On 2020-06-14 18:28:21, user Dana Mulvany wrote:
The featured snorkel mask looks like it could also be used to provide a view of the wearer’s mouth for speechreading purposes. That can be extremely important for the high numbers of professionals and patients with significant hearing loss, who can be enormously incapacitated by not being able to understand most people due to not being able to lipread them.
Could attention be paid to how to minimize fogging?
On 2020-06-15 15:45:17, user Schwebe Pan wrote:
Some previous studies have suggested that smoking might reduce the risk of infection with Covid-19, but I am unaware of studies claiming that smoking might reduce the severity of the disease. On the contrary, the current state of the art is that smoking is a risk factor for more severe outcomes. Why, then, is this study trying to check a claim for which there is no evidence but not the actual question of interest?
On 2020-06-16 05:17:57, user Sukhyun Ryu wrote:
This manuscript was published, please follow the link below<br /> https://wwwnc.cdc.gov/eid/a...
On 2020-06-20 22:42:57, user many wrote:
Within the boundary of assumptions, the paper is mathematically accurate. I have a list of technical questions, which if addressed could make the paper noteworthy. See here: https://publons.com/publon/...
On 2020-05-19 22:56:46, user Jared Roach wrote:
In response to some of the tweets: <br /> This report out of Snohomish County, WA suggests a case in December 2019. https://www.seattletimes.co...
On 2020-05-20 00:57:13, user David Philpott wrote:
For the discussion: If you wish to make a comparison with influenza, please give a citation for this "a (0.1%, 0.2% in a bad year)". I have not found a reference for fatality risk for influenza using serologies that is in the 0.1-0.2% range. Typically, those numbers are for doicmented symptomatic cases which is not what is being addressed in this manuscript. Rather, the available evidence is much lower for influenza, perhaps in the range of 0.01%. See here for example: https://www.ncbi.nlm.nih.go...
On 2020-05-20 18:56:53, user Sander Greenland wrote:
Here are two papers that deal with the general causality theory of collider bias and related phenomena:<br /> Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37–48.<br /> Greenland S. Quantifying biases in causal models: classical confounding versus collider-stratification bias. Epidemiology 2003;14: 300-306. <br /> See also Ch. 12 of Rothman Greenland Lash, Modern Epidemiology 3rd ed. 2008.
On 2020-05-21 13:02:18, user Fred Douthwaite wrote:
A vaccine will not protect us from each successive mutated or novel virus. Correcting the underlying zinc deficiency that is the common denominator in the Covid-19 comorbidities is the answer.
The federal government should be stockpiling supplemental zinc for distribution to vulnerable groups.
Zinc deficiency is estimated to contribute to over 800,000 deaths per year - primarily in third world countries. This time, zinc deficiency has impacted the whole world. Correcting this problem is long overdue.
On 2020-05-21 19:46:25, user TS Francis wrote:
There are a lot of problems with this study making me embarrassed to have graduated from Columbia. The report repeats the obvious, that forced social distancing reduces the infection rate, and the report does this with impressive mathematical models but in total the research is misleading in a number of areas.<br /> The report states "a substantial number of cases and deaths could have been averted". This may be true in the measurement period, likely the cases and deaths occur after the measured period. In other words, you prove what we all know that the "control measures" slow down the virus but don't stop it. Even the data shows "control measures" don't stop the cases and deaths.<br /> Assumptions - You are only looking at a snapshot in time. Of course, social distancing slows the virus. Absent a magic cure or herd immunity, the virus will pick back up again after "control measures" are removed. There is an implied assumption that a person saved by "control measures" won't die from the virus soon after your measurement period.<br /> You are assuming Death is a good measure for public policy. Everyone will die, it is a given. Loss of life is what should be measured and this can be estimated based on Covid morbidity by age and life expectancy tables. At the same time you should estimate how much life was taken by your "control measures". Using data from Sweden and my state, I have done this and the loss of years of life from "control measures" far exceeds the loss of years of life saved. <br /> Obviously the objective of the research is to promote a certain public policy to save lives. But it does the analysis without looking at the costs which can be weighed using years of life. Overall, very impressive modeling but not useful except for promoting a biased agenda.
On 2020-05-22 18:43:01, user Madhurjya P Bora wrote:
This paper has lifted many portions of texts in several places from the paper https://hal.archives-ouvert... by Eber Dantas, Michel Tosin, Americo Cunha Jr, especially in the Trust-Region-Reflective section.
On 2020-11-18 22:09:57, user Owen Parry wrote:
10 days in hospital without the intervention.
Is this a fair trial?
On 2020-05-11 10:20:02, user Paul Revere wrote:
How many people died in the US in April 2019 VS April 2020?
On 2020-04-19 00:43:04, user JK wrote:
Model is surely under estimating cumulative deaths by Aug 4th - trajectory suggests 80k - 90k...believe this was one of the earlier IHME projections
On 2020-04-19 06:51:19, user DFreddy wrote:
reference 2 -> link not correct
Report 12 - The global impact of COVID-19 and strategies for mitigation and suppression [Internet]. Imperial College London. [accessed 2020 Apr 7];Available from: http://www.imperial.ac.uk/m... epidemiology/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/
On 2020-04-19 19:15:06, user Michael A. Kohn, MD, MPP wrote:
From the 3439 people who showed up for testing, they were able to obtain 3330 valid specimens on which to perform the Premier Biotech serology test. Of these, 50 were positive. That’s 50/3330 = 1.5% . They tried to adjust for the fact that the people who actually showed up were not representative of the county population’s sex, race, and zip code distribution. But the main potential source of error is the accuracy of the test. At a low sero-prevalence like this, a small proportion of false positives can result in a large overestimate. They ran the Premier Biotech test on 30 serum specimens drawn prior to the pandemic and it was negative on all 30. If the error rate on truly uninfected individuals is 0.5%, and the test properly identifies 91.8% of previously infected individuals, then the true sero-prevalence is 1.1%. As the authors say, “Additional validation of the assays used could improve our estimates and those of ongoing serosurveys.” Having reviewed the test accuracy studies of this and other lateral flow immunoassays (http://covid-19-assay.net/ ), I believe we will end up with a true sero-prevalence of about 1% in Santa Clara County. But the authors made a reasonable estimate and did a great job of collecting this data and reporting their results and assumptions.
On 2020-04-19 20:45:06, user John Smith wrote:
people who thought they have been exposed to covid-19 would want to get a free test. Others who thought they don't have the virus and have been in lockdown for a month would not go out of the house for the free test. This means you're selecting only the people who have been exposed and invalidates the study.
On 2020-05-20 17:22:05, user Omari wrote:
Anybody know the false positive rate of the test used?
On 2020-04-17 18:22:03, user Anon wrote:
The authors state: "We used Facebook to quickly reach a large number of county residents and because it allows for granular targeting by zip code and sociodemographic characteristics." This gives an inaccurate impression of how participants were recruited. I participated in the study, but don't have a facebook account. In truth, anyone with a link could have registered to participated in the study. So the author's claims here are dubious on the evenness of recruitment.
In this survey we were only allowed to have one adult get test. Naturally, we selected the person with the most relevant symptoms (me). So there's an element of self selection going on here as well.
On 2020-04-18 04:24:55, user Vasyl Zhabotynsky wrote:
The conclusion seems to heavily rely on the fact that specificity is really 99.5%<br /> If specificity is 98.5% (which is still in the confidence interval for the estimate of specificity), one would expect to get 50 positive tests from 3330 tests (as stated in second paragraph of page 7) in a completely disease free population.
On 2020-04-18 10:07:24, user Dean Karlen wrote:
Ignore this pre-print. They have insufficient evidence due to a weak measurement of the false positive rate. Consider that they saw 50/3330 in the test, and use the manufacturer false positive measurement of 2/371. I estimate the p-value (probability for seeing something as anomalous or more anomalous under the null hypothesis) to be about 0.08. There is weak evidence that even one of the 50 had COVID-19. And they are using that data to make an extraordinary claim?
It appears that none of the 26 comments below pick up on this point...
If you need help thinking about this problem, under the null hypothesis, ask yourself
Is it anomalous to see 50 or more positive tests in a sample of 3330 (all negative) when there was also an independent measurement of 2 positive tests in a sample of 371 (all negative)? Easiest to estimate by taking the first datum as a measure of false positive rate (50/3330) and the expected number of positives in the sample of 371 is therefore 5.6. Seeing 2 or fewer is not unlikely: p=0.08.
In fact the experiment was flawed in its design. With a poor false positive measurement they would have no chance to measure the expected small fraction of individuals with COVID antibodies. Why did they even embark on the study, when it was doomed to fail?
I hope this pre-print can be retracted somehow, and the community informed to not take this result seriously!
On 2020-03-25 00:18:42, user Sinai Immunol Review Project wrote:
Summary of Findings: <br /> - Retrospective study of 59 patients assayed key function indicators of the kidney–including urine protein, blood urea nitrogen (BUN), plasma creatinine (Cre), and renal CT scan data. <br /> - Found that 34% of patients developed massive albuminuria on the first day of admission, and 63% developed proteinuria during their stay in hospital; and 19% of patients had high plasma creatinine, especially the terminal cases. <br /> -CT analyses of 27 patients showed all patients to have abnormal kidney damage; indicate that inflammation and edema of the renal parenchyma very common.
Limitations: <br /> -No analysis of immunity-dependent damage and cytokines in blood/plasma/urine. Will be worth correlating disease progression with cytokine production, immune activity and kidney function. <br /> -Extrapolating to earlier SARS-CoV studies provides the only rationale for viral-damage in kidney and resultant pathologic immune response (understandable for this clinical study).
Importance/Relevance: <br /> -Multiple lines of evidence along this study’s finding point to the idea that renal impairment/injury is a key risk factor in 2019-nCoV patients similar to what has been reported for SARS-CoV [1]; this may be one of the major causes of virally-induced damage and contribute to multi-organ failure. <br /> -ACE2 expression in kidney proximal tubule epithelia and bladder epithelia (https://doi.org/10.1101/2020.02.08.939892) support these clinical findings. <br /> -Study argues for closely monitoring kidney function, and applying potential interventions including continuous renal replacement therapies (CRRT) for protecting kidney functions as early as possible, particularly for those with rising plasma creatinine.
References:
Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-04-20 17:09:11, user Michele Faucci Giannelli wrote:
Could you add the fraction of asymptomatic in Table 2. I.e. provide it broken down by age? This can really help in modelling the infection beyond Vo'. Thanks!
On 2020-04-20 17:25:20, user Dylan Skola wrote:
Can anyone see where they're presented the MAF of the mutations? How many were fixed in the isolate and how many represented intra-host quasispecies at low abundance?
On 2020-04-21 09:37:53, user Walter Langel wrote:
The article describes the calculation of the time-dependent reproduction number Rt for the present Coronavirus pandemic. These calculations recently resulted in values below 1 and had an enormous impact on political decisions in Germany. <br /> As a physical chemist I have major concerns on the validity of these results:<br /> (1) The calculations are based on a kinetic model with originally eight compartment, which has later been refined by them to as much as 14 compartments. This affords a huge number of parameters, which are known with limited precision. The authors try to circumvent this problem by using various combinations of values for these parameters. <br /> Unfortunately the most important fit parameter R1, which describes the feedback from infected individuals to non-infected, was not quoted. I have fitted the total confirmed infection data for Germany, China and Italy in https://www.medrxiv.org/con... by a simple logistic function with very few parameters. For Germany the effect of the lock down is clearly manifested around March 21st: The fits of the data before and after lock down predict final values of 340 000 and 180 000 infected individuals, respectively (see supplement to my paper). In the paper by Meyer-Hermann et al. the lock down should be seen as a sudden decrease in R1, if not buried in statistic scatter. The missing values of R1 are thus crucial for the validation of their compartment models.<br /> (2) The values of Rt , which are the fundamental result of their calculation, are superimposed by an oscillation with significant amplitude beyond noise (Figure 2(B)). I suspect that this is an artifact of their approach to evaluate the reproduction factor in time windows of seven days. This should be checked by repeating the calculation with variable time windows. As small differences in the asymptotic value of Rt (say 1.2 or 0.8) already have a huge influence on political decisions in Germany, it is urgently important to verify, if the final value is independent of such artifacts.
On 2020-04-22 10:39:55, user Niall Toibin wrote:
***First Point***
Obviously the state of the patient and their progression may have influenced the decision to prescribe HC. To quote the paper
QUOTE<br /> baseline characteristics corresponding to clinical severity varied across the three groups of patients and could have influenced the non-randomized utilization of hydroxychloroquine and azithromycin<br /> UNQUOTE
This is the context in which the following has to be taken
QUOTE<br /> A total of 368 patients were evaluated. Rates of death in the HC, HC+AZ, and no HC groups were 27.8%, 22.1%, 11.4%, respectively.<br /> UNQUOTE
No media outlet should report the second quote without the first.
***Second Point***
The authors attempt to account for this obvious bias - the patient's state influencing the decision to use HC.
They compute propensity scores (for different clinical outcomes) for HC use and HC+AZ use based on all baseline characteristics.<br /> i.e. they attempt to look at people who are equally sick in each cohort and see if HC made a difference.
There is a problem with their attempt to account for these baseline characteristics (Age, BMI, pulse, breaths per minute, heart rate, blood pressure, blood count etc.)
Clearly we need to know patient's baseline characteristics at the start of treatment.<br /> (We don't know the dates on which the decisions were made to start HC treatments. We only know the dates of admission.)
If we don't know their medical states on the date of that decision we can't discount that HC was more likely to be tried on the desperate cases. This is the main issue the authors identify and are trying to overcome. Without which the study is meaningless.
But (page 21)<br /> QUOTE<br /> Patient demographic and clinical characteristics, including those associated with the Covid-19 disease severity, were evaluated ***at date of admission,***<br /> UNQUOTE
How the patients illnesses had progressed and what state they were in when it was decided to start them on HC neither we nor the authors have any idea.
On 2020-06-29 02:58:37, user David F. Priest wrote:
Study has not been peer reviewed and was funded by Suez which has a joint venture in China with the state-controlled China Everbright International Limited.
On 2020-07-08 14:37:20, user rede2fly wrote:
Association does not indicate causation. The study has no control for the Covid-Quarrantine-Frustration factor. The author began the project with the intent to show causation and failed. The research was funded by anti-firearm organizations with the same goal.
Why is no one talking about WHO is doing the shooting and WHO is getting shot?
On 2020-07-09 08:15:09, user Yerlan wrote:
This work has been published in the Electronic Journal of General Medicine, DOI: https://doi.org/10.29333/ejgm/8346
On 2020-04-16 17:25:11, user forevertheuni wrote:
This is tricky:
Can you do a graph with "tests per capita" as a variable in this? I think that it would abate some differences.
I think that on how robust the testing has been plays a bigger role in this, because it reduces the % per million inhabitants. Which is usually a correlative on how resources are put into healthcare in general, and where vaccines are probably well implemented.
Then you have another big and totally opposite confounder, if you don't to tests...you don't have reported cases, and you will go down the graph (and that in some cases correlates with low income places, that will have the BCG because tuberculosis is very prevalent).
Well, I still appreciated the article, but there are many variables to be explored.
On 2021-05-19 22:26:18, user Michael Plank wrote:
This paper has now been published in the New Zealand Medical Journal and is available open access at: https://www.nzma.org.nz/jou...
On 2021-05-29 20:55:44, user Robert Clark wrote:
Seriously, it’s like some researchers opposed to the concept of EARLY treatment of COVID will go to any lengths to provide evidence against it, even if it crosses the line of scientific ethics.
Sorry, to have to say this but the authors no longer have any credibility on this issue.
Extremely important to recognize the importance of this: to provide evidence against IVM researchers have had to change data to fit their conclusion.
What does that tell you about the effectiveness of IVM?
Robert Clark
On 2021-07-27 11:05:46, user JustinReilly wrote:
As the article is now published, I urge everyone to go there and submit a comment! Thank you!
On 2021-01-26 19:45:53, user ingokeck wrote:
The title is misleading, as the data is not about viral load, but instead only on the density of the E gene in the samples. Also, it seems the samples were not normalized to the amount of human dna in the sample. Without that the Ct values can vary by 10 and more just because of differences in the collection, which renders the analysis done in the article useless, unfortunately. Please see Dadouh et al at https://pubmed.ncbi.nlm.nih...
It would be great if the authors could find out if maybe some samples have been normalized and then restrict their analysis to them. The result would still be interesting.
On 2021-01-28 09:56:34, user Georges Borgès Da Silva wrote:
Despite some methodological reservations, the effect on the reduction in hospitalizations seems possible.<br /> Statistical significance is limited and does not take into account the multiplicity of comparative tests (absence of Bonferroni correction). The composite outcome is questionable.<br /> It will be necessary to take into account an increased risk of pulmonary embolism in the context of a problematic association (difficult to prescribe colchicine + anticoagulants).<br /> Note a slightly favorable level of comorbidity in the treated group compared to the placebo group.<br /> Our doubts might have been dispelled if the trial had not been interrupted before its end.
On 2021-01-31 19:53:18, user Lisa Brosseau wrote:
This is not how one should test filter efficiency. This instrument is designed to test the fit of a respirator. It samples at a relatively low flow rate and compares the concentrations of particles inside and outside of the facepiece to arrive at a fit factor. Filter testing requires a completely different set of test conditions, such as those used by NIOSH for evaluating performance of respirator filters. If I were reviewing this paper for a journal I would reject it outright for failing to use this instrument correctly and for the correct purpose. If you were to perform a more thorough literature review you would find that the filters of surgical masks, face covering materials and respirators have been correctly tested using NIOSH-type methods. You would also find that the filters of surgical masks and face covering materials would not perform to the high level of performance you report here.
On 2021-02-01 01:14:00, user gogettem wrote:
Sam Moore is quoted in Telegraph as trying to justify extending lockdown post vaccination saying “The vaccines are not going be 100 per cent effective at stopping serious disease. So if you manage to get, say, 85 per cent of people to take it and it turns out to be 90 per cent effective, that's still 25 per cent of people who could die from it, which is a lot of people," But if the Case Fatality Rate for Covid is 1% overall and the most vulnerable will be vaccinated first, then the CFR for the 25% will be much less - let’s be pessimistic and say 0.1%. So only 0.025% could die from it. A lower rate than annual flu. Even at 1% the impact will only be 0.25%, still tiny. No way this can justify the carnage lockdown is inflicting on jobs, lives, people’s futures in the devastated private sector. How can we have any confidence in the response to Covid when we hear this kind of exaggerated fearmongering from those driving it?
On 2021-02-03 22:16:44, user Eric Goodyer wrote:
Whilst I can see how the authors support their claim that the efficacy is good after 21 days, I cannot see any data to support their later claim that it would continue to be effective for a further 9 weeks. I am not saying they are wrong but I cannot see any data here to support that claim
On 2021-02-06 10:16:20, user andreiakhmetzhanov wrote:
(SMs) I think the version of R is 4.0.3, not 4.3 :-)
On 2021-02-08 21:37:49, user Raymond Lam wrote:
Note that another study on this topic, using the same database, was published recently: Rhee SJ, Lee H, Ahn YM. Serum Vitamin D Concentrations Are Associated With Depressive Symptoms in Men: The Sixth Korea National Health and Nutrition Examination Survey 2014. Front Psychiatry. 2020 Jul 30;11:756. PMID: 32848932
Although our analysis methods were slightly different, we came to the same conclusions, so we will not be submitting this to a journal, but wanted to have it available to other researchers.
On 2021-02-10 09:58:44, user ad4 wrote:
Thank you for this thorough consideration of Knock 2020. I hope this can be influential in shifting our policy away from harmful lockdowns. I wonder if you had also considered that a) reported COVID-19 deaths are likely also to be an overestimate and b) that the IFR used in Knock (2020) is likely to be exaggerated? https://www.who.int/bulleti...
On 2021-02-13 13:49:05, user Matt wrote:
Very interesting paper. On the topic of Figure 3 in the paper and the relationship between "Relative predictive performance with UK (reported in figure 2) compared to PC distance with UK", have you looked at the performance using other measures than PC distance? The outgroup f3 statistic is often proposed as an alternative measure to Fst that is less sensitive to recent drift (for example as in - https://www.nature.com/arti... "https://www.nature.com/articles/srep42187.pdf)") and more sensitive to overall population divergence time, and Fst has a relationship to PC distance as you've established.
For an example, I've quickly compared the stated relative predictive performance from your paper to an outgroup f3 set I had to hand: https://imgur.com/a/Vz5KLDG. Potentially there could be a closer and more linear relationship with the outgroup f3 statistic than PC distance in some ranges (the range of Han Chinese->European populations). Restricting to this range, R^2 is 0.96 against R^2 for PC Distance of 0.89. However the predictive performance in West African ancestry populations would seem to be outlying the prediction from an outgroup f3 statistic (with the inclusion of the West African population, R^2 drops for f3 statistic to 0.84, against improvement to 0.93 for PC distance). Alternatively the distribution suggests a potential exponential or power relationship between outgroup f3 statistic and relative predictive power. Of course this is just a proxy (from a different dataset!) and a comparison using your datasets directly might be more informative.
It might be interesting to quantify if another measure that better reflected real population divergence times among present day people might be even more predictive of relative performance. Discrepancies between Fst and divergence time might be important for a naive baseline sense of portability of scores to Indigenous American populations in particular, where Fst seems to be particularly high relatively to estimated divergence times from European populations (e.g. divergence time from European populations is probably lower than Han Chinese, which is reflected by the outgroup f3 statistic, while Fst and PC distance is quite a bit higher, reflecting a strong population bottleneck).
On 2021-02-13 17:05:48, user Harold Erickson wrote:
The question raised below seems much more important than the Qt estimate of viral load. What fraction of people tested positive after vaccination? What were their symptoms? Hopefully this will come soon
On 2021-02-25 11:19:58, user Manuel Riegner wrote:
Have the participants of the control group been symptomatic or asymptomatic ?<br /> What kind of symptoms did they include in their study ? ( The Oxford study with Astra Zeneca did not include gastrointestinal symptoms neither fatigue, muscle pain, headache or any psychological symptoms ).
On 2021-02-14 13:10:19, user Rafael Green wrote:
Hi,<br /> In the article it's written:<br /> "Summing up the excess mortality estimates across all countries in our dataset gives 2.1 million excess deaths. In contrast, summing up the official COVID-19 death counts gives only 1.3 million deaths, corresponding to the global undercount of 1.6 million deaths."<br /> but 2.1 - 1.3 = 0.8 (and not 1.6)<br /> am I missing something?<br /> Thanks,
On 2021-03-17 14:25:05, user John Smith wrote:
Hi<br /> I look forward to seeing what you come up with for Turkmenistan who have 'officially' recorded no cases of Covid but more than 1000 deaths have been unofficially noted including that of one senior government official and hundreds of medical staff.<br /> The country also has a rule implemented that all grave markers must be flat so as not to be seen from the air which indicates high mortality trying to be hidden.
On 2021-02-14 21:31:21, user Dr. Stefan Pilz wrote:
This manuscript has just been published by the European Journal of Clinical Investigation:<br /> https://onlinelibrary.wiley...<br /> Many thanks for the interest in this publication!<br /> Best wishes,<br /> Stefan
On 2021-02-15 22:13:34, user Robert van Loo wrote:
in one of the figures the thetas for old and voc seem to be interchanged, theta voc put at 25 % and old at 22 %, should be reverse?
On 2021-02-20 19:30:05, user Valerie DeLaune, LAc, CNMT wrote:
There is an assumption in the paper: "Third, the likelihood of exposure to SARS-CoV-2 may be dependent on vaccination status to a greater extent in the real world than it is in the context of a randomized trial. That is, vaccinated individuals may feel more comfortable participating in social situations that pose a higher risk for infection, whereas this bias did not exist by definition in the context of the observer-blinded clinical trials." I would think the opposite would be true. Those who are most concerned about getting the vaccine as quickly as possible would be those already taking the most precautions both before and after vaccination i.e. wearing a mask, physical distancing, so also would be less likely to contract COVID in the first place.
On 2021-02-21 01:30:58, user Offer wrote:
Were the vaccinated tested at the same rate as the unvaccinated after enrollment date?<br /> As the tested unvaccinated population is larger than the tested vaccinated population following enrollment date is larger - we also don't know how frequently the unvaccinated were tested after enrollment date compared to the vaccinated population. (We only know they were tested 1 or more times, but not the actual test rate).
On 2021-02-20 21:09:56, user Rony wrote:
The paper is now published in PLOS ONE:<br /> https://journals.plos.org/p...
On 2021-02-21 19:05:04, user hugh_osmond wrote:
The assumptions regarding vaccine administration are already out of date, with the program well ahead of that assumed; this makes a very significant difference to outcomes. Take up rates amongst most vulnerable groups are also ahead of those assumed. Data suggest that just one dose reduces likelihood of hospitalisation by close to 100% (after two weeks) so assumption that 40% of deaths will still occur amongst those vaccinated appears ludicrously pessimistic. The study seems to take no account of those who are resistant/immune following infection (c. 15 million in UK), which will combine with number immune through vaccination to significantly reduce R. The fatality rate assumed amongst those infected in subsequent waves appears significantly higher than currently being experienced amongst the relevant age groups and certainly appears to take no account of improvements in treatments and newly available drugs. We now have better data on the effectiveness of the vaccines at preventing transmission, so the lower estimates in the study can be disregarded. The study also takes no account of the known seasonal effects, as seen last year after lockdown was released.
The combination of all the above suggests that the likely outcomes will be approximately 1/10-1/20 of those calculated, making the conclusions of limited relevance.
On 2021-02-25 15:12:43, user jubel wrote:
"It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms." – do these 80% refer only to infected people who were hospitalized, or are mild cases (no hospitalization) included? That would be very important to know.
On 2021-02-27 22:24:10, user ABO FAN wrote:
The latest version, version 3, seems to have the wrong reference number. For example, in the text, No. 11 is supposed to be a GTTC document that started in July 2020, but the corresponding MHLW page is from April 2020.<br /> Also, I do not think it is appropriate to use regression analysis to examine the effect of Emergency status. This is because even if an Emergency status is declared "after" the mobility and R(t) start to decrease, it will still be statistically significant. Figure 4 suggests that this is the case.
On 2021-03-01 19:34:43, user Alexander Buell wrote:
Dear authors,
my students and I have studied your paper in our course at DTU (Quantitive analysis and modelling in protein science). We have reproduced some of your analysis and we have noticed something that we wanted to hear your views on. In the methods section you mention that "For the MAAP measurements, varying fractions of human plasma samples were added to a solution of the antigen of concentrations varying between 10 nM and 150 nM..." At the same time, if we look at the red and yellow binding curves in Figure 2 a) they cannot have been measured at a RBD concentration above 100 pM. Indeed, we were unable to fit the data with a concentration of RBD higher than 100 pM or so.<br /> This would mean that you have added 99% serum and 1% labeled protein at 10 nM. Is this the case? Is the instrument really sensitive enough to get good signal at sub-nM concentration?<br /> Thanks in advance for your clarification!<br /> Alexander Buell<br /> Professor of Protein Biophysics at DTU
On 2021-03-02 06:39:50, user Azad Ghulam wrote:
Where is the supplementary material?
On 2021-03-02 13:11:50, user Syarranur Zaim wrote:
Hello, I’ve read the article and your article on vaccination was very enlightening. If possible, can you provide the code for your mathematical model and also the source of datasets for my reference.
On 2021-03-03 00:47:52, user Bin Jiang wrote:
Dear readers,
I am the corresponding author of this article. Please kindly notice this article has been published in the Environment International Journal. Please check out the article at the following two webpages:
Bin JIANG<br /> Ph.D., UIUC, USA<br /> Co-Chair, Research and Methods Track, Council of Educators in Landscape Architecture (USA)<br /> Founding Director, Virtual Reality Lab of Urban Environments and Human Health<br /> Associate Professor, Division of Landscape Architecture, Faculty of Architecture<br /> The University of Hong Kong, Hong Kong
On 2021-03-07 14:54:48, user Dr Ish Midha wrote:
Changing strains of SARS CoV -2 are pose big epidemiological and therapeutic challenge. Retinol has great impact on immunity and there is possible role of differences in retinol metabolism behind immune dysregulation that hallmarks severe Covid-19. During infections there is decreased mobilization of retinol stores as well as decreased conversion to active form ATRA.
Since there exist correlation between low circulating retinol level and severity of infections especially measles and supplementation of under 5 children with retinol is associated with decreased infection related mortality and morbidity.<br /> Thus, it may be interesting to assess serum retinol levels in patients with severe Covid-19 and study the impact retinol supplementation on outcome.<br /> If found favourable, supplementation at community level may augment circulating retinol level in population aborting the peak of on going peak of pandemic.<br /> Retinol supplementation being rapid acting and easy intervention may be of use during peak of pandemic.
On 2021-03-09 21:18:20, user Antonio Beltrão Schütz wrote:
In this meta-analisis, the I2 is very rise to be accept and CI to recovery time is, also, very big to be accept. Therefore, the results of this meta-analisis are not confiable. Is possible that personal interpretation of Grade parameters has contribute to increase I2
On 2021-03-15 13:44:09, user Daniel Mølager Christensen wrote:
Congrats on an important and well-written paper. I'm particularly interested in your eTable 4d. It's an important analysis as it in my opinion seems unreasonable to compare hospitalized patients to a matched general population when investigating clinical sequalae. Seems like there would be conditioning on the future if COVID-19 hospitalization status was determined after time zero of follow-up. If that was not the case; how did you in that analysis handle patients that were hospitalized with COVID-19 after start of follow-up?
On 2021-03-21 23:57:56, user M K wrote:
Run a PCR for the moderna mRNA. Did that get transferred as well?
On 2021-03-23 14:35:54, user Malcolm Semple wrote:
Hi Folk, Your search strategy missed "Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020; 369 doi: https://doi.org/10.1136/bmj... (Published 22 May 2020). This paper describes predictors of mortality, and describes length of stay. Other papers missed include all global ISARIC reports in MedRxiv. These alone would give you an additional samples size of 300,000 cases.
On 2021-03-24 00:01:30, user Elle wrote:
I'm surprised the paper doesn't discuss weight as a factor. If you look at the last figure, you'll see that nearly all categories are overweight (both controls and long haulers), with many with a BMI >30 (obese).
On 2021-03-24 05:27:18, user Eik Dybboe Bjerre wrote:
Dear Authors, Please find a comment to your paper here. On page 31, line 608-609. You write:" None of the 78 published articles from the 31 trials were free from incomplete, 609 inconsistent, or selectively reported outcomes". As I can see in your review of trials, the trial I were responsible for (The FC Prostate Community trial) only fail on 1 criteria, the ability to present a "statistical analysis plan (SAP) ". As per ICH-GCP guidelines and the detailed guideline from Cochrane on assessing bias then a SAP should be in place before unblinded data was accessed. In the FC Prostate Community trial. The data collection was fully done by a web-based system and all data was keept logged and confined. As stated in publications of the results);the trial randomised 1.participant in June (15th)2015 an the SAP was published in Nov 2015 on clinicaltrials.gov. No participants had at that time complete the full 6 month intervention period. The data had not accessed from the database I only have detailed knowledge on the trial I was responsible and I acknowledge that it is difficult to evaluate if the SAP was in place in a timely matter. It is of course an important aspect but as it is not possible to assess without contacting authors/trialist I suggest the author group to consider the judge on this domain. Also if you look at the COMPare project, this do not evaluate the SAP. <br /> Best regards<br /> Eik Dybboe Bjerre
On 2021-03-25 20:16:01, user Peter Novák wrote:
Seems, that nothing has stopped the paper from publication.
On 2020-12-08 13:43:42, user Iveta Nagyova wrote:
Might be interested to read also our article published in The BMJ Opinion „Could Slovakia’s mass testing programme work in England?“ https://blogs.bmj.com/bmj/2...
On 2021-04-02 11:10:37, user Rudy Faelens wrote:
It's obvious why, in the first wave, GP's didn't get higher infection rates. The physical exam was replaced by a pure telephonic anamnesis, diagnosis, and therapy. Pure horror. Unethical. <br /> Definitely safe for the GP, but at a severe sometimes lethal cost of false diagnoses and wrong therapies.
On 2021-04-09 15:15:15, user Martin Bleichner wrote:
We read this preprint in our journal club and have collected some comments I would like to share. <br /> Overall, we liked the approach and the straightforward message of the paper. <br /> Comments regarding the paradigm<br /> • Do you control somehow for word length? In the given example, “swift” is shorter than “swrfeq”. <br /> • Are word combinations repeated? I.e., do participants see ‘swift horse’ as well as ‘swrfeg horse’? In that case, participants may remember that they saw a similar item before. Hence, memory could play a role<br /> Controls and Patients<br /> • The ACE-R scores overlap between the two groups (range controls 83 – 100, range MCIR (64-99). Isn’t it then surprising that the results in figure 8 show such a good separation?<br /> Signal Analysis<br /> • The ERP subtraction was only done for the cap. Based on those results, it was concluded that it does not make a difference, and hence this approach was not used for the cEEGrid data. Since the segmentation of the ERP components depends on the data quality that differs between the two devices, this transfer might not be valid.<br /> • It is stated that the lexical retrieval effect is absent in the MCI-group, but in figure 3, the alpha rebound, for example, seems to be present in both groups to some degree. Furthermore, in figure 4, the main difference between the conditions (bottom TRF) is between 600 and 800 msec), i.e., exactly before the alpha rebound kicks in (around 800 msec figure 3). <br /> Comparison Cap cEEGrid<br /> For Figures 7 and 8, individual electrodes were used. It would be interesting to know how variable that was across subjects and how often the different electrodes were chosen. Furthermore, given that the results of the individualized electrodes and standard electrodes are comparable, it would be interesting to see the spectra of all channels. <br /> • The electrodes used for referencing and re-referencing are not completely clear to us. Unfortunately, different people use different names for the electrodes A layout-plot of the cEEGrids with indications of gnd, ref, etc. would be helpful.
Figures<br /> • The figures are difficult to compare to each other (different units [% signal change for the cap, but t-values for the cEEGrid] in same-colored color bar, different time axis, etc.) E.g., in figure 6 Top TRF x-axis is from 0 to 1.4, Bottom TRFs from 0 to 1. Figures are differently scaled along the x-axis.<br /> • Please indicate in the figures the important time points (word off-set, onset, etc.)<br /> • Explain the ROC-curve in detail. What data goes in exactly? Should be added to the method section.
On page 20 the is a space missing between “The” and “current”.
On 2021-04-13 09:35:53, user Economy Decoded wrote:
There are also claims that there have been cases of vaccine wastage and shortage in production funds. Massive exports have also been cited as one of the reasons for vaccine scarcity. India has exported 64 million doses of vaccines to 85 countries in the form of “gifts,” commercial agreements signed between the vaccine makers and the recipient nations, and under the Covax scheme, led by the World Health Organisation (WHO). The experts have pointed out that vaccine shortages have become a problem in some parts of India due to supply bottlenecks. They claimed that vaccine makers had oversold their capacities while taking orders from all over the world. The steadily rising cases of COVID-19 and the issues related to “vaccine scarcity” are significant challenges to making India free from COVID-19. There is an urgent need to plan and prioritize providing vaccinations to achieve the target of inoculating 400 million vaccine doses by July, as stated by the Ministry of Health and Family Affairs. This piece has also taken a look at the current shortage of vaccines India is facing: We Analysed Whether The COVID-19 Vaccine Shortfall Is Due To Exports Or High Domestic Consumption https://edtimes.in/we-analy...
On 2021-04-23 10:30:29, user Paul-Olivier Dehaye wrote:
Following criticism that the results announced do not match the data (for instance at<br /> https://pdehaye.medium.com/...<br /> or<br /> https://lasec.epfl.ch/peopl... )
the main author seems to now (2021.04.21) acknowledge problems.
From https://www.youtube.com/wat... :
“What I also need to mention here is... We do see this time advantage [but] this is also early[?] days. We have not been able to dissect each and every secondary survey. This is still ongoing, but what is kind of confusing is that about 8 of those 43 specifically mentioned that they received the exposure notification before they were called up by manual contact tracing. So there is an accumulation of those people in those groups but then you still have a few [sic, given it’s 35/43!?] where manual contact tracing was first and then exposure notification was second. So the picture is still a bit blurry, but overall I think we are getting closer and we are doing additional analysis”.
Note that, in accordance to the "Data/Code" section presented on medrxiv ("We are open to sharing individual participant data that underlie the results reported in this article, after de-identification upon reasonable requests to the corresponding author. Data requestors will need to sign a data access agreement."), the author of this comment has requested access to the data, but his request has not been acknowledged.
On 2021-05-02 07:00:13, user Peter McIntyre wrote:
This is an interesting and detailed analysis. One metric not provided is whether any identified infections arose from persons who had no travel history outside Australia. Such persons from NZ have not been required to quarantine on arrival in Australia so comparison of this metric would be helpful for policy assessment. A long and extensive list of potential interventions and/or policy changes to reduce or eliminate infections in MIQ is provided but their relative cost/ time to implement or difficulty not discussed. Vaccination is listed as only of value if transmission is eliminated - while this is the case if the target is no instances of infection in the context of a population unprotected by Immunization, once vulnerable populations are protected and risk of adverse outcomes from infection greatly reduced, this will have a major impact on the cost-effectiveness of a continued zero infection target and therefore on cost-effectiveness of listed interventions. <br /> Although this retrospective review is valuable, forward thinking is now needed to estimate future cost effectiveness
On 2021-08-11 09:43:54, user Sebastian wrote:
Could you please add some other vaccine that "reprogram" the immune-system like BCG or MMR (compare Netea et al. 2020)? You present (indirect) the reprogramming as a new effect of mRNA vaccines, what isn't exact enought in my opinion.
On 2021-07-19 03:08:20, user Miles Babbage wrote:
OK, authors, there are problems here. You don't have the sample numbers to make the claims you do, and your data do not bear it.
Interferon alpha effect you report here seems, from your own graphs, to be an artifact of changes between the first and the second dose, not between vaccine and lack of vaccine. I.e. you have a very minor shift up at t2, which makes the drop at t3 significant - but there is no actual effect between t1 and t3.
For TNFa data, the R848 seems to be based on one single patient who had a strong increase at t2 that declined in t3. The only significant observation that holds is the one with candida, which then brings up the problem of sampling (test enough things, and you'll get a result somewhere). You need to state your statistics much more clearly.
There is an possible trend here, but that trend a) needs to also be interpreted in the light of known post-viral effects on the innate immune system (such as e.g. those seen with post-influenza effects on bacterial resistance), and b) needs to stated as trend, not as a definite finding.
On 2021-08-18 04:23:00, user Turncoat Don wrote:
Why don't you just make the data available to everyone? Just post a link here.
On 2021-08-18 13:24:26, user Justin -O'Sullivan wrote:
We have a povidone iodine product 0.58% (zero surfactant) which showed in vitro inactivation of SarsCoV2 published with the awareness of public health England. Can't understand why this strategy not used more. Would be interested to know what final volume of irrigation fluid was and was it standardised in protocol?
On 2021-08-22 13:37:42, user ingokeck wrote:
Dear authors,
Thanks for publicizing this research. I notice that the main point of your article, figure 1C, is purely based on a model you derived, however I was not able to find the data that went into it, i.e. positive and negative cell cultures plotted against the Ct values for the samples in each group.
I also notice that the probability for culture positivity you present in your model is vastly different from the source you quote for it (19. van Kampen et al. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19)). To be more specific, the form of the curve you present for the non-vaccinated sample is vastly different from the one you cite, and your patients are vastly more infectious, with 50% positive cell culture probability already at 10^5.1 copies/ml, while van Kampen et al. writes 10^8.5 copies/ml. This corresponds to a difference in Ct-Values of 11! I cannot think of any sensible explanation for this difference.
To me, it looks like there must be errors in your model. It would help a lot if you revise your model and publish a scatter plot of positive/negative cell cultures per Ct/Values for both groups, as well as positivity against days of sample taken after symptom onset.
On 2021-08-26 05:03:33, user David Lang wrote:
I can't deeply assess the paper but please note the term "survivorship bias" does not appear once.
On 2021-09-14 13:39:06, user Henri van Werkhoven wrote:
Dear colleagues,
With interest did we read this manuscript which fueled a lively discussion during our journal club of the department of infectious diseases epidemiology at the University Medical Center Utrecht. The authors address a relevant research question. If there is a substantial difference in the risk of SARS-CoV-2 infections between previously infected and vaccinated individuals – as suggested - this may have consequences for social distancing, testing recommendations, and for projections of the impact of vaccination on future COVID-19 trends. However, we have several concerns regarding generalizability, selection bias, information bias, and confounding that we would like to address. We focus our discussion on model 1: the comparison of the fully vaccinated non-infected group (group 1) to the infected non-vaccinated group (group 2).
In regard to generalizability:<br /> - Due to the matching process, only 4% of the available data is used (i.e. for model 1 only 32430/736559) and as a consequence the study population is fairly younger (with expectedly less comorbidity) than the source population (i.e. vaccinated individuals, infected individuals). Therefore, the study population may not be representative of this source population which severely limits the external validity of results for all vaccinated/infected people.<br /> - Naturally, subjects who died due to previous SARS-CoV-2 infection were not included in the study. Yet, without information on morbidity and mortality and contribution to the spread of SARS-CoV-2 from the primary infection, the results of the study are not informative for the question whether people without previous SARS-CoV-2 infection should be vaccinated or await natural infection. <br /> - All three study groups – vaccinated or infected at baseline (28th of February) – were established upon future information (no infection, no additional vaccination after June 1, 2021), which severely limits the use of the results for today’s decision making.
In regard to selection bias:<br /> - People with a SARS-CoV-2 infection between February 28, 2021 and June 1, 2021, or those who received a first (infected group) or third vaccine (vaccinated group) between February 28, 2021 and August 14, 2021 were excluded from this study. Thus the study population of group 2 consists of previously infected people that do not take the opportunity to receive a booster vaccine, which may well be the less vulnerable people with a lower baseline risk of getting infected/hospitalized. This would bias the estimate in favor of the infected group.<br /> - Similarly, though at a smaller scale, people who died from COVID were not included in the analysis. This decreases the vulnerability of the infected group for secondary infections and/or hospitalization. This too would bias the estimate in favor of the infected group.
In regard to information bias:<br /> - A difference in willingness to test between the vaccinated and previously infected group can result in biased estimates. Vaccinated people may be more on guard in regard to COVID-19 symptoms (especially if they adhere less to regulations because they are vaccinated) and will be tested more frequently. This can bias the estimate, again in favor of the infected group. However, this form of bias should not have affected the outcome hospitalization due to COVID-19, for which differences had the same direction. Yet, the number of those endpoints was low, limiting statistical power.
In regard to confounding:<br /> - The authors acknowledge absence of information about health behavior, such as social distancing and masking. If the vaccinated group would adhere less to these preventive measures due to a sense of safety, this would also bias the estimates in favor of the infected group.<br /> - A potential important aspect is the young average age (36 years) of the study population. As they were all fully vaccinated before February 28th, we thought that a large proportion may have been health care workers, who have a higher chance of exposure to SARS-CoV-2, and thus infection after vaccination. This would also bias the estimate in favor of the infected group.
We have scrutinized the paper in search of the fatal flaw; the one major methodological limitation that could explain the extreme effect in favor of the infected group, as reported. We conclude that it is not there, as we don’t think that any of the above biases can explain all of the effect. However, we did found several weaknesses that each have the potential to yield a modest bias, all in the same direction. Five modest biases may yield a large effect estimate. We, therefore, consider the question whether natural immunity provides better protection than full vaccination with Pfizer/BioNTech’s COVID vaccine remains unanswered.
The authors (Annemarijn de Boer, Valentijn Schweitzer, Marc Bonten and Henri van Werkhoven, all at University Medical Center Utrecht) acknowledge all other journal club participants for their time dedicated to discussing the paper.
On 2021-07-07 10:03:14, user ateamrdr wrote:
This is very interesting. What about the issue of transmission? If you do come into contact with the virus after having been previously infected, you might recover faster, but are you more likely to transmit if you have natural immunity vs vaccine immunity?
On 2021-08-02 01:30:17, user Carter Clough wrote:
What was the date or time frame of this study? Was this prior Delta?
On 2021-08-04 07:40:19, user Philippe Meisburger wrote:
Question : should this finding be proven true, would it imply that someone who's got vaccinated (2 doses) before he/she ever got Covid 19 will benefit from the same level of protection convalescents have once they'll successfully fight a potential breakthrough infection ?
On 2021-07-10 20:28:13, user Michel Prémont wrote:
Interesting study but I have two questions.
Thank you.
On 2021-02-09 05:10:52, user David Brewer wrote:
What honey was used in the HNS study?
On 2021-07-11 16:46:18, user A Clarke wrote:
Also, what effectiveness are you measuring? Symptomatic desease it seems however hospitalization and death is the real problem and this report out of the UK seems to say those rates are higher among the vaccinated; see page 13; https://assets.publishing.s...
On 2021-07-13 14:03:05, user Olga Mazlova wrote:
“Patients admitted to hospital were eligible for the trial if they had clinically suspected or laboratory confirmed SARS-CoV-2 infection and no medical history that might, in the opinion of the attending clinician, put the patient at significant risk if they were to participate in the trial… Patients with known hypersensitivity to aspirin, a recent history of major bleeding, or currently receiving aspirin or another antiplatelet treatment were excluded.”<br /> So, after having excluded patients with initially extreme blood viscosity values, you left the wide middle part of the normal (Gaussian) curve of blood viscosity value distribution. It means that the trial participants probably had normal or somewhat low or, on the contrary, somewhat elevated – but underdiagnosed - blood viscosity. Why did you prescribe aspirin to the whole range (except extremes and control, of course) – and not only to those predisposed to elevated viscosity of blood?.. It is logical that the dose of aspirin should be increased proportionally to the excess of the blood viscosity values. Patients with initially normal blood viscosity may need only minimal (preventive) doses of aspirin or need none. Patients with low blood viscosity can be at risk of bleeding, so the substance should not be prescribed in such cases. There should be a personalized approach to the patients, with analyzing their blood tests and even tiny individual symptoms.
On 2021-07-14 18:07:08, user Alexander Domnich wrote:
It seems that this SRMA lacks of data elaboration/transformation procedure at all. For instance, in our paper (icluded in this review) it has been clearly stated that no false positive results were detected. It is therefore obvious that the specificity is 100%. The overall specificity with 95% CIs was reported "The overall sensitivity and specificity were therefore 78.7% (95% CI: 73.2%–83.3%) and 100% (95% CI: 94.7%–100%), respectively". The authors instead stated that the specificity estimate was not reported. I agree that we had not reported the 100% specificity for each test analyzed in order to save the space. To us, it was clear.<br /> You only needed to calculate the 95% CI from the available raw data.
On 2021-07-19 14:49:56, user Julia R. Gog wrote:
A final version is now published at Royal Society Open Science:<br /> https://doi.org/10.1098/rso...
(Since medRxiv V2: Expanded detail in the methods section, new section in SI on sensitivity to higher d, fixed typos and minor changes for clarity throughout)
On 2021-07-23 12:04:42, user Harry Matthews wrote:
Very fascinating work. I read it with great interest. I think some Supplementary Tables are missing, though. In the supplementary pdf I see up to Supplementary Table 3. But in the caption of main figure 5 there is reference to a Supplementary Table 6.
On 2021-07-26 20:40:57, user Double_Up wrote:
So far, so good. No infectious agents added to the SARS-2 shot, that's a plus, and if Phase 3 goes as well this Medicago-GSK shot may be safe enough for many I know to take, with so many possibly having SARS-2 already but no way to prove it since tests are garbage and antibody tests are about 50% accurate at best. Safety over hype. People I work with cannot take any SARS-2 shots due to medical conditions they have but they're being treated like cattle, horrible.
On 2021-07-29 08:23:00, user J Tulloch wrote:
You should probably be comparing to the Liverpool paper looking at THIN data rather than the Cairns paper as they are much more comparable. (https://bmcinfectdis.biomed... "https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-020-05018-2)")
On 2021-07-30 14:06:06, user Luiz wrote:
Even though, the vaccine protects the people from the very dangerous infection of covid -19, 15 people died in the vaccinated group and 14 died in th unvaccinated group?
On 2021-08-01 16:33:46, user RationalSkeptic wrote:
"Most participants who initially received placebo have now been immunized with BNT162b2, ending the placebo-controlled part of the study. "
Um, doesn't the defeat the purpose here? Isn't this trial suppose to be ongoing until Feb 2023?
On 2021-08-01 20:08:35, user Billium wrote:
After six months monitoring >45,000 patients, there were 14 deaths in the placebo group and 15 deaths in the vaccine group. This not only demonstrates lack of efficacy in the most important endpoint, but highlights the extremely low fatality rate of Covid-19 in most people.
On 2021-08-04 12:24:22, user Will Helm wrote:
regarding the 29 deaths, this number seems under-rated to me. <br /> Guess what, here's a novelty, PEOPLE DO DIE, with or without Pfizer shot. <br /> 29 dead over a six months period for 44'000 people is certainly consistent with the US demographics which actually would yield 230 deaths for all causes. Something's not right here.<br /> Check mortality table
On 2021-08-07 08:58:47, user john vegan wrote:
So, one reading is that the treatment reduces 50% the deaths.<br /> Another reading is that the covid death rate in the placebo group is 0.00009 (2 / 21,921 = 0.00009), which is double than the treatment group, but Influenza and pneumonia deaths (15.2 / 100.000 = 0.000152 (1)) are 68,8% higher (0.000152 / 0.00009 = 1.688) than the covid deaths.
So, should we have this treatment in our arsenal ? <br /> Yes.
Should it be mandatory for everyone ?<br /> Considering the fact that the treatment for influenza is not mandatory, then this treatment should also not be mandatory.
However, this is just my opinion which may be wrong, and if it is wrong I would like to hear why it is wrong
On 2021-07-31 00:05:48, user Arthur wrote:
Check these stats from results section.<br /> The percentages of white, black and Latino participants do not add up to 100%.
Please check before publishing , these are some things that makes a publication lose credibility.
Participants were 49% female, 82% White, 10% Black/African American, and 26% Hispanic/Latinx; median age was 51 years.
Then
Of vaccinated participants, 58% had >=2 months follow-up post-dose 2, 49% were female, 86% were White, 4.6% were Black/African American, and 12% were Hispanic/Latinx.
On 2021-08-08 08:39:21, user Armand Sarkizians wrote:
Hello,
I am trying to replicate the code for some parts of this interesting paper.
please can you let me know:
Figure 'c', page 18m, found in the supplementary material. has the Y-scale been scaled, or that is simply plotting the Err.
On 2021-11-30 09:20:59, user Glenn LGG wrote:
Crucially the study also misses clear criteria for testing (including symptoms - if any) and the number of people subjected to PCR testing (under which regimen?) in each cohort.<br /> Arbitrary PCR testing does not imply any true event.
On 2021-12-01 08:32:45, user Wencke wrote:
It seems that all omicron patients coming to Germany were fully vaccinated and PCR tested within 24 hours before the flight. This immune escape correlates to this study:<br /> Increases in COVID-19 are unrelated to levels of vaccination... <br /> https://link.springer.com/a...
On 2021-12-01 23:56:02, user Athanasios Chalkias wrote:
The article has been published in Clinical Hemorhelogy and Microcirculation and can be found here: https://content.iospress.co...
On 2021-12-03 03:11:46, user virtualcappy wrote:
How many of the reinfections in November were associated with omicron and what fraction of the total infections in November were from omicron vs other variants? Without that information the data does not seem to support the authors' conclusions that omicron is responsible for the increased risk of reinfection.
It stands to reason, risk of reinfection will eventually increase with time, as naturally acquired immunity wanes. Further, it is to be expected that the risk of reinfection will increase with a variant that has variation in sequence. The question is how much does each of these factors contribute to an overall reinfection rate and this paper doesn't seem to do much to answer that.
The authors also should suggest some plausible mechanism for the decrease in reinfection rate with time through the end of wave 3. Why would a mutated virus be less likely to reinfect? Seems more likely due to the dynamics of the immune response over time than genetic variation. Why then would the authors not consider that mechanism for the increase in reinfection rate in wave 4?
Anyway, the New York Times picked up on this preprint already and the headline suggests that "prior infection is little defense". This is a far cry from "substantial and ongoing increase in the risk of reinfection" which could be from multiple factors and 2-3x increase in risk of reinfection is not "little defense". The authors should provide better context to head off alarmism.
On 2021-12-03 01:31:09, user Alex Johnson wrote:
This analysis did not address infection after vaccination, which we know is happening with Omicron. I'd like to see the rate of reinfection compared with the rate of breakthrough infection, before I get too excited about reinfection.
On 2020-04-23 06:40:04, user Sergey Morozov wrote:
Very actual study that brings new information to fill in the gap on the histological features of lungs in those who died of COVID-19. The paper seems methodologically correct and based on multicentre (2 hospitals) study with histological assessment performed by 2 pathologists blinded to the results of each other. For better compliance GPP, I would suggest to add the information of immediate cause of death to the description of the study population; the results of analysis of concordance of the results of tissue evaluation performed by 2 pathologists, who were involved to the study. As pre-existing chronic obstructive pulmonary disorders are described in 3 patients, could these cases influence the results of pulmonary fibrosis assessment? It would be nice also, if the past-malignancies localizations also were described. The statements are logical and are based on the described results. This study is rather explorative by nature and larger studies are necessary to make an association between different aspects that characterize the disease flow (including co-morbid pathologies, medications used, laboratory deviations, etc) with histological features<br /> observed in pulmonary tissues much clear.<br /> I have no conflict of interests in the regard to this review.
On 2021-12-06 18:46:16, user Griefer HD wrote:
The authors claim at the beginning of the abstract AND at the beginning of the introduction: "Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic." This appears to be a foregone conclusion.
I am no a mathematician and thus cannot evaluate the elaborate models used in the study. But it is obvious that the models hinge on the R value and are very sensitive to even small changes in R. And the assumptions going into the R values used in the study are flawed: the authors estimate that 67-76% of the R value is caused by the non-vaccinated population. This is in stark contrast to the incidence in the vaccinated and non-vaccinated population ad reported by the RKI on a weekly basis. And even the RKI states that these reported incidence value probably under-report the incidence among the vaccinated population. Consequently, the authors report that "In order to obtain breakthrough infection rates in adolescents on the order of observed symptomatic breakthrough cases we assume a vaccine efficacy of s = 92% for adolescents." Which again is in stark contrast of RKI's own estimate for efficacy of 67% (weeks 42 to 44). And while the authors cite two studies showing that infected vaccinated individuals are equally like to transmit the disease that non-vaccinated (and only citing one study showing lower transmission), they state that "Considering these results, we assume a conservative transmission reduction of r = 10% for breakthrough infections" (for vaccinated individuals) - a baseless claim, to say the least. In addition, the authors also resort to slander: "As individuals that are not opposed to vaccination typically adhere to protection measures more consistently..." This goes against my own observation in my direct vicinity, where non-vaccinated are very aware of the risks they are taking and are often more careful. While vaccinated individuals seem to take greater risks, as for example shown by the large number of infections of vaccinated at '2G' Halloween parties. A more risk-aware behavior of the non-vaccinated would also explain the seemingly increased infection rate among vaccinated individuals in the UK (for example in the age bracket 40-49, as reported by UKHSA) compared to non-vaccinated peers.
And finally, I would like to point out that the state of Berlin introduced 'selective NPIs' (read: 'lockdown' of the non-vaccinated) and the Mayor of Berlin advocates introducing such measures on a nationwide basis. At the same time, the state of Berlin is the main funding body for the host University (Humboldt University) of the majority of authors. The authors fail to acknowledge this conflict of interest.
On 2021-12-12 13:59:45, user Andrew Hayward wrote:
“This research is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20029)”
On 2021-12-14 14:10:03, user Ergellegre wrote:
We would all benefit for this proposition to be widely considered for replacing the current 'crude' model adopted in the UK to assess risk level. The fundamentals are well established, irrefutably so.
On 2021-12-16 04:37:21, user Jordan Atchison wrote:
A little concerned about the comparison to the NNT for ASA of 333. That value of 333 is calculated over a span of 6.6 years, but it's unclear over what time period the author's NNE applies to. Is it 1 prevented transmission per day, 1 prevented transmission per average length of incubation period, or some other time period?
On 2021-12-18 02:12:44, user Peachyjenniekag21 wrote:
The main concern i have over the implications of this report are how this could impact conditions and protocols of prison inmates and populations. An unfortunate reflex seems to be rigid and stringent focus on isolation efforts as opposed to abundant supply of safe and effective treatment in congregate settings...
On 2021-11-29 10:58:19, user Andy Bloch wrote:
This study had just 13 unvaccinated participants with no known prior SARS-CoV-2 infections. To say that it was "underpowered" is an understatement. It's incorrect to conclude "we found no statistically significant difference." Add this paper to the long list of articles that mistakenly interpret statistical significance. The authors and reviewers should read this comment: Scientists rise up against statistical significance.
On 2021-12-23 21:35:13, user Dan Ostermayer wrote:
is the negative VE for 91-150 days a typo?
On 2022-01-12 11:43:46, user kdrl nakle wrote:
There is nothing in this paper worth beyond what is already expected. The numerical predictions will likely be erroneous. I have no idea why would anybody want to write the stuff like this that wil be outdated in two weeks time.
On 2022-01-14 18:11:36, user Maxwell Neal wrote:
A revised version of this article has been published by the journal npj Vaccines and is available at https://doi.org/10.1038/s41...
On 2021-10-16 13:53:34, user Sam Smith wrote:
Thanks for the great study, but when will you publish results what happens if one takes Sputnik light as a booster? I am only interested in boosters that give >90% protection against delta, because in Israel 3 doses of Pfizer gives >90% protection.
On 2021-10-20 08:13:57, user ClearSkys wrote:
"...the vaccination coverage rate is inversely correlated to the mutation frequency of the SARS-CoV-2 delta variant"
Correlation != causation
Motives are questionable especially when the authors then go on to recommend the public health policy based solely on the correlation.
On 2021-10-29 12:26:39, user Yehonatan Knoll wrote:
Good study of a bad question. <br /> Ten months into the vaxx campaign, why is there no similar, comprehensive study following vaxxed and unvaxed, *starting with the date of vaccination rather than that of infection*. This is the only pertinent question, now that a biannual booster is required of the vaxxed.
On 2021-10-29 17:15:41, user kdrl nakle wrote:
This is a very troubling report as we really need to differentiate "regular" deaths from the ones that are consequence of (and caused by) vaccination. This report is vague on that and it is going to be used against vaccination, no doubt.
On 2021-11-14 13:53:40, user Marc Middleton wrote:
I don't even have to read the whole (not yet peer-reviewed and thus questionable) article to see that the conclusion, which anti-lockdownists like to draw from it, is faulty. It's already stated in the abstract that "efficient infection surveillance and voluntary compliance make full lockdowns unnecessary". People of the studied population obiously had enough common sense to contrain their contacts, which OF COURSE reduces viral spreading without the need for lockdowns! But as we have seen in several countries, not all people are as smart as the Danish...
On 2021-11-15 05:09:26, user John Davies wrote:
Might be good to make it extra clear in plain English that these are background rates - not actual vaccine side effects, as they appeared to me at first glance.
Other lay people might use these data to perpetrate the antivax argument.
On 2021-11-16 02:36:21, user Peter Renzland wrote:
The last sentence in the "Results" seems difficult to reconcile with the first sentence in the "Conclusions":
We observed no difference in the LoS for patients not admitted to ICU, nor odds of in-hospital death between vaccinated and unvaccinated patients.<br /> vs.<br /> Vaccinated patients hospitalised with COVID-19 in Norway have a shorter LoS and lower odds of ICU admission than unvaccinated patients.
On 2021-11-16 17:30:31, user Marcelo Sauaf wrote:
Authors using ONLY the term "faster" about the viral cleareance while this "faster" meant mere 2 DAYS less than unvaccinated evidentiate their POLITICAL bias on the subject. Why don't they QUANTIFY in the conclusion the "faster" was mere 2 days less than unvaxxed - AND that the contagious phase (PCR ct = 25) is tipically up to 9 days ??
On 2021-11-17 10:29:38, user David P. Herrmann wrote:
This article was peer-reviewed and published in the journal Hearing Research.<br /> https://doi.org/10.1016/j.h...
On 2021-11-20 09:36:30, user Amador Goodridge wrote:
Great ongoing work of Amanda et al bringing to the light of scientific evidence the dramatic situation of migrants. While looking forward findings and results of this study, hope this warning help Panama together with other agencies continue to reinforce POC,<br /> & clinical diagnosis as well as on-site treatment strategy in order to assure the public health. Congrats!
On 2021-11-22 18:02:48, user Timeisrelative wrote:
This is an excellent paper. I have a few minor comments related to the word choice and clarity that I hope are helpful to you.
1)The uses of the words "rate" and "rate of change" are problematic in this context. I think it would be more clear to use different words. A "rate" usually describes how much of something happens over a specified unit of time. So the "rates of change in antibody titres during 3-6 months" might be about 10%/per month. Your metric is defined as:
rate of change = [(Ab titre 6 months after the 2nd dose - Ab<br /> titre 3 months after the 2nd dose [12]) / Ab titre 3 months after the 2nd dose] × 100 (%)
I believe this metric would be better described as simply the "change" or "percentage change" instead of the "rate of change" since it doesn't have a unit of time in it's denominator. This phrase "rate of change" occurs at many times throughout the paper and I believe they all should be replaced with "change" or "percentage change".
2) I was confused by the meaning of this line near the end of the results section:
because the Ab titres 3–6 months after vaccination were significantly higher in women than in men.
If I correctly assumed your intention, I think this line could be written more clearly as: "because the Ab titres were significantly higher in women than in men at both 3 months and 6 months after vaccination"
3) I think it would be helpful to specify in the table headings and in the chart axes labels whether the measured titres were 3 months or 6 months post vaccination. This information is in the paper and the caption of the figures, but it would be clearer if, for example, the headings of tables 1 and 2 were "Ab titre at 6 months, median (IQR), U/mL" and the x-axis label of figure 2b was changed similarly.
On 2021-11-24 00:22:39, user Nik Kolb wrote:
Could you please double check if the German vaccination data in ECDC are handled correctly for your calculations? The burden is unexpectedly high.<br /> It might be that the lack of more detailed age groups than 3 categories (<18 years, 18-64, 65+) resulted in a wrong attribution of the vaccine coverage. I could not find a method how you "interpolated" the vaccine coverage by age group, but Supplementary Figure S1 suggests that it does not really reflect the true vaccine coverage in each age group. While the true coverage sadly is unknown in Germany, a telephone survey among german speaking participants conducted by RKI given some hint about the true coverage: https://www.rki.de/DE/Conte...
On 2021-11-25 15:30:42, user kdrl nakle wrote:
When you write nonsense like this:<br /> ***<br /> The rate of detected reinfection after two doses of vaccine was 1.35 (95% CI 1.02 to 1.78) times higher in those vaccinated before first infection than in those unvaccinated at first infection.<br /> ***<br /> in your abstract then I know it is not worth reading any further.
On 2021-11-29 13:08:12, user TheBigWakaWaka wrote:
There's something that needs explanation.
In table S2, the raw <br /> ratios of unvaccinated cases over unvaccinated person-time (45% vs 55%, <br /> single dose vaccinated cases over single dose person-time (24% vs 19%), <br /> and double dose vaccinated cases over double dose person-time (30% vs <br /> 26%) are pretty close.
Nevertheless, the Cox coefficients indicate<br /> a strong difference. This means that very strong confounding effects <br /> are at play here: this would need commenting. Usually such a strong <br /> difference between "corrected" effects and raw effects indicates a <br /> weakness in the study, that should at least be commented.Based <br /> upon the raw ratios one would think there's no effect of extra <br /> vaccination ; based upon the Cox coefficients, there's a very strong <br /> effect.
On 2021-11-29 13:59:27, user Samer wrote:
The article is published in Frontiers in Immunology https://www.frontiersin.org...<br /> https://doi.org/10.3389/fim...
On 2023-01-02 11:42:16, user Lance wrote:
It seems that the authors indulged in the pharma-friendly practice of starting the clock on exposure 7 days post-exposure:
"Individuals were considered bivalent vaccinated 7 days after receipt of a single dose of the bivalent COVID-19 vaccine... Curves for the non-vaccinated state were based on data while the bivalent vaccination status of subjects remained “non-vaccinated”. Curves for the bivalent vaccinated state were based on data from the date the bivalent vaccination status changed to “vaccinated”. "
This is particularly egregious given the potential for these vaccines to increase infection risk in the period immediately following vaccination. What little VE is reported here for the bivalent could itself be an illusion, disappearing upon proper treatment of the data.
On 2020-12-27 02:14:14, user valley_nomad wrote:
What is the definition of mortality rate in this study? The numbers seem to be way too high if it is CFR (case fatality rate).
On 2021-12-21 21:03:38, user Mike B wrote:
Fantastic early news on boosting to increase circulating antibodies to provide Omicron protection. I hope we see a matching case study to correlate clinical data. Although the author declared the limitations regarding waning, it is critical to determine the waning pattern of boosted response.<br /> Taking "likely to be similar" as a starting point, the data appears to show significant loss of circulating antibodies 6 months post vaccination. This a critcal clinical issue in the USA because the high number of elderly/institutional vaxxed early in 2021 and subsequently boosted in Aug/Sept timeframe to enhance protection against Delta. For this highly risk population, boosted protection may already have significantly waned leaving less than expected protection just as Omicron begins to dominate. Without data on waning attached, the study may set false expectations of protection and open questions on continued booster use. <br /> One way to ameliorate the issue is to extend the study, collect samples pre and post a 4th dose at 50 and 100 micrograms. Thus will settle discussion and improve application towards clinical use.
On 2020-07-31 13:04:08, user Anne Wyllie wrote:
Since the OGD-510 has been shown to be detrimental to SARS-CoV-2 RNA,how does the buffer in the 610 compare?
On 2021-08-18 15:53:47, user K Meijer wrote:
Does this assume the previously infected peeson still has antibodies left? What about someone who had Covid Beta variant middle December 2020, tested positive for IgG antibodies late in January with lab test, but showed hardly any antibodies remaining with rapid antibody test early August 2021?
On 2020-07-04 15:08:27, user Robert van Dijk wrote:
Interesting paper! I have a few questions/comments. <br /> - is the model really just a ResNet-50 with the final classification layer fine tuned? Sounds amazing haha! <br /> - if you’re looking to apply the model in real clinical practice I think it’s good to think about how it would fit in the workflow. I think it’s already great that it does not output a diagnosis, but actually the step before it. Transparancy is very important especially in the clinic, so I could still expect that they want the model to explain it’s own decision as well. Does it allow for highlighting (using bounding boxes) the cells it has identified? <br /> - from what I have learned sensitivity is often more important than specificity in a clinical setting, but that differs of course per specialisation. So perhaps fine tuning towards that may be beneficial<br /> - great that you mention limitations of the model. Think that’s going to be essential especially with regard to specific cell types.
On 2021-01-17 01:45:40, user Oguzhan Alagoz wrote:
AN updated version of this paper is published by Annals of Internal Medicine:<br /> https://www.acpjournals.org...
Full updated citation is:<br /> Alagoz, O., Sethi, A. K., Patterson, B. W., Churpek, M., & Safdar, N. Effect of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the United States: A Simulation Modeling Approach. Annals of internal medicine, M20-4096.
On 2020-08-14 13:42:47, user Nikita Michaels wrote:
From the paper: "Because the amount of virus present in the samples was low and thus unsuitable for common next-generation sequencing approaches, Sanger sequencing based on a gene-walking approach with over-lapping primers was used to obtain the virus<br /> sequence." Probably any air sample would have led to the same results given the "right" primers. But they did not use another sample of "non-contaminated" air to perform the same test so the results are without any value. Terrible, how these studies without peer review end up influencing public regulations.
On 2020-08-14 15:40:32, user Ricky Turgeon PharmD wrote:
This article has generated some discussion on Twitter, including a thread where I provide some comments. https://twitter.com/Ricky_T...
In particular, I hope that the authors can revise and/or provide responses to address the following concerns:<br /> 1. Please provide the rationale for selecting July 31 as the date for interim analysis. Please also provide details regarding this interim analyses, including pre-specified stopping rules, who had access to the data. Although this manuscript is labeled as a "preliminary report", it would be valuable for the authors to explicitly state whether this trial is ongoing, and whether any changes to the conduct of the trial were made based on this interim analysis.
In version 1 of the article on this site, the Methods section had a sentence that stated "No concealment mechanism was implemented". This was subsequently removed in version 2 yesterday. Please clarify what is meant by this. Did the authors mean to imply that allocation concealment was not performed, or was this an erroneous statement intended to describe the unblinded nature of the study? Please also describe the process for treatment allocation and how allocation concealment was maintained.
The authors describe a change in the primary outcome in terms of timing of CRP measurements. However, I note that the clinicaltrials.gov summary of this trial previously had an entirely different outcome as the primary outcome, with CRP only described as an exploratory/tertiary outcome. The authors should describe the timing and rationale for switching the outcome from a clinical one (need for supplemental oxygen in the first 15 days post-randomization) to the inflammatory biomarker CRP.
Despite changing the timing of CRP measurements, data on this modified primary outcome of CRP was missing in a large proportion of patients at day 5, and in the majority of patients at day 8. Further details should be provided regarding the reason for missing data, how this was handled in their analyses, and how this should temper conclusions.
Finally, performing an interim analysis and disseminating their results in the midst of an open-label trial with subjective endpoints can pose challenges to maintaining impartiality. The authors should describe how they will mitigate potential allocation, performance, detection and attrition bias during the remainder of the trial.
I hope that the authors will seriously consider these comments.
Sincerely,<br /> - Ricky Turgeon
On 2020-08-17 22:56:26, user Irene Tosetti wrote:
identical to the French test (already for sale in Europe) called Easycov, a saliva rt-lamp. Good job.
On 2020-07-15 11:28:56, user One bird one cup wrote:
The CDC quotes a current best estimate for planning purposes as .0065. I'm not sure if that's just for the US. The above study is the only study they list that contributes to that estimate. I'm sure I'm missing something here but I just don't understand the numbers. I will look closer, though.
On 2020-08-25 08:12:24, user Bart Rijnders wrote:
The analysis is done with days after diagnosis (I presume the positive PCR this is) and not days since start of symptoms and the most important variable. This means that the diagnosis can thus be made somewhere between >14 days preceding hospital admission (if the test was done by a GP of testing venue) but can also be several days after hospital admission (e.g. when the first PCR is false negative but the second is positive)
Important bias may happen when patients who get tested easily / earlier on in the disease course while still outside the hospital also can get hospitalized easier (e.g. good health insurance). These patients will be overrepresented in the "treatment within 3 days after diagnosis" group. I do not see how this was (and can) be accounted for.
The study should therefore analyse the treatment effect in function of symptom duration at time of plasma transfusion as well and how this relates to a possible therapeutic effect of plasma. Hope this will be possible
On 2021-02-09 09:47:38, user Alex wrote:
Hi guys, interesting paper. I’m curious as to how you justified a change in well-being pre-during when baseline data was collected during the peak?
On 2020-09-07 03:47:09, user Stephen D wrote:
Your conclusion is faulty. Note that nothing in your data implies that anything should be made "mandatory". Your modeling might imply that if everyone wore a mask 24/7, the effect would be to reduce the probability of a certain increase in infections. But it cannot in principle have any implication as regards legal responses by governments. This is not science. Your conclusion is framed using political and ethical concepts implied in the term "mandatory" that are in principle not amenable to science, and cannot be inferred from data or models.
On 2020-09-09 11:10:26, user Andrew Broadbent wrote:
Draft comment by – T Andrew Broadbent CES Economic & Social Research info@ces.org.uk
Overview<br /> This important paper claims to transcend the large number of ‘conventional’ epidemiological ‘SEIR’ models (Susceptible, Exposed, Infectious, or Recovered) of the current pandemic. Its fascination lies in its attempt to ‘compare SEIR models of immune status’ and derive results more directly from the data available.<br /> It uses ‘Bayesian inference’ on data from 10 countries from 25 Jan to 20 June 2020 to estimate the daily proportion of people in each country who are (i) not exposed to infection(ii) not susceptible even though exposed (iii) not infectious even when susceptible. These sub-populations are what the authors call ‘dark matter’. It concludes that many more of the population are ‘effectively immune’ than generally understood, and so the second wave can be indefinitely postponed or suppressed without successive lockdowns as concluded from some of the conventional models.<br /> The immediate issues and queries seem to be :<br /> (i) Suppression is said to depend on an effective Track and trace system, as with some ‘conventional’ models. The practical policy implications are thus not very different from studies which suggest maintaining restrictions until infection is very low, so as to enable managing with track and trace, without needing to reimpose universal lockdown.<br /> (ii) The proportion of the population isolating/shielding dominates the results (eg Germany) . This is social behaviour and government policy, not ‘dark matter’ in the way susceptibility and infectibility may be, being more biologically determined.<br /> (iii) The parameters in the model are estimated within limits determined from external sources This may include the predominant ‘effective population’ parameter – the population who are not shielding.<br /> (iv) ‘Effective herd immunity’ is a somewhat troubling term – given overtones of ‘let the old die’ in some policy discussions of ‘herd immunity’.<br /> (v) It claims to incorporate all the different data collection biases in different countries, such as testing people with or without infection. Would it be worth including countries with early success in suppressing infection – Taiwan, S Korea, China, New Zealand etc?<br /> ‘Effective’ herd immunity?<br /> It references other studies which also look at ‘heterogeneity’ of the population, where the first wave either kills or makes immune the more susceptible population – so that a second wave necessarily involves a less susceptible population and will tend to be lower than the first wave, other things being equal.<br /> It concludes that ‘effective herd immunity’ following the first wave of infection is much higher than suggested by the proportion of people who have been infected and recovered and may now be immune – ‘seroprevalence’ . This is now in the range 5-7% in the UK.<br /> The term ‘herd immunity’ prompts wariness following the UK government’s early discussions which were interpreted as contemplating 60-80% of the population becoming infected with 500K-1 million deaths. ‘Culling the old and infirm’ was one interpretation. The paper concludes that having less than 20% of the population infected and recovered could be enough to dampen a second wave.<br /> The second wave - a ten-fold reduction in infection and death?<br /> A main claim of the paper is that the second wave could be postponed indefinitely, or if not, have a factor of 10 fewer infections and deaths than predicted by some SEIR models, (deaths peaking at 30-100 per day in the UK, compared with 1000 per day at the peak of the first wave) .<br /> But this projection, has in common with the conventional models, a heavy reliance on an effective FTTIS (‘Find, Test, Trace, Isolate, Support’) system in order to isolate those infected or exposed to infection. But the paper suggests that only 25% efficacy of FTTIS is needed, compared to the present official target of 80%.(?)<br /> Dark matter – very high?<br /> ‘Dark matter’ seems a very high proportion of the population. From one illustration (figure 2) dark matter results in under 20% of the population being infected. Almost 50% of the total population are not exposed (shielding/sequestered), so that only half the populations is ‘effective’ in the epidemic. Of those who are, 50% are not susceptible, and of those who are susceptible 50% are not infectious. <br /> The proportion of the total population which is non exposed (self isolating, shielding, sequestered) would seem to be very dependent on people’s behaviour and on government instructions, and thus on the social context and time lapse of the pandemic. The other components of ‘dark matter ‘ - susceptibility and infectibility – seem more biologically determined, not so subject to behaviour and social and policy context.<br /> Data – why not include countries with greater success in suppressing the first wave?<br /> Although the FTTIS is said to be enough to limit or suppress the second wave without a ‘lockdown’, the Bayesian inference was conducted on countries, many of whom who were in some kind of lockdown for at least part of the period. They are the 10 countries with high death rates.<br /> The data is from USA, UK, Canada, Spain, France, Italy, Belgium, Germany, Mexico, and Brazil . It would have been interesting to include countries which largely succeeded suppressing the virus in the first wave, with either very short sharp lockdowns, or early interventions of intense FTTIS, namely Taiwan, South Korea, China, Hong Kong, Singapore, New Zealand. <br /> Many model parameters are influenced from outside the model. (?)<br /> There ar 25 parameters listed in the model, and their levels and potential variation – are apparently influenced by external empirical studies outside the model, and are listed as ‘priors’. This apparently influences the final estimated parameters after the model has been run. (?)<br /> Parameters include the effective population, the probability of going out, social distancing threshold, critical care capacity threshold (per capita), Infection, proportion of non-infectious cases, effective number of contacts, effective number of contacts: work, transmission strength, infected period , infectious period , proportion of non infectious people etc. etc.<br /> Rich findings – country by country results – ‘effective population’ dominates?<br /> The paper suggests that only Spain, and Brazil don’t exhibit the heterogeneity embodied in the model – in that their whole population seems to participate in the epidemic – their ‘effective population’ is equal to the whole population, with almost no one isolating, or shielding.<br /> The country comparisons involve changing the input parameters, so as to eliminate each component of heterogeneity in turn. The parameters - effective population, non susceptibility, social distancing threshold, decreasing seropositivy are each removed in turn.<br /> Germany and Canada have by far the smallest proportion of ‘effective population’ due to their high levels of shielding – this seems to determines their relatively good performance and low level of deaths, - it would be useful to learn more about how far this parameter is set ‘prior’ to the model.<br /> There is much less variation in the proportion of the effective population susceptible to infection – from ~67% in Spain (operating on an effective population almost equal to the whole population) , to ~47% in Canada.<br /> Similarly there is low variation in the proportion of susceptible people who are non infectious – from ~60% in Canada and Italy and to ~45% in Germany, France , and USA .<br /> Some more details<br /> The claim is that the analysis can incorporate all kinds of real world fuzziness in the data - by modelling latent variables such as the bias towards testing people with or without infection or, the time-dependent capacity for testing. ‘Everything that matters —in terms of the latent (hidden) causes of the data—can be installed in the model, including lockdown, self-isolation and other processes that underwrite viral transmission’.<br /> This is a ‘LIST’ model with four factors (Location, Infection, Symptoms and Testing). It models the probability of people being in different states, and produces two outputs – positive cases, and deaths .<br /> The states in each factor are::<br /> Location – Home, Work , Hospital, Isolated, Removed<br /> Infection – Susceptible, Infected, Infectious, Sero negative, Seropositive<br /> Symptoms – Health, Symptoms, Severe, Deceased<br /> Testing – Untested, Waiting, Negative, Positive<br /> Each individual in the population has to be in one state, and only one state, within each of the four factors.
On 2020-09-13 19:32:19, user Qunfeng Dong wrote:
An updated version of this manuscript is now accepted for publication by Journal of the American Medical Informatics Association Open Access on Sep, 13, 2020.
On 2021-08-30 11:33:43, user KTHPIO7 wrote:
Do the results account for the faulty data based on PCRs of >40 ct?
On 2021-02-27 06:50:50, user Suriati Jamalludin wrote:
good exploration. i'm digging the source that online learning during emergency response are facing the problem mental health disruption among student. therefore the learning continuity is not easy when student in a bad condition of mental health. how do i use the reference as a support. may i have the reference format this article for citation?
On 2021-02-28 16:23:44, user Larisa Tereshchenko wrote:
Now published in the Journal of Innovations in Cardiac Rhythm Management: https://www.innovationsincr...
On 2021-09-01 02:33:45, user Andrea Boggan wrote:
"Survival of the Flattest." This new variant will wait it's turn until Delta is through delivering its blow, and when Delta is done, it and the others waiting in the wings will step forward and compete for fuel.
On 2021-03-17 10:01:33, user Bernhard Brodowicz wrote:
As the aim of this study was to determine the prevalence of SARS-CoV-2 it is essential to define the parameters when a test is rated as SARS-CoV-2 positive or negative (as a qualitative analytical test result, ct-value cutoff, handling of different results for viral targets…). Neither the paper itself, nor in the supplementary data, gives an evidence about how positive and negative test results were delimited. Especially as different analytical setups were used, validation data of the different RT-qPCR setups should be reported, discussed and comparability should be shown. When reporting quantitative analytical results (viral loads in children and adults via ct-values), the method should be validated for its quantitative purpose (including standardization). Especially when using different analytical setups this is crucial to assure the reporting of valid and comparable results. Looking on ct-values given in Supplementary Figure 2b the results from Graz (first round), which used a FDA authorized and (also for pool samples qualitative) validated diagnostic kit, showed comparable results for both targeted genes (E and ORF1a/b) and suggests robust positive results for 9 samples in pupils. For other assays the human housekeeping gene RPP30 (RP2) was used as sample control. In clinical diagnosis it might be useful (specially to reduce the risk of false negatives) to also report test results as positive when only one viral target is detected or RPP30 (RP2) was absent, but only when covered by method validation results. However, in a study, where different analytical setups were used, it should be further investigated and discussed, when viral target N2 and ORF1b were report as positive results also with high ct-values (> 40) and in the absence of RPP30 (as it is suggested by Supplementary Figure 2b). This could question the validity of the analytical setup used in this study and is calling for the presentation of the validation parameters of the different RT-qPCR setups to interpret comparable results.
On 2021-07-27 12:56:21, user James Jarvie wrote:
This paper is referred to as evidence that vaccine is superior to naturally acquired immunity. However, the paper appears to me to suggest that vaccine is as-good-as naturally acquired immunity (using naturally acquired immunity as the benchmark).
On 2020-10-14 02:40:05, user Robert Stephens wrote:
Could it be that a more recent HCoV infection increases the likelihood of the dysfunctional 'back boost'. If such is the case then perhaps this partially explains the lower second wave CFRs seen in many European countries. Maybe the Sars-CoV-2 mitigating behaviours (distancing/ masks etc) have also reduced the incidence of HCoV infections in the preceding 6 months - thereby reducing the frequency and amplitude of the back boost.
Dr Robert Stephens MB BS FACD
On 2021-03-30 12:09:03, user jgas wrote:
Has there been furtheer follow-up beyond 72 hrs?<br /> This data would really help to clarify possible mechanism of serious adverse effects emerging with the roll-out of the Oxford adenovirus vector vaccine to frontline workers across Europe and the safety of use of these adenovirus vectors per se.
On 2021-04-09 12:49:29, user Francesco Pilolli wrote:
In spite of the difficulties encountered by other studies evaluating the efficacy of therapies in outpatients, this work describes an impressive statistically significant reduction in the hospitalization rate.<br /> This study reports a share of hospitalised patients in the “recommended” cohort (2,2%) similar to the one described in the placebo groups of the Pfitzer-Biontech (2,5% https://www.fda.gov/media/1... ) and Moderna (3,3% https://www.nejm.org/doi/fu... ) vaccine studies between symptomatic cases (these studies did not exclude hospitalised cases at the onset), but it describes an impressive 14,4% share of hospitalised cases in the “control” group.<br /> This rate is much higher than the one described in the placebo group in the Bamlanivimab and Etesevimab study in high-risk outpatients (7% https://www.fda.gov/media/1... ) and in the COLCORONA trial again in the placebo group of high-risk outpatients (5,8% https://www.medrxiv.org/con... ).<br /> It’s peculiar that this study (carried out on general population and excluding severe cases at onset) describes a hospitalization rate in the control group much higher than that observed in other studies in high-risk patients, considering also hospitalisation at onset.<br /> I think that the majority of the difference of the hospitalisation ratio between “recommended” and control group could be explained by the choice of selecting 88 out of 90 cases of the control group from people infected in the first wave in the province of Bergamo, one of the most severely hit zones in Italy.<br /> The control group required swab or serological positivity but the swab test capacity was limited in Italy during the first wave and it is very unlikely that all the symptomatic people underwent a serological test. During the first wave many symptomatic people were at home without having undergone any swab. The limited test capacity causes a high underestimation of paucisymptomatic and mild cases resulting in a high rate between hospitalisation and tested cases.<br /> In fact the Italian ratio between hospitalisation and tested cases from March to May 2020 (the same infection period of 88 out of 90 patients of the control cohort) was 36,4% compared to the 7,8% observed from October 2020 to January 2021 https://www.epicentro.iss.i...<br /> This difference was much higher for the province of Bergamo. Data about daily hospitalisation by province are not public but we know the cases ( https://lab24.ilsole24ore.c... and deaths ( https://www.istat.it/it/fil... ) by province until December 2020. While the ratio between Italian cases and deaths was 14,8% from March to May 2020, it falls to 2,2% from October to December, in the same periods the rate deaths/cases in the province of Bergamo was 23,4% (almost one patient with positive swab every four died in the first wave) and 1,5% (less than the Italian average).<br /> Therefore, it is very likely that the majority of the difference in the hospitalisation rate between the “recommended” (from the second wave) and the “control” cohort (from the first wave in the most hit zone in Italy) is explained by the different historical moments which were characterised by a large difference in test capacity and many symptomatic people at home without getting tested during the first wave.
On 2021-08-03 10:01:20, user Alan Yoshioka, PhD wrote:
There are several numerical discrepancies and questions about methods that should be resolved before any conclusions can be drawn from the study.
When was it decided to exclude patients whose RT-PCR results had a cycle threshold value >35 in the first two consecutive [tests]? When was it decided to adjust the Kaplan–Meier analysis for symptom onset?
Please reconcile the discrepancy between the "mild" in study title and the "mild to moderate" in the description of the mandate of the isolation hotels. The inclusion criteria do not appear to specify the severity of disease, which would apparently then depend on the admission criteria of the hotels.
In Table 1, stated percentages of patients who are male do not match raw numbers of 69/89 for all patients and 36/47 for ivermectin, respectively; instead (corresponding to females accounting for 21.6% in the abstract) 78.4% = 69/88, and 78.3% = 36/46.
The abstract says 16.8% were asymptomatic at baseline, which does not complement the 80.9% symptomatic in Table 1, nor the 69 symptomatic patients in Figure 3. Perhaps I am missing something, but it is not clear why 37 and 35 symptomatic patients in Table 1 do not match the numbers of subjects at risk, 36 and 33, on Day 0 in Figure 3.
Table 2 presents results from RT-PCR testing at days 4 to 10. Day 2 is said to have been added to the protocol along with Day 4, but no explanation is given for why data from Days 2, 12, and 14 are not also shown in the table.
I'm not a specialist in lab tests, but I'm afraid I am having trouble understanding the post hoc analysis based on a convenience sample of 16 samples on Day 0. Does Table S2 mean there were then 26 samples taken on Day 2?
I am mildly puzzled by the alignment of the dots in Figure 2: most appear to lie on a grid, but a few sets of points are slightly raised or lowered. Is this a normal occurrence?
On 2022-06-22 18:31:49, user Elisabeth Bik wrote:
I have serious concerns about the data integrity of this paper, in particular about Figures 2 and 3. Some constellations of data points in these images appear to be duplicated within or across panels, and the lowest/highest values of the X axis (which should be the same across the four panels within a figure) appear to unexpectedly vary. This paper has been published in Sleep Science in 2020, under DOI: 10.5935/1984-0063.20190133 and I have posted my detailed concerns on PubPeer at https://pubpeer.com/publica...<br /> The PubPeer entry lists other concerns about this paper as well, including concerns about the ethical approval process and the p values in Table 1, raised by PubPeer user 'Meliosma donnellsmithii'.
On 2024-02-26 17:17:08, user Ciarán McInerney wrote:
Please, justify why<br /> statistical significance of individual values in your omnibus PheWAS protocol<br /> warrants an indication of predictive performance? Firstly, looking at main<br /> effects in an omnibus assessment commits the Table 2 fallacy (doi: 10.1093/aje/kws412).<br /> Secondly, the p-value associated with an odds ratio is a statistic related to<br /> the validity of the parameter estimate in a hypothetical null world. It can and<br /> should only be used for making statements about the model used to estimate the<br /> parameter of interest (in your case, the odds ratio). It has nothing to do with<br /> the quantifying the association. Thirdly, why do you select features based on the<br /> p-value but not the magnitude or direction of the association statistic to<br /> which it refers? A feature with a very large magnitude might be clinically<br /> meaningful for many patients, regardless of how spread the distribution of that<br /> feature’s values are.
On 2024-04-09 15:52:16, user Tarachopoiós wrote:
This looks like an intersting analysis. One question was around the correlation of predictors to TTFT. So growth rate requires time-series data to be estimated did you account for the time taken to estimate growth rate? One option would have been to use a joint longituinal time to event model to account for immortal time bias. How did you account for immortal time bias in your analysis i.e. the fact that tumour growth rate isn't known at your time zero? Or is it known? That wasn't clear in the methods.
On 2024-04-27 19:47:53, user Rebecca L. Roop wrote:
Thank you for the work and dedication to create this article for publication. I suffered through TSW for 24 months after only using various classes of topical steroids for 12 months. That period of my life was absolute hell.
On 2024-04-28 13:06:50, user Gina Dee wrote:
I’m so happy and relieved to see research being done to better understand TSW. My daughter suffered through TSW starting at the age of 2 and it was a nightmare. We had to struggle through with minimal support from doctors. I hope this study and further studies help to lessen the occurrence and better treat the condition.
On 2024-05-01 03:00:07, user Bernadette wrote:
Firstly my thanks to each one of you. This paper gives me hope that diagnostic and treatment guidelines for ‘TSW’ can be developed. I have a H/O of 70+ years of skin problems. Having struggled for 4 years with skin rashes which present to me, and to a Sydney based GP with an interest in TSW, as being consistent with TSW, I have experienced the frustration of presenting to dermatologists who say TSW is not an ‘accepted’ skin condition even though I have experienced and photographed my red sleeve, elephant skin on my ankles and wrists, non stop oozing on my face, neck and ears, non stop skin flaking, hair loss, heat, pain and intense itching. I have experienced the isolation and depression too often associated with this condition. Recently I have focused on managing heavy staph and fungal concentrations and have seen significant improvements. So I’m hoping this paper will act as a catalyst for a comprehensive focus on TSW so that we, those affected, can access medical expertise without running the gauntlet of being dismissed and belittled. Again. Thank you.
On 2024-05-08 14:58:49, user mira wrote:
Thank you for carrying out this research. This preprint needs to be published so people and the medical world can finally stop gaslighting us and telling us it's "just eczema". I have been suffering for years with TSA and after quitting steroids my life has been terribly changed because of TSW. We are so overlooked, desperate for knowledge and solutions, we are a suffering community.
On 2024-05-09 14:39:59, user Ana Angel wrote:
Very important piece of research for the thousands of us suffering from this condition. More research is needed! <br /> I stopped all forms of steroids 4 years ago. I’m now much much better, but still flaring on my elbow creases. We need treatments to shorten these lo g recovery times
On 2024-08-13 07:20:46, user Christine Markwalter wrote:
The peer-reviewed version of this article can be found here: https://doi.org/10.1038/s41467-024-49080-9
On 2024-08-13 07:22:59, user Christine Markwalter wrote:
The peer-reviewed version of this article can be found here: https://doi.org/10.1371/journal.ppat.1011442
On 2024-08-15 15:39:08, user Vanessa Merker wrote:
The final version of this article has been peer-reviewed and published online.
Citation: Chou C, Wiredu SO, Von Imhof L, et al. Health services interventions to improve the quality of care in rare disease: a scoping review. Therapeutic Advances in Rare Disease. 2024;5. doi:10.1177/26330040241262810
Available open-access at: https://journals.sagepub.com/doi/10.1177/26330040241262810
On 2024-10-19 15:28:14, user Steve Laurie wrote:
Great work - congratulations. Let's hope WGS becomes standard of care in NICUs some day soon.
Just wanted to let you know that you have duplication of text in the Methods in the current version, lines 144-153 and 153-162.<br /> I also doubt that citation 67 at the end of the paragraph is the one you meant to cite.
On 2024-11-04 08:17:51, user Oren Miron wrote:
Short video summarizing the findings: https://www.youtube.com/watch?v=NFVkE_nwSEk
On 2024-12-03 21:07:38, user xPeer wrote:
Courtesy review from xPeerd.com
This manuscript investigates the genetic underpinnings of gene expression noise (variability in mRNA expression) and its contributions to complex trait variation. By leveraging single-cell transcriptomics from 1.23 million peripheral blood cells across 981 individuals, the study identifies expression noise quantitative trait loci (enQTLs) in seven immune cell types. Key findings include distinct enQTLs independent of traditional expression QTLs (eQTLs), with implications for hematopoietic traits and autoimmune diseases. This comprehensive analysis highlights gene expression noise as an overlooked molecular trait impacting genetic variation in complex traits.
Strengths include the integration of large-scale single-cell data, robust methodological frameworks, and a novel focus on noise QTLs. However, the work’s translational potential and certain mechanistic aspects require refinement.
Major Revisions<br /> 1. Mechanistic Depth<br /> Limited Exploration of Noise Regulation Mechanisms:
While the authors identify enQTLs enriched in chromatin marks (e.g., H3K27ac, H3K4me3), the functional pathways connecting these marks to noise modulation are underexplored (Section: Functional Enrichment, p.8). Including mechanistic validation, such as CRISPR perturbation experiments targeting key SNPs, would enhance understanding.<br /> The interplay between noise and transcriptional bursting models (e.g., initiation frequency vs. burst size) remains superficially addressed. Expanded quantitative modeling of burst kinetics could better explain noise-associated traits (Section: Discussion, p.9).<br /> Post-Transcriptional Contributions:
The discussion briefly mentions mRNA stability but does not evaluate post-transcriptional regulation’s role in noise. Experimental validation, such as ribosome profiling or RNA decay assays, could substantiate these claims.<br /> 2. Population Diversity and Generalizability<br /> Limited Ancestral Representation:
The cohort comprises Northern European ancestry individuals, limiting the generalizability of enQTL findings. Noise might vary due to ancestry-specific SNP frequencies or regulatory architectures (Section: Methods, p.3). Validation in diverse populations is critical for ensuring broad applicability.<br /> Cell-Type Specificity:
Some findings, such as HVGs shared across cell types (e.g., HLA genes), require validation in other tissues or disease models. Cell-specific functional assays could strengthen the biological relevance of these findings.<br /> 3. Statistical and Computational Robustness<br /> Unexplained Variance in enQTL Effects:
While enQTLs explain certain GWAS loci, the authors do not quantify the proportion of unexplained variance attributable to unaccounted mechanisms (Section: GWAS Colocalization, p.10). Comparative analysis with polygenic risk scores or partitioning heritability methods would contextualize enQTL contributions.<br /> Colocalization Analysis Limitations:
Colocalization methods prioritize high-probability overlaps (PP.H4 > 0.7), but alternative loci with moderate probabilities (e.g., PP.H4 > 0.5) might merit inclusion. Revisiting loci with expanded statistical thresholds could yield additional insights.<br /> 4. Functional Insights<br /> Overemphasis on Chromatin Features:
While the enQTL analysis emphasizes chromatin states, the link to noise-specific regulatory dynamics is unclear. Functional experiments, such as live-cell imaging of noise dynamics in specific chromatin contexts, would substantiate claims (Section: Functional Enrichment, p.8).<br /> Underexplored Relationship Between enQTLs and Disease:
The finding that autoimmune risk variants correlate with attenuated noise is intriguing but not mechanistically explained (Section: Discussion, p.9). Immune activation studies in enQTL-defined contexts could clarify whether lower noise promotes immune tolerance or other phenotypes.<br /> Minor Revisions<br /> 1. AI Content Analysis<br /> Estimated AI-Generated Content: ~15-20%.<br /> Stylistic Observations: Repetitive phrasing (e.g., “highlighting noise as an important mediator”) and predictable transitions suggest AI-assisted drafting in some sections.<br /> Epistemic Impact: Minimal; technical content is original, but editing for stylistic variation is recommended.<br /> 2. Figures and Data Presentation<br /> Figure Annotation:<br /> Figures (e.g., Figures 3-5) lack precise legends detailing axes, significance thresholds, and methodological descriptions.<br /> Supplemental figures require clearer integration into the narrative (e.g., referencing HVG enrichments in Figure S2).<br /> Data Accessibility:<br /> Raw data from single-cell noise calculations and SNP annotations should be made available as supplementary files for reproducibility.<br /> 3. Terminology Consistency<br /> Inconsistent Definitions:
Terms like “expression noise” and “transcriptional variability” are used interchangeably but should be clearly defined early in the manuscript.<br /> Confusing Use of Abbreviations:
HVG, enQTL, and eQTL acronyms require standardized introduction and consistent usage across sections.<br /> 4. Citations and References<br /> Key Omissions:<br /> Recent advances in single-cell variability analysis (e.g., newer methods beyond tensorQTL) are underrepresented. Including citations for innovative noise quantification approaches (e.g., scVI) would modernize the references.<br /> Recommendations<br /> Mechanistic Studies:
Employ experimental tools (e.g., CRISPRi/a, live-cell reporters) to validate enQTL roles in noise dynamics.<br /> Integrate transcriptional bursting models to elucidate enQTL regulatory mechanisms.<br /> Enhance Population Scope:
Expand cohort analysis to include non-European populations.<br /> Incorporate ancestry-aware computational models to assess demographic variability in enQTLs.<br /> Data Presentation Improvements:
Add supplemental raw data files for transparency.<br /> Expand figure annotations and connect supplemental content to main findings.<br /> Expand GWAS Interpretation:
Investigate enQTL roles in non-immune traits to broaden the study’s impact.<br /> Compare enQTL contributions with existing functional annotations (e.g., enhancers, transcription factor binding sites).
On 2024-12-15 08:46:43, user Ujváry István wrote:
Note the correct chemical name:<br /> bis(2,2,6,6-tetramethyl-4-piperidinyl) sebacate
(BTMPS is a piperidine derivative; it is not a pyridine derivative!)
On 2024-12-23 02:34:26, user IA Signore wrote:
Now published as Signore, I. A., Donoso, G., Bocchieri, P., Tobar-Calfucoy, E. A., Yáñez, C. E., Carvajal-Silva, L., ... & Colombo, A. (2024). The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population. Genes, 15(11), 1352.
On 2025-01-02 09:48:12, user Teresa Ramírez García wrote:
Dear Dr. Witt: We have read with great interest your article published in preprint format. In this article, an aspect that we consider confusing is mentioned in relation to our work [1]. We refer to the authors' assertion that “in the FCSRT only 4 words need to be learned in three learning trials, whereas the VLMT requires learning of 15 words in 5 trials” [2].
In this regard, we would like to point out that the FCRST requires you to effectively memorise 16 words spread across 3 trials, not just 4 words, as stated in the original paper by the author who developed the exam. Because it aligns with the original test scales by age and cognitive reserve of the patients in the Spanish population, this test can also be explained in the Neuronorma project, which we use in Spain. Since we discovered a baremation based on the Spanish population [3], this is also the reason why this test is typically utilised in Spain.
1.- Serrano-Castro PJ, Ramírez-García T, Cabezudo-Garcia P, Garcia-Martin G, De La Parra J. Effect of Cenobamate on Cognition in Patients with Drug-Resistant Epilepsy with Focal Onset Seizures: An Exploratory Study. CNS Drugs. 2024 Feb;38(2):141-151. doi: 10.1007/s40263-024-01063-6. Epub 2024 Jan 24. PMID: 38265735; PMCID: PMC10881647.
2.- Witt JA, Badr M, Surges R, von Wrede R, Helmstaedter C. Negative Impact of Cenobamate on Cognition: Dose-Dependent and Independent Effects medRxiv 2024.12.23.24319533; doi: https://doi.org/10.1101/2024.12.23.2431953
3.- Peña-Casanova J, Gramunt-Fombuena N, Quiñones-Ubeda S, et al. Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the Rey-Osterrieth complex figure (copy and memory), and free and cued selective reminding test. Arch Clin Neuropsychol. 2009;24(4):371-393. doi:10.1093/arclin/acp041
Ramirez-Garcia T and Serrano-Castro PJ.
Hospital Regional Universitario de Málaga.
Instituto de Investigacion Biomedica de Málaga (IBIMA-Plataforma Bionand).
On 2025-01-16 06:08:40, user xPeer wrote:
Courtesy review from xPeerd.com
Summary:<br /> The manuscript titled "Typhinder: Rapid, low-cost colorimetric detection of Salmonella Typhi bacteriophages for environmental surveillance" presents a novel colorimetric assay designed to detect Salmonella Typhi (S. Typhi) bacteriophages in environmental water samples. This study primarily focuses on areas with poor sanitation infrastructure, including regions in Brazil, Côte d’Ivoire, Nepal, and Niger, demonstrating high sensitivity and specificity of the assay. The work indicates potential applications in public health surveillance, particularly in resource-limited settings, by providing a cost-efficient method (approximately $2.40 per sample) that does not require sophisticated equipment.
Potential Major Revisions:
Validation and Methodological Robustness:<br /> One key concern is the validation of the colorimetric assay only against the double agar overlay method. More comprehensive testing against additional molecular techniques like PCR/qPCR, which are considered gold standards for pathogen detection, is essential to determine the assay's accuracy and reliability under diverse environmental conditions. This gap was acknowledged in the discussion section.
Sample Diversity and Detection Limit:<br /> The study demonstrates that the detection limit is 28 PFU/mL, which although sensitive, may need further optimization to ensure applicability in environments with even lower pathogen concentrations. Additionally, the research did not provide adequate comparative data from different environmental contexts, such as varying water sources with potential inhibitors like antibiotics, which could affect assay reliability.
Comprehensive Data Analysis:<br /> The study's reliance on environmental surveillance data lacks integration with epidemiological data and molecular-based assessments of typhoid burden. Correlating phage detection with rates of clinical typhoid fever incidents would offer stronger evidence of the assay's utility in public health management. Future studies should aim to establish these correlations more explicitly.
Potential Minor Revisions:
Typographic and Grammatical Errors:<br /> 1. Page 2, Line 1: "particularly in low-resource settings with inadequate sanitation." - Repetition of the phrase "particularly in low-resource settings", consider rephrasing for clarity.<br /> 2. Page 6, Line 3: "require precise data on where typhoid is most prevalent, yet current surveillance methods are expensive and limited in scope..." - The sentence structure could be improved for readability.<br /> 3. Page 9, Line 5: "Antimicrobial resistance among S. Typhi strains poses serious challenges to effective treatment and may lead to higher mortality..." - Consider rephrasing for clarity.
Formatting Issues:<br /> The figures and tables should be better integrated into the text for improved readability. For example, citing Table 1 and Figure 2 explicitly within the corresponding discussion for context will aid readers' understanding.
AI Content Analysis:<br /> - Estimated AI-generated content: Given the extensive detail and specific nature of the subject, it is estimated that the manuscript has less than 5% AI-generated content.<br /> - Highlighted AI-detected sections: The introductory summary and some instances of repetitive phrasing suggest possible AI involvement.<br /> - Epistemic impact: Minimal as the core research contributions and data seem original and substantive.
Recommendations:
Enhanced Validation:<br /> Incorporate a broader range of validation techniques, particularly molecular methods like qPCR, to establish the assay's robustness across different environmental samples and contexts.
Addressing Limitations:<br /> Include detection methods for concurrent fecal contamination to provide contextual data, enhancing the reliability of typhoid phage detection results as environmental indicators.
Future Studies:<br /> Focus future research on correlating phage presence with clinical incidence of typhoid fever, and explore structural analysis of phage-host interactions. This will substantiate the assay's efficacy in public health interventions and policy-making.
Overall, the manuscript provides a promising tool for typhoid fever surveillance in low-resource settings, with significant public health implications. Addressing the detailed critiques will strengthen the manuscript and its potential impact.
On 2025-02-05 20:08:07, user Daniel Corcos wrote:
Gotzsche and Jorgensen claim to have found a high level of overdiagnosis after mammography screening. However, the method they use does not allow them to distinguish between cancers related to overdiagnosis and those caused by X-rays. Yet, when measuring the delay in the appearance of excess cancers, it becomes clear that, in addition to the excess corresponding to the lead time due to detection, there is a significant excess of delayed-onset cancers, which are therefore caused by X-rays ( https://www.biorxiv.org/content/10.1101/238527v1.full ; Corcos D & Bleyer, NEJM, 2020). These cancers explain the failure of screening at decreasing breast cancer mortality observed at 13 years by the authors.
On 2025-02-10 12:39:18, user MINGXIN LIU wrote:
This preprint has been published in International Journal of Medical Informatics and can be accessed at: " https://doi.org/10.1016/j.ijmedinf.2024.105673 ."
The title of the published version has been changed to "Evaluating the Effectiveness of advanced large language models in medical Knowledge: A Comparative study using Japanese national medical examination". Readers are encouraged to refer to the published version for the final peer-reviewed content.
On 2025-02-12 20:00:36, user Aron Troen wrote:
Review Part III
Results and Discussion<br /> Quantity of food trucked in: No source is cited for the figure of a pre-war baseline of 150-180 food-transporting trucks per day. This number is inconsistent with Israeli and UN sources. According to a document published in June by the Food Security Cluster, only 23% of UN recorded incoming goods to Gaza (not including fuel) before 7 October were food or food production inputs ( https://fscluster.org/sites/default/files/2024-06/Gaza%20imports%20and%20food%20availability%2015_may_V2%202.pdf) "https://fscluster.org/sites/default/files/2024-06/Gaza%20imports%20and%20food%20availability%2015_may_V2%202.pdf)") . If one is to rely on those UN statistics, the pre-war monthly average of trucks carrying food into Gaza was 2,288 (an average of approximately 100 trucks per working day in a normal month). Another UN source is the OCHA online Gaza crossings dashboard according to which during Jan-Sep 2023 a total 27,434 trucks carrying food entered Gaza, representing a monthly average of 3,048 trucks. <br /> The comparison in Figure 1 between the mean daily number of trucks for each week during the war with the "pre-war number of food-carrying trucks" per working day is highly misleading since it assumes that the number of working days remained steady. The distortion is significant because between 21 October and 5 May the crossings were open almost every day, as opposed to the 5-day work week in the period before the war. The following chart shows the monthly figures of UNRWA and COGAT compared to the monthly pre-war average of 2,288 trucks carrying food.
Compare it with Figure 1 from the article, which tells an entirely different story for the same period (blue columns represent trucks carrying food) in which is all but one week at the end of April the number of trucks carrying food was below the pre-war average:
Contribution of different food sources [to the northern and southern regions] (Table 1 & Figure 4)<br /> The result and discussion devote substantial attention to the relative distribution of food between the northern and southern regions. The governates designated as North and South Gaza are not explicitly defined. The only explanation for how the author determined the distribution of food deliveries between Northern and southern-central Gaza is as follows:<br /> "Until Israel re-opened the northern Erez and Erez West crossings, trucks had to leave south-central Gaza to resupply the north. We reconstructed the number of these trucks over time based on published information and data shared by WFP. As no data on content were available, we simulated their caloric equivalent by repeatedly sampling from the empirical distribution of calories per truck obtained from the UNRWA dataset (see below and Figure S1, Annex). The remaining trucked food was attributed to the south-central region."
The breakdown of that amount between northern and central-southern Gaza is based on an incomplete dataset (Commodities Received.xlsx) that appears to be missing the bulk of supplies by the private sector, appearing in the COGAT data ( https://gaza-aid-data.gov.il/main/) "https://gaza-aid-data.gov.il/main/)") , and which provided a significant share of supplies to the north. The dataset shows that during January and February 84 trucks were delivered to the north (according to the Logistics cluster). According to the same file, during March and April there only 20 private sector trucks delivered aid to the north. However, according to COGAT, deliveries to the north at that time were carried out mostly by the private sector, which are not fully covered by UN data. The flow of aid within Gaza and its regional distribution is difficult to ascertain. Media sources have provided conflicting reports from different sources. But they underscore the need to clarify precisely how the study assigned the regional food supply. For example, a story by the Associated Press from February 28 2024, reported that the UN had not been involved in aid deliveries to the North that month. According to one of COGAT's reports, during the first half of March they "facilitated over 150 aid trucks to the north" ( https://gaza-aid-data.gov.il/media/qtvbs5u0/humanitarian-situation-in-gaza-cogat-assessment-mar-15.pdf) "https://gaza-aid-data.gov.il/media/qtvbs5u0/humanitarian-situation-in-gaza-cogat-assessment-mar-15.pdf)") . In addition, COGAT claimed in a tweet from March 25 that UNRWA had not submitted a single request for delivering food to northern Gaza in six weeks ( https://x.com/cogatonline/status/1772316633605812511) "https://x.com/cogatonline/status/1772316633605812511)") . Thus, the methodology for determining the distribution of aid between northern and southern-central Gaza appears to be flawed since it almost entirely disregards aid deliveries by the private sector, which had a significant share of the total deliveries to the north during that period. Findings and conclusions that are contingent on this issue cannot be fully evaluated until this is corrected.
Main findings
The authors insinuate that the shortfall in the adequacy of food aid is solely due to intentional Israeli actions. For a subtle example of this the authors write that “Patterns in the diversity and caloric value of food trucked-in suggest that humanitarian actors may not have optimised the selection of what aid was allowed into Gaza.”. The food diversity findings suggest the humanitarian actors, who are responsible for deciding what is supplied to Gaza may not have optimized the selection of the aid. However, the use of the word “allowed” insinuates that the fault for this lies with Israel. The correct word should be “delivered”. Israel is responsible under international law for facilitating the entry of humanitarian aid. It is not responsible for selecting, procuring or delivering the aid. The fact that there was a considerable decline in food availability the first months of the war should not be surprising. Israel did not initiate the war, and should not be expected to have in place the logistics capacity for providing food to over 2 million conflict-affected people immediately after a strategic surprise attack. These major efforts, facilitated by the international community acting together with Israel, eventually yielded results as demonstrated by the study’s findings (eg. “a steep increase in food availability occurred from late April 2024, coinciding with the reopening of crossings into northern Gaza, and by June acute malnutrition prevalence appeared to be relatively low…”. [As noted above, “reopening” is a misleading term for the conversion of the Hamas-damaged Erez crossing from a pedestrian to a trucking terminal].
Similarly, one might ask why the Hamas failed to prepare for the needs of the Gazan civilian population under its governance, while it demonstrably prepared meticulously for the attack that was intended to provoke retaliation.
The authors seem intent to find Israel alone at fault, to encourage political pressure on Israel. They criticize “operations to deliver food via air or sea [as] cost-inefficient and a poor substitute for diplomatic pressure to merely reopen crossings”, stating in passing that “the 230M USD cost of the JLOTS operation [43] was higher than the entire humanitarian aid budget for the Central African Republic in 2024”. A back of the envelope calculation examining this assertion, and using WFP statements that their “emergency response [in Gaza] requires USD 740 million to provide support for up to 1.1 million people monthly” ( https://www.un.org/unispal/wp-content/uploads/2024/04/WFP-Palestine-Emergency-Response-External-Situation-Report-18-23-April-2024.pdf) "https://www.un.org/unispal/wp-content/uploads/2024/04/WFP-Palestine-Emergency-Response-External-Situation-Report-18-23-April-2024.pdf)") , shows that USD 740 per 1.1 persons monthly translates to 22.4 dollars per person per day. This means that the cost of the air-dropped food was only 29% higher than the delivery of land-based humanitarian food-aid. Thus, an equally plausible alternative interpretation of the resource expenditure might be that the air and sea operations, involving cooperation of USA, Jordanian, Israel and other Arab militaries to assist the Palestinian civilian population, could be considered a valuable attempt to circumvent the challenges to land-based humanitarian aid-operations during fierce fighting between Hamas and the IDF, as well as a means of exerting diplomatic pressure on the combatants. The policy implications and cost effectiveness of political pressure to increase food influx via land crossings are not obvious.
Comparing the resources allocated by the international community to the Palestinian population versus the long list of other pressing humanitarian crises, out of proper concern for emergency-affected civilian populations, is indeed a vexed question. Clearly, a critical and balanced discussion of this issue is beyond the scope of this paper. However, if one insists on raising this important question, one might also question the efficiency of the billions of dollars donated to Gaza over the past decade by the international community, including from UNRWA, and how the funds, which were intended for civil and humanitarian development, were misappropriated by Hamas for a massive military buildup to the attack including the construction of hundreds of kilometers of military tunnels and the stockpiling tens of thousands of rockets and launchers, embedding them in their civilian population ( https://www.wsj.com/world/middle-east/hamas-gaza-humanitarian-aid-diverted-cf356c48; https://govextra.gov.il/unrwa/unrwa/#:~:text=Update%206%2F8%2F24%3A,massacre%20are%20credible%20and%20true; https://www.nytimes.com/2024/12/08/world/middleeast/hamas-unrwa-schools.html?unlocked_article_code=1.f04.lcW3.n2kj8akEfM-M&smid=nytcore-ios-share&referringSource=articleShare; https://www.atlanticcouncil.org/blogs/new-atlanticist/how-to-reform-unrwa-to-improve-palestinian-lives-and-israeli-security/) "https://www.atlanticcouncil.org/blogs/new-atlanticist/how-to-reform-unrwa-to-improve-palestinian-lives-and-israeli-security/)") .
Limitations
The authors acknowledge several of the more obvious limitations and assumptions described above. However, they minimize or arbitrarily dismiss these weaknesses and proceed to make tendentious interpretations in support of their preferred policy implications. For example, they write that they relied heavily on a single UNRWA dataset “which appears highly complete and well-curated” without explaining how they make that subjective and unsupported assertion. The authors are demonstrably aware of the controversy and limitations of the data, yet they feign ignorance and avoid placing the data in the context of the known controversy writing that the data “may be biased by systematic under- or over-reporting UNKOWN TO US”. This knowingly downplays and misrepresents the CERTAIN under-reporting of UNRWA trucking data which the official disclaimer states clearly on the online dashboard and in the dataset that they provide for review: “We [UNRWA] are unable to provide comprehensive monitoring of cargo for the following reasons: i) safety and security concerns, which continue to prevent UN staff from maintaining constant presence at Kerem Shalom, therefore severely impacting our ability to cross-reference UN cargo, and record data from INGO, Red Cross and commercial trucks, and ii) delays and/or denials in approvals for UN to retrieve, count and move UN humanitarian aid from Kerem Shalom to other parts of the Gaza Strip, which mean that we are unable to fully verify all trucks which have transited the land crossings. We will resume presentation of comprehensive data once the situation at the crossing allows.” Similarly, the acknowledgement of “considerable uncertainty about population denominators” does not logically lead to the conclusion that this would “…have only marginally affected our estimates”.
Policy Implications
The conclusion of the article makes politicized recommendations that are disconnected from the findings. The authors’ recommendation to “reinstate UNRWA’s role as an independent and experienced on-the-field monitor” is unsupported, and the summary dismissal and evaluation of COGAT data as “not of sufficient quality to guide decision-making”, reflects bias rather than a balanced analysis. Considerations relating to the role that international actors can and should play is determined by far more complex factors that are the partial shipping data analyzed here.
The claim that Israel, “as the de facto occupying power”, did not ensure sufficient food availability to Gaza (while acknowledging the relatively short period of deficiency), vastly oversimplifies the complex dynamics of the conflict and the multifaceted factors affecting food availability. This claim appears intended to promote the use of the study as “evidence” supporting “forensic efforts” (in the courts) to prove allegations that “Israel deliberately has starved Gaza’s population”, presenting as fact a disputed interpretation of Israeli combat operations in Gaza as constituting occupation, and hence its obligations under international law, while ignoring weighty arguments to the contrary. This view also ignores corresponding obligations of Hamas as the governing power in Gaza, and the role of international humanitarian actors. The legal questions on this point are far beyond the scope of this review, but there is no basis in the data provided to make this claim – it is simply presented as an unsubstantiated assertion. In order to evaluate the morality, legitimacy or legality of the Israeli military strategy in response to the Hamas attacks and terror infrastructure, including its impact on food availability, it is necessary to examine and understand the strategy challenges in conditions of military asymmetry, the large-scale use of human shields to protect Hamas forces, and urban warfare as exist in Gaza. The authors of this article appear to be unaware of this central dimension in the issues they are claiming to address. Given the slanted narrative, the selective and biased use of data and their interpretation, and the far-reaching and unsupported conclusions, it is difficult to escape the impression that this study is aimed at providing a prosecution with ostensibly credible academic findings, rather than advancing open-ended research in support of humanitarian efforts.
Timely and reliable data are crucial to address the critical needs of the war-affected civilian population of Gaza. There is no doubt that data “on the civilian impacts of the war in Gaza”, and “situational awareness on food security in Gaza” are “important to inform appropriate humanitarian response”. It is also undoubtedly true that “humanitarian actors should review whether there is adequate coordination and technical expertise in place to ensure that what food is allowed into Gaza is both calorically efficient and diverse enough to maintain the best-possible diet, especially for population groups most vulnerable to malnutrition”. How a retrospective simulation of the food supply informs “situational awareness” is less obvious. Slanted, simplistic and politicized framing of the findings that ignore complexity, place the onus on Israel alone, and overlook the role of Hamas, the agency of Palestinian civil society, and the responsibility and obligations of the international community, do not advance scholarly discourse, nor will it strengthen the cooperation that is urgently needed to strengthen humanitarian efforts to benefit the civilians of Gaza.
On 2025-02-16 02:16:55, user Michael Pazianas, MD wrote:
Low BMD can be a common finding in both osteoporosis and renal osteodystrophy—two distinct histological diagnoses with distinct pathophysiology. While a low BMD and a T-score below -2.5 are often used to define osteoporosis, this finding does not necessarily indicate an osteoporotic etiology. Non-osteoporotic causes should be considered.
In this study, the authors included patients with CKD who were diagnosed with osteopenia or osteoporosis based solely on BMD measurements, rather than bone biopsy findings. However, low BMD in these patients could stem from other forms of renal osteodystrophy, such as adynamic bone disease or osteomalacia, rather than true osteoporosis.
"Given this premise, a more accurate and clinically relevant title might be: 'Low BMD Prevalence in Cardiovascular Kidney Metabolic Syndrome: Implications for Mortality.' The current title promotes an overly simplified approach that risks making the already challenging task of successfully managing these patients—particularly those with CKD—an even more distant prospect. This concern is especially relevant because antiresorptive therapies, commonly prescribed for osteoporosis, are contraindicated in adynamic bone disease, a pathology prevalent in CKD, as well as in osteomalacia."
On 2025-02-21 05:08:41, user Evan Stanbury wrote:
The paper refers to "a chronic debilitating condition after COVID-19 vaccination, often referred to as Post-Vaccination Syndrome" (which it calls PVS). This should not be confused with a common chronic debilitating condition after viral infection, often referred to as Post-Viral Syndrome (also PVS). This paper could confuse many, so it would be better to call the sick cohort something different from "PVS".
On 2025-02-22 17:25:17, user Shawn M wrote:
The study's questionnaire has significant design flaws. The main issue is how the questions are worded - they repeatedly ask about 'health conditions that you have had as a result of vaccine injury.' This phrasing assumes vaccines caused these health problems before even asking the question. It's like asking 'When did you stop stealing?' instead of 'Have you ever stolen anything?'<br /> This problematic wording can influence how people respond in two ways. First, it might lead people to automatically connect their health issues to vaccines without considering other possible causes. Second, by focusing only on vaccine-related problems, the questionnaire misses important information about people's overall health that could explain their symptoms.<br /> These issues make it difficult to trust the study's findings because we can't tell if the health problems reported were actually caused by vaccines or if they happened for other reasons that weren't explored.
On 2025-02-23 19:55:05, user Jan Páleš wrote:
Hello, I cant find any Ees formula in the reference 10...
On 2025-03-07 03:42:49, user mehrdad alemi wrote:
The COVID-19 pandemic posed unprecedented challenges for countries worldwide. Despite international sanctions, Iran managed to respond effectively to this crisis by relying on its domestic capacities.
Among the actions taken by Iranian scientists, researchers, and physicians:
Production of Domestic Vaccines: Iran became one of the countries producing COVID-19 vaccines by developing domestic vaccines such as Noora.
Expansion of Diagnostic and Treatment Capacity: The development of diagnostic kits, the increase in the number of equipped laboratories, and the production of medical equipment, including ventilators, contributed to better crisis management.
Healthcare System Management: The establishment of field hospitals, the strengthening of medical infrastructure, and the implementation of health restrictions at critical times played a significant role in reducing infection and mortality rates.
Research and Innovation: The publication of reputable scientific articles and the conduction of clinical studies on Iranian vaccines strengthened Iran’s scientific standing in this field.
On 2025-03-10 13:27:10, user Feng Gu wrote:
Hi, Please check the published version here: https://www.doi.org/10.1038/s41746-025-01501-9
On 2025-03-30 09:52:42, user Isatou Sarr wrote:
Over time, immunity from both vaccination and previous infection can decrease, leading to an increased risk of breakthrough infections. This phenomenon is particularly noticeable as the immune response fades, and the virus continues to evolve.This waning immunity presents a challenge for public health strategies that rely heavily on initial vaccination or infection-induced protection. Boosters become crucial in reinforcing the immune system and restoring protective antibody levels, especially for vulnerable populations such as the elderly or those with underlying health conditions. Moreover, the emergence of new variants, often with mutations that allow them to evade existing immunity, further complicates the picture. These variants can spread more easily and cause illness in individuals who were previously protected, necessitating ongoing adaptation of vaccines and preventative measures to keep pace with viral evolution. Continuous monitoring of variant spread, vaccine effectiveness, and the duration of immunity are essential for informed decision-making and effective mitigation strategies.
On 2025-04-09 12:24:29, user Breck wrote:
My colleague wants to know why you didn’t mention any testing for the flu. Did you test for the flu?
On 2025-04-10 22:33:05, user Will wrote:
My first comment should be: <br /> I noticed that Table 2 mentions Covid 19 under "Abbreviations" but in the actual table there is no Covid 19 variable. Could you clarify that please?
On 2025-06-04 21:03:24, user Meg McSorley wrote:
The unadjusted risk estimates are exactly the same as the adjusted, down to the confidence intervals and p-values?
Where is table 1, comparing baseline characteristics of the comparison groups (vaccinated and unvaccinated)? This would inform which covariates should be included in the model. This is actually the most important table because the vaccinated likely do have different characteristics than the unvaccinated that would affect the risk estimates.
How was influenza ascertained? Self-report? Employee Health testing? Why aren’t raw numbers reported?
Is there a reference for the Vaccine Efficacy calculation? Is there a statistical rationale for this calculation?
On 2025-04-23 20:04:44, user Maiwenn wrote:
Hi - this article is now available in the WHO Bulletin: https://pmc.ncbi.nlm.nih.gov/articles/PMC11978406/pdf/bullworldhealthorgan-103-4-BLT.24.292167.pdf <br /> DOI: 10.2471/BLT.24.292167
On 2025-04-30 07:16:59, user Frédéric Derbré wrote:
This preprint is now published on Cell Reports : https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00219-0?uuid=uuid%3A08f5e5ab-1371-444d-b8cf-ba64672613f3 Is it possible to inform the readers on Medrxiv ?
On 2025-05-28 23:11:26, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( http://evoheal.github.io/) "http://evoheal.github.io/)") really enjoyed this paper. Here are our highlights.
Investment in ensemble forecasts resulted in better calibration and less variable predictions
Work bridged the gap between theory and policy; builds infrastructure for future data integration
Climate zone analysis balanced the desire for model generalizability, the need for sufficient historical data to characterize each zone, and the risks of unrealistically grouping diverse regions together.
Limitations of individual models including the provision of confidence intervals was honestly presented
On 2025-06-04 05:27:15, user Sira Jam Munira wrote:
This paper is now published in the “Journal of public health research” and the link-
On 2025-06-04 17:34:59, user Sarah Jorgensen wrote:
Questions for the authors: <br /> From the results, 11 children started GAHT within 12 months of GnRHa initiation and another 20 within 12-24 months (total 31, 31/94 (33%)), yet in the discussion, "more than half of the participants had initiated gender-affirming hormones over the 24-month follow up period." Could the authors resolve this apparent discrepancy?
59 patients were assessed at 24 months. If 31 were not assessed because they started GAHT, there still appears to be 4 children unaccounted for. Were they lost to follow-up? What was their status at last assessment?
Details on psychiatric medications at baseline and initiated during follow-up would be of interest and could be considered for inclusion as time-varying covariates in models.
Given that 4-9 years have elapsed since GnRHa initiation, why was this analysis limited to 24 months follow-up? At the very least it would be of interest to know vital status and how many ultimately went on to receive GAHT versus desisted.
On 2025-06-07 15:03:33, user Nicolai Krekiehn wrote:
The git link does not work for me. I would like to test an use your model. Who is the contact?
On 2025-06-26 00:25:38, user Pegah Khosravi wrote:
We are pleased to share that this preprint has now been peer-reviewed and published in Biological Methods and Protocols. The final version of the article is available at: https://doi.org/10.1093/biomethods/bpaf032
On 2025-07-14 22:06:03, user Pedro Rocha wrote:
This work has been published in its entirety in a peer-reviewed journal. The published version is available at: https://journal.unoeste.br/index.php/cv/article/view/5016/3709 , doi: 10.5747/cv.2024.v16.v379
On 2025-07-15 16:59:57, user zlmark wrote:
The preprint relies on survey data that shows clear evidence of sampling protocol violations, including improper household selection, failure to screen for residency, and geographic deviations confirmed by GPS data.
In several cases, inconsistencies appear to have been retroactively edited to align with protocol.
Data from two teams—Gaza9 and Gaza3—raise particular concern, with demographic anomalies and mortality figures that suggest possible manipulation or fabrication.
These issues compromise the representativeness of the sample and call into question the reliability of the resulting estimates.
A full analysis of these issues is available here: <br /> https://markzlochin.substack.com/p/design-vs-execution-in-gaza-mortality
On 2025-07-29 02:44:01, user Qiwen Yang wrote:
The article has been published at: https://doi.org/10.1093/ehjqcco/qcaf064
On 2025-08-08 09:31:31, user David Fournier wrote:
Dear authors, commenting on the recent Nat. comm. release, did you actually studied the direct connection in the samples from encode ad brains you studied between histone modifications and actual expansions? i dont see a plot of histone modifications versus repeat expansions directly plotted from the same individual. Did you check that? Thanks.
On 2025-08-15 08:47:16, user Jouke- Jan Hottenga wrote:
Nice paper!
Population stratification severly influences HWE, which is known as the Wahlund effect. Hence, also the much larger SNP removal in mixed populations.
Imputation and phasing software in general assume HWE. Which might a) be a reason to apply HWE beforehand and b) will thus likely result in all markers also being in HWE post imputation.
With kind regards, Jouke
On 2025-08-20 13:21:16, user Anthony Clanton wrote:
Thank you for the opportunity to comment on your well-constructed manuscript. We appreciate the authors’ efforts to advance the STARD-IONM framework and promote rigor in reporting diagnostic accuracy for IONM. We would like to highlight the importance of clarifying partial recovery scenarios within the STARD-IONM framework. While the manuscript provides valuable discussion on reversible and irreversible signal changes, it does not explicitly define or address partial recovery—cases in which IONM signals improve but do not return to baseline. These common scenarios are clinically relevant and may reflect incomplete injury or partial mitigation. To improve clarity and consistency, we suggest considering the following additions:
• Clearly define “partial recovery” and distinguish it from full recovery and persistent deterioration.<br /> • Include guidance on how to classify and report partial recovery in diagnostic accuracy studies, particularly when calculating sensitivity, specificity, and predictive values.<br /> • Provide illustrative examples or decision frameworks to support consistent interpretation and reduce bias in outcome classification.
We would also like to emphasize that the STARD-IONM checklist does not currently call for authors to specify which muscles, nerves, or anatomical structures were included in the intraoperative monitoring plan, nor does it recommend reporting which signals changed and then recovered or failed to recover. While item 10a under “Test Methods” may implicitly suggest this level of detail, making this expectation explicit would be beneficial. Such information is frequently absent in studies evaluating IONM, yet it is essential for interpreting outcomes and ensuring reproducibility.
Thank you again for your commitment to transparency and community engagement. These additions could further strengthen the STARD-IONM framework and help ensure it serves the entire research community effectively.
Kent Rice, Kevin McCarthy, Anthony Clanton & Adam Doan
On 2025-08-28 17:40:53, user gzuckier wrote:
Just a typo of some sort, I assume, but<br /> "-0.85 (95% Cl: [-0.48 --0.37]) for dose 3" can't be correct.
On a related note, however, I can't avoid a nagging suspicion of bias from the fact that some of the estimates used to support <br /> "concerning evidence of a higher-than-expected fetal loss rate" <br /> are not statistically significant is missing from the paper and must be intuited by the reader, as in <br /> "1.9 (95% CI: 0.39-3.42]) for dose 3";<br /> particularly when it's specifically noted with respect to estimates involving COVID infections <br /> "all the 95% CIs of the respective observed-to-expected differences included 0 (Table S8)."
The main findings, however, do not have this problem.
My humble suggestion is to include these other findings as "suggestive but not reaching statistical significance."
Or perhaps, since the results given for week 14 seem to be significant but are diluted by nonsignificant later results, that should be pointed out?
On 2025-09-21 16:07:08, user practiCalfMRI wrote:
The authors should address the issue of brain/CSF motion as a potential source of error in the MISL difference images.
On 2025-10-05 04:11:20, user Mohammad Golam Kibria, PhD wrote:
The GitHub code link doesn't work.