), “Additionally, recent clusters of COVID-19 cases linked to a…restaurant in Wuhan are suggestive of airborne transmission.”
Although the index case having lunch on Jan. 24 was from Wuhan, the restaurant was in Guangzhou. Indeed, its location far from Wuhan so early in the spread of Covid increased Guangzhou CDC’s confidence that the other patrons were infected by the index case and no one else.
On 2020-08-13 07:57:05, user Zeit wrote:
Very interesting manuscript. I think it may be wise to remove isotopes from your data as it seems clear that you have associations of monoisotopic peaks and their isotopic peaks with phenotypes. If you correlate the retention times of ions most correlated with each other by area count/signal, it should reveal that they are non-independent ions.
On 2020-08-13 20:06:24, user Rhyothemis wrote:
Could the low number of deaths in Kenya be at least partly attributable to low per capita protein consumption? It seems as though many countries with low per capita protein consumption rates are reporting relatively low per capita COVID death rates. Mechanistically, such an association (if it exists) could be related to lower baseline mTOR activation.
On 2020-08-15 14:01:48, user Dom_Pedulla wrote:
Joao not only had Dude made some very good points, but in observational trials like this, everything depends on the nitty gritty data. I notice the huge qualifier "recent" in the results sentence, noting that carefully since in many studies these kinds of adjectives disclose or hint at certain erroneous tendencies or conclusions in even in "meticulous peer-reviewed studies". I am requesting the paper to analyze for myself, and suspect strongly that what it may show is a "benefit" for only the very recently vaccinated, and that either long after it either ends up being a net liability as regards COVID death risk, or that the timing isn't possible to discern because the investigators avoided studying all but the recently vaccinated.
We'll see.
On 2020-08-15 23:30:43, user Nan wrote:
To those who tweeted and regarded this as evidence that masks don't work,
This article does NOT imply masks don't work. If one wishes to draw such a conclusion, a direct comparison is required on the disease risk when wearing masks versus not. From both the fifth and sixth comparison in the figure and a related article (https://www.bmj.com/content... "https://www.bmj.com/content/369/bmj.m1442)"), masks are better than not wearing at all! This article only says physical distancing is very important for cloth and surgical masks. It means that besides wearing normal masks, I should be cautious about a strict physical distancing. This agrees with common sense that the more protections (e.g., masks, distancing, etc.) we have, the safer we are.
Also, is physical distancing always easy and tangible to follow? The answer is no. You cannot guarantee that you are always in safe distances with other people in the street. In contrast, masks are a lot more perceptible. They reduce exposure to the contaminated air. Masks are also a sign of caution. A sign that everyone should protect their community by reducing transmission.
On 2020-08-16 19:15:38, user Skadu SkaduWee wrote:
One of the fundamental assumptions of the paper is the use of a previously tested positive saliva sample to prepare the serial dilutions used for the limit of detection studies. However, the authors omit to declare how this initial copies/ul value was arrived at and by whom.
On 2020-08-17 09:34:02, user buddinggenetics wrote:
The principal author has stated in media that the cost per test is $10, however in the text the cost is listed as $1.29-4.37/sample. Pricing should be consistently stated to avoid misleading the public and/or scientific community. Also, the text states that the price per sample is low, which is a relative term, and gives no price estimates of other established tests for comparison.
Multiplexing the samples is a fundamental improvement of testing, however there is insufficient evidence to show eliminating the N2 primer set is justified. There needs to be an analysis of how many inconclusive test results (N1 positive and N2 negative/ N1 negative and N2 positive) would now become positive or negative tests as a result of eliminating the N2 primer set. Also, in Supp Fig 2, the data appear irregular with a bimodal distribution when a Gaussian distribution would be expected. The authors do not discuss the reason for this in the text. Furthermore, the failure of the N2, E, and ORF1 sets may be due to the HEX fluorophore. Would they have worked using a different fluorophore? Would the authors have eliminated N1 if they had by chance used HEX on N1?
The Source Data files are not posted.
On 2020-08-17 18:18:04, user OxImmuno Literature Initiative wrote:
On 2020-08-18 12:42:45, user Saban Öztürk wrote:
The peer-reviewed and published version of this article can be accessed using the following DOI:<br /> https://doi.org/10.1002/ima...
Also, this link can be used for direct access to this article: <br /> https://onlinelibrary.wiley...
On 2020-08-18 18:07:44, user Eric Vallabh Minikel wrote:
Excellent, important study, with carefully considered conclusions from the authors. Some readers may assume that if plasma NfL can become elevated 2y before onset, then NfL could be used as a prevention trial entry criterion, a primary endpoint, or a basis for deciding which patients are eligible for drug access/reimbursement. Importantly, the authors of this paper do not assert that their data support those applications. I believe there are three key considerations here that should be factored into any clinical application of plasma NfL quantification in pre-symptomatic genetic prion disease: genotype (rapid vs. slow PRNP mutations), age (affects reference range for NfL), and cross-sectional (as opposed to longitudinal) number of people in a prodromal state at any given time. I have written a detailed blog post here: http://www.cureffi.org/2020...
On 2020-08-18 20:34:55, user Lauren Call wrote:
I found this study through a link in a CNN article, along with the quote: “Gommerman said since scientists have not seen a record of re-infection, even with as widespread as the pandemic is, that strongly suggests the body's immune system is working well against this threat, and re-infection is less likely.” I am surprised they haven’t “seen” a re-infection, because I’ve had 2 positive COVID-19 tests, separated by 3 months, with a negative antibody test in between. Both times I had classic coronavirus symptoms, but they were distinctly different cases.
On 2020-08-19 11:00:05, user AbsurdIdea wrote:
Have I understood this right: " Vitamin D dose was not significantly associated with testing positive for COVID-19."? So taking vitamin D does NOT reduce the probability of testing positive for CoViD-19...Then, why take it against CoViD-19? For the rest - correlation or causation? Healthier people are likely to have a higher probability of sufficient vitamin D, conversely, people in poor health for any reason are likelier to have low vitamin D. Also there is a difference between becoming infected i.e. the virus actually entering into a person and propagating and the degree of illness and complication once being infected. This study does not appear to address these factors. Finally the phrase "499 had a vitamin D level in the year before testing" does not make sense. All people have some level of vitamin D.
On 2020-08-19 17:59:56, user petsRawesome1 . wrote:
"Of the 43 patients randomized to ConvP 6 (14%) had died while 11 of the 43 (26%) <br /> control patients had died."
That sounds like the study showed promise on the key metric, mortality, it just did not have enough data when it was stopped. It would be good to be very clear about the reasons for discontinuing the study, as the New York Times of Aug 19, 2020 is quoting this paper as "Last month, one such trial in the Netherlands was stopped when researchers realized that patients given plasma showed no difference in mortality"
On 2020-08-20 01:45:02, user giorgio capitani wrote:
How it can be proved without any doubt that the virus present in the aerosol actually infects a person? it can present but be harmless. Where is the evidence of the actual trasmission of the infection? the presence in the aerosol is not evidence of the transmission of the virus it's another pair of shoes. Or somebody can be infected and others not. How can you tell one thing from the other? they are two different moments: the presence of the virus in the aerosol, the actual transmission of the virus.
On 2020-08-21 13:38:24, user Susan Levenstein wrote:
If I understand the paper correctly, its most striking result is the isolation of Patient 1's virus from the VIVAS air sampler located 4.8 m away. But according to the Figure, Patient 1 had to walk right past that air sampler, closer than 1 m, every time he went to the bathroom. Couldn't that be a simpler explanation for how it picked up his virus?
On 2020-08-23 16:11:32, user Ang wrote:
Hello there,<br /> below a question for someone with the right competence.
True the approach of this work is great, it might result that they are right or wrong we'll see, starting from the review outcomes. However a common person would ask: "Why can't we do a direct and conclusive experiment about transmissivity through aerosol?". A direct experiment is to put a never infected person in the same room with a SARS-CoV-2 ill person, without the physical possibility to exchange any particle between them except air/aerosol. 100 person would cover a good statistics in terms of age, gender, time of exposure and other characteristics of the volunteer. Is this possible? How can be that in the entire world we cannot find 100 voluntaries that are available for the following experiment. Why is this something not done yet?
On 2020-08-24 04:45:43, user Bill Pilacinski wrote:
Now it will be important to identify those in the population who are immune so that the early limited supply of vaccine can be used for those susceptible individuals of high priority as we attempt to reach herd immunity.
On 2020-08-24 06:26:34, user Stan Himes wrote:
For COVID-19 you should include co-morbidity data, without this key information (which may be contained in full article) the data presented is worthless.
On 2020-08-24 15:34:57, user Eva Lendaro wrote:
Hello,<br /> I question regarding what does the vector beta account for. it supposedly includes policy dummies of businesses, restaurants, movie theaters, and gyms being allowed to reopen but in practice it is not very clear how these are accounted for. Is the capacity at which they were allowed to reopen considered? are the categories considered separately?
I would also like to point out this systematic review on this exact topic published on may 26th, 2020 on bmj that is nowhere mentioned in this article but would be rather important to include for completness.
https://www.bmj.com/content...
Best Regards,<br /> Eva
On 2020-08-25 21:29:56, user Chris Raberts wrote:
I am not sure how the authors can use a study that speaks of N95 and 12-16 layered cloth masks and come to a conclusion like this. (reference 31).
In a recent comment (https://www.thelancet.com/j... "https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30352-0/fulltext)") the same authors speak of a range of 6% to 80% of mask benefits regarding reduction in transmission. I wonder what percentage was used in this paper, but given the results I'd assume it is on the higher end. Also that paper does not speak to schools, mostly to health care settings.
More transparency would be great but overall this paper looks like agenda and not science :/.
On 2020-08-27 11:22:55, user pto wrote:
I thought the index cases of that conference were all local residents of the Boston area. If so, that certainly wouldn't rule out a previous introduction a few weeks earlier. Say when international university students returned to Boston 3 to 4 weeks earlier.
On 2020-08-27 13:31:15, user Joe Psotka wrote:
Using data from Florida creates misleading expectations because Florida's decrease in March and April was largely from Snowbirds' and part time residents' departure from the State. Some people estimate that one-third of Florida's winter population leaves in the Spring to avoid the summer heat.
On 2020-08-27 23:26:12, user Vinci P, MD wrote:
There might be other explanations for better prognosis in post-menopausal women taking oestradiol: they were probably healthier than women not taking oestradiol, because HRT improves health. <br /> In addition I cannot understand why all post-menopausal women have better prognosis than men, since their estrogens are similar to those of men. Maybe it is the absence of testosterone, and not the presence of oestradiol, which makes the difference.<br /> Could you comment this, please?
On 2021-06-30 12:12:11, user SinoBen wrote:
The language used here is seething with something I've not commonly found in the typical scientific paper.
On 2020-08-29 21:03:28, user Sam Well wrote:
I cannot believe your work is being "advertised" as a hail mary when you conclude..."Additional results from larger randomised controlled trials are needed"
Please refer to the "sale" of your source:<br /> https://www.eurekalert.org/...<br /> https://www.dailymail.co.uk...
On 2020-08-30 15:05:05, user Henry Johnson wrote:
Does anyone know whether similar experiments have been done with woodwind instruments. I'm particularly interested in clarinet. The instruments work differently. The sound comes out of a variety of places...
On 2020-09-04 19:44:32, user Art Framer wrote:
Excuse my ignorance but it seems that the tests for covid 19 are looking for the virus itself. Wouldn't the tests have a higher rate of success if they looked for signs of the body's reaction to the virus?
On 2020-09-07 16:03:04, user Joe B wrote:
We don't know how long ago the vitamin D levels were obtained in these patients. This is especially true in the COVID patients, because we have no idea if they truly were "deficient" at the time of their infection. Additionally, you never tell us in the methods that you were going to examine supplementation, and how you were going to do that (and assure adherence). Can vitamin supplements not be purchased over the counter in the countries involved in this study? Finally, I assume you categorized people by "sex" and not "gender" as sex if the term used for male/female DNA based differences.
On 2020-09-07 16:34:55, user A. Baluja wrote:
Hi! The last version for the reference [Baluja et al, 2020] has been finally peer-reviewed and published in:<br /> https://www.tandfonline.com...
The updated version of the preprint:<br /> https://www.medrxiv.org/con...<br /> Thank you for continuing research in such important topic!
On 2020-09-09 19:12:11, user Michael Bishop wrote:
I don't believe the authors' data, which would imply that SARSCOV2 was circulating with little increase or decrease in Dec 2019 - Feb 2020 until suddenly taking off in late Feb early March.
On 2020-09-10 16:51:51, user Thomas Waterfield wrote:
Thanks Sunil. It was great to chat the other day.
We have produced a protocol that is currently with BMJ Open. The data presented here relates to the first clinic appointments (16th April to 3rd of July) for all participants. The symptom data was reported using RedCap data capture with retrospective reporting of illness episodes prior to the attendance from the beginning of the pandemic in February. In all instances the symptoms were reported without the participant knowing their antibody status. Data were entered by trained members of the research team.
On 2020-09-13 01:19:25, user mzbaz wrote:
There is an unfortunate typo in the horizontal axis unit label of Fig 3b, which should be "minutes" not "hours", consistent with the "15 Min Rule" vertical line, as well as the discussion in the text.
On 2020-09-16 19:31:28, user Ibrahim Hussin wrote:
I believe the precipitation in the weather type of Drizzle has effect on the outbreak
On 2020-09-18 16:27:11, user kdrl nakle wrote:
These types of papers that are masquerading as science are nothing more than speculations. Even IMHE forecasts from this Spring are laughable now. This is in the same venue.
On 2020-09-22 03:41:51, user Direk Limmathurotsakul wrote:
This article has been accepted for publication at the Emerging Infectious Diseases (EID). https://doi.org/10.3201/eid...
On 2020-09-24 10:16:48, user Camila Hobi wrote:
I would like to congratulate the authors for this paper! Wonderful idea! The hypothesis that children can be protective rather than harmful is very plausible! Unfortunately since the beggining of pandemic people are saying the opposite based in misbeliefs and not in science. It’s very important to test this hypothesis in other countries. Reading this paper, I asked myself “why keep schools closed?”
On 2020-09-27 03:43:34, user LB wrote:
It is well known that magnesium absorption is an issue with elevated gastric pH from PPIs. <br /> Please evaluate the possibility that the individuals who had a history of taking PPIs might have had magnesium deficiency, which altered their immune response to SARS-CoV-2.<br /> - Linda Benskin, PhD, RN
On 2020-09-27 22:00:20, user nan wrote:
It would be be helpful to know the dosage.
On 2020-10-04 20:57:37, user William James wrote:
Do you think you should refer to the paper by Kojaku at al [Submitted on 5 May 2020 (v1), last revised 14 Sep 2020 (v3)] at https://arxiv.org/abs/2005...., which largely represented this analysis formally some months earlier? [I have no conflict of interest.]
On 2020-10-15 22:40:24, user Marm Kilpatrick wrote:
Dear Dr. van Beek and co-authors,<br /> Thank for your this important work!<br /> In your Table 1 you appear to be grouping results for multiple assays together:<br /> Panbio™ COVID-19 Ag rapid test (Abbott), and Standard Q COVID-19 Ag (SD Biosensor);<br /> and COVID-19 Ag Respi-Strip (Coris BioConcept), and GenBody COVID-19 Ag (GenBody Inc)<br /> I *think* you did this because they had similar LODs but it'd be more informative if you could show results for each assay independently. <br /> It would also help to know the sample sizes for each of the assays in each group of patients.<br /> Finally, specificity is a potential issue with these rapid antigen assays. Did you test samples that were negative by PCR to determine this (acknowledging that PCR could miss viral RNA, especially if not done at the same time)?<br /> thank you,<br /> marm
On 2020-10-22 11:33:50, user Paul Peerbooms wrote:
It would be interesting to see the protective effect of the flu-vaccination when only staff with contacts with patients is considered.
On 2022-10-24 17:42:56, user CDSL JHSPH wrote:
Dear Mekkes et. al.,
Thank you for sharing your work with us! Creating models to predict neuropathological diagnosis based on temporal signs and symptoms is very significant research, and I’m looking forward to seeing where this heads in the future! I enjoyed reading this paper, especially since it introduced me to techniques and concepts I was previously unfamiliar with. That being said, while reading I did notice some parts that I think could be given further clarity in order to make this paper more accessible to those not within the immediate field. There are a lot of abbreviated disorders mentioned, and I noticed that some were explained in the introduction however I could not find the proper matching terms for disorders abbreviated in later sections. I think it would be a great benefit if there were a word key with the disorders and their corresponding abbreviations. Especially since different disorders may be represented by the same acronyms, so googling it may not provide the reader with the correct one. Also, I was wondering if you plan to do another study focusing on optimizing these models to diagnose mental illnesses and psychiatric conditions in a separate paper? I understand the main focus on brain disorders and neurodegenerative diseases since those can be linked to prominent neuropathological changes, but when reading the abstract I was given the impression that mental illnesses would be focused on to a larger degree than I noticed in the paper. I would love to see any future steps you take with this, especially since the alterations in cognition and behavior associated with mental illnesses can be observed from live patients, and don't necessarily have to be inspected retroactively like from brain donors.
On 2022-10-24 18:20:28, user Jordan Ross wrote:
This study was aimed to understand the complexity of diagnosing brain disorders in society today. The author utilizes data in statistical analyses to identify this crossover and give rise to further research in the field. This journal article emphasizes the use of a computational pipeline to establish a series of clinical synopses regarding a series of brain disorders from donors that were previously diagnosed. Donors from the NBB underwent a series of cross-disorder research to identify signs and symptoms associated with psychiatric diagnoses. Overall the article had great analyses and synthesis of ideas pertaining to the mechanisms of symptomatology and how it differentiates across brain disorder profiles. One thing to note is organization in this article. Maybe incorporate these main figures within the results section with titles and keys for each to limit confusion to the reader. Additionally, in the methods section (2.7.3 Observational profiles of the signs and symptoms), the author referred to a figure but did not implicitly state which one. It is rather stated as: (Figure number?). This should be better examined. Lastly, the addition of implicitly stating the need for further research is a great way to highlight the need for experimental study amongst the reader. With this, it would be helpful to highlight your targeted audience within this article (i.e. professional, student, etc.). If you have a targeted audience with a scientific background you will not need to go into further depth to define terminology and specific neurological pathways in this study. Overall, job well done and very interesting study!
On 2022-11-08 15:26:46, user W_R_1 wrote:
Hi,
Thanks for the very interesting and useful paper!
I wondered if the stated z-value calculation (p26, next-to-last line) has a typo of a "-" sign rather than a "+" in the denominator?
I was also curious as to the properties of this test statistic in general, given that the two beta estimates aren't necessarily independent (i.e. when calculating the variance of the difference between the two beta estimates, their covariance can't necessarily be assumed to be 0?)?
On 2022-11-26 06:41:30, user Soichiro Obara wrote:
Dear Prof. Schindler,
First I (Soichiro Obara, the PI of this project) apologize for being replying to your great comments.
Second, our project has modelled the European APRICOT as you commented. We would sincerely appreciate your suggestions from the viewpoint of the author of the APRICOT.
On this matter, before launching this project, we got in contact with the principal investigator of the APRICOT, Prof. Walid Habre, who has been giving many suggestions and comments to us.
As you might have concerns regarding data acquisition, he also gave comments as to how tough the data acquisition and cleaning in the APRICOT.<br /> Hence, this time, we assume that we may need to change the case report form (actually modelling the CRF of the APRICOT) after a pilot study which hopefully will be conducted February or March in 2023.<br /> (I have heard from Prof. Habre that a pilot study was conducted in three centers to examine the feasibility of the protocol and the CRF in the APRICOT.)
If possible, I would appreciate your "specific and direct" suggestions as to which data items in the APRICOT might be difficult or be unnecessary to collect.
Again we would sincerely appreciate your great suggestions and comments, if you kindly gave, in the future again.
I am very pleased if you kindly get touch with me on behalf of the Asian Society of Paediatric Anaesthesiologist research committee.
Soichiro Obara (Japan)<br /> e-mail address: soichoba1975@gmail.com
On 2022-12-09 13:17:21, user Maja wrote:
I applaud the authors for their meaningful work on reporting COVID-19 trials. The results are fascinating. Only 19% of COVID-19 clinical trials were published within three months of completion, which shows how much work has to be done regarding research transparency, publishing trial results, and responsibility while conducting clinical trials. Timely publication of results and avoiding research waste in clinical trials should be a priority, especially during global public health emergencies, such as the COVID-19 pandemic.
On 2022-12-27 15:27:14, user nobiggie wrote:
The most interesting piece of information here seems to be that the more shots you get the more likely you are to get sick but somehow it goes unmentioned
On 2023-01-06 12:54:34, user Mike Verosole wrote:
A more recent, peer reviewed publication involving a much larger population set showes that indeed vaccinated people were more likely to be infected vs previously infected individuals, however, the vaccinated group was about 25-35% less likely to be hospitalized or experience death. Maybe this paper will be reviewed in light of this new publication
On 2023-02-19 17:11:56, user citrate reiterator wrote:
The simplest explanation for the vaccine dose trend is that it’s not broken out by previous infection status and date. Other data in the paper confirms that recent past infection is very protective against the omicron subvariants. The more shots you have, the more likely you probably were to be omicron-naive at the start of the study period. As Luis Cruz points out below, even past research from this same group has not previously found a dose-response relationship of this type. Also, when they actually fit a model that takes these confounders into account (the proportional hazards model), they find that there is a modest preventative effect of the bivalent booster — which is not consistent with a dose-dependent increase of risk following vaccination.
On 2023-01-16 01:47:52, user Brian Piper wrote:
This is a timely review on an important topic!
In evaluating the safety of the relaxation of take-homes, it might be helpful to consider that “The number of clients receiving methadone increased from 306,440 in 2011 to 408,550 in 2019 and then decreased to 311,531 in 2020” [1]. This reduction from 2019 to 2020 of 97,019 patients is a 23.7% decline!<br /> The increase in the number of overdoses involving methadone may only appear equivalent to the increase in all opioid overdoses when one does not take this factor (i.e. using the population size and not the number of methadone patients as the more appropriate denominator) into account.
The authors also are encouraged revisit the important policy research in England and Scotland which identified sizable declines in methadone overdoses following implementation of supervised administration [2].
Citations
National Survey of Substance Abuse Treatment Services (N-SSATS): 2020 Data on Substance Abuse Treatment Facilities. Table 3.2.
Strang et al. Impact of supervision of methadone consumption on deaths related to methadone overdose (1993-2008): analyses using OD4 index in England and Scotland. BMJ 2010;341:c4851.
On 2023-02-09 23:16:58, user Constantinos C wrote:
The published version can be accessed via the following link: https://www.nature.com/arti...
On 2023-02-20 07:41:20, user Martin Schecklmann wrote:
I miss in the discussion the meaning of relevant dose of TMS with respect to e-field. We could demonstrate that F3-based method is similar to neuronavigation-based method with respect to clinical efficacy even if induced e-field was lower for F3-method (doi: 10.1016/j.brs.2021.01.013). In addition your sample size is very low and correlation between clinical efficacy and e-field may be biased by outliers (figure 3).
On 2023-02-22 16:16:10, user Robert Clark wrote:
Major flaw in your analysis. <br /> You noted the report by Peterson et al included cardiac arrest cases where they survived:<br /> “This study identified 173 confirmed SCD cases (and 158 SCA cases with survival), so on average 43 SCDs per year.”
Since there has been a great increase in awareness of cardiac arrests and arrhythmia in athletes there have been a great increase in AED’s (automatic defibrillators) available and those trained in CPR.
Then many of those cases even in just the last couple of years survived who would have died previously. So you should have also counted the number of cases who had cardiac issues who also survived in the current, pandemic era.
Additionally Maron et al described other causes other than just cardiac arrest as the cause of the sudden deaths. Since the Goodsciencing report considers several kinds of serious life threatening illnesses arising in athletes you should also have done a separate count of those cases in the Goodsciencing report, again both of those who died and those who survived.
Robert Clark
On 2023-03-02 18:33:37, user Daniel Park wrote:
Fascinating. Lines up well with other evidence including the Lewnard et al. study showing interactions b/w pneumococcal carriage and SARS-CoV-2. We also saw similar patterns with viral load (+ severity) and pneumococcal carriage with human endemic CoVs: https://journals.lww.com/pi...
On 2023-04-04 06:16:50, user Kevin J. Black wrote:
I think you'll want to examine the spatial delayed response task, which Tamara Hershey and other have studied in Parkinson disease, e.g. https://pubmed.ncbi.nlm.nih...
On 2023-05-16 21:45:14, user Maria Log wrote:
This is really very strange and disappointing that you consider the use of private e-mail as an indicator of fake articles. Since my years as a PhD student I always use, and I use now and will use my personal email in all my articles. There are many reasons for that, but the most crucial one is that I (as well as any other researcher)can change my affiliation and then my institutional e-mail will be inactivated and ones who are interested in my publication will not be able to get an answer to their questions. So the use of an email linked to the organization is unfair and irresponsible. Here is a link to my google scholar profile: https://scholar.google.com/... - good luck with finding any fake articles!
On 2023-05-24 14:24:40, user Shaffi Fazaludeen Koya wrote:
The article is now published in Lancet SEA journal,<br /> https://www.thelancet.com/j...%20in%202016%E2%80%932020 "https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00130-5/fulltext#:~:text=The%20pooled%20hypertension%20control%20rate,28.0%25)%20in%202016%E2%80%932020").
On 2023-06-07 16:45:42, user Nathan Pearson wrote:
In this study (of American patients to whom bivalents were available only as booster), all bivalent recipients by definition got 3+ total mRNA vaccine doses, while the current preprint text's control group got 2+.
As such, to compare equal dose counts (if not timing relative to prior waves etc.), can the authors please add a sub-analysis of peer bivalent (original or BA.4/5) vs. 3+ dose (not 2+) monovalent recipients?
Not doing so inherently confounds any additional benefit of bivalent vs. monovalent formulation with a group difference in total doses per participant.
Thanks
On 2023-06-07 17:10:10, user Nathan Pearson wrote:
Also: in adding such needed apples-to-apples comparison of bivalent vs. monovalent peer boostees (i.e., with 3+ doses in each otherwise background-similar group), can the authors take care to control for time since last dose?
I.e., if bivalent boostees got boosted at most k months before data freeze (so were tracked for severe COVID for <=k months after last dose), likewise tally cases of severe COVID in monovalent boostees <=k months (but not longer) after their last dose.
Thanks.
On 2023-06-22 16:56:53, user Benjamin Shuman wrote:
This is interesting work. The descriptions of methodological variation (task, muscles measured, amplitude normalization method) certainly impact how the studies compare to one another. However, there is no discussion of filter parameters and its impact on the interpretation of synergy metrics. With more aggressive filtering the resultant processed EMG signal is less variable which also has a direct impact on the number of synergies extracted or tVAF1/DMC. The Collimore article is noted for having the least number of synergies identified but also has the most aggressive LP filtering (4hz). There may be additional trends in the article findings when looked at through a filtering lens and I would encourage the authors to consider this. Finally, please note that DMC is a linear transformation of tVAF1 (Steele 2015). As such the trends in DMC are directly comparable to tVAF1.<br /> Thanks,
On 2023-07-10 13:53:43, user Carlos Menck wrote:
This work was published in Carcinogenesis and the correct citation is: <br /> Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, SarasinA, Menck CFM. Mutational signatures and increased retrotransposon <br /> insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print. <br /> PMID: 37195263.
Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, Sarasin A, Menck CFM. Mutational signatures and increased retrotransposon
insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print.
PMID: 37195263.
On 2021-12-21 15:35:35, user Greg Mock wrote:
Has any association of PANS and the NOD2 gene been studied?
On 2023-10-24 02:18:23, user CDSL JHSPH wrote:
Dear Dr. Bi et al,
Thanks to your work on influenza, which has provided a new proof that the residual repeat vaccination effect might be explained by different rates of subclinical infection between repeat and non-repeat vaccinees via two proposed mechanisms, the infection block hypothesis and enhanced vaccine immunogenicity and protection post-infection.
As a reader who doesn't know much about the field ,I can give you some reading feedback for your reference.<br /> First I think your article provides three important pieces of evidence. <br /> 1,Repeat vaccinees were vaccinated earlier in a season by one week.<br /> 2,Clinical infection influences individuals’ decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type.<br /> 3,Adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination.<br /> 4,Adjusting for subclinical infection could theoretically attenuate this effect.
On the basis of your good work, I would like to offer a bit of advice for readers who are not experts in this area. First is the article structure section. I hope this provides some perspective to help you publish. First of all the name of the title may be too long for non-specialized readers. It may lose some of the attention. Furthermore the explanations of within-season waning, recent clinical infection, and subclinical infection could have come in the INTRODUCTION instead of the METHOD before being mentioned. Another thing is that I think you could put in the conclusion that has some summarizing words underneath the fig so that readers might find them easier. Also the part you put in the appendix about theoretical modeling some of it is necessary for understanding the model, if you could summarize it necessary and put it in the body methodology would help understanding.
The next aspect is about research, first of all your work is very relevant and on this basis you are perfectly placed to capitalize on this aspect. First of all you can try to find some way (like sampling) to compare your theoretical model with the results of the data response you got, so that you can speculate about the effect of the vaccine in reality and the possible number of Subclinical infections.
After that comparing your model with real data would be an interesting aspect. And this can emphasize the correctness of your model and increase the credibility of your article. Although overall this article has been highly relevant with enough realistic data.
However, on this basis, one can consider whether the data from different regions (the five regions sampled) are very different? For example, are the probabilities of tuning into a vaccination strategy similar in different regions, and do repeat vaccinees in each region tend to get vaccinated earlier in the flu season than non-repeat vaccinees? These comparisons of data from different regions can be added to the article as they relate to the reproducibility and generalizability of your model's and conclusions.
Finally, thank you for this article, which provides very good evidence for the causes of Reduced effectiveness of repeat influenza vaccination, and this article has the advantage of incorporating a lot of details that were not considered in previous studies and provides a good interpretation of the errors, providing new ideas and theoretical models for the field. And I personally learned a lot of research ideas from you through this article, thank you for your work!
On 2023-11-03 16:41:46, user Dr. Hans-Joachim Kremer wrote:
It is not well described, what exatly is control. The estimates in Figure 1 are not comprehensible given the few details provided. <br /> Fig 1: It is written that Pfizer bivalent vs. Moderna bivalent is shown, however, the text suggests that Pfizer bivalent was compared with its own contol (before vaccination). What is true? If in fact you compared them to their own control: Where are respective details?<br /> It appears that in that Figure the "all" in the first row has a different meaning from the "all" in the third row. Note that the "16" of the first row, denoted as "all", is repeated in the fifth row, but denoted as "new".<br /> Anyhow, the "new" is not explained in the text and the "all" might have a meaning different from common understanding, as it appears that it stands for "after matching", so not really "all".
On 2023-12-07 15:20:52, user Alex Liber wrote:
This article has now been published: Liber AC, Faraji M, Ranganathan R, Friedman AS. How Complete Are Tobacco Sales Data? Assessing The Comprehensiveness Of US Tobacco Product Retail Sales Data Through Comparisons To Excise Tax Collections. Nicotine Tob Res. 2023 Nov 2:ntad214. doi: 10.1093/ntr/ntad214. Epub ahead of print. PMID: 37933997.
On 2023-12-07 19:39:10, user Chihua Li wrote:
The study has been published: https://bmcmedicine.biomedc...
On 2023-12-14 17:58:32, user Michelle Carras wrote:
Very curious about this. It's worth considering, but the authors had some big challenges with their sample, which is a relatively well-off, very White population, a bit older than the US average. They have a very high rate of premorbid asthma (about 2-4x the usual), but the thing I find most curious is the use of naltrexone (20% of the sample) and ivermectin (18% of the sample) to treat these new symptoms. Since these uses are very off-label, it makes me wonder about the types of patients who get them. Same with intermittent fasting as a non-pharmacological treatment. I'd be interested in seeing other responses to this and whether it ultimately gets published.
On 2024-01-04 18:43:48, user John Beach wrote:
As a non-doctor with no opinion on ivermectin, I read<br /> "Effect of Ivermectin 600 ug/kg for 6 days vs Placebo"<br /> to learn.
My questions:
1.a.) What is being compared to what?<br /> Placebo isn't identified in the paper or the Supplemental material. Where can I look up the substance used as placebo?
1.b.) Does the placebo contain folic acid or folate? Or a placebo containing Magnesium, Sodium, or Potassium in some form?
Reason for question 1: is it possible the study assumed the placebo was inert, but fortuitously discovered instead another treatment that works against COVID with efficacy equal to ivermectin?
2.a.) Is there a supplement-to-the-study that shows the outcomes sorted by Cytochrome polymorphism?
Reason for question 2:<br /> CYP polymorphisms can affect how our bodies respond to virus.<br /> Some CYP polymorphisms can prevent a patient from even metabolizing the treatment.<br /> Furthermore, CYP3A4 mediates metabolism of ivermectin, but COVID-19 reportedly decreases metabolism of drugs by CYP3A4
e.g. via GoogleScholar, I found<br /> https://tandfonline.com/doi... <br /> "The impact of COVID-19 infection on cytochrome P450 3A4-mediated drug metabolism and drug interactions"
Back to my questions...
2.b.) Is there a way to determine if ivermectin was metabolized by a study participant, and if the molecule was circulating in their system? If so, was there any variability in study outcomes when those outcomes were sorted by Cytochrome polymorphism?
3.a.) Has any past research shown that the placebo ingredients interfere with any of the following?
If so, evidence suggests that these, and therefore the intended placebo that affects these, change human biology relevant to the COVID disease process.
It looks like a lot of good work was put into the Duke study, and so I am trying to learn from it. Any answers you can provide are much appreciated.
On 2024-01-16 18:40:48, user Weina Jin wrote:
This is the initial paper preprint. The published paper is at: http://dx.doi.org/10.1016/j.artmed.2023.102751
The updated preprint and supplementary materials S1-S3 can be found at:<br /> https://www2.cs.sfu.ca/~hamarneh/ecopy/aiim2023.pdf<br /> or <br /> https://github.com/weinajin/multimodal_explanation/tree/main/neurosurgeon_clinical_study_data_analysis/paper_preprint
Video paper presentation:<br /> https://www.youtube.com/watch?v=rhLQQsc8Z8Y&t=29s
On 2024-02-03 10:02:01, user Ryan wrote:
published https://rdcu.be/dxHNq.
Benjamin, R. Reproduction number projection for the COVID-19 pandemic. Adv Cont Discr Mod 2023, 46 (2023). https://doi.org/10.1186/s13...
On 2024-03-18 13:01:53, user Varsha Gouthamchand wrote:
Follow the link https://doi.org/10.1093/bjr... for an extended and published version of this article.
On 2024-04-27 22:04:26, user Linette Roungchun wrote:
I am a TSW sufferer and this work is important. I have suffered with TSW on and off since the young age of 3, so to see the science backing up the fact that TSW is in fact, a separate entity from Eczema is so validating and healing. Huge thank you to Kelly, Kathy, and Dr. Ian Myles!
On 2024-04-28 00:05:10, user pfwindsor wrote:
Thank you for the opportunity to comment on this excellent TSW study. I am not a scientist, but have more than 10 years of personal experience with TSW and have studied and published material through the American Academy of Dermatology. I also moderated ITSAN’s online forum for several years where I reviewed thousands of posts and responded to thousands of questions about TSW. I am currently involved with ITSAN research advocacy and would be happy to offer additional input.
Line 33 and Line 66 refer to “prolonged exposure” and “longterm use” of TCS. While my own experience with TSW involves longterm use of TCS, the anecdotal data suggests that some TSW cases follow a short term course of TCS treatment, such as for poison oak. Others report TSW symptoms emerging after decades of TCS use. Our theories are centered around multiple variables for susceptibility to TSW including, but not limited to length of use. We also considered potency, quantity and length of exposure and areas of application. Your study doesn’t seem to address these other potentially significant variables.
Line 67 further states that TSW is related to “abrupt cessation” of TCS use. This may be the case for your small study cohort, but it has not been studied or established within the TSW community.
Line 73 -74 states that “improvement is seen through avoidance of TCS therapy”. This is misleading. Overwhelming anecdotal evidence suggests that TSW symptoms that emerge after cessation of TCS become more acute before they resolve.
Line 306-307 suggests a threshold of 4 months of TCS exposure. Is this an arbitrary threshold identified for purposes of your study or is there a source that proposes or establishes this threshold? I am not familiar with any particular exposure timeframe associated with a risk of developing TSW. In fact, given the anecdotal evidence of TSW with short term exposure, this may not be a valid metric.
On 2024-04-28 21:04:46, user Marly Peel wrote:
PLEASE! PLEASE! PLEASE do more research & acknowledgement of this TERRIBLE problem! I just recently discovered info on this & it all matches my symptoms exactly. Imagine a horrific itch that you cannot scratch enough to make it STOP! I don't think I've ever been this close to panic as I have during those times. There's no way to sleep or rest AT ALL when this is happening. That doesn't even include the flaking, shedding, red & raw areas, irritated & antsy feelings one can have. WE NEED HELP! Plus, there needs to be a HUGE WARNING included with every top.steroid...that doctors know about & take to heart. Top.steroids might be a quick "fix"...but they DO NOTHING to address the underlying reason for the problem in the first place. It's time for common sense to COME HOME!
On 2024-04-27 18:39:09, user Haley DelPlato wrote:
As a young adult whose life has been put on hold for the past 3 years due to Topical Steroid Withdrawal, I can't thank you enough for this work!
Seeing studies about TSW not only helps validate my pain that so many medical professionals have dismissed, but also contributes greatly to advancements in dermatopathology looking forward. The current stigma that makes TSW such a controversial concept NEEDS to be eradiated, a complex task that ultimately relies on substantiated clinical proof to combat misinformation. Unfortunately, the current scope of dermatopathology has kept so many folks unaware of TSW and trapped in harmful cycles of topical corticosteroid addiction. I hope this will be the first of many legitimate works seeking to uncover the truths about this tragic condition so future generations will be believed, treated, and cared for with dignity, in ways the current dermatological standards simply haven't allowed for.
Appreciate the strides this study has taken toward a more compassionate reality for TSW sufferers!
On 2024-04-28 12:21:49, user Henry wrote:
How much Berberine is required for the improvements to begin? Really interested in this study, thank you!
On 2024-04-28 19:43:09, user Laura Mihalidesz wrote:
I am a TSW sufferer and made a signifacant change to the worse in my condition when TS were introduced to my life 30 years ago, stopping the usage a age 26 and going into TSW the symptoms are uncoparable to eczema. This study is important to understand what harm can TS cause in the long run and after 9 years in TSW I still suffer from symptoms. The importnce of this research is not a question but answer to many patient's questions and finding and developing treatment methods.
On 2024-05-07 17:04:04, user Katy Ross wrote:
There is a current flawed premise that ‘adherence’ to steroid treatment is a good idea and anything querying whether that’s a good idea is deemed to be a phobia, as opposed to a clear, rational and well reasoned concern.
I am out the other side of topical steroid withdrawal syndrome, and have lost a lot to the trauma it brings (in all matter of ways). Sufferers like me are desperate for science-backed research, and it’s great to see progress here. It will be fantastic when a diagnostic code has been approved and further research is available to differentiate TSW from other skin conditions so that patients can be acknowledged and treated appropriately.
The former Chair of the British Dermatology Council said that as many as 1 in 10 patients may have TSW and it may be seriously under-diagnosed, and the hashtag #TSW has racked up more than one billion views on TikTok - I applaud anyone who’s working towards ending this huge amount of unnecessary suffering.
On 2024-05-10 04:50:19, user Kaitlin Lee wrote:
Thank you for this long overdue research. It gives me hope that in the near future TSW will be universally diagnosable, treatable, and most importantly, prevented. I have had eczema for ten years, just a few patches here and there, which never interfered with my livelihood. Now 5 months into TSW, so many things have been put on hold for me. People suffering from TSW need treatment and support and medical professionals need to start acknowledging this condition.
On 2024-04-30 16:10:19, user Cameron Daniels wrote:
Hello! My name is Cameron Daniels and I am a Undergraduate Student at Sam Houston State University. As part of a class project we were told to pick a preprint and provide the authors with relevant comments that could help their study. I really enjoyed reading your study and learning about new data collection techniques. Here are my comments on your paper that could help you improve the paper:
* List the number of cycles for the first round of PCR in the patient samples. You did it for the in vitro experiment, but you must've forgotten about it in the patient one. The number of PCR cycles helps to be able to replicate your experiment and also see if too many cycles were run because the more cycles that occur leads to the more mistakes, chimeras, and faulty sample reads. Consider looking at this article to validate my response: Sze MA, Schloss PD.2019.The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. mSphere4:10.1128/msphere.00163-19.https://doi.org/10.1128/msphere.001...
* Specify filtering for chimeras and PCR errors, which is a must to ensure accurate data.
* List the number of sample reads for the ASV units. The sample reads will help understand your data better.
* Make sure to include coverage measures and do it before filtering of singletons. Calculating Good's Coverage or Chao1 helps to ensure that representative sampling occurred.
* Great introductory paragraphs but seems to have many long run-on sentences. Consider breaking up those sentences for readability.
* A preserving agent is mentioned numerous times as important to capsule function. What is this agent and what concentration?
* Fig4A visualizes beta diversity. Should also use an appropriate statistical test, such as ANOVA, too determine if the community compositions are significantly different with probiotic addition.
* The probiotic contains two specific strains, while the microbiome data is from V3-V4 sequencing, and so cannot identify at the species or strain level.
* An increase in Lactobacillus or Bifidobacterium is expected for the probiotic-treated patients. What would be more interesting is to analyze if certain other taxa are significantly increased or decreased in abundance by the probiotic strains, as the question of how those two strains affect the rest of the community is scientifically important and would enhance the literature.
* Fig5 and 6 show that stool sampling vs capsule are different. Authors also need to include analysis to show that capsule and endoscopy are not different, since capsules are proposed as a non-invasive alternative.
On 2024-06-08 00:47:24, user Renzo Huber wrote:
The manuscript entitled “Laminar multi-contrast fMRI at 7T allows differentiation of neuronal excitation and inhibition underlying positive and negative BOLD responses” is a methods paper that estimated metabolic changes (CMRO2) across cortical layers.
The subject matter is relevant for the field. (layer-)fMRI suffers from the interpretability challenge of ‘only’ capturing an indirect measure of neural activity. This study aims to estimate neural energy demand more directly with a newly re-implemented multi-contrast sequence of CBV, CBF, and BOLD.
The method is benchmarked on previously established tasks (finger tapping) and applied on visual retinotopic stimuly.<br /> The study is clearly described and the results on positive responses look robust and convincing.<br /> The results on negative responses are weak and less clear and less convincing, though. <br /> One advantage of this study compared to previous laminar CMRO2 studies is that it does not rely on a Grubb coefficient that relates CBV and CBF. Instead, the study at hand measures both parameters concomitantly.
There are some model assumptions that are not really justified (detailed below).
I recommend the editors to publish this manuscript given the authors make a few small revisions.
Detailed comments are below:
1.) The Davis model on CMRO2 estimation is based on many assumptions that might not be valid for the spatial scale of laminar fMRI with GRASE. I believe the authors could spell out the assumptions that they are making and discuss if and how much they matter for the conclusions.
1a) The Davis model is based on the Fick’s principle. This assumes that delivered oxygen (via CBF) is either (i) sitting in the voxel -CBV , (ii) metabolized -CMRO2 or (iii) drained away - BOLD. Its a mass-balance principle. This assumption is valid for conventional 3mm voxels that cover the entire vascular tree. But for laminar resolution this is not valid anymore. The exchange (CBF) is happening in different layers than the draining (BOLD). So in superficial voxels, when there is a BOLD signal change without any CBV or CBF change, the Davis model results in unphysiological results.
1b) The Davis model is solely parametrizing venous CBV that is contributing to the BOLD signal. The Davis model does not include arterial CBV. In the study at hand, the authors take VASO and it’s estimation of total CBV, in the equation that is meant for venous CBV only. Given that venous CBV is weaker, slower, and has a different sensitivity to superficial layers [Huber 2014 10.1016/j.neuroimage.2014.04.022], this can result in skewed estimations of CMRO2. Previous studies on laminar CMRO2 have used a scaling factor to account for this [Guidi 2016 10.1016/j.neuroimage.2016.06.030]. The study at hand does not account for the mismatch between total CBV and venous CBV.
1c) The power law that equates BOLD signal changes with oxygenation changes is originally estimated based on a supralinear effect: “a linear large vessel component is combined with small vessel contributions, which tend toward a quadratic effect on relaxivity according to the Luz-Meiboom model for diffusion-mediated exchange on the capillary scale” (Davs paper 1998). In my understanding, this has always been applied with gradient echo BOLD. In the study at hand, the authors apply the same relationship to GRASE BOLD. Based on modeling work in [Scheffler 2021, https://doi.org/10.1002/mrm...], the vessel sensitivity and the relationship between intra and extravascular BOLD is dependent on vessel radius and flip angle. This is different from GE-BOLD which does not have these dependencies. This makes me wonder if it's justified to use an universal beta value in the Davis model for GRASE BOLD. Maybe beta varies a lot across layers and areas?
2.) The study by Bohrhaus et al 2023 also used laminar CBV, CBF and BOLD to estimate CMRO2 with a layer peak that seems much more superficial (monkeys) than the results shown here. The authors could acknowledge that this study exists and include it in the reference list?
Bohraus, Y., Merkle, H., Logothetis, N.K., Goense, J., 2023. Laminar differences in functional oxygen metabolism in monkey visual cortex measured with calibrated fMRI. Cell Reports 42, 113341. https://doi.org/10.1016/j.c...
3.) It seems that the profiles in Figs. 3,4 are group results. It is not clear if the corresponding maps are single participant maps. Are the inflated brains in Fig. 4 averages in FS-average?
4.) It is not clear to me to which experiment the results in Fig. 3 refer to. The heading suggests its experiment 1. The figure caption seems to suggest it refers to experiment 2.
5.) I think it would be helpful to add a zero line in Fig. 5d. It's not clear if the author hypothesizes that the superficial layer sees negative changes or if the deeper layers see positive changes.
6.) I found Fig. 8 a bit misleading. The scanner plots are mixing many different sources of variance. The spread across points might contain true spatial patterns as well as intersubject variability e.g. different fMRI gain due to different venous baseline oxygenation [Lu,et al., 2008. https://doi.org/10.1002/mrm...]. So it’s not clear what a higher correlation means. In the Davis model, CBF dominates the estimates of CMRO2. Thus, any thermal noise in CBF will be expected to translate to noise in CMRO2 estimates; Making them not independent parameters. Thus, I am not sure if the higher correlation in CBF-CMRO2 is an excelent measure to investigate which parameter is most closely related to CMRO2. But it also doesn’t hurt to keep the figure in there.
7.) In the discussion, the authors discuss their beta value with respect to the literature. I think it would be helpful to mention that beta is not solely a tissue property constant. It is expected to be different across field strength, TE and BOLD contrast (GE-SE).
8.) Typo in discussion “rang from 0.9…”
On 2024-08-02 13:43:41, user Paul Levay wrote:
This preprint has been superseded by:
Levay P, Finnegan A (2024) The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid). Journal of the Medical Library Association, 112(3): 316-328. https://jmla.pitt.edu/ojs/jmla/article/view/1806
On 2024-10-03 03:31:24, user Max Lamparth wrote:
This paper is nowpublished through the CoLM conference proceedings with new results: https://openreview.net/forum?id=1pgfvZj0Rx
On 2024-10-19 20:32:11, user CDSL JHSPH wrote:
I thoroughly enjoyed reading your paper and found it to be a significant contribution to the field of tuberculosis treatment. The exploration of model-based methods, particularly MCP-Mod, to enhance traditional qualitative approaches is both timely and necessary. Your findings highlight the effectiveness of these methods in detecting duration-response relationships, especially in small sample trials, which is a crucial aspect of optimizing treatment strategies.
I am particularly intrigued by the potential for these model-based approaches to be applied beyond TB. It would be fascinating to investigate whether the insights gained from your research could be translated to other bacterial infections, such as Staphylococcus or Streptococcus species, as well as to the treatment of viral and fungal infections. This could open new avenues for improving antibiotic and antiviral therapies.
Additionally, your suggestion to incorporate patient characteristics and risk factors into the analysis is commendable. I believe that further exploration of how different design parameters—such as sample size and time intervals—affect the accuracy of the model could provide valuable insights. Understanding how to integrate more patient-centric data into real-world clinical settings would enhance the applicability and scalability of your findings.
Thank you for your valuable contribution to this important area of research. I look forward to seeing how your work evolves and the potential implications it may have for personalized treatment strategies in the future.
On 2024-10-18 00:43:20, user CDSL JHSPH wrote:
Thank you for sharing your research. I have read your paper and deeply appreciate the necessity and advantages of using model-based methods, such as MCP-Mod, to improve traditional qualitative approaches. The model-based methods you proposed have demonstrated excellent performance in detecting the duration-response relationship, especially in the context of small sample trials, and their potential application is very promising. Additionally, your suggestions for incorporating patient characteristics and risk factors pave the way for more personalized treatment strategies in the future. However, I wonder if it would be possible to further explore how different design parameters, such as sample size and time intervals, might affect the accuracy of the model, and how more patient-centric data could be integrated into the analysis in real-world clinical settings. I believe this could help enhance the understanding of the model’s applicability and scalability. Thank you once again for your valuable contribution to this field, and I look forward to seeing more of your research in the future.
On 2024-12-05 12:13:20, user xPeer wrote:
Courtesy review from xPeerd.com
Summary<br /> The study "Pre-existing anti-polyethylene glycol antibodies in pregnant women and newborns" investigates the prevalence and levels of pre-existing anti-PEG antibodies in pregnant women and their newborns, revealing significant safety concerns over the use of PEGylated drugs in these populations. The study highlights maternal age and certain lifestyle factors, such as cosmetic use and consumption of take-out food, as influencing the prevalence and levels of these antibodies. The implications for public health lie in the potential reduced efficacy and increased adverse reactions to PEGylated drugs.
Potential Major Revisions<br /> 1. Study Design and Population Detail Improvement: The current study design section provides a basic overview of the population criteria (pg. 7) but lacks deeper context about the broader representativeness of the sample size and demographics. Additional detail on potential regional and healthcare-specific biases can help contextualize the findings better for international readers.
Methodological Clarification: Some methodological aspects, such as the exact ELISA techniques used and their validation, are mentioned cursorily (pg. 11). A more comprehensive separate methodological section could provide greater clarity and benefit reproducibility.
Detailed Analysis of Influencing Factors: The discussion of influencing factors like maternal age and cosmetic use (pg. 11) needs expansion to delve into how these were statistically analyzed and how robust these findings are. The differences in antibody prevalence and levels based on lifestyle factors should be discussed with more supporting data.
Potential Confounders: Addressing potential confounding variables not examined in the study could enhance its robustness. Consider expanding the discussion around potential other environmental and genetic factors influencing anti-PEG antibodies not addressed in this study.
Discussion on Clinical Implications: While the study raises concerns about the safety of PEGylated drugs, it stops short of providing specific clinical recommendations or guidelines. This section could be expanded to address more direct implications for clinical practice and public health policies (pg. 10).
Recommendations<br /> 1. Expand and Detail the Methodological Section: Expand where necessary, especially focusing on the validation and comparison of ELISA techniques used between this study and previous studies.<br /> 2. In-depth Statistical Analysis: Include more detailed statistical tables and charts to back the discussions around influencing factors and antibody levels.<br /> 3. Address Confounders: Identify and address other potential influencing factors and confounders that were not examined and discuss their potential impact on the findings.<br /> 4. Clinical Guidelines Discussion: Provide a more detailed discussion with possible clinical guidelines or recommendations addressing the raised safety concerns about the use of PEGylated drugs in seropositive populations.
Potential Minor Revisions<br /> 1. Typographical Errors: Correct minor typographical errors, such as any found in the description and presentation of data in figures and tables (pg. 7).<br /> 2. Formatting Consistency: Ensure that formatting is consistent throughout the document, particularly around headings and subheadings for better readability.<br /> 3. AI-Generated Content Analysis: No significant AI-generated content was detected in the document. The content is likely produced by human authors, given the nuanced arguments and specific scientific context presented.
By addressing these points, the study could provide a more thorough and accessible analysis of its findings, enhancing its contribution to the understanding of pre-existing anti-PEG antibodies in pregnant women and newborns.
On 2024-12-06 02:09:34, user xPeer wrote:
Courtesy review from xPeerd.com
Summary
The preprint titled "RGnet: Recessive Genotype Network in a Large Mendelian Disease Cohort" introduces RGnet, a novel tool for analyzing recessive genotypes in large cohorts, focusing on compound heterozygotes and homozygotes. The study applied RGnet to the SLC26A4 gene within a cohort of individuals with hearing loss, identifying significant pathogenic variants and demonstrating the tool's potential for advancing the understanding of recessive genetic disorders. The paper highlights the novelty of RGnet, the methodology involving variant preprocessing, phasing, network construction, and permutation-based enrichment analysis, and presents the results from its application to the CDGC cohort.
Potential Major Revisions
Example: "RGnet is available from GitHub at https://github.com/jiayiiiZeng/RGnet " (page 1) but does not provide direct links or instructions for data access.
Robustness of the Methodological Framework:
Example: "This study employs a combination of trio-based phasing, read-based phasing, and an expectation-maximization phasing algorithm" (page 3). However, specific reasons for selecting these methods are not provided.
Statistical Analysis:
Example: The paper states that "100,000 permutations were performed" without detailing the basis for this choice (page 5).
Ethical Considerations:
Potential Minor Revisions
Line 58, page 2: "research3,4" should be "research" followed by proper citations.
Formatting Issues:
In the reference section, ensure that all references, such as URL links, are formatted and hyperlinked correctly. For example, repeat the formatting used for URL links like " https://doi.org/10.1101/2024.12.02.24318353 " for other references as well.
AI Content Analysis:
Recommendations
Provide a more granular explanation of the methodological decisions, particularly around the choice of phasing methods and permutation tests.
Enhance Data Accessibility:
Ensure that all datasets and supporting materials are accessible, with clear instructions for researchers wishing to replicate the study or apply the RGnet tool.
Incorporate an Ethical Review Section:
By addressing these major and minor revisions, the paper can be significantly strengthened, ensuring clarity, reproducibility, and ethical adherence, which are vital for advancing research in genetic studies.
On 2025-02-24 23:45:15, user Stephen Goldstein wrote:
I am now pasting in the review done by Gemini 2.0 Flash Thinking Experimental. I have not edited it.
Prompt: Please conduct an extremely rigorous, journal-agnostic peer review of this manuscript.
Response:<br /> I have now thoroughly read the manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination" by Bhattacharjee et al., available at https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1 .
Here is an extremely rigorous, journal-agnostic peer review of this manuscript:<br /> Extremely Rigorous, Journal-Agnostic Peer Review of Manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination"<br /> Manuscript DOI: https://doi.org/10.1101/2025.02.18.25322379 <br /> Manuscript URL: https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1.full-text
Overall Assessment
This manuscript by Bhattacharjee et al. investigates the complex and highly sensitive question of immunological and antigenic signatures associated with chronic illnesses reported following COVID-19 vaccination. The authors employ a multi-faceted approach, combining clinical data from a prospective cohort study with deep immunophenotyping and autoantigen profiling. The study's aim is to identify potential immunological correlates of these post-vaccination chronic conditions, which is a crucial area of investigation given ongoing public health discussions and concerns.<br /> While the study addresses a highly relevant and important question, and utilizes advanced methodologies, this review finds that the manuscript in its current form presents significant limitations in its methodology, analysis, interpretation, and overall conclusions. From a rigorous, journal-agnostic perspective, the manuscript requires substantial revisions and further validation before it could be considered to provide robust and reliable scientific evidence. The current findings are preliminary and require cautious interpretation, and the manuscript overreaches in its conclusions and implications.
Major Strengths (Limited in Impact by Weaknesses)<br /> Addresses a Highly Relevant Question: Investigating potential immunological links to chronic illnesses post-COVID-19 vaccination is undeniably important for public health and vaccine confidence.
Prospective Cohort Design: The prospective cohort study design is a strength, allowing for systematic data collection and comparison between vaccinated individuals with and without reported chronic illnesses.
Multi-Omics Approach: The use of multi-parameter flow cytometry for immunophenotyping and autoantigen microarray profiling are advanced techniques that offer the potential to identify complex immunological signatures.
Clinical Data Integration: The study attempts to integrate clinical data (symptomology, diagnoses) with immunological data, which is essential for translational relevance.
Major Weaknesses and Critical Concerns (Requiring Substantial Revision)
Case Definition of "Chronic Illnesses": Critically Vague and Subjective. The definition of "chronic illnesses" post-vaccination is alarmingly vague and relies heavily on self-reported symptoms and broad diagnostic categories (e.g., "dysautonomia," "POTS," "fibromyalgia"). There is no clear, objective, and validated case definition used. This introduces significant subjectivity and potential for bias in patient selection and outcome assessment. The lack of specific inclusion/exclusion criteria for these "chronic illnesses" undermines the scientific rigor of the entire study. It is unclear if these are new-onset conditions post-vaccination, or exacerbations of pre-existing conditions, or simply background rates of these conditions temporally associated with vaccination.
Lack of Appropriate Control Groups: The study compares "Vaccinated individuals with chronic illness" to "Healthy vaccinated controls." This control group is insufficient. Crucially, there is no unvaccinated control group with similar chronic illnesses. Without this, it is impossible to determine if the identified immunological signatures are specific to post-vaccination illnesses, or are simply signatures associated with these illnesses in general, regardless of vaccination status. Furthermore, a "healthy unvaccinated" control group would be essential to assess baseline immunological differences related to vaccination itself.
Causality vs. Association: Fails to Establish Causality. The study is fundamentally correlational. It fails to provide any robust evidence for a causal link between vaccination and the reported chronic illnesses. The temporal association alone is insufficient to establish causality. The manuscript over-interprets correlational findings as suggestive of causal relationships, which is scientifically unsound. Confounding factors (e.g., pre-existing conditions, other exposures, reporting bias) are not adequately addressed or controlled for.<br /> Small Sample Size and Statistical Power: The sample sizes for the "chronic illness" groups are relatively small (e.g., n=15-20 per group in some analyses). Given the complexity of immunological data and the heterogeneity of "chronic illnesses," the study likely lacks sufficient statistical power to detect subtle but potentially meaningful differences, and is highly susceptible to false positives. The statistical analyses presented are descriptive and lack rigorous correction for multiple comparisons, further increasing the risk of spurious findings.
Over-Interpretation and Over-Generalization of Findings: The manuscript overstates the significance and generalizability of its findings. The conclusions are presented in a manner that suggests a stronger link between vaccination and chronic illnesses than is warranted by the limited and preliminary data. The discussion section speculates broadly on potential mechanisms without strong empirical support from the study itself. The manuscript risks contributing to vaccine hesitancy by presenting preliminary and correlational data as stronger evidence than it is.
Limited Validation and Reproducibility: The findings are not validated in an independent cohort. Without external validation, the reproducibility and generalizability of the identified immunological signatures are highly questionable. Given the small sample sizes and potential for batch effects in omics data, validation is absolutely essential.
Lack of Clinical Characterization and Phenotyping: Beyond broad symptom categories, the clinical characterization of the "chronic illness" groups is superficial. Detailed clinical phenotyping, including objective measures and validated diagnostic criteria, is lacking. This makes it difficult to interpret the clinical relevance of the identified immunological signatures. The manuscript does not adequately address the heterogeneity within these broad "chronic illness" categories.<br /> Potential for Reporting Bias and Selection Bias: Individuals who believe they have experienced adverse events post-vaccination may be more likely to participate in studies investigating vaccine-related illnesses. This introduces potential reporting bias and selection bias, which are not adequately addressed in the manuscript.
Ethical Considerations and Responsible Communication: Given the sensitive nature of the topic and the potential for misinterpretation, the manuscript needs to demonstrate a heightened awareness of ethical considerations and responsible scientific communication. The current manuscript, in its over-interpretation and lack of caveats, falls short in this regard. The potential for public misinterpretation and the fueling of vaccine hesitancy needs to be carefully considered and mitigated.
Recommendations for Substantial Revision (Minimum Requirements for Consideration in a Rigorous Journal)<br /> Develop and Implement Rigorous, Objective Case Definitions:
This is the most critical revision. The authors must develop and apply clear, objective, and validated case definitions for each "chronic illness" category. This requires moving beyond self-reported symptoms and broad diagnoses. Consult with relevant clinical specialists to establish specific inclusion and exclusion criteria based on objective clinical findings, validated diagnostic tools, and established disease classifications. Vague terms like "dysautonomia" and "POTS" are insufficient without precise operationalization.
Include Essential Control Groups: Absolutely essential revision. The study must include:<br /> Unvaccinated individuals with similar chronic illnesses: To determine if the immunological signatures are specific to post-vaccination illnesses or are general features of these conditions.
Healthy unvaccinated controls: To assess baseline immunological differences related to vaccination itself.<br /> Focus on Establishing Causality (or Acknowledge Purely Correlational Nature): The authors must either:<br /> Significantly temper their interpretations and conclusions to reflect the purely correlational nature of the study. Avoid any language that implies or suggests causality. Clearly state the limitations of correlational findings. <br /> OR<br /> Implement methodological approaches to strengthen causal inference. This would be extremely challenging in this observational study design, but might involve more sophisticated statistical methods to address confounding, mediation analysis, or focus on subgroups with stronger temporal associations. However, even with these, establishing robust causality will be very difficult.
Increase Sample Size and Statistical Power: Larger sample sizes are needed, particularly for the "chronic illness" groups, to improve statistical power and reduce the risk of false positives. This may require multi-center collaboration and a significant expansion of the study.
Rigorous Statistical Analysis and Correction for Multiple Comparisons: Implement more rigorous statistical analyses, including appropriate correction for multiple comparisons in omics data. Focus on robust statistical methods and validation of findings.
External Validation in Independent Cohort: Validation of the identified immunological signatures in an independent cohort is essential to assess reproducibility and generalizability. Without validation, the current findings are highly preliminary and should be interpreted with extreme caution.
Enhance Clinical Characterization and Phenotyping: Provide much more detailed and objective clinical characterization of the "chronic illness" groups. Include validated clinical assessments, objective measures, and explore heterogeneity within these groups.
Address Potential Biases: Acknowledge and discuss the potential for reporting bias and selection bias. Implement strategies to mitigate these biases where possible, or at least transparently discuss their potential influence on the findings.<br /> Responsible and Cautious Communication: The manuscript must be rewritten with a much more cautious and balanced tone. Avoid over-claiming, over-generalization, and any language that could be misconstrued as definitive proof of vaccine-induced chronic illnesses. Emphasize the preliminary and correlational nature of the findings, and the need for further rigorous research. Clearly discuss the limitations of the study and the potential for misinterpretation.
Recommendation for Publication<br /> In its current form, this manuscript is not suitable for publication in any rigorous peer-reviewed journal. It suffers from critical methodological flaws, significant limitations in its analysis and interpretation, and overreaches in its conclusions. Substantial revisions are absolutely necessary to address the major weaknesses outlined above.<br /> Without these extensive revisions, the manuscript risks contributing to misinformation and vaccine hesitancy without providing robust and reliable scientific evidence.<br /> If the authors are able to undertake the very significant revisions recommended, particularly regarding case definitions, control groups, causality, and validation, the manuscript might then be reconsidered for publication in a specialized immunology or clinical immunology journal, but even then, would require extremely careful and balanced framing of the findings. Publication in a very high-impact journal like Science or Nature remains highly unlikely given the fundamental limitations in the study design and the sensitive nature of the topic.
In conclusion, while the research question is important, the current manuscript requires a fundamental re-evaluation of its methodology, analysis, and interpretation to achieve scientific rigor and responsible communication of findings.
On 2025-02-21 05:12:28, user Evan Stanbury wrote:
Re "PVS participants also had lower anti-spike antibody titers, primarily due to fewer vaccine doses", ie the people with more vaccine doses had less PVS. This contradicts the hypothesis that vaccines cause Post-Vaccine Syndrome, since the dose-response relationship contradicts the hypothesis.
On 2025-02-21 05:14:49, user Evan Stanbury wrote:
A Machine-Learning (ML) model was able to distinguish fairly well between the "sick" and "not sick" cohort. But most ML models are incapable of explaining why a decision was made.<br /> In particular, ML cannot distinguish between the following two hypotheses: (A) "PVS is a Post-Vaccination Syndrome" (ie Iatrogenic) vs (B) "PVS is a Post-Viral Syndrome" (ie a symptom of disease). This is an important distinction since the the reported training data does not include patients with Long COVID.
On 2025-02-24 06:06:47, user Daniel Corcos wrote:
This work is a good start to appreciate the existence and organic nature of a post-vaccination syndrome. What worries me is the low number of control subjects. A much larger number of control subjects would allow adjustments to be made, particularly regarding the number of vaccine doses received.
On 2025-03-26 03:22:48, user Pavel Montes de Oca Balderas wrote:
It seems risky to claim so vehemently that vaccines saved lifes when the studies cited to backup this claim did not consider the syndrome described here that could also lead to death. <br /> Moreover, one of the references used here to demonstrate that vaccines saved 14 millions lifes through a math model has been called a "fiasco" because simple maths with world data records DO NOT fit their model. Also the authors of that paper have conflicts of interest as some are sponsored by the WHO and others.
Longdom Publishing SL<br /> https://www.longdom.org <br /> The discrepancy between the number of saved lives with COVID-19 vaccination and statistics of Our World Data.
On 2025-03-10 17:26:08, user Ruhollah Dorostkar wrote:
I am Dr. Ruhollah Darskar, a specialist in medical virology with 20 years of research experience in the field of virology and vaccines.<br /> The article... has been published without considering the facts of the covid-19 virus and the complications of this virus and the corona pandemic, and unfortunately without mentioning the round-the-clock efforts of Iranian scientists in the field of identifying and controlling the corona disease.<br /> For example, due to the lack of diagnostic kits in the world, at the beginning of the spread of the disease, more than 20 of my colleagues and I worked around the clock in the field of rapid virus detection and identification and the launch of the virus detection kit.
In order for our activities to be accurate and completely scientific, my colleagues and I have been away from home for a long time and were stationed at the hospital and many researchers and colleagues have also been infected with the corona disease.
Considering the publication of thousands of articles about the scientific activities of the Corona pandemic in Iran, it is unethical to create ambiguity about the scientific activities of Iranian researchers.
On 2025-03-14 14:22:15, user Amir wrote:
The paper’s statistical approach is troublingly opaque; without a detailed account of the one-way ANOVA assumptions or the Prophet model’s parameter choices, the reported findings on vaccine delays and excess mortality remain unconvincing and methodologically fragile.
While the manuscript highlights a correlation between delayed vaccination and excess deaths, it fails to adequately control for crucial confounding variables such as healthcare infrastructure, socioeconomic status, and demographic differences, making its causal claims appear overly simplistic.
Claiming that 50,000 excess deaths resulted directly from delayed vaccination campaigns is a bold assertion that lacks the necessary evidentiary support; the absence of comprehensive sensitivity analyses and diagnostic metrics renders this figure speculative at best.
On 2025-03-15 20:37:50, user Ali Molaverdi wrote:
The authors appear to conflate correlation with causation when attributing excess mortality to political populism, an interpretative leap that undermines the scientific validity of their conclusions and calls for a more cautious analysis.
On 2025-04-10 20:11:03, user Jeffrey_S_Morris wrote:
This study's conclusion of -27% negative effectiveness does not seem to be supported by the study, given they did not account for testing bias, which happens to also be 27%, with vaccinated testing on average a 27% higher rate than unvaccinated.
To their credit, the authors acknowledged this in the following plot:<br /> https://uploads.disquscdn.c... <br /> Here it can be seen in my replotting of their Figure 1a scatterplot on the log y axis (after extracting the data by applying AI tool to their scatterplot image), with the 27% increase being the (geometric) mean testing rate (vaccinated/unvaccinated) over the days they plotted.
https://uploads.disquscdn.c...
Incidentally, taking simple means (or fitting linear regressions) for a sample of ratios is not good statistical practice since the <1 and >1 parts are asymmetric, so instead the geometric mean (averaging on the log scale) should be used. For example, if one day is 4x higher for vaccine and one day 4x lower, they should average to be equivalent. The average on the raw scale (4 + 0.25)/2 = 2.125 would imply a mean 2.125x increase, which is incorrect, while a geometric mean (averaging on the log scale and then exponentiating) would get the correct result. 2^{log_2(4)+log_2(0.25)} = 2^(2 + -2) = 2^0 = 1. That is why I used geometric mean in the plot above and plot in the log scale, and think the authors should do the same in their paper.
While they acknolwedge the increased testing rate, the text of the paper dismisses it as a potential source of bias by claiming the test positivity rate is equivalent in vaccinated and unvaccinated. I agree with their logic that if test positivity were identical in vaccinated and unvaccinated, then the 27% higher testing rate could simply be a result of a 27% higher infection rate, and not from testing bias.
However, the analysis they present to support this assumption is not justified and seems flawed. They perform a linear regression of the ratio of testing positivity (vaccinated/unvaccinated) by day over time, and because the confidence bands intersect zero they conclude the test positivity is no different between vaccinated and unvaccinated, and thus the difference in testing rate is not a bias, but from the negative effectiveness that they conclude is true.<br /> https://uploads.disquscdn.c... <br /> However, this analysis is problematic for numerous reasons:<br /> 1. It is not clear why a regression over time should be done to answer this question, and not clear why one would assume any time trend is strictly linear. It would make much better sense to compute a (geometric) mean over time, or if wanting to model time trends to use a smooth nonparametric function.<br /> 2. Computing means or modeling time trends on ratios should not be done on the raw scale, but the log scale, for the reasons discussed above.
Plotting these numbers on the log scale (again, after using AI tool to extract it from their scatterplot image in the paper), I computed the geometric mean test positivity, and find it to be 0.80, meaning the "average" test positivity over time is 20% lower in vaccinated than unvaccinated, certainly not the same.
https://uploads.disquscdn.c... <br /> This lower test positivity is obscured in their original plot on the raw scale, since the ratios <1 got compressed and ratios>1 expanded.
If you have a situation with vaccinated having 1.27x the testing rate and 0.80x the test positivity, this would correspond to an infection rate that is 1.27 x 0.80 = 1.016x higher infection rate. This would correspond no difference in infection rate, certainly not a 27% increased infection rate in vaccinated.
While not a formal analysis, this demonstrates that vaccinated having a 27% higher testing rate along with a 20% lower test positivity rate could result in a 27% higher rate of confirmed flu infections even if the infection rate was equivalent between vaccinated and unvaccinated.
In that case. the 1.27x increased testing rate would be a testing bias that produces a spurious 1.27x confirmed infection rate even if the infection rate were not higher in the vaccinated.
Based on this, one cannot tell from the study whether the 1.27x increased rate of confirmed flu infections is from negative effectiveness (as claimed), or from the testing bias (which is not adjusted for in the analysis).
The authors cannot rule out the possibility that their results are caused by the testing bias, which is not accounted for in their analysis.
Thus, I don't think the conclusion of -27% VE is valid.
At most, they could say there is no evidence of any vaccine effectiveness vs. infection, but cannot conclude a significant negative effectiveness because of failure to account for the testing bias.
Of course, there are designs to adjust for this testing bias -- test negative designs -- but the authors eschew this design, seemingly because it gives odds ratios rather than relative rates which they express concern that they are not as intuitive to grasp.
To me, that seems like a relatively minor issue relative to testing bias of sufficient magnitude to drive spurious results.
If I were reviewing this paper, I'd require them to adjust for the testing bias, and ideally perform a test negative design, even if considered a secondary analysis.
Of course test negative designs have their own limitations and potential biases, but at least considering it as a secondary analysis would be useful to see if they obtain equivalent results using that design and, if not, should raise questions on whether they should boldly conclude negative effectiveness in this study, or instead more carefully conclude a lack of evidence of vaccine effectiveness in their cohort.
These concerns are also summarized in an http://x.com thread
On 2025-04-23 12:00:02, user Pei Meng wrote:
I read your paper with great interest, but I have a question regarding the following statement:
“This approach identified putative drivers for all aut-mCAs (Table 1). Annotation of driver genes as tumor suppressors or proto-oncogenes revealed that every + aut-mCA contained proto-oncogenes as putative drivers, notably every – aut-mCA contained tumor-suppressors as putative drivers, and = aut-mCAs contained either proto-oncogenes and tumor-suppressor drivers.”
I noticed that TP53 is located on chromosome 17p, and from Extended Figure 2, there appear to be some losses on chromosome 17q. These 17q losses don't seem to contain tumor suppressors as putative drivers. Could you please clarify how these cases were classified, and whether 17q losses were considered as – aut-mCAs without tumor-suppressor drivers?
On 2025-05-08 07:52:10, user Nino Stanic wrote:
Why was there no inclusion of a control group, or even an average expected mortality line added to the graphs?
On 2025-05-01 19:06:19, user John R wrote:
Now compare to the unvaccinated.
On 2022-05-18 18:22:54, user Yosuke Tanigawa wrote:
Hi Chelsea,
Congrats on the impressive work and the talk at #BoG22. I am curious if your results would also help resolve the pathogenicity of rare SNVs or rare short indels. For example, is it possible to say rare (smaller) variants disrupting the boundaries of TADs identified from your genome-wide scan (Fig. 4) would likely be pathogenic? Suppose many pathogenic variants are enriched at such TAD boundaries at a well-characterized locus (perhaps MEF2C locus). In that case, it may be possible to gain insights into the pathogenicity of VUSs at other TADs. Thanks!
Best,<br /> Yosuke
On 2022-06-06 19:03:39, user Chris wrote:
During this period, the test positivity rate was about 7%. Some of those tests may be random (schools, needed for travel, etc.), but it seems odd that the rate of having COVID would be lower for people who go get a test than for people who just pick up the phone and answer a survey - even if it related to a 2 week period. Note the COVID tests would pick up people who were infected over some period in the past as well.
On 2022-06-17 14:18:31, user Peter J. Yim wrote:
The trial registration at ClinicalTrials.gov listed three primary endpoints: <br /> 1. Number of hospitalizations as measured by patient reports. [ Time Frame: Up to 14 days ]<br /> 2. Number of deaths as measured by patient reports [ Time Frame: Up to 14 days ]<br /> 3. Number of symptoms as measured by patient reports [ Time Frame: Up to 14 days ]
The publication reports the outcomes for none of those endpoints.
This article is close to irrelevance on the question of the efficacy of ivermectin in COVID-19.
On 2022-07-15 12:36:40, user Bob wrote:
So what happened with the peer review process?<br /> 238 days have passed, about 2/3rd (66.85%) of a year, or 8 months have passed and no updates. It hasn't been declined and claimed to be wrong, nor has it been approved ...
Either the paper did a proper job or it didn't ....
On 2022-07-23 18:11:27, user Rogerblack wrote:
The mental health scales used in this protocol are in general wholly inappropriate for someone with a condition where they are significantly fatigued, facing an uncertain future, with difficulty doing normal activities of life.
This studies depression and anxiety measure ASSUMES A HEALTHY PATIENT. 'little energy', 'trouble concentrating' 'moving slowly' = a minimum score of 3 due to physical symptoms of longcovid/fatigue. If very exhausted, this can easily rise into the 'severely depressed' range.
It is not unreasonable to use the PHQ-9 or similar as a screening measure of disease severity.
To use it in a patient population suffering from fatigue, concentration problems, ... is guaranteed to cross-read between those symptoms and anxiety - it is useless without a careful assessment of each question to find if you are measuring MH, or physical symptoms.
In past papers with this issue (many), you find unjustifed claims like (PHOSP-COVID) "The physical, cognitive and mental health burden experienced by COVID-19 survivors was <br /> considerable. This included symptoms of anxiety and depression in a quarter" without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no interpretable (scale valid) depression, and it's a scale artifact.
PHQ9 and similar scales are designed for patients without significant physical <br /> comorbidities to the mental state they are trying to measure. The normal scale cuts are only valid for this purpose.
To use them at face value is as meaningful as noting that a symptom of Orthostatic hypotension is to wobble on standing up, and concluding that patients with one leg are likely to have OI because nearly all wobble on standing up.
I am a patient with ME/CFS who has been noticing this same issue for many years.<br /> Please at the very least carefully consider scale validity per-patient severity and avoid making MH statements that come from inappropriate scale use. If you must use unmodified PHQ-9 and friends, this must come with a large warning that it is only a severity measure.
On 2022-07-28 13:28:19, user ??????? ?????? wrote:
This preprint has been published in the Journal of Medical Virology here: https://onlinelibrary.wiley...<br /> The title was slightly changed which I guess is why it was not picked up automatically.
On 2022-09-08 01:25:06, user Martin Hensher wrote:
Some people who have tweeted this article have taken your Long COVID prevalence estimates as being representative of Long COVID arising from Omicron / BA.5 infections. Yet my reading is that you have explicitly not counted Long COVID symptoms arising from the new COVID infections in your study period (June 2022); therefore people reporting Long COVID in this study do so from a prior infection, which could in fact have occurred at any time during the pandemic until June this year. Interpreting your Long COVID prevalence estimate as prevalence arising from recent BA.5 infection is therefore not correct - which some have done, but I hasten to add the authors have not! Would you agree that is a fair assessment?
On 2022-09-19 01:01:30, user Miles Markus wrote:
A question (a subject for future research) arises, indirectly, from this impressive analysis.
Relatively few hypnozoites are present in chronic Plasmodium vivax malaria. But there is a large, concealed, asexual parasite reservoir.
So does tafenoquine prevent a significant proportion of P. vivax malarial recurrences by killing many non-circulating, extra-hepatic merozoites (see reference below)?
Or are the vast majority of recurrences of P. vivax malaria relapses (i.e. recurrences that have a hypnozoite origin), as has been suggested by various researchers?
REFERENCE (the link below can be clicked on): Markus, M.B. 2022. Theoretical origin of genetically homologous Plasmodium vivax malarial recurrences. Southern African Journal of Infectious Diseases 37 (1): 369. https://doi.org/10.4102/saj...
On 2024-02-05 00:31:50, user disqus_qMy1DU5jUb wrote:
The preprint challenges the validity of two modelling papers by comparing COVID-19 mortality data across Japanese prefectures, assuming these differences reflect the impact of varying vaccination coverages. This comparison is flawed due to unaccounted variables like population density, demographics, urbanization, and epidemic stages. Moreover, it overlooks the interconnectivity between prefectures in virus spread. The critique simplistically equates, for example, Tokyo and Saitama as identical except for their vaccination rates, which is highly questionable.
On 2024-02-20 15:54:40, user John wrote:
Chesekes et al (2022) utilise 2 different defibrillators in their trial.<br /> Zoll X series - rectilinear biphasic - 120,150, 200 j protocol<br /> Lifepak 15 - truncated exponential biphasic - 200, 300, 360 j protocol<br /> Both have a 15% variance in actual energy delivered.<br /> Is there consideration to be made to the range of energy delivered to the VC and DSD cohorts?<br /> VC - 200J Zoll 360J Lifepak<br /> DSD - 400J Zoll 720J Lifepak<br /> Should future trials use a single type of defibrillator to remove this as a possible confounding variable?
On 2024-02-28 20:15:40, user Robert Chambers' vestiges wrote:
Great paper especially the common detection. in cases of encephalitis. Also nicely list past reports of these viruses in diverse systems.
Could it be that these gemykibivirus actually infect some unidentified protozoa that is actually causing the disease?Many other CRESS-DNA viral genomes seem to infect protozoa as reported in https://pubmed.ncbi.nlm.nih...<br /> https://www.mdpi.com/1422-0...
On 2024-03-14 13:13:14, user Rune Wilkens wrote:
This is a very interesting study! Why not discuss and highlight that 20% of the "cirrhosis" patients have PBC? One of the big drivers of the difference between IBD and non-IBD looks like being PBC ("immune-mediated") or even viral. This provides a different picture.
On 2024-04-11 17:36:56, user JMIR Publications wrote:
Join JMIR Publications & PREreview for a Live Review of this preprint: Assessing the Incidence of Postoperative Diabetes in Gastric Cancer Patients: A Comparative Study of Roux-en-Y Gastrectomy and Other Surgical Reconstruction Techniques - by Tatsuki Onishi medRxiv: https://hubs.la/Q02stzCL0
April 19, 9am PT / 12pm ET / 4pm UTC
Learn More & Register: https://hubs.la/Q02stwwt0
On 2024-05-14 18:33:34, user Chris Buck wrote:
It seems to me that the simplest explanation for these data is that a lot of people are walking around with an H5N1 infection in their gut. It's certainly a follow-up hypothesis worth testing. I've posted a more detailed argument on my Substack page: <br /> https://open.substack.com/pub/cbuck/p/h5n1-is-messing-with-texans
On 2024-08-21 16:23:52, user DUPA- Preprint Review wrote:
Overall, this is a well-designed and conducted analysis that provides valuable insights into comorbidity patterns among early COVID-19 deaths in the United States. The manuscript presents important findings on the morbidity patterns associated with COVID-19 mortality and offers valuable insights for public health strategies. The latent class analysis (LCA) is a widely utilized clustering method for investigating comorbidities, which effectively addresses the issue of collinearity among comorbidities in high-risk populations. It could help identify disease patterns and understand disease relationships. The findings give researchers and health departments detailed knowledge to quickly identify vulnerable populations and provide protection in these public health emergencies. However, addressing the suggestions outlined above will enhance the clarity, transparency, and impact of the study. Therefore, we recommend the manuscript for publication with minor and major revisions.
Major Comments:
In Materials and Methods section, line 6 of the second paragraph, it is noted that cardiovascular disease (CVD) includes a variety of diseases/conditions with different prevalence and severity. For example, hypertension may have a significantly higher prevalence compared to other diseases within the CVD group, potentially leading to a disproportionate representation. Is it possible to list the prevalence of individual diseases in the supplementary material? Additionally, It would be beneficial to separate the diseases that have more than 60%(or other value)prevalence as the sensitivity analysis. This approach could enhance the stability of the study by avoiding amplifyfication of the effects from individual diseases with high prevalence. On the other hand, it also provides more details and discussion for the formation of the present results.
Minor Comments:
In the Abstract, Results section line 3: the phrase “A low frequency of comorbidities” is not precise. Use several words to express “where the prevalence of each comorbidity group was less than that of the entire sample” could be clear.
In the Introduction, paragraph 1: <br /> The study effectively reaffirms the importance of cardiovascular disease and diabetes. Including a comparison with other studies conducted during the same period would provide valuable context.
In the Discussion section, paragraph 2, line 3-6:<br /> Cardiovascular disease was present at 23%, even in the "minimal prevalence" category, which includes cardiovascular disease and diabetes, prominent cardiovascular disease without diabetes, and "minimal prevalence." Is there a difference in the distribution of each disease? Could this same/different distribution further explain the large proportion of "minimal prevalence" in people over 85 years old?
In the Discussion section, paragraph 2, line 7-11:<br /> What are the mechanisms behind the high rankings for kidney disease and chronic lung disease.
In the Discussion section, paragraph 2, line 1:<br /> In addition, please briefly state the underlying mechanisms behind cardiovascular disease and diabetes, such as mechanisms of interaction between cardiovascular disease and diabetes.
In the Discussion Section, paragraph 4, line 2:<br /> The discussion effectively interprets the findings, particularly identifying the "minimal prevalence" class. But besides the eldest group, the proportions of this class still lead in other age groups. Is there any other explanation for why the "minimal prevalence" class still experienced significant mortality? It would also be helpful to provide more details based on citations 28,29(or other literature) to explain the reasonableness of proportions. This additional detail could offer deeper insights into the underlying factors contributing to their outcomes.
On 2024-08-28 04:54:25, user John Orchard wrote:
My own comments having read many excellent replies from the COVID cautious and COVID stoical community on X in the last 24 hours are summarized at https://x.com/DrJohnOrchard/status/1828598891027333208
On 2024-10-12 09:28:56, user Alireza Mani wrote:
This manuscript has now been published. Please see the published version at: https://www.nature.com/articles/s41598-024-74351-2
On 2024-10-13 17:50:05, user Tom Hagan wrote:
Consider that niclosamide is highly lipophilic (LogP of 3.91) and the distal rectum’s inferior and middle veins have been employed to systemically transport many rectally administered, insoluble drugs. Would niclosamide have greater bioavailability if simply compounded as rectal suppositories? Apparently - from searching the journals- this has never been attempted.
On 2024-11-15 15:00:42, user Chandni Khemai wrote:
This paper has been accepted for publication in the Journal of Integrated Care, published by Emerald Publishing Limited.
On 2025-02-20 16:46:44, user Caleb Hartman wrote:
SN Table 5 and SN Table 6 appear to present the exact same mutational signature activities data for copy number signatures. The signature column labels for both tables appear to be decomposed COSMIC signature labels. I think SN Table 5 needs to contain de novo extracted copy number activities values. Would the authors be able to update this discrepancy?
On 2025-02-26 23:25:34, user Guido Mazzone wrote:
The sentence " This stop gain variant has not been previously reported and it is not present in <br /> gnomAD and 1000 genomes databases" is not correct anymore.<br /> ERMARD(NM_018341.3):c.1523G>A is actually present in gnomAD 4.1.0 with global AF=1/1613140 and South Asian AF=1/90712.<br /> This is interesting because the patient is South Asian.<br /> https://gnomad.broadinstitute.org/variant/6-169776457-G-A?dataset=gnomad_r4
On 2025-03-14 16:31:28, user Andrea Bellavia wrote:
The paper is now published on EBPH https://riviste.unimi.it/index.php/ebph/article/view/28145
On 2025-04-01 17:33:08, user Richard DiBenedetto wrote:
A number of Havana Syndrome victims reported a sudden onset of symptoms resembling effects of a concussion. Impairment similarity with areas of the brain affected by pesticides does not conclude cause of sudden concussion. Sudden symptoms from my personal experience were more like radio frequency, EMP or radar type technology. There has been much research on microwave bioeffects which is worth further study for a cause of Havana Syndrome.
On 2025-04-22 13:57:17, user Maarit Piirtola wrote:
Dear reader,
Behalf of the study group, I'm happy to make an announcement that the paper has been published in the Journal of Affective Disorders ( https://www.sciencedirect.com/science/article/abs/pii/S016503272401841X) "https://www.sciencedirect.com/science/article/abs/pii/S016503272401841X)") .
Best wishes, <br /> Maarit Piirtola
On 2025-04-29 13:39:34, user Guignabert wrote:
Dear Dr. Sajid Shahul and colleagues, <br /> Reference 3 is incorrect and should be replaced with the following: Guignabert C, Savale L, Boucly A, Thuillet R, Tu L, Ottaviani M, Rhodes CJ, De Groote P, Prévot G, Bergot E, Bourdin A, Howard LS, Fadel E, Beurnier A, Roche A, Jevnikar M, Jaïs X, Montani D, Wilkins MR, Sitbon O, Humbert M. Serum and Pulmonary Expression Profiles of the Activin Signaling System in Pulmonary Arterial Hypertension. Circulation. 2023 Jun 13;147(24):1809-1822. doi:10.1161/CIRCULATIONAHA.122.061501. Epub 2023 Apr 25. PMID: 37096577. <br /> Thank you.
On 2025-05-22 10:50:12, user Naoki Watanabe wrote:
We are pleased to announce that this preprint has undergone peer review and has been published in a formal journal. Please refer to the final version of the article.
Watanabe, N., Watari, T., Hosokawa, N., & Otsuka, Y. (2025). Alistipes Bacteremia in Older Patients with Digestive and Cancer Comorbidities, Japan, 2016–2023. Emerging Infectious Diseases, 31(4), 652.
On 2025-06-12 12:45:41, user Juri-Alexander Witt wrote:
The article has now been finally published in CNS Drugs:<br /> https://link.springer.com/article/10.1007/s40263-025-01196-2
On 2025-06-23 20:40:10, user FletchKennedy wrote:
This write-up in Nature News (linked below) makes some good points. The creators of Otto-SR call it an "end-to-end agentic workflow using large language models (LLMs) to support and automate the SR workflow from initial search to analysis," but then they really only talk about the screening and data extraction processes. They also indicate that they used the pre-existing search strings when re-running the Cochrane Reviews.
I'm not saying that speeding up the screening and data extraction processes aren't valuable, they are; but building a proper Cochrane search string can take months. Saying you did 12 work-years of traditional systematic review work in two days is a pretty bold claim when you're really only doing two parts of the process.
On 2025-07-03 15:36:38, user Iraq Body Count wrote:
We commend the authors of the Gaza Mortality Survey (GMS), and in particular their Palestinian survey colleagues, for producing the first rigorous estimate of violent deaths in Gaza since 7 October 2023 which is completely independent of deaths documented and collated by Gaza’s Ministry of Health (GMoH). Also significant is that it contains the first data-driven attempt to estimate non-violent deaths, which has so far been lacking from any other source.
Also welcome is that, while the GMoH’s numbers are notably lower than those in GMS, its authors recognise that “By naming individual victims one by one, the GMoH endows each person with a measure of human dignity.”
In their concluding section titled “The Future”, the authors go on to state that “Undercounting of violent deaths by the GMoH is likely to persist.” However the level of this undercount cannot be consistently derived from a single snapshot survey, for the simple reason that the GMoH documentation is continually being backfilled, as we have discussed extensively elsewhere: https://iraqbodycount.substack.com/p/gazas-internal-list-of-the-killed .
The number the authors provide for the “comparable” period to GMS is one which the GMoH put out in early January 2025: 45,650. However in the GMoH’s list published March 2025, which the authors refer to elsewhere, this number had grown to 48,440. Latest GMoH data (15 June 2025) show that they have further increased their number of verified violent deaths for the period to 49,048 individuals.
In addition to the deaths listed by GMoH, another 4122 identified dead were known to them by 10 April 2025 but had yet to be verified for addition to their list. (See: https://iraqbodycount.substack.com/p/gazas-victim-details-and-victim-deniers ) On past evidence, most if not all of these names will eventually be included too.
In fact, had the GMS been conducted a year earlier (January 2024) the gap between its estimates and deaths listed by GMoH would have been markedly wider, as the GMoH has increased its numbers for that early period from an initial 14,121 to 26,987 (an increase of 91%). As the backfilling has progressed, the shortfalls have become appreciably smaller. A notable and predictable pattern has been that the higher the intensity of killing, the more has needed to be completed later. At any rate, these efforts by the GMoH have been constant (not to say noble and brave) and are likely to continue to reduce the difference between competently estimated and actually-recorded casualties.
So any figure given for the level of difference between a survey and the GMoH is temporary, provisional, and dependent on the date at which the GMoH data was accessed.
This exemplifies some of the difficulties in comparing a snapshot view such as is obtained by a survey with an ongoing casualty recording effort conducted on a daily basis. Any such comparison needs to be done with appropriate caveats which, if not included, might have the unintended effect of setting in stone a particular estimate of official “undercounting”, thus undermining essential casualty documentation efforts, particularly where such efforts are already being impeded by the most awful circumstances on the ground.
Hamit Dardagan and John Sloboda, Iraq Body Count, London, UK<br /> 3 July 2025
On 2025-07-06 08:41:23, user xu-sheng zhang wrote:
Dear medRxiv staff
I just want to inform you that our article has been [published in The Lancet Regional Health - Europe 2025; https://doi.org/10. 1016/j.lanepe.2025. 101364<br /> with a title: "Cost-effectiveness of vaccination strategies to control future mpox outbreaks in England: a modelling study". could you please help signpost to it.<br /> Best wishes<br /> Xu-Sheng Zhang
On 2025-07-08 12:22:49, user Md Rakibul Hasan wrote:
The article have recently been published in a journal, please see the following link
Hasan, M. R., Sultana, N., Panthi, S., Hasan, M., Jahan, S., & Hasanat, M. A. (2025). Fasting Plasma Glucose as a Primary Screening Test for the Diagnosis of Gestational Diabetes mellitus: Fasting Plasma Glucose and Gestational Diabetes mellitus. Journal of the Medical College for Women & Hospital, 21(1), 43–51. https://doi.org/10.3329/jmcwh.v21i1.80952
On 2025-07-13 08:45:42, user Ben Auxier wrote:
In their pre-print Brackin et al. [1] present data suggesting nosocomial infections (that is, infections arising from the clinical environment) of patients infected with A. fumigatus. This is a surprising finding, given the near universal abundance of this fungus. As I detail below, there is no evidence of transmission chains within a hospital. Rather, the analyses presented fall victim to the statistics of detecting matches within populations of differing sizes, related to what is commonly referred to as “the birthday paradox”. The main data in this paper consists of whole genome sequencing data from 182 isolates from 15 patients (>2 samples from each patient), 101 isolates from patient’s homes, and 102 isolates from a medical centre that all patients visited. From these data, three comparisons are made between a) case samples and general environmental samples, b) case samples and their own home and c) case samples and the reference clinic.
The authors find that there are links for a), consistent with reports over the last several decades that A. fumigatus populations are highly recombinant, but includes widely dispersed clones [2–5]. More interestingly, they find no links for b) but abundant links in c), which would be consistent with hospital spread. However, while the sample sizes in b) and c) are equivalent, the comparisons are not. Across the 8 cases (average of 11.3 isolates per case) where the housing was also sampled, an average of 12.6 isolates per house (101 total) were used for whole genome sequencing. This leads to ~1000 comparisons being made, due to substructure in the data. Notably, since some patients have long-term infections of one genotype, this number is an overestimate due to within-patient correlations. Then, all 182 patient isolates (more than the 8 patients sampled) are compared against all 102 isolates from the medical centre, producing over 18,000 comparisons. Thus, using a null hypothesis of no difference between patient-hospital and patient-home data, since there are ~20X more patient-hospital comparisons (and 20% of patient samples match a hospital sample), a naïve expectation would be 1% of patient-home comparisons to be clonally related, likely detectable in the ~1000 comparisons.
Unfortunately, their analysis falls into the “birthday paradox”. Briefly stated, this paradox reflects the fact that while the chance that you share a birthday with someone else is 1/365, the chance that two people share a birthday within a classroom of 30 students is not 8% (30/365), but instead a surprisingly high 70%. This is because in the classroom situation you not only have a larger group, but also many more combinations. The chance of sharing a birthday can be considered as the chance of sampling two identical genotypes from a population of clones. Thus, while roughly equal numbers of isolates from homes and the reference center were used for genome sequencing, this difference in structure means that comparisons with patient isolates are unequal. However, the birthday paradox shows that this math is not intuitive and the chance of finding matches increases non-linearly. So, while perhaps 10 matches should have been expected between patients and their homes, which would already be a tenuous link, the expected number is effectively zero due to the smaller sample size.
The actual sites of infection for A. fumigatus is important to discern. The cryptic nature of initial infections makes this a challenging task, requiring creative experimental or observational studies. However, I would argue simply identifying clonal matches provides insufficient evidence..
References:<br /> 1. Brackin, A. P. et al. Genomic epidemiology links azole-resistant Aspergillus fumigatus hospital bioaerosols to chronic respiratory aspergillosis. 2025.07.04.25330042 Preprint at https://doi.org/10.1101/2025.07.04.25330042 (2025).
Chazalet, V. et al. Molecular Typing of Environmental and Patient Isolates of Aspergillus fumigatus from Various Hospital Settings. Journal of Clinical Microbiology 36, 1494–1500 (1998).
Rhodes, J. et al. Population genomics confirms acquisition of drug-resistant Aspergillus fumigatus infection by humans from the environment. Nat Microbiol 7, 663–674 (2022).
Shelton, J. M. G. et al. Landscape-scale exposure to multiazole-resistant Aspergillus fumigatus bioaerosols. 2022.11.07.515445 Preprint at https://doi.org/10.1101/2022.11.07.515445 (2022).
Snelders, E. et al. Widely dispersed clonal expansion of multi-fungicide-resistant Aspergillus fumigatus limits genomic epidemiology prospects. mBio 16, e03652-24 (2025).
On 2025-07-14 12:23:02, user Massimiliano Mazza wrote:
Yes, the paper has been recently published here:
https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1488860/full
On 2025-08-10 13:57:05, user zerihun woldesenbet meja wrote:
A timely and impactful study on HIV care in Ethiopia. The research team highlights the key risk factors for second-line ART failure, urging better adherence and continuity strategies. I hope this study fills a critical data gap and guides targeted interventions to improve patient outcomes.
On 2025-08-26 09:25:30, user Constant VINATIER wrote:
Feedbacks about your preprint : https://doi.org/10.1101/2025.07.31.25332504
About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. The protocol allows for an authentication and authorization procedure, where necessary. As your data contains sensitive data, we suggest to make it Findability, Accessibility, Interoperability, and Reuse ( https://www.go-fair.org/fair-principles/) "https://www.go-fair.org/fair-principles/)") by providing some details on this procedure.<br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .<br /> Comments :<br /> Dear authors,<br /> You did not publicly share your data but adequately justified why (confidentiality of data from patient records) and clearly explained the procedure for obtaining it, thank you. But is it possible to openly share the code ?
On 2025-09-16 11:36:42, user Ayan Dey wrote:
Hello this article is now published. Please see BMJ Mental health for the final version. https://mentalhealth.bmj.com/content/28/1/e301663
On 2025-10-07 13:23:10, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
The study examines the effect of cigarette taxes on smoking behaviors (participation, cessation, and intensity) and whether these effects differ by polygenic indices and timing of exposure to cigarette taxes.
The authors find that cigarette tax exposure during adolescence is a determinant of lifetime smoking status (cigarette tax is a deterrent of smoking participation), and the effect of cigarette taxes during adolescence is significantly higher for individuals with a higher genetic predisposition for smoking. The authors also find that ordinary least squares models underestimate the detrimental effects of smoking on chronic disease.
Future studies can explore other genetic ancestries and within-family GWAS.
Highlights the importance of youth-targeted tobacco taxes, taking into account the risk of initiating smoking.
On 2025-10-15 20:48:18, user jpirruccello wrote:
The updated version of this manuscript has been published; please see https://pubmed.ncbi.nlm.nih.gov/40175013/
On 2025-10-18 14:47:45, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
The authors used metagenomic studies from the PARSIFAL study to determine if there is a difference in ARG/species abundance between 1) children who live on farms and 2) attend a Steiner school and geographically-matched reference groups.
High-abundance taxa were similar across all groups. Most differences were observed within low-abundance and often individualized taxa
GLM between ARG load and other study variables found that BMI, length of time having been breastfed, and age had significant negative relationships with ARG load, regardless of lifestyle
On 2025-10-21 22:10:03, user Carlos A. Fermín-Martínez wrote:
The published version of this work is now available at: https://academic.oup.com/jcem/advance-article/doi/10.1210/clinem/dgaf225/8110154
On 2025-11-20 17:36:10, user Ceejay wrote:
This study is very interesting.
There are various exercise regimes popularly described in recent years for Long Covid sufferers, two often mentioned are 1. Graded Exercise Therapy and 2. Exercise Pacing. The former is characterised by a manageable but significant exercise level which is steadily increased, the latter is a strict regime of minimal exercise and only slowly increasing this as the person is able. A key difference between these methods is probably that Pacing is designed at such a low level of exertion that it avoids provoking PEM (Post-Exertional Malaise, starting a few days after the exercise), whereas Graded Exercise Therapy may provoke PEM and if properly monitored the exercise level should be reduced. Some opinion in favour of Pacing even states that GET is contra-indicated in LC. Part of the problem here is a lack of clarity in describing the two different exercise regimes.
It would therefore help to know more about the exact exercise regimes you applied (lines 146 to 149):
* List all exercises, and whether each was strength or cardiovascular exercise.
* What were the criteria for starting exercise, and for incrementing or decrementing exercise (lines 149 to 153) during the 32 months.
* Was PEM monitored for?
* Would the schedules adopted fit into either of the two popular descriptions (GET vs Pacing) or could this even be dynamically changeable according to progress?
* What was the typical level, duration and frequency of the maximum exercise undertaken by month 32 or at the asymptomatic point.
* Do you have data on the periods to achievement of asymptomatic status (or end of trial)? Fig 3 provides no data at periods between 6 and 32 months.
* Figure 3 suggests that by month 32, 45.5% of participants were still not asymptomatic, so can this statistic infer anything about the suitability or advisable emphasis of the exercise program?
* Can the data from your study enable arbitration between GET and Pacing? e.g. could the prognostic point of 21 days inform the ongoing exercise program?
Thank you.
On 2020-04-07 03:18:15, user Tomas Hull wrote:
Even if 2 strains of coV-2 exist, one more lethal than the other, the confirmation is far away. <br /> Why not look at the similarities between the populations of Italy, Spain, France, China? Antibiotic resistance is well documented. Smoking, comorbidities and their treatments, lead to upregulation of ACE2 receptors and therefore could account for lethality of the same virus strain and the supposed statistical anomalies...
On 2020-06-23 19:40:15, user Roman Shein wrote:
In an interview Hendrik Streeck claimed the specificity to be 99%. How? In March, when all mayhem just started to envelop! How were these test verified?<br /> At the same time, the very same Hendrik Streeck has co-authored a paper (published), that the antibody tests are rubbish, not fit to diagnose Covid. The specificity is around 88%. I admit the paper concerns antibody test usage at much earlier stage of the disease, but nevertheless his own assessment seems to contradict the claim about 99%.
On 2020-06-25 15:24:39, user Nojan Aliahmad wrote:
great work with very good control. The impact of unregulated cytokines and inflammatory compounds (such as CRP) on COVID-19 is a very important discussion. Future clinical trials can show how effective will be vitamin D supplements in reducing these unregulated compounds.<br /> Dexamethasone is the first drug showing success in reducing the mortality rate of COVID-19 in clinical trials. It also works on the principal of reducing unregulated cytokine.
On 2020-06-26 09:28:20, user Dena E. Utne wrote:
Al this study shows is that there wasn't a lot of Covid-19 around when they did the study. There wasn't enough Covid-19 around at the time of the study to make claims about the safety of gyms. It is disappointing to see the BBC summarizing the conclusions of this article, when the conclusions are not supported by the actual science. I don't think this article will or should pass peer review.
On 2020-06-29 16:14:10, user xanthoptica wrote:
Zero SARS-CoV2 infections in control group, one SARS-CoV2 infection in treatment group acquired before treatment (gym attendance - unclear if individual was prevented from going to gym based on positive test). Essentially zero statistical power. This study only tested whether there was enough coronavirus around Oslo to cause transmission at the gym in any conditions...and there was not.
On 2020-06-26 16:22:15, user disqus_XufFG9Zovr wrote:
Has this been adjusted for time?
Do the masks just slow the spread and delay herd immunity?
Is the total death in the community less over all time for mask wearers or is it just a technique to flatten the curve?
Mortality per day is not an adequate goal. Total death must be considered as well.
On 2020-07-03 03:58:20, user Pedro wrote:
Considering that many doctors prescribe ivermectin as strongyloidiasis prophylaxis before the administration of high doses of corticosteroids, and that the use of dexamethasone has been shown to be effective in reducing mortality in patients with Covid-19 in RECOVERY Trial, was there any difference in the use of corticosteroids between the groups in this study?
On 2020-07-07 16:15:43, user Michael Hombach wrote:
Very interesting data!<br /> Pearson’s r quantifies the extend of the linear relation between two variables. Both variables are assumed to be continuous. Heavy-tailed distributions of the data, e.g. many values at the lower or upper end, might highly influence both Pearson’s r estimate. In addition, Pearson's correlation is not sufficiently robust against outliers.<br /> Spearman’s rank correlation ? is appropriate for both continuous as well as discrete ordinal variables. In contrast to Pearson’s r it does not assess the linear relation but the monotonic relation between two variables, based on the rank of the absolute values. Spearman’s ? is therefore better suited for heavy-tailed distributions than Pearson’s r. <br /> The paper includes already a proper calculation of agreement rates between the serological assays to the NT titre measurement values. The authors additionally use Pearson’s r to conclude about the performance of the assays. The paper and the conclusion would highly benefit from additionally presenting Spearman’s rank correlation coefficient since NT dilution rows depict non-continuous data that are heavy-tailed at the upper end. A final conclusion and discussion should be initialized based on both the agreement rates and the correlation of the assays based on Spearman’s rho. E.g. applying Spearman’s correlation to the presented line listing data based on R-package ‘spearman.CI’ (literature: de Carvalho, M. and Marques, F. J. (2012). Jackknife Euclidean likelihood-based inference for Spearman’s rho. North American Actuarial Journal, 16, 487–492.) we found rhos of 0.6714, 0.6768, 0.5854, 0.7583, 0.8131 for EI S1 IgA, EI S1 IgG, DiaSorin S1/S2 IgG, Abbott N IgG, and Roche N Ab, respectively.
On 2020-07-11 18:20:51, user Joan Saldana wrote:
Dear authors, since your average force of infection term lambda includes N in its denominator, I don't see why expressions (2) and (3) of R0 also include N. Suppose \rho=0 (exposed are not infectious) and x=1 for everybody. In this standard case, the infection term in (1) is beta·S·I/N and, then, R0=beta/gamma. From (2) and (3), however, it follows that in this case R0=(beta/N)/gamma, which is not correct. Am I missing something from the model? On the other hand, the values of R0 in the figures are reasonable, so perhaps this is typo. Thank you!
On 2020-07-12 18:43:47, user Mario Moisés Alvarez wrote:
Please share with us your opinion on this contribution. We really want to raise awareness on the importance of massive testing particularly in densely populated cities. <br /> Very Best. Stay safe.
On 2020-07-13 09:10:17, user OxImmuno Literature Initiative wrote:
On 2020-07-14 13:01:28, user Hedda von Rosa wrote:
Was selenium considered and if so what was the rationale for not including in the protocol?
On 2019-07-17 16:54:37, user Guyguy wrote:
EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI
Tuesday, July 16, 2019
The epidemiological situation of the Ebola Virus Disease dated July 15, 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,512, 2,418 confirmed and 94 probable. In total, there were 1,676 deaths (1,582 confirmed and 94 probable) and 703 people healed.<br /> 423 suspected cases under investigation;<br /> 11 new confirmed cases, including 5 in Beni, 2 in Mandima, 1 in Mabalako, 1 in Vuhovi, 1 in Katwa and 1 in Komanda;<br /> 8 new confirmed cases deaths:<br /> 3 community deaths, 2 in Beni and 1 in Mandima;<br /> 5 deaths at Ebola Treatment Center, including 4 in Beni and 1 in Goma;<br /> 3 people cured out of Ebola Treatment Center including 2 in Butembo and 1 in Katwa.
136 Contaminated health workers
The cumulative number of confirmed / probable cases among health workers is 136 (5% of all confirmed / probable cases), including 41 deaths.
163,533 Vaccinated persons
The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 19 May 2018.
75,321,895 Controlled people
NEWS
Follow-up of the situation of the pastor's contacts who traveled to Goma
On Monday, July 15, 2019, 37 high-risk contacts and 40 Goma confirmed case contacts were vaccinated at the Afia Himbi health center where the patient had been isolated before being transferred to the Ebola Treatment Center. In total, 97 contacts in the broad sense have already been listed to date. Vaccination will continue until all identified contacts have been vaccinated.<br /> Among the contacts identified were two women from the pastor's family traveling with him. After the pastor's transfer to CTE, they hid in Goma and some people thought they fled to Bukavu in South Kivu province. Fortunately, the two women were found in Goma on Tuesday and will be vaccinated.
On 2019-08-03 19:22:31, user GuyguyKabundi Tshima wrote:
Dear all, here is the daily bulletin on the evolution of the response to the Ebola Virus Disease outbreak of 01 August 2019. The field information verification process has been more painful because of the sensitivity of the events on the ground. .<br /> Please be indulgent for the delay.
EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI
Thursday, August 01, 2019
Epidemiological Status of Ebola Virus Disease as of 31 July 2019
Since the beginning of the epidemic, the cumulative number of cases is 2,713, of which 2,619 confirmed and 94 probable. In total, there were 1,823 deaths (1,729 confirmed and 94 probable) and 782 people healed.<br /> 423 suspected cases under investigation;<br /> 13 new confirmed cases, including 5 in Beni, 2 in Mabalako, 2 in Mandima, 1 in Nyiragongo (Goma), 1 in Vuhovi, 1 in Katwa and 1 in Mutwanga;<br /> 10 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Beni and 1 in Mandima;<br /> 7 Ebola Treatment Center (ETC) deaths, including 3 in Beni, 2 in Mabalako, 1 in Komanda and 1 in Goma;<br /> 1 death at the ETC of Beni;<br /> 6 people cured out of ETC, including 5 in Beni and 1 in Katwa;<br /> One health worker, living and vaccinated, is among the new confirmed cases in Beni. The cumulative number of confirmed / probable cases among health workers is 149 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
As a reminder, the recommendations of the Ministry of Health are as follows:<br /> Follow basic hygiene practices, including regular hand washing with soap and water or ashes;<br /> If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu civil protection hotline directly;<br /> If you are identified as an Ebola patient contact, agree to be vaccinated and followed for 21 days;<br /> If a person dies because of Ebola, follow the rules for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.<br /> For all health professionals, observe the hygiene measures in the health centers and declare any patient with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect the sanitary measures recommended by the Ministry of Health, it is possible to ensure that this case of Ebola detected in Goma is only a sporadic case that does not cause a new outbreak.
Follow-up of the situation of contacts of the second case confirmed Ebola of Goma<br /> 151 contacts have been reported around the 2nd confirmed case of EVD in Goma since 22 July 2019. Among these contacts, 118 have already been vaccinated, including 70 at high risk (CHR) and 48 contact contacts (CC);<br /> The girl and the woman of this case of Goma constitute to date the 3rd and 4th positive cases of EVD recorded in Goma;<br /> The sister of this same case, who fled to the province of South Kivu, was found in Biara in the health zone of Muti Muresa. 40 contacts have already been vaccinated around this contact this Thursday, August 1, 2019, including 9 high-risk contacts and 31 contacts.
A traditional healer among the confirmed cases of Mabalako<br /> This is a 25 year old man, living and vaccinated on July 20, 2019 (geographical vaccination). He practiced self-medication on July 24-29, 2019 with a gradual worsening of symptoms.
It was taken to the CTE after validation on July 30, 2019 after the alert launched by a Community Relay (ReCo). It was confirmed MVE on July 31, 2019. 22 contact persons are listed around this case, whose investigations are ongoing.<br /> The confirmed case of Lubero on the run<br /> The confirmed case of July 25, 2019 in Lubero Health Zone (ZS), who fled into the community, is reported to be in Lukanga in the Masereka SZ, 17 km from Lubero. A team went there on Thursday, August 1st, 2019 for its transfer to CTE.
80,481,013<br /> Controlled people<br /> 98 entry points (PoE) and operational sanitary control points (PoC).
149<br /> Contaminated health workers<br /> 1 health workers, living and vaccinated, are among the new confirmed cases of Beni.<br /> The cumulative number of confirmed / probable cases among health workers is 149 (5% of all confirmed / probable cases) including 41 deaths.
Source: The press team of the Ministry of Health.
On 2019-09-30 05:15:29, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT SEPTEMBER 22, 2019
The epidemiological situation of the Ebola Virus Disease dated September 22, 2019
Monday, September 23, 2019
Since the beginning of the epidemic, the cumulative number of cases is 3,168, of which 3,057 are confirmed and 111 are probable. In total, there were 2.118 deaths (2007 confirmed and 111 probable) and 975 people healed. <br /> • 343 suspected cases under investigation; <br /> • 4 new confirmed cases, including: <br /> • 1 in North Kivu in Butembo; <br /> • 3 in Ituri, including 2 in Mandima and 1 in Mambasa. <br /> • 3 new confirmed deaths, including: <br /> • 2 community deaths, including 1 in North Kivu in Butembo and 1 in Ituri in Mambasa; <br /> • 1 confirmed death in Ituri in Mandima. <br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 160 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Beginning of trainers training in Goma on good clinical practices related to the second Ebola vaccine <br /> • The Ebola Virus Disease Response Information Management Coordinator, representing the Technical Secretariat, Mathias Mossoko, launched on Monday in Goma the training of trainers which runs from 23 to 28 September 2019 on the good clinical practices (PCBs) related to the second Ebola vaccine. <br /> • This training benefits from the expertise of CVD's Malians on the transmission of notions about good clinical practice. It aims to provide participants with the standards applicable to the design, conduct, monitoring and stopping of studies, to teach them the activities of audit, analysis, reporting and documentation with the guarantee that these studies 'rely on sound scientific and ethical principles. It is also intended to introduce participants to the correct documentation of the clinical properties of the vaccine tested or evaluated. <br /> • The Response Information Management Coordinator called the attendance participants to demonstrate better actors for the implementation of good practice in this second vaccine. <br /> • For its part, the chairman of the Immunization Committee, Stéphane Hans, said that this five-day training announces the forthcoming launch of the second vaccine that will come at any time in the targeted health zones. "We welcome this supplementary vaccine very positively compared to the first vaccine. This second vaccine has the advantage of preventing all strains of the Ebola virus. It is therefore positive for the population that will receive it, "he said while inviting all communities targeted by this vaccination to take ownership of this activity, once launched. <br /> • The training on good clinical practice will revolve around several presentations on different topics, among others, the Ebola virus disease, the responsibilities of the INRB for the QA system, study vaccines (storage, management, chain cold and accounting), inclusion and follow-up of pregnant women, community involvement and informed consent, etc. <br /> • This training was organized for the different actors involved in this project, including doctors, epidemiologists, clinicians and pharmacists. A total of 25 people from Kinshasa, including the INRB, UNIKIN, CUK and specialized programs and North Kivu, including the Provincial Health Inspectorate (IPS), the Provincial Division of Health Centers (DPS) and Health Zone Coordinating Offices (BCZS) are participating in this meeting. <br /> As a reminder, the recommendations of the MULTISECTORAL COMMITTEE ON THE RESPONSE TO THE EBOLA VIRUS DISEASE are as follows: * <br /> 1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes; <br /> 2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu Civil Protection toll-free number; <br /> 3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days; <br /> 4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination. <br /> 5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding). <br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
VACCINATION <br /> Opening of an expanded vaccination ring around two confirmed cases from 19-21 Sept 2019 in the Madidi health area in Mambasa, Ituri. Another satellite ring was opened at the Kitatumba General Referral Hospital in the Butembo Health Zone in North Kivu around the case notified on 22 09 2019. This case started the disease in the health area of Kasindi in Mutwanga, North Kivu. <br /> • The Expanded Program of Vaccines has received 4320 doses of vaccine at the national level; <br /> • Since vaccination began on August 8, 2018, 226,722 people have been vaccinated; <br /> • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.
MONITORING AT ENTRY POINTS <br /> • High-risk contact was intercepted at Kangote PoC in Butembo, North Kivu. This is a 28-year-old unvaccinated woman on the 14th day (D14) follow-up who was listed around a confirmed case in the Katwa Health Zone. During her interception, this woman presented some signs related to the #Ebola Virus Disease. She was sent to the Butembo CTE for treatment. <br /> • Kituku PoC providers in Goma, North Kivu, were assaulted by about 20 onlookers called "Maibobo" who were avenging one of their drowned during the night of 20 to 21 September 2019. These providers feel insecure and ask to be supported by officers of the National Police (PNC) or FARDC. <br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement) at the sanitary control points up to 22 September 2019 is 96,998,860; <br /> • To date, a total of 111 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
LEXICON <br /> • A community death is any death that occurs outside a Ebola Treatment Center. <br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.
On 2019-10-04 22:38:03, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 03 OCTOBER 2019
Friday, October 04, 2019
Since the beginning of the epidemic, the cumulative number of cases is 3,201, of which 3,087 are confirmed and 114 are probable. In total, there were 2.139 deaths (2025 confirmed and 114 probable) and 999 people cured.<br /> 451 suspected cases under investigation;<br /> 3 new confirmed cases, including:<br /> 1 in North Kivu in Beni;<br /> 2 in Ituri, including 1 in Mambasa and 1 in Mandima;<br /> 2 new confirmed deaths in North Kivu, including 1 in Beni and 1 in Mabalako;<br /> 4 people healed from the CTE in North Kivu in Beni;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.<br /> Day 18 without response activities in the Lwemba Health Area in Mandima, Ituri.
LEXICON<br /> • A community death is any death that occurs outside a Ebola Treatment Center.<br /> • A probable case is a death for which it was not possible to obtain biological samples for confirmation in the laboratory but where the investigations revealed an epidemiological link with a confirmed or probable case.
NEWS<br /> The 10th Ebola Virus Disease epidemic in the DRC reaches its 1000th cure<br /> - The thousandth cured of the Ebola Virus Disease came out Friday of the Mangina CTE in Mabalako in North Kivu Province;<br /> - Indeed, this 1000th cured is part of four healed Friday of this CTE. It is about a woman, quarantine gone, case contact of her nephew with the Air of Health of Lwemba with Mandima in Ituri. As soon as she felt the fever, she went to the Health Center, where she was detected as a suspected case and transferred directly to the CTE. She was confirmed and followed her treatment until recovery. She advises the population to go quickly to the Health Center and not fear the CTE to cure Ebola Virus Disease;<br /> - Among these four cures, there is also a health provider. This is an ambulance hygienist, the 1001 st healed, who was contaminated during the unloading of his personal protective equipment (PPE). He recommended a lot of protection and precautions to all hygienists when removing PPE. And in case of possible contamination, do not panic, but rather go quickly to the Health Center for appropriate treatment;<br /> - For the Ebola Epidemic Epidemic Response Coordinator, Dr. Faustin Bile Saka, these healers will be the ambassadors for the response in their respective communities and testify that when we arrive early we have the chance to come out healed like them. He handed out the certificates of release to the cured, with the various partners of the response, WHO and IMC to the 1000, 1001, 1002 and 1003 th cures of the Ebola Virus Disease in the DRC;<br /> - As a miracle, the 10th epidemic of the Ebola Virus Disease began around the end of July 2018 and declared in early August 2019 in Mangina and it is still in Mangina, where came the 1000th cured.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-10-07 13:54:02, user GuyguyKabundi Tshima wrote:
EPIDEMIOLOGICAL SITUATION
EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI OCTOBER 05, 2019<br /> Sunday, October 06, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,204, of which 3,090 are confirmed and 114 are probable. In total, there were 2,142 deaths (2028 confirmed and 114 probable) and 1004 people healed.<br /> 414 suspected cases under investigation;<br /> No new cases confirmed;<br /> 1 new confirmed death at CTE in Ituri in Komanda;<br /> No one healed out of ETCs;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.<br /> Day 19 without response activities in the Lwemba Health Area in Mandima, Ituri, where the dialogue continues in the community.
NEWS
Reconciliation between displaced people from Lwemba to Biakato and communities left in Lwemba in Mandima in Ituri
The Lwemba communities that moved to Biakato in Mandima in Ituri reconciled on Sunday 06 October 2019 with the communities that had remained in Lwemba in the presence of the response team led by the Deputy General Coordinator, Dr. Justus Nsio Mbeta, head of the Cheffery and Mandima MCZ, coordinator of Mangina's sub-coordination of the response, as well as some partners from the Ministry of Health, including WHO, MSF, UNICEF and United Nations ;<br /> - From this meeting, follows the following recommendations: setting up a community committee to support the response, the local recruitment of sensitizers in the monitoring of community-based surveillance, decontamination and the workforce in the community. burning houses. The Ministry of Health has promised the next supply of drugs to Lwemba;<br /> - The Deputy General Coordinator for the Ebola Virus Epidemic Response, representing the Ministry of Health and the Technical Secretariat of the CMRE, Dr. Justus Nsio Mbeta, took this opportunity to recall the regulatory role of the Ministry of Health and the role of each partner involved in the response;<br /> - For the community victim of the fire, they ask for the guarantee of their security, the emergency humanitarian aid, the compensation of their destroyed property and the reconstruction of their burned houses, the commitment or the hiring of all victims in the various services at all levels, the immediate arrest of all the alleged perpetrators of these uncivil acts and the care of the children affected;<br /> - These fires occurred following the death of a nurse from Lwemba, confirmed with Ebola Virus Disease. His death sparked the uprising of the population to burn down the houses and other property of all the unknowns of Lwemba. This remains the cause, even, the cessation of the activities of the response in this Health Area for more than 15 days;<br /> - The leaders of the Lwemba community also asked for the construction of the houses for the displaced, the organization of an intercommunal dialogue session by the Administrator of the territory or his delegate and the rehabilitation of the road leading to Lwemba ;<br /> - In the response, WHO is responsible for epidemiological surveillance, communication and prevention against infection (IPC) and immunization, UNICEF is in charge of communication, psychosocial care and PCI, MSF and ALIMA take care of the treatment of patients in Ebola treatment center and PCI and psychosocial support within CTE, WFP brings food products to contacts, IOM deals with Entry and Control Points (water supply, soap and chlorine);<br /> - As for the National Institute for Biomedical Research (INRB), Dr. Nsio stated that he is in charge of the diagnosis and gives MSF and ALIMA the medicines to treat patients with CTE.<br /> - The World Health Organization has pledged to rebuild burned houses, to provide community surveillance (community watch) and investigations of all suspected cases, as well as to build a transit center in LWEMBA, while UNICEF has pledged to improve communication and awareness through the use of space, to support ICH, decontamination and psychosocial, to provide water sources and to build latrines in 5 priority schools;<br /> - On the other hand, Médecins Sans Frontières intends to help the community of Lwemba to resume primary health care, to organize triage in the Health Zones present in the village and to break the PCI, as well as to train sensitizers;<br /> - At the end of this Lwemba meeting, all partners, including WHO, UNICEF and MSF, met around the Deputy General Coordinator at the Biakato Reference Health Center to review the joint and shared planning of activities in Lwemba.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-10-16 13:01:03, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 13, 2019<br /> Monday, October 14, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,220, of which 3,106 confirmed and 114 probable. In total, there were 2,150 deaths (2036 confirmed and 114 probable) and 1033 people healed.<br /> 383 suspected cases under investigation;<br /> 2 new confirmed cases at CTE in Ituri in Mandima;<br /> No new confirmed deaths have been recorded;<br /> 1 person healed out of CTE in Ituri in Mambasa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Governors of North Kivu and Ituri Raise Awareness on Ebola Virus Disease in Biakato, Ituri<br /> - The Technical Secretariat of the Multisectoral Ebola Epidemic Response Committee (CMRE) in collaboration with the Governor of North Kivu, Carly Nzanzu and Ituri, Jean Bamanisa, organized this Monday October 14, 2019 an awareness raising day on Ebola Virus Disease in Biakato, Ituri;<br /> - This tripartite awareness-raising aimed to share the experience of North Kivu on Ebola Virus Disease and to show that the movement of people between the two provinces can encourage further spread of this epidemic in the region, as much as the last four cases recorded in North Kivu (in Beni and Kalunguta) came from Biakato;<br /> - The governor of North Kivu has indeed responded favorably to the invitation of the Technical Secretariat of the CMRE because he wants to reinforce the surveillance in his province and refuses to see his province plunge into the epidemic;<br /> - To achieve their objectives the two governors were accompanied each by a strong delegation, where one finds the presidents of their provincial assemblies and some influential deputies of their respective countries;<br /> - In addition, the Ebola Virus Epidemic Response Coordination Team, which has been in the Mambasa Health Zone in Ituri for the past week, has been monitoring Bavalakaniki Control Points and Mabakese in this health zone.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-19 17:15:54, user Guyguy wrote:
EPIDEMIOLOGICAL SITUATION OF THE EVOLUTION OF THE EBOLA VIRUS DISEASE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI IN THE DEMOCRATIC REPUBLIC OF THE CONGO AT NOVEMBER 17, 2019
Monday, November 18, 2019
• Since the beginning of the epidemic, the cumulative number of cases is 3,296, of which 3,178 are confirmed and 118 are probable. In total, there were 2,196 deaths (2,078 confirmed and 118 probable) and 1,070 people healed.<br /> • 407 suspected cases under investigation;<br /> • 4 new confirmed cases in North Kivu, including 2 in Mabalako, 1 in Beni and 1 in Oicha;<br /> • 1 new death of confirmed cases, including:<br /> o 1 new community death in North Kivu in Oicha;<br /> o No new deaths among confirmed cases in CTEs;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;
NEWS
NOTHING TO REPORT
VACCINATION
• 147 people were vaccinated, until Saturday, November 16, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;
• Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 253,545 people have been vaccinated;
• Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;
• This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been approved.
MONITORING AT ENTRY POINTS
• New positive case among Mukulya Checkpoint alerts in Beni, North Kivu. It is a lifeless body of a 35-year-old man from Oicha for burial at Kabasha in Butembo;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 117,987,763 ;
• To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-10-28 19:46:45, user Mark Yarbrough wrote:
Does anybody have any code examples of how to extract data in the proper structure from the MIMICIII data, (I have official access), into PheWAS for r? I/We are working on our practicum and focusing on type - 2 diabetes - we also want to try clustering on such data using K-mediods for mixed-type features in many columns - with ICD9 codes one-hot encoded.
On 2020-01-25 10:47:13, user stucash wrote:
I am not sure if this is due to the "preprint" nature of paper, but a few points that look a bit suspicious:<br /> 1. The actual data set used to conduct the estimation was not disclosed in paper;<br /> 2. The research method for estimation was also not disclosed in paper<br /> 3. Reasoning for the employed assumptions and not others? Reasoning for the employed transmission model and not others? Apparently this should be part of research method elaboration yet there's none. <br /> 4. Do all med papers come in this short?? This paper is just too descriptive and only estimation results were presented.
I'd really wait for a full-fledged version, I am reluctant to call this research.
On 2020-01-26 02:10:01, user Dzogchen wrote:
The r0 estimated here at 3.8 seems significantly higher than first estimates by WHO and is likely the biggest factor and assumption above. Only thing I think we can say for certain is r0 is > 1 at this point.
On 2020-01-26 06:35:13, user Wes Wong wrote:
Is the methods supplement available anywhere? I can't find it
On 2020-01-27 17:59:25, user robertinventor wrote:
Just to say the author of this paper tweeted that they now estimate it as 2.5 95% CI 2.4, 2.6 for R0 which would change all the projectons.
This version says 3.8 (95% confidence interval, 3.6-4.0),
Likely those confidence intervals need revising to, if it changes so much with an extra day of data. It is a non peer reviewed preprint.
On 2020-01-27 18:30:54, user Cyborg Gabe wrote:
In reviewing the supplementary model details, I note that the rate of exposure in each city is assumed to be directly proportional to the number of infected individuals in that city. However, if quarantine measures being taken in affected areas are at all successful, then this assumption will not be correct. Instead, a declining proportion of the infected will infect others as successful quarantines take effect. I suspect that implementing this change in the model would significantly change the model predictions, though it would require some method of estimating the success of the quarantines.
On 2020-02-02 21:55:47, user hvoltbb wrote:
Also see this one:<br /> https://doi.org/10.1016/S01...
Those people did exactly the same thing as in this article but with better traffic data, and better discussion. They also paraphrased the terms pretty good.
On 2020-01-28 21:44:55, user Nan-Hung Hsieh wrote:
A minor comment for your result. According to the code you shared, the interval you used in the paper is confidence interval, not credible interval. For example, on page 4, the 95% credible interval should be [4.5, 7.5]. You can use bayestestR package to double-check the result.
On 2020-02-06 06:41:02, user Ben Berman wrote:
I want to point out that we recently reported a strong association between global hypomethylation and a proliferative gene expression signature (including key cell cycle markers like FOXM1) in a pan-cancer analysis of TCGA tumors (Zhou and Dinh et al., Nat. Genetics 2018 https://doi.org/10.1038/s41... ). We also reported increased copy number alterations and transposable element insertions in the hypomethylated tumors. We proposed a passive demethylation mechanism, whereby late replicating regions are less efficiently maintained during mitosis in both normal and cancer cells, resulting in both age- and cancer-associated hypomethylation. In our pan-cancer analysis, we found relatively high expression of DNMTs and UHRF1 in hypomethylated tumors, so your findings of low expression in these genes may be a consequence of your normalization to proliferation markers, or something ovarian cancer specific (we did not include ovarian cancer in our analysis, since we did not have 450k data for that cancer type). Please let me know if you have any questions.
On 2020-02-16 21:25:17, user Paul Curto wrote:
You can check this site for daily updates:
https://www.worldometers.in...
The formula which you may use to provide a first-order estimate for <br /> how many deaths daily may occur within a given number of days can be <br /> expressed by:
1.1 raised to the power of the number of days into the future from today, times the current daily death toll
The 1.1 is the ratio of today's death toll divided by yesterday's <br /> death toll as of February 12, 2020. We may use a three day running <br /> average to smooth out the data for spurts of death.
If you use this data and formula, you get over half a million deaths per day within 90 days.
You get over 10 million deaths per day after 120 days.
You get a number in the billions by Thanksgiving.
So much for a seasonal flu. This is a weaponized killer of billions of people.
Since the cat is out of the bag and we still allow cruise ships and <br /> aircraft to use the facilities of over 27 nations outside of China that <br /> have infections, we won't be far behind, at most a few weeks, before we <br /> succumb. Expect a very sad Christmas, indeed.
On 2020-02-27 04:02:53, user ShangShang Gao wrote:
They recruited 125 patients from Nanjing Second Hospital, of which 103 were patients with new coronary pneumonia. The official data till 2.27 showed a total of 93 confirmed diagnoses in Nanjing. How did this sample data come?
On 2020-02-28 01:21:33, user RQ wrote:
It was an easy method to calculate the true T value and CFR without any indigestible mathematical formulas or models requiring severe calculating conditions. Actually, when different T was assumed, if it was smaller (bigger) than the true T, calculated daily CFRs would gradually increase (decrease) to infinitely near the true CFR with time went on. Left of true T is decreasing, right is increasing,so T could be easily determined, then the true CFR could be calculated. The calculated true CFR had accurately predicted the death numbers more than two weeks continuously
On 2020-03-02 16:40:24, user Abed Ghanbari wrote:
We estimated that 18,300 (95% confidence interval: 3770 – 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing.
How did you reach to these numbers?
On 2020-03-08 05:57:17, user James Nokes wrote:
Highly informative paper. Thank you. A few points/questions:
Table 1 indicates it is contact-based surveillance with higher proportion male than female contrary to the results text.
How was temperature measured and what was the definition of fever?
How were nasal samples collected (eg nasopharyngeal swab, per-nasal swab, aspirates). Did the method differ for contact and case-based surveillance?
Assessing severity status - (i) can you clarify if moderate required all three of fever, respiratory symptoms, and radiographic evidence of pneumonia? What is included in 'respiratory symptoms'? (ii) How did you measure oxygen saturation?
Table S1. It would be useful to include the proportions with fever. The proportion of cases from symptom-based surveillance with shortness of breath (4%) or difficulty breathing (3%) is remarkably low.
On 2020-03-16 13:08:45, user Karl Milhon wrote:
I have been pushing for people to investigate the role of children in transmission very hard since the Chinese CDC first put out their descriptive epi piece. there are numerous articles and quotes pointing toward this element of Covid 19 transmission but it appears that no one is truly trying to get at the problem. Simply utilizing serology testing to do quick and dirty seroprevalence studies would provide some insight. Singapore has developed and utilized some decent serologic tests. I do not understand why this is not being more aggressively pursued.
On 2020-03-11 10:09:13, user Bob Phillips wrote:
Needs the units for bilirubin and ALT, and a very clear description of WHEN these lab tests were taken ('predicting' severe disease when a child has severe liver dysfunction on an ICU isn't that useful)
On 2020-03-13 23:55:07, user Cadence C wrote:
Singapore health ministry stated that pre-symptomatic transmission is not a prominant mode. How did the authors conclude that 40% to 80% there are asymptomatic transmission ?
On 2020-03-20 11:57:18, user Romain G. wrote:
No data about hypertension and diabetes mellitus in these patients, which increase risk for COVID-19 infection and severity. Should be interesting to cross the informations. Here, it is pure speculations. Has to be reviewed, but many other parameters have to be included.
On 2020-03-23 16:03:24, user Ing. Alessandro Zivelonghi wrote:
no ambient temperature is reported in the study... ?????!!!!!
On 2020-03-24 14:01:51, user Sinai Immunol Review Project wrote:
Main findings<br /> The authors collected data on 25 COVID-19 patients (n=11 men, n=14 women) using standard laboratory tests and flow cytometry. All patients were treated with antibiotics. Twenty-four of the 25 patients were also treated with anti-viral Umefinovir and 14 of the patients were treated with corticosteroids. 14 patients became negative for the virus after 8-14 days of treatment. The same treatment course was extended to 15-23 days for patients who were still positive for the virus at day 14. <br /> The authors found a negative association between age and resolution of infection. Patients with hypertension, diabetes, malignancy or chronic liver disease were all unable to clear the virus at day 14, though not statistically significant.<br /> Elevated procalcitonin and a trend for increased IL-6 were also found in peripheral blood prior to the treatment.<br /> A trend for lower NK cell, T cell and B cell counts in patients was also reported. B cell, CD4 and CD8 T cell counts were only increased upon treatment in patients who cleared the virus. NK cell frequencies remained unchanged after treatment in all the patients.
Limitations of the study<br /> 73% of the patients who remained positive for SARS-CoV2 after the 1st treatment, and 43% of all patients who cleared the virus were treated with corticosteroids. Corticosteroids have strong effects on the immune compartment in blood{1}. The authors should have accounted for corticosteroid treatment when considering changes in T, NK and B cell frequencies.<br /> Assessing if IL-6 concentrations were back to baseline levels following treatment would have provided insights into the COVID-19 cytokine storm biology. Patients with higher baseline levels of IL-6 have been reported to have lower CD8 and CD4 T cell frequencies{2}. Correlating IL-6 with cell counts before and after treatment would thus have also been of interest.<br /> The report of the laboratory measures in table 2 is incomplete and should include the frequencies of patients with increased/decreased levels for each parameter.<br /> Correction is needed for the 1st paragraph of the discussion as data does not support NK cell restoration upon treatment in patients who cleared the virus. NK cells remain unchanged after the 1st treatment course and only seem to increase in 2 out of 6 donors after the 2nd treatment course in those patients.
Relevance<br /> Previous reports suggest an association between disease severity and elevated IL-6 or pro-calcitonin concentrations in COVID-19 patients3,4. IL-6 receptor blockade is also being administered to patients enrolled in clinical trials (NCT04317092). This report thus contributes to highlight elevated concentrations of these analytes in COVID-19 patients. Mechanisms underlying the association between viral clearance and restoration of the T cell and B cell frequencies suggests viral-driven immune dysregulation, which needs to be investigated in further studies.
References
Review by Bérengère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-03-25 03:52:12, user Renee Chan wrote:
Hi Dr Liao, Prof Zhang and Prof Zheng, May I know if you have done any fixation of the cell isolated from BAL before doing the downstream procedure using 10X genomics?
On 2020-03-29 17:21:35, user Sinai Immunol Review Project wrote:
Title: A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China
Keywords: disease severity – clinical data – Neutrophils/lymphocytes ratio – CRP – D-dimer
Main findings:<br /> 377 hospitalized patients were divided into two groups: severe and non-severe pneumonia. The laboratory results of their first day of admission were retrospectively analyzed to identify predictors of disease severity.<br /> After adjusting for confounding factors from chronic comorbidities (such as high blood pressure, type 2 diabetes, coronary heart disease, and chronic obstructive pulmonary disease), the independent risk factors identified for severe pneumonia were age, the ratio of neutrophil/lymphocytes counts, CRP and D-dimer levels.<br /> To further increase the specificity and sensibility of these markers, they showed that their multiplication [(Neutrophil/lymphocyte count) * CRP * D-dimer] was a better predictor of disease severity, with higher sensitivity (95.7%) and specificity (63.3%), with a cutoff value of 2.68.
Limitations:This study included 377 hospitalized patients. Among them, 45.6% patients tested positive for SARS-Cov-2 nucleic acid test results, and others were included in the study based on clinically diagnosis even if the molecular diagnosis was negative. Thus, additional studies are needed to verify this on a larger number of covid-19 certified patients and the cutoff value might be adjusted. Also, all the patients that did not have the clinical characteristics of severe pneumonia were included in the non-severe pneumonia group, but usually patients are also divided into moderate and mild disease.
Also, studying different subset of lymphocytes could lead to a more specific predictor. Another study showed that the neutrophils to CD8+ T cells ratio was a strong predictor of disease severity [1]. Another more precise study showed that the percentage of helper T cells and regulatory T cells decrease but the percentage of naïve helper T cells increases in severe cases [2]. Taking these subpopulations into account might make the predictor more powerful.<br /> Other studies also noted an inverse correlation between disease severity and LDH [3] or IL6 [4] levels, but the authors here do not discuss LDH nor IL6 levels, although this could help to strengthen the predictor.
The study is based on the results obtained on the first day of admission, studying the dynamic of the changes in patients might also be interesting to better predict disease severity.
Relevance:This study confirms that the neutrophil to lymphocyte ratio can be a predictor of disease severity as shown by many others [2], [5], [6]. The novelty here is that they show that a combination with other markers can enhance the specificity and sensibility of the predictor, although the study could be improved by taking into account sub-populations of lymphocytes and more biological factors from patients such as LDH and IL6.
References:<br /> 1. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 2. Dysregulation of immune response in patients with COVID-19 in Wuhan, China | Clinical Infectious Diseases | Oxford Academic. https://academic-oup-com.do.... Accessed March 29, 2020.<br /> 3. Clinical findings in critical ill patients infected with SARS-Cov-2 in Guangdong Province, China: a multi-center, retrospective, observational study | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 4. Mortality of COVID-19 is Associated with Cellular Immune Function Compared to Immune Function in Chinese Han Population | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 5. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. - PubMed - NCBI. https://www-ncbi-nlm-nih-go.... Accessed March 29, 2020.<br /> 6. Neutrophil-to-Lymphocyte Ratio Predicts Severe Illness Patients with 2019 Novel Coronavirus in the Early Stage | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.
Review written by Emma Risson as part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-03-30 13:40:15, user Viktor Szathmáry wrote:
This study has fundamental flaws, both in the chosen metrics as well as the stats, plus misleading visualizations.
Details: https://www.facebook.com/53...
On 2020-03-31 15:52:10, user Pedro Thompson wrote:
Is it valid to compare an Italy with a destroyed health system against a Brazil just beginning the problem? I mean, is the mortality rate a constant, in the same country during all the epidemic?
On 2020-04-01 15:52:32, user Peter Hansen wrote:
Comments.<br /> This is a very noisy signal, because data are changing very much from week to week. Current country statistics are more influence by: <br /> 1) The time where COVID-19 began to spread in each country<br /> 2) The degree of preventive measures (e.g. compare Denmark and Sweden).<br /> So all figures in the paper might look very different 2 weeks from now.
Smaller comments.<br /> Would be nice to have a table with actual data for each country, rather than just highlighting selected countries in the figures. BTW.
Where is France in Figure 2? <br /> Number of deaths per capita in France is relatively large (higher than Denmark). <br /> The BCG was mandatory in France for school children between 1950 and 2007, and for healthcare professionals between 1947 and 2010. (Whereas Denmark stopped BCG in the mid 1980s).
On 2020-04-02 02:05:24, user Jesus Bejarano wrote:
Hi, all how can I compute (or derived) the R0 from this model?
On 2020-11-25 05:08:10, user ArthurConanDoyle wrote:
Layman here, w/Covid. Wondering why you don't use sputum for greater accuracy?
Of course, it's not as easy or ubiquitous as saliva, but maybe a sample option?<br /> The major point being accuracy is almost everything, other factors count, but...
On 2020-11-27 19:55:21, user John Butler wrote:
There seems to be either something wrong with the risk calculator or the paper text. If I choose White Female age 70-74, no comorbidities, it says 18.9% higher. I take it that means "multiply the base rate time 1.189". If I switch that to "male" it reports 119.1% higher, which would, to be consistent with the female, mean "multiply the base rate times 2.191". However, if I select Hispanic Male, 80-84 with Chronic Kidney Disease, it report 601.8%, the text reports "6 times higher". All this suggests that the White Female 70-74, as an example, is inconsistent with the form of the others.
On 2020-12-02 20:32:21, user Roberto Etchenique wrote:
Great work !! We had done the same procedure, but a bit cruder, for City of Buenos Aires, Argentina. Our results for our country (not published) are coincident with these ones. https://www.medrxiv.org/con...