On 2020-10-20 09:17:45, user Anne Hartmann wrote:
Dear Prof. Kähler, <br /> thank you very much for your study. <br /> Unfortunately, we think that some aspects are not considered correctly and some mentioned conclusions therefore can not be drawn. We summarized our comments in a statement on our blog (statement is available in German as well as English)<br /> https://blogs.tu-berlin.de/....<br /> Kind regards<br /> Anne Hartmann, member of the research group of Prof. Kriegel
On 2020-10-20 14:15:11, user Robbert Bentvelsen wrote:
This trial was published: N Engl J Med 2020; 383:494-496 DOI: 10.1056/NEJMc2016321
On 2020-10-21 14:36:25, user Stephen B. Strum wrote:
It would be important to see if hospitals in urban settings have superior outcomes re death rates versus rural medical facilities. It would also be important to know if the issue of viral load as it relates to the population wearing masks (e.g., high-mask wearing versus intermediate vs low-mask wearing) plays a role.
On 2020-10-22 21:10:27, user Critical Dissection wrote:
Dear author,
After reading your article, here are my comments. I will start out with positive; the abstract was greatly laid out. I like how it is broken down to individual parts. It helped me navigate that section better. Your discussion section hits a variety points discussed in the paper and wrap it up nicely. Now to discuss certain things that were missing. Presentation is very important, and the paper lack the proper flow to achieve that presentation, for example table 1 was not present as a unique table but broken down into two pages which can be confusing for some. The figures were not explained, and conclusion had to be made from the caption and some information. A major issue in deciding if this method works is the sample size and lack of a control population. Further trials would have to be done as indicated in the study limitations, bias should be minimized in next group and a control group containing patients needing ablation but never had one before would be recommended.
On 2020-10-27 00:19:10, user Critical Dissection wrote:
Dear author,
Thank you for posting this article! It was truly very informative and will likely have important implications in resolving disorders of the heart, or possibly in other diseases and organs one day. I appreciated how thoroughly you expanded upon the criteria, explained considerations and acknowledged limitations, particularly in the discussion section. Additionally, patients' medical history and data were depicted very well through the tables in the Results section, so this was helpful in providing additional background. Overall, the methods and discussion sections were very detailed and provided excellent insight on this topic.
I have some feedback and recommendations for this study and article that I believe will help to improve clarity and reach a wider audience. First, it may be helpful to include more background about atrial flutters and ablation techniques in the introduction section. This would allow a more diverse audience of readers to understand the paper's contents without referring to external sources. Further, the results were not explained in great detail, so it was slightly difficult to interpret the figures presented. There was a more substantial mention of these results in the discussion section, but there may be some merit in including a direct explanation of each figure. Lastly, the small sample size likely caused bias in the results, as mentioned in the study limitations. The study would reach a larger category of patients if the criteria were less specific, so I would love to see a follow-up study, perhaps with expanded scope.
Overall, this article was very interesting! I do not have much background in the field, so I found some parts difficult to understand without reviewing external sources, and I believe there are some improvements that could be made to make this article more accessible to the public and the study more generalizable. Thank you, and I hope to see some future studies on this topic!
On 2020-10-23 06:01:42, user Martijn Hoogeveen wrote:
Pre-proof is nu online https://authors.elsevier.co...
On 2020-10-24 19:58:34, user Per Sjögren-Gulve wrote:
Why not use multiple logistic regression and examine age plus additional predictive variables (continuous awa categorical) together + interaction terms? Studies can be numbered/ID:d and included as one predictive variable in a common dataset. In that way, differences in distribution of the other predictive variables between the studies can be considered or - if there are no such differences - rejected and datasets pooled.
On 2020-10-26 08:37:35, user Jan wrote:
Very nice study! But I'm wondering how much antibody levels und numbers of specific B and T cells in the blood really tell us about protection. Is there any data out there about numbers specific plasma cells in the bone marrow or presence of tissue resident memory cells, e.g. in the lung - maybe from autopsies?
On 2020-10-26 13:14:19, user Stefan Dombrowski wrote:
I am not a fan of these respositories. They may well contain research that has been rejected by the peer review process and thus cannot find a legitimate outlet. And, now that this study has been submitted to the world via this repository it very likely cannot be subjected to the gold standard of peer review-- the double blind peer review process. CNBC and other media outlets have been sloppy by disseminating this study broadly given its lack of scientific vetting.
On 2020-10-28 06:10:32, user DenSvenskeSkeptikern wrote:
My question is this; is this a cross-section study? They just tested cognitive abilities whose results were below the average otherwise and then conclude the cognitive decline must be chronic? I mean, you would need to do follow-up to even begin suggesting it is chronic, right?
If things improve even though it might take weeks or even months, then it isn't chronic is it? Also, wouldn't you suffer (maybe temporary) cognitive decline if you end up in the ICU because of how intense that is for your body no matter why you ended up there?
Is it likely my questions would be indirectly answered as the article goes through the peer-review process where they might realize the logical flaws or the possibly too weak evidence they base their conclusions on?
On 2020-10-28 11:35:36, user David Simons wrote:
I have interpreted the inclusion criteria for the "Severe SARS-CoV-2 infection" group to include those within the biobank that died during March to July. If that's not the case and it's only individuals who died with COVID-19 on their death certificate you need to make this clearer. I understand that there have been a high proportion of COVID-19 related deaths in the community but this has definitely not been the only cause of death in these 4 months. If you are intent on using this to include individuals I think you'd need to run a sensitivity analysis on your results to investigate what happens when you exclude these individuals from your analytic sample.
Further, an in-hospital test is not an adequate proxy for disease severity. The reference you site can also not clearly support that statement. There are multiple reasons for in-hospital testing of non-severe individuals. Some of these include; staff of the hospital (or family member of staff), at risk groups (i.e. those attending the hospital for regular dialysis or chemotherapy) and those that attend the emergency department but do not get admitted to hospital. There are several ways you can mitigate against this depending on what data you have available. One option would be to use length of stay combined with in-hospital mortality to support your definition of severity, for example, if a significant proportion of your participants are admitted and discharged within less than 2 days it's unlikely they have severe disease. A further option if available would be to explore their requirement for supplementary oxygen, enrollment into RECOVERY or similar trial with inclusion of only severe disease or treatment with dexamethasone/remdisivir. If none of these are possible having a sensitivity analysis where you remove those with known comorbidities that increase the probability of asymptomatic screening or where the disease may not be severe at testing (i.e. renal dialysis patients or chemotherapy patients) and healthcare workers may strengthen this assumption.
Hope these are helpful comments.
On 2020-10-28 17:49:44, user Sam Wheeler wrote:
How often should a healthy 40-year-old person take the flu shot, for maximum protection? Every 2 months, until real covid-19 vaccine is available?
Does vaccine brand matter? Egg-free vaccines better? Egg-free flu vaccines are unavailable in most European countries, where could an European consumer buy them and how?
On 2020-10-28 21:36:35, user IJ wrote:
The SNPs were found using a GWAS that controlled for vitamin D supplementation, and thus measured the genetic association with unsupplemented vitamin D levels. However, the question we are interested in is the relationship of COVID-19 outcomes with actual vitamin D levels, including supplementation for those who are already taking supplements. Since the decision to supplement is affected by unsupplemented vitamin D levels, this study needs to account for supplementation.
In particular, those with low vitamin D levels are more likely to be advised to take supplements. Could it be that those with genetic predisposition towards low levels might be taking supplements that raise their vitamin D levels, on average, more than is needed to compensate for the genetic predisposition? In that case, genetic predisposition for low vitamin D could _negatively_ correlate with actual vitamin D level, which would reverse the interpretation of the results.
On 2020-10-29 07:12:38, user reality tester wrote:
orange county prevalence 12% equates to 7.6 x higher than reported? 61,000 cases reported x 7.6 = 463,000 divide by 3.1 mil population equals 15% at least as of today... add 35% of those who have innate immunity as research published in Science and Nature indicates, and OC is at herd immunity threshold... no wonder hospitalizations are decreasing on 7 day moving averages, and daily deaths likewise dropping, despite more "cases" as tallied by positive swabs ...
On 2020-10-29 16:33:54, user Najmul Haider wrote:
Dear Readers-- the original article of this Preprint is Published as a Peer review article at "BMJ Global Health". You can download the published article from here: https://gh.bmj.com/content/...
On 2020-10-30 07:38:22, user Rajeev A wrote:
Dear Sir,<br /> Thanks for the answer to a question I was waiting for.<br /> In America COVID has surpassed the road accident death stats already.<br /> Thanking You<br /> Yours sincerely<br /> Rajeev
On 2020-10-31 09:30:37, user Paolo Benna wrote:
In a meta-analysis related to EPHX1 polymorphisms, Gui-Xin Zhao et al. [1] used the Newcastle-Ottawa scale (NOS) [2] for assessing the quality of the case series to be included in the study. The same Authors in this meta-analysis [3] use, for the evaluation of other polymorphisms, some of the series already included in [1]. Nevertheless, they attribute a different NOS score to these in the two meta-analysis. In detail:<br /> Hung CC (2012): 8 [1] and 6 [3]<br /> Yun W (2013): 5 [1] and 8 [3]<br /> Zhu X (2014): 5 [1] and 8 [3]<br /> Daci A (2015): 8 [1] and 6 [3]<br /> I think a clarification in this regard is appropriate, since the discrepancy is not easy to understand.<br /> Yours sincerely,<br /> Paolo Benna
References<br /> [1] Zhao G, Shen M, Zhang Z, Wang P, Xie C, He G. Association between EPHX1 polymorphisms and carbamazepine metabolism in epilepsy: a meta-analysis. Int J Clin Pharm. 2019; 41: 1414–1428. https://doi.org/10.1007/s11...<br /> [2] Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomized studies in meta-analysis. The Ottawa Health Research Institute. 2013. http://www.ohri.ca/programs...<br /> [3] Zhao G, Zhang Z, Cai W, Shen M, Wang P, He G. Associations between CYP3A4, CYP3A5 and SCN1A polymorphisms and carbamazepine metabolism in epilepsy: a meta-analysis. medRxiv 2020.03.03.20030783. https://doi.org/10.1101/202...
On 2020-11-01 19:22:55, user kdrl nakle wrote:
Sample of 34?
On 2020-11-02 00:25:13, user Noriaki Kurita (????), MD, PhD wrote:
This paper was eventually published from BMC Nephrology.<br /> https://bmcnephrol.biomedce...
On 2020-11-03 14:18:18, user Jonas Svensson wrote:
Paper now published in Journal of Thrombosis and Thrombolysis:
On 2020-11-03 17:26:53, user Nicanor Austriaco, OP wrote:
The preprint has been published at the Philippine Journal of Science: https://philjournalsci.dost...
On 2020-11-05 02:41:54, user Robert Stephens wrote:
This is a nice study!
"Interestingly, among children with symptoms compatible with COVID-19, only 11% (1/9) of those tested with RT-PCR were positive, while 60% (12/20) seroconverted. "
Perhaps some of the PCR -ve / seropositive children had gastrointestinal disease. Was there a pattern of symptoms for this group? Faecal / rectal PCR might have been interesting.
Dr Robert Stephens MB BS FACD
On 2020-11-05 16:58:58, user Sorin Draghici wrote:
Hi, Thanks for your great work. Your preprint refers to patients 1 through 20 plus patients A-D. The associated GEO dataset GSE150316 has only patients 1 through 12 but then A through J. The GEO data set also has 7 samples allegedly from placenta. Can you please clarify this? Which 20 patients are referred to in the paper? How about the placenta sample?
On 2020-11-06 23:19:58, user Ali K wrote:
Another good proof point showing CD4 and CD8 responses to a dual vaccine approach. Interesting perspective to use previously infected serum
On 2020-11-07 10:52:37, user Jesper Kivelä wrote:
Ollila and coworkers have erroneously [based on their data and R code (1)] calculated standard errors (SE) for individual studies without first taking natural logarithm of upper and lower bound of confidence interval (CI) as would be appropriate in the case of ratio measures, like relative risk (RR).
For example, SE was 0.204 for one of the included studies [reference 17 in study by Ollila and coworkers (2)], which is smaller than correct SE of 0.643. Naturally, too small SE will produce too narrow CI, which is evident, for example, from the Figure 3A in their study (2).
I replicated result highlighted in the abstract (2) based on a maximum follow-up [RR 0.61 (95% CI 0.39 to 0.96)] using R meta package and its metagen function. Replicated RR was 0.77 (95% CI 0.57 to 1.05) across 5 studies based on random-effects model with the use DerSimonian-Laird estimator for between-study variance. In sensitivity analysis, RR was 0.72 (95% CI 0.39 to 1.33) using the recommended methods for random-effects modeling with a small number of studies (3).
I first pointed out calculation errors in the study by Ollila and coworkers at Twitter, and as of submitting this comment statistical code provided by the authors in (1) is still under review for possible changes and corrections.
References
On 2020-11-07 13:40:48, user kdrl nakle wrote:
Why would you need a surrogate? You did not explain anything about the relation between this virus and SARS-CoV-2. The title is misleading.
On 2020-11-13 09:01:22, user Suneet Sood wrote:
Sir, I applaud the very well-written study. The data is valuable. I do suggest that we should be cautious with the conclusions, however. The aim of this study was "to evaluate the impact of SORT interval on clinical outcomes". It was not "to evaluate the impact of SORT interval on clinical outcomes in SORT groups <=9 vs > 9". In other words, the <= 9 and > 9 groups were not declared a priori. I think a better conclusion would be "Our study suggests that the results in these two groups are different, and should be confirmed by a trial in which patients are randomized into these two groups."
On 2020-11-16 08:49:06, user Mike Maglothin wrote:
I've seen several studies attributing all excess deaths to CoVid. I agree... BUT.. what they show in their modeling is a correlation to CoVid. The excess deaths could very easily be from delayed procedures or people being unwilling/unable to get a procedure done in a timely fashion. I know many procedures were delayed, especially during the beginning of the pandemic when hospitals were being "reserved" for CoVid. Would be interesting to see the Excess death correlation only after August.
On 2021-09-13 15:48:07, user Bennie Schut wrote:
In a followup it might be interesting to compare myocarditis requiring hospitalizations in both vaccine and covid groups. Not all myocarditis requires hospitalization and being infected now seems more of a when than an if. We already know covid causes myocarditis, so for risk assessment we would need to understand if one is better than the other. This study doesn't show this yet. But very interesting nevertheless.
On 2021-08-20 23:43:57, user Chris Raberts wrote:
Can the authors explain how they conclude that lowering the particles in the air reduce the chance of infection? Seeing the sheer amount of particles exhaled this seems like a drop in the bucket, even at 50% reduction.
If you cant swim it doesnt matter if you fall in a lake or the ocean.
On 2021-08-21 04:36:50, user Fergal Daly wrote:
This applies linear regression to cumulative cases against NPI scores. It does not specify any model that justifies this. Simple models suggest a linear relationship between NPI scores and estimated R_t or log(case-growth). No model would suggest a linear relationship between these two. In the simplest example, if NPIs bring R_t below 0.9 it leads to very few cumulative deaths, with no much difference between very strict and less-strict, as long as R_t is < 0.9. Conversely, all NPI that leave R_t above 1.1 , lead to explosive growth and very similar large numbers of cumulative deaths. The relationship is highly non-linear and applying linear regression has no justification. The statistically significant outcome must be either chance or systematic result of the mis-specification.
On 2021-08-27 04:39:32, user William Brooks wrote:
The authors use cumulative deaths from June 2020 but don't explain why they omit deaths before June 2020 (i.e., the <br /> whole first wave). Since many of the deaths during the omitted period <br /> occurred in states with strict NPIs such as Maryland (Fig.1a), this probably biases the results in favor of stricter states since they would have had smaller susceptible populations after the first wave than other states. Another study got around this problem by excluding northeastern states from the main analysis of the summer wave and including them in the analysis of the autumn/winter wave [1]. Because different NPIs were introduced/lifted at different times in different states, it would be interesting to see how consistent the correlation between NPI strictness and cases/deaths is during different waves.
Also, Fig. 3a shows that case trajectories are clearly effected by geography, so rather than directly compare two states with different NPI strictness from different regions (Maryland and Tennessee), it might be more informative to compare two states with different NPI strictness from the same region (e.g., Louisiana and Florida).
On 2021-08-23 09:38:01, user Valerio Marra wrote:
Now published in Scientific Reports doi: 10.1038/s41598-021-95004-8
On 2021-08-23 23:20:52, user Toa_Greening wrote:
The said method "aspirin once daily until discharge" was not meet as only "5040 (77%) received aspirin on most days following randomisation(>=90% of the days from randomisation". Therefore the aspirin treated group data of 7351 is contaminated with patients who did NOT have "aspirin once daily until discharge".
It is recommended to redo the analysis using only the "5040 (77%) received aspirin on most days following randomisation(>=90% of the days from randomisation" as the aspirin group.
On 2021-08-24 08:23:27, user Meerwind7 wrote:
I like to praise that an assessment like this is possible only in a "No-Covid" environment where extensive contact tracing of individual cases is possible.
The conclusion about the difficulties to contain transmission even in this setting, i.e. with rare infections that allow extensive contact tracing ("individual-based interventions such as case isolation, contact tracing and quarantine"), points to the near-impossibility to contain Delta in the larger part of the world, even with more voccination.
On 2021-08-24 18:06:02, user Skeptic wrote:
23andMe has an article about this on its website, in which the company listed the WRONG reference SNP number. According to this pre-print, it's rs7688383, but 23's 6/2/21 article claims it's rs7868383. In any case apparently the v.5 chip did not include this SNP as I can't find it in the raw data for any of the five kits I manage at 23.
Kind of important to proof read, 23andMe, if you expect to develop and maintain credibility: https://you.23andme.com/p/8...
On 2021-08-25 06:23:02, user L Wong wrote:
This pre-print was submitted to the peer reviewed "Japanese Journal of Radiology" and was accepted on 6th of Jan, 2021. The content had been revised according to the reviewers suggestion and comment and the title of the article was revised as "Convolutional neural network in nasopharyngeal carcinoma: How good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?”. Readers can find the latest version of the article in the link:
.
On 2021-08-26 05:47:36, user MarcoBonechi wrote:
You assume 2-3 students being infected at the beginning. Out of 500 students. 2.5/500=0.005 i.e. 500 cases per 100k.<br /> That's 10x actual Aug-2021 US rate at 46 (https://www.nytimes.com/int... "https://www.nytimes.com/interactive/2021/world/covid-cases.html)").<br /> 18x the CA rate of 26.
Your study has <10% chance of happening?
Please explain.
You should redo the study using several scenarios using randomized chances of a student being positive from outside.
Then also randomize symptoms, as symptomatic cases will stop spreading or be caught altogether before reaching school.
Then also randomize mask failure rate, badly worn masks, ineffective masks etc..
Finally add testing with weekly or twice-weekly universal antigen with their success rate.
You got to put more work!
On 2021-08-26 16:37:49, user Larry Melniker wrote:
The issue with Dr Hoffe conjecture is connecting D Dimer results, which are nonspecific, with serious ischemic events, which require specific testing results. He may be speculating on a True-True, but unrelated phenomena; otherwise D Dimer would be a routine part of ACS rule out work-up.
On 2021-09-10 15:49:38, user skeptonomist wrote:
The paper shows very conclusively that the vaccine reduces infection rate. Because the overall death rate among those infected is small (on the order of 1-2% at most), the expected number of deaths in the placebo group is not large enough for a meaningful test of how death rate is affected.
On 2021-08-27 17:40:44, user David Wells wrote:
Table 1a shows that your 'vaccinated individuals' group exhibited higher rates of comorbidities. Comorbidities are therefore possibly correlated with vaccination. The model results show insignificant comorbidity effects, suggesting the possibility that your 'vaccination' effect is really (or partially) a case of stolen significance. Did you try removing the vaccination variable to find out if comorbidities then become significant? Or what if you matched on comorbidity rates, not just demographics?
On 2021-08-28 02:02:35, user Jonas Ferris wrote:
While it may be that natural immunity offers more protection than vaccine immunity, there seems to be some problems here:
How can you adjust for the issue that some in the previously infected group died, presumably those most susceptible to symptomatic infection while the vaccinated group likely has many of these most susceptible still in the group?
As the overall infection rate seems quite low (<2%) in the vaccinated group, though many multiples of the even lower numbers in the previously infected (and death screened) group (leading to sensationally high multiples of up to ’27-fold risk’) is it possible that many of these infected could have been deceased had they not been vaccinated?
I understand there are adjustments for comorbidities, but there is no real way to determine who would have died from a group with comorbidities yet they may not exist in the previously infected group.
Why are there so few people above 60 in the study (<5%) when this age groups is over 15% of the population over 16 and the very age group that is most likely to have serious symptomatic infection? How many went to the hospital from this group in both the vaccinated and previously infected groups?
Early seekers of vaccines were likely more at risk of death from Covid than those that were not as worried and didn’t (or couldn’t) get a vaccine in Jan/Feb.
Your two groups are basically those that were fearful of catching Covid and those that didn’t see it as much of a risk to them. These are groups that may have very different risks of testing positive for Covid even if they both received vaccines at the same time.
Those that received a vaccine after almost a year of watching out for the virus may have acted in a more risky fashion after getting vaccinated - the pendulum swung even further than the no vaccine group (who may not have known they were somewhat immune)?
Given these shortcomings, it seems like a more reasonable conclusion than natural immunity is 7 fold+ stronger than vaccine immunity after a few months, is that while both natural immunity and vaccine immunity offer similar substantial absolute protection from serious infection, for those in an age group already less likely to have serious infection, that has already made it through one infection without dying a significant population screening event of those most susceptible to serious Covid infection, symptomatic infection from Covid is less likely than for those that have self-identified as at risk and have been vaccinated for but not exposed to Covid.
As fears of wanning immunity may lead to over consumption of limited resources of Covid vaccines globally, a conclusion that is more likely to lead to the unvaccinated seeking vaccination while discouraging the already vaccinated to seek an aggressive booster timeline would be more appropriate as opposed to one that could rationalize seeking natural immunity and encourage frequent boosters to the previously vaccinated.
On 2021-08-28 14:41:37, user RC Cyberwarrior wrote:
I have read comments based on medical studies that individuals who previously had SARS COV2 were 2 -4 times more likely to suffer adverse reactions to the covid vaccines, if vaccinated post initial infection. Some speculate this reaction was related to Antibody-Dependent Enhancement.
On 2021-08-30 00:15:54, user chris amos wrote:
An important paper and carefully conducted study, but it would be useful if the authors would provide a figure or table starting with the overall cohort size, indicating the total numbers of events according to vaccination versus infection or first vaccination among infected. Given the data that are provided I do not know how to accurately calculate a positive predictive value of having been vaccinated, which is another statistic that is of interest. Also, when the authors refer to the analyses as 'multivariate', I think the more accurate way to refer to the analyses is "multivariable". Multivariate would mean that multiple outcomes (vaccinated only, infected only or vaccinated and infected) are jointly modeled, but it seems like the comparison groups are analyzed in separate analyses.
On 2021-08-30 22:42:54, user Chris Curry wrote:
You would think this would be common knowledge seeing as all a vaccine does is simulate a person getting infected in order to force their body into building immunity to the virus. If natural immunity wasn't a thing then vaccination wouldn't be a thing either, but for some reason the country has decided that you have to be either "pro vaccine" or "anti-vaccine" without entertaining any sort of nuance.
On 2021-08-31 22:30:55, user Fully wrote:
Thank you for the interesting and easy-to-understand study - and the clear results: Recovered people are actually much better protected against the now predominant delta variant of Covid-19 and thus less contagious than vaccinated people, even if the infection occurred more than 6 months ago.<br /> Policymakers in Europe, who grant recovered people the same rights as vaccinated people for only 6 months after their infection, should now remove their 6-month rule based on your study results.
Thank you for this from someone who has recovered since one year, who does not want to be vaccinated, because he did well with the disease - me.
On 2021-09-02 09:13:39, user zlmark wrote:
There are several issues with the way the cohort in this study have been formed - the most critical one is the age distribution:
The 60+ group extremely underrepresented - the cohorts contain about 5-6% of people aged 60 and above, whereas they amount to about 31% of the vaccinated people in Israel. And since their own regressions show that the age is a major factor in infectability, such underrepresentation can seriously affect the risk ratio estimates.
On 2021-10-16 15:43:33, user Alex wrote:
Oh and on natural immunity, myself, partner and two children had COVID March 2020, both antibody tests came back May 2021 positive. Currently waiting for the results of an updated one…. We have also not had anything with similar symptoms since but in two weeks I fly to Barcelona with a tonne of red tape because I’m not vaccinated and I don’t find it fair…My partner loses her job along with 40 people in Hampshire social care next month because they opted for no vaccine, a big gap in care looking after our grandparents - good luck with that!- my point is,why is natural immunity not accepted??it’s simple to test for so questions need to be raised!
On 2021-08-30 07:51:38, user Candice Chaplin wrote:
It states that the GENECUBE® HQ SARS-CoV-2 (TOYOBO Co., Ltd.) reagent was approved in October, 2021. As a layman, I don't quite understand this.
On 2021-08-30 14:40:54, user Nathan Johnson wrote:
Hi Sean, table 2 is the attention getting graph with the large drop but it mixes tests at all different ages so it's harder to read. It'd be better to see a graph by time for separate groups of 3 months old, 6 month old and 12 months old (or similar). Since table 4 shows "Overall, we note no significant reductions in development trends." taking out the older groups who didn't drop should make the drop in 2021 even more dramatic, no? Also if masking was used in first few months in children born prepandemic without a drop, could point more strongly to prenatal cause.
On 2021-10-08 05:09:07, user Anya Dunham wrote:
Hi Sean and team, as a scientist and a mom of a 2020 baby, I read your paper with interest. Similar to Pasco, I wondered about the effects of masks. I am also wondering whether babies might have exhibited some form of a 'freeze' response, as some might have not left their homes or neighborhoods much... In an exaggerated example, I would probably do okay on a cognitive test in my home or your lab, but perhaps not so well if I were abducted by aliens... which a lab setting might feel like to babies born during the pandemic.
Similarly, there could be a novelty effect. Given that babies learn by figuring out patterns and experiencing novel events, I wonder if everything in the lab visit was so new that babies who didn't 'freeze' had a harder time paying focused attention to the task at hand. (I see you already mentioned something similar below.) I imagine even following a shape with their eyes might be more challenging if baby is greatly distracted by the novelty of a visit. I can see my summer 2020 baby having this challenge, although he has amazing focus when playing independently at home. I think some measure(s) from the home environment taken by the family would be important here.
Lastly, how did the families join the study? Did they self-identify? (As a side note, I would have liked to see more details in the Methods section - perhaps I am missing an Appendix?) At least where we live, getting an appointment with a pediatrician has been much more challenging during the pandemic. So I wondered if families who had some concerns around their babies' development (even subconscious ones) could have been more likely to join.
On 2021-08-30 15:16:42, user Jeff Brender wrote:
For those wondering about the decrease in PhD respondents from the last version<br /> From the Methods section<br /> "To be included in the analysis sample, participants had to complete the questions on vaccine uptake and intent, and report a gender other than “prefer to self-describe.”. This exclusion was made after discovering that the majority of fill-in responses for self-described gender were political/discriminatory statements or otherwise questionable answers (e.g. Apache Helicopter or Unicorn), and that as a group, those who selected self-described gender (<1% of the sample) had a high frequency of uncommon responses (e.g., Hispanic ethnicity [41.4%], the oldest age group [23.2% >=75 years] and highest education level [28.1% Doctorate]), suggesting the survey was not completed in good faith. "
On 2021-08-30 16:11:49, user Eduardo Amorim ????????? wrote:
Can you please explain how mf is calculated? You ms says "Mf was calculated as described previously [3]." But ref. #3 doesn't explain how mf is calculated -- at least I can't see it.
On 2021-08-31 10:15:07, user Isatou Sarr wrote:
Excellent paper,
the route of therapeutic administration usually plays a pivotal role in immune cells activation, type as well as robustness. Mucosally induced immunological tolerance has become an attractive strategy for diagnostics and treatment of diseases, although there is a need to fully understand the dynamics of mucosal-tolerance immunotherapy as well as efficient antigen delivery and adjuvant systems.
Additionally, the genetically diverse human subjects who also differ significantly in their mucosal flora, nutritional status and previous immunological/environmental exposure, all of which are factors that can been affect mucosal vaccine efficacy.
On the brighter side of life :)))), if practical assays for assessing mucosal immune cells reactivity in research settings are developed as well as methods for predicting efficacy of candidate mucosal immunotherapeutics, harnessing the therapeutic potentials of the<br /> mucosal immune pathway can be a reality.
Thank you.
On 2021-09-01 04:18:17, user John Smith wrote:
Surgical face masks at best have a 3.4 fold decrease in aerosols if worn perfectly, but in this case the typical imperfect fit would drop this down to about a 1 fold decrease. The math in this simulation is far off the mark compared to detailed peer reviewed experiments. Too many incorrect assumptions made in the simulation.
On 2021-09-01 17:28:59, user Elle Tigre wrote:
What was the specific time-frame for the 4-fold increase in evolutionary rate?
On 2021-09-04 19:24:55, user melanoficus wrote:
Very encouraging results. I wish these investigators great success in their endeavours to find and implement beneficial treatment protocols that will save lives of those severely effected.
On 2023-01-15 02:42:48, user Peter lange wrote:
I agree with the other commenters. The description of training is inadequate to determine if the dogs are detecting acute and chronic stress, which canines have been trained to do with high reliability. Without further information the conclusions are unsupported by evidence presented.
On 2022-01-13 17:30:19, user jetbundle wrote:
How were the dogs trained? Were they trained on the sweat of infected (symtomatic or asymptomatic?) people or on isolated viruses?
The authors should answer this. That makes the difference whether the dogs simply identify sick patients or whether it has anything to do with the virus.
On 2023-08-08 19:34:44, user Xiaoping Liu wrote:
The author has published this paper in PLoS One with a revised title: "Analytical solution of l-i SEIR model – Comparison of l-i SEIR model with conventional SEIR model in simulation of epidemic curves". PLoS One. 2023; 18(6): e0287196.<br /> Published online 2023 Jun 14. doi: 10.1371/journal.pone.0287196
On 2021-12-25 16:37:09, user Markus wrote:
In the light of the negative vaccine efficiency, why do they conclude that there is the need for massive rollout of vaccinations and booster vaccinations? The vaccines appear to undermine the natural immunity.
On 2022-01-09 17:05:04, user rubenroa wrote:
Any reason for the increased Odd in vaccinated people against other studies in Israel which conclude: "Vaccination with at least two doses of COVID-19 vaccine was associated <br /> with a substantial decrease in reporting the most common post-acute <br /> COVID19 symptoms."https://www.medrxiv.org/con...
On 2022-01-10 23:20:22, user Litawor wrote:
The previous version of this preprint additionally described adjusted <br /> analysis with important covariates related to vaccination status and <br /> vaccination timing. Why is that analysis omitted in this version? <br /> Matching does not remove the need for statistical adjustment.
On 2022-01-13 09:48:09, user zlmark wrote:
There seems to be some discrepancy between the actual calculations and the conclusions drawn in the Discussion section.
Assuming that Copenhagen data provides us with a more reliable estimate of the gatherings size distribution, as the authors themselves seem to suggest, limiting the gatherings of 100+ gives us about 40% reduction in the number of infections in a single infection cycle.
And given that Omicron mean serial interval is estimated to be around 2.2, this means that about 3 infection cycle happen in a week, and 40% reduction in single cycle leads to about 80% reduction in a week.
On 2022-01-13 14:50:32, user Erik Petersen wrote:
One of the findings that is going to be predominantly taken from this study is that, "vaccinated individuals have significantly lower IVTs." However, upon looking at the data in Figure 4A specifically, we see just under 3 (2.9?) FFU/ml in unvaccinated individuals compared to ~2 FFU/ml in vaccinated individuals. Would you please explain how this constitutes a "significant" reduction?
On 2022-01-17 19:49:07, user AW wrote:
Some errors in text and tables I’m afraid. In text you report the IRR for men <40 years as “7.60 (2.44 - 4.78)” for 3rd dose for Pfizer which clearly is nonsensical -looks you have used the 95%CI for second dose repeated in error. And you have reported the number of events as * for 3rd dose Pfizer in men under 40 years in table rather than number - should have a numerical value.
Given these are probably the most important impactful data you present it’s a bit embarrassing to not get this right - but shows why peer -review is needed (and makes me wonder what else might be incorrect)
On 2022-01-19 16:21:44, user xavier wrote:
Hi, fig 4/inset d, shouldn't line one of the first line read "infection WA1" instead of "infection B1"? <br /> Thanks
On 2022-01-23 21:31:37, user maa jdl wrote:
This paper is a total nonsense!<br /> Why applying the Benford law?<br /> There is no reason. And the paper does not contradict that!<br /> On the contrary.<br /> You just need to look at the data to understand WHY the Benford law doesn't apply!<br /> This is what I did and ONE simple picture can reveal it in a much clearer way than a long paper with a lot of references. This can be done with no references at all! The chi² test is useful there only to give numbers on what is obvious from the picture.
On 2021-10-13 17:03:08, user constantinos schinas wrote:
very interesting article. can you breakdown the calculation for the ie. <br /> 13,080 tests, 100 positives, 20% FNR and 0,8%FPR, in a way we can replicate it in an excel document? In two cases, stable 20%FNR and variable 0-40% FNR.
thank you in advance
On 2022-01-27 21:22:51, user Michael Klar wrote:
They have NOT done their homework:
This investigation uses no suitable surrugate for human aerosols. These consist mostly of mucin5 and this is a hydrogel. Hydrogels behave differently than the one used Serum. This is reflected in the Shrinkage factor of 2.5 versus 4-5 in humans.
The results of the preprint should not be evaluated, as another previously published study shows that the liquid composition is crucial for the inactivation rate:
On 2022-01-28 20:26:50, user Dylan Arroyo wrote:
What happens to the patient with a suicidal ideation while they wait for sobriety? Are they restrained/sedated? Do they wait in the waiting room until they have sobered up before they can be seen by a social worker?
On 2022-02-02 06:38:43, user Marcel Zwahlen wrote:
The fulll paper is now out here on Ann Int Medicine
On 2022-02-02 19:32:41, user Eric D wrote:
This is on Sky News as<br /> BA.2 "More likely to infect vaccinated people"!
SSI report is ambiguous<br /> https://en.ssi.dk/news/news...
The headline<br /> "BA.2 is more transmissible than BA.1 but vaccinated persons are less likely to be infected and to pass on infection"<br /> contradicts a sentence that looks badly-written or edited<br /> "In addition, comparing the risk of household members being infected in BA.2 relative to BA.1 infected households, was higher in vaccinated and booster vaccinated than in unvaccinated, which suggests immune evasive properties of the BA.2 variant."
On 2022-02-08 07:24:12, user Ole Stein wrote:
Misleading and biased conclusions based on wrongfully datatreatment, where they have mixed vaxed with unvaxed, and so unvaxed included all vaxed less than 14 days since last shot and all vaxed include unvased post illness. Such mix is not just unetical, but makes the conclusion completely useless as it does not say anything about contamination between vaxed and unvaxed as they are mixed in their data input. The report should be discarded and removed as fraudulent science.
On 2022-02-07 07:06:22, user kdrl nakle wrote:
5 days after sympton onset? Apparently flawed sampling. You showed nothing as Omicron has faster clearance anyway.
On 2022-02-07 23:20:52, user A440 wrote:
The report says: "The analyses were adjusted for [...] booster dose and time since last dose among the vaccinated."
For those of us wondering whether to get a booster dose, it would be good to know more about how this adjustment was done.
On 2022-02-17 10:51:48, user Sandeep Sharma wrote:
where do I find the latest on COVAXIN approval in Germany?
On 2022-02-22 02:12:34, user Juliet French wrote:
Nice paper. You may want to check out one of our papers. Moradi Marjaneh et al, Genome Biology 2020. PMID: 31910864. Some similarities between yours and ours.
On 2022-02-23 03:43:25, user Sam Wigginton wrote:
Deaths in South Africa are still climbing steadily three months after the Omicron infection peak (according to Worldometer). The case fatality rate (assuming 3 week lag) appears to have risen from 0.7% three months ago to about 8% now. What's going on?
On 2022-03-23 16:58:16, user Stefan Baeuml wrote:
It would be interesting to have a follow up study in the presence of Omicron. In particular, given the increased likelihood of breakthrough infections, it would be interesting to see if the likelihood of the symptoms mentioned in the 'Results' section still remains within the background of people without SARS-CoV-2 infection.
On 2022-04-08 12:13:33, user Tommy Brothers wrote:
The peer-reviewed version of this manuscript is now published online in Drug and Alcohol Dependence
On 2022-04-11 18:32:10, user ReviewNinja wrote:
Thanks you for this fast publication. This publication confirms:<br /> - that people can be reinfected with BA.1 after delta infection<br /> - that people can be reinfected with BA.2 after BA.1 (but that this seems rather a rare event)
However, this publication shows some clear limitations that would need some discussion:<br /> - This publication gives an advice on testing policy, but does not discuss the testing policies at the moment of the study. This is important as this policy changed over the period of the study and was different during some periods for vaccinated and non-vaccinated individuals. Also a correction for testing behavior over time per age group would be useful. <br /> - The publication compares the number of reinfections (01/12 to 10/03) to the vaccinated population on 10/03. As the advice for vaccination for children 5-11 only came out on 15/12, this is an overestimation for the whole study period. The same is true for boosters in the younger age groups. <br /> Vaccination % at the different periods in the study: <br /> %For each age group (age in 2021) on: 01/12, 01/01, 07/02 and 10/03<br /> 5-11y (2 doses): 9 (probably most already 12y), 10, 20, 41<br /> 12-17y (3 doses): 0.5, 2, 11, 33<br /> 18-44y (3 doses): 7, 25, 70, 75<br /> 45-64y (3 doses): 13, 56, 88, 89<br /> The changing vaccination rate over time should be taken into account, or this comparison should not be made. Furthermore, most measured reinfections were in the first study period (<feb 7:="" 91="" of="" the="" 96="" reinfections).="" some="" other="" points="" to="" discuss:="" -="" the="" conclusions="" (and="" abstract)="" are="" rather="" strong.="" to="" advise="" a="" change="" in="" (pcr-)testing="" policy,="" at="" least="" reinfection="" versus="" residual="" pcr-detection="" should="" be="" compared="" discussed.="" reinfection="" during="" this="" short="" period="" measured="" in="" this="" paper="" is="" 0.16="" and="" 0.01%="" (with="" off="" course="" all="" biases="" and="" limitations).="" we="" know="" from="" a="" challenge="" study="" that="" 1="" 3="" young="" people="" (in="" these="" conditions="" in="" this="" small="" study)="" still="" test="" (low)="" positive="" after="" 28="" days="" for="" example="" (https:="" <a href="www.nature.com" title="www.nature.com">www.nature.com="" articles="" s41591-022-01780-9).="" -="" how="" was="" the="" n-gene="" cut="" off="" determined="" here?="" (it="" would="" be="" of="" added="" value="" to="" also="" confirm="" which="" percentage="" really="" resulted="" in="" detectable="" virus="" (specially="" for="" study="" period="" 2).)="" additionally,="" a="" look="" at="" the="" viral="" loads="" might="" be="" of="" added="" value,="" as="" done="" by="" the="" study="" by="" stegger="" (ref="" 9).="" they="" suggest="" a="" more="" transient="" infection="" upon="" reinfection="" with="" ba.2="" after="" ba.1.="" (would="" be="" nice="" to="" know="" the="" testing="" indications="" for="" these="" people="" as="" well.)="">
On 2022-05-11 01:52:14, user bioRxiv wrote:
This preprint is participating in the Comment-a-thon pilot initiative by bioRxiv/medRxiv at the Biology of Genomes CSHL meeting. You can enter the competition if you are registered for this conference by signing up using the link provided at the meeting. Remember to add #BoG22 to your comments.
On 2022-06-14 13:31:27, user Peter J. Yim wrote:
This comment is to clarify that the study showed an unequivocal benefit from ivermectin in COVID-19. From the abstract, the primary outcome considered in the study was: "...time to sustained recovery, defined as achieving at least 3 consecutive days without symptoms." The outcome did not reach statistical significance for that outcome. However, for the related secondary outcome "mean time unwell" the outcome was statistically significant and favored ivermectin.
MTU was estimated "...from a Bayesian, longitudinal, ordinal regression model with covariates age (as restricted cubic spline) and calendar time." The principal finding of the study was that there was a statistically significant difference in MTU between the treatment and control groups: -0.49 (95% CrI: -0.82, -0.15) where CrI refers to "credible interval". The negative range of the 95% credible interval indicates that MTU was lower for the treatment group than the control group.
The authors conclude that the trial "...did not identify a clinically relevant treatment effect ...". The magnitude of the treatment effect found in this trial may or may not be clinically relevant, but clinical relevance is not a statistical quantity and establishing it was not a goal of the trial.
On 2022-06-14 12:41:29, user Robert Clark wrote:
I was puzzled in Fig. 3 that the numbers for the severe cases was 39 for placebo and 51 for IVM. I thought this was measuring the comparative effect of ivermectin for the severe cases. But I see in the Supplementary appendix in eTable 1 that this is just giving the numbers in this category on entrance to the study.
But this raises another problem. For a randomized trial the number of severe cases assigned to the placebo and treatment groups should be close. Yet the IVM group got 24% greater number of severe cases. That’s a discomfortingly large difference for a randomized trial. Clearly this could create a bias against the treatment regimen.
Reviewing the further eTable 1of baseline symptoms, we see several categories of symptoms that would be key indicators of severe disease such as dypsnea, difficulty breathing, were assigned significantly more severe cases to the IVM group compared to the placebo group.
That shouldn’t happen in a randomized trial. I suspect something went wrong with the randomization. This could create such a serious bias against the treatment that a disclaimer should be placed on this study that its randomization procedure is being reviewed.
Robert Clark
On 2022-06-18 09:09:00, user David Escors wrote:
The preprint is still a work in progress before submitting it for publication. It has an error in the definition of the cohort and in Table 1. The correct statement defining the cohort previous to the correction of the manuscript would be :"The majority of patients were smokers, 75% male and the mutational status of the tumors was not evaluated in 96.4% of the patients".
On 2022-08-01 00:35:38, user JJ wrote:
Looks like some numbers are inconsistent, eg 146 vs 158 and 303 vs 301 in the unmasked group.
On 2022-08-06 10:16:33, user Jef Baelen wrote:
No inclusion/exclusion criteria are mentioned? It includes a study from 2004, long before SARS-CoV-2 emerged. The Caruhel study was not performed on COVID-19 patients. The çelebi study used a cycle treshold cut-off of 38 and evaluated 20 parameters of which masks was only 1. The results of this study were wrongly extrapolated in table 1. Very dubious studies included in this meta-analysis!
On 2020-05-26 22:31:23, user guost wrote:
Unfortunately there is absolutely no info about what kind immunoassay this LIAISON XL platform is. The company Diasorin website for is totally useless in that regard.
On 2020-04-24 07:57:39, user Sinai Immunol Review Project wrote:
Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking <br /> Mahapatra et al. MedRXiv [@doi:10.1101/2020.04.05.20054254v1]
Keywords<br /> • Drug prediction<br /> • Machine learning<br /> • Docking
Main Findings<br /> The COVID-19 pandemic has ravaged hospitals: the disease can present severe complications (acute respiratory failure in particular), and yet no specific drug exists to date. Time being of the essence, it is therefore essential to explore drugs already on the market for other indications. These drugs, however, must be tested in COVID-19 patients and thus selection of limited candidates is important. The authors argue that an important step in accelerating the selection of promising drugs can be done in silico.<br /> The authors use machine learning (ML), training their algorithm on a dataset obtained from in vitro targeting of SARS Coronavirus 3C-like Protease with existing drugs. The trained algorithm was then used to screen drugs available in the Food and Drug Administration’s Drug Bank. Using the Drug Bank dataset, the authors also performed a docking study -a process used to predict in silico the orientation and conformation of a molecule when bound to its receptor. Since SARS-CoV-2 spike protein is considered to play an important role in infection by binding ACE-2, docking was also applied to study the stability of drug-spike protein complexes. The results of the ML and docking were aligned, and antiretroviral Saquinavir was identified as a potentially promising therapy for COVID-19.
Limitations<br /> The authors train their algorithm on SARS Coronavirus 3C-like Protease, as inhibitors of this protein should prevent the virus from replicating in the host. However, the authors note that the most promising target seems to be SARS-CoV-2 spike protein. Moreover, the training dataset is the result of in vitro studies, and may have limited relevance in vivo.<br /> Overall, preclinical studies and then potential clinical trials would need to be performed before administering this drug to COVID-19 patients though, admittedly, clinical validation of an existing drug could happen faster than the development of new drugs entirely. Saquinavir has been studied in vitro by Yamamoto et al.[1], and shown little promise in SARS-CoV-2 treatment so far.
Significance<br /> Repurposing of existing drugs is can be advantageous to develop treatment strategies. An in silico approach could help identify potential therapies, although they must be confirmed in clinical trials before being administered on a large scale.
References<br /> Yamamoto et al. Nelfinavir inhibits replication of severe acute respiratory syndrome coronavirus 2 in vitro. BioRXiV preprint, 2020
Credit<br /> Reviewed by Maria Kuksin as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2024-04-28 02:04:59, user Ian Myles wrote:
Small correction, the Clinical trial number is NCT04864886
On 2024-04-27 18:42:07, user Kim Brumfield wrote:
Thank you to Dr. Myles and his team for doing this research. My daughter has 20 years of prescribed steroid use for eczema without informed consent of the risk of topical steroid addiction and withdrawal. The dermatologists she saw used step therapy which eventually resulted in tachyphylaxis. The topical steroids stopped working after reaching hichest potency and now she is suffering from "eczema on steroids" which is really topical steroid withdrawal. Please help us find the cure for this horrible iatrogenic disease.
On 2024-04-27 19:07:49, user Toby wrote:
So glad to see that this problem is being taken seriously. I have suffered from eczema all my life and from TSW as described here for many years. I hope this research will result in new treatments for this awful condition.
On 2024-04-27 19:50:44, user Eve Benson wrote:
I am starting my 8th year of TSW. The first five years were torture, the last few have been manageable. I can identify with every symptom listed in this study. I can also identify with the stress of seeing several "well-meaning" doctors whose only choice of treatment was putting me back on steroids, which I refused thanks to the education I received from online communities of thousands of people who were suffering like myself. More studies like this one are needed. TSW is real. Sufferers of TSW deserve appropriate medical care and care from practitioners who understand the disease and thus provide appropriate treatment. It is time for medical institutions to step up and address TSW.
On 2024-05-02 15:45:09, user Kelly Barta wrote:
This is such an important and groundbreaking study in its showing a differentiation between Atopic Dermatitis and Topical Steroid Withdrawal Syndrome, which has been one of the big debates within the medical community. Patients need and deserve acknowledgement and support from their health care providers, but understandably, this is unable to happen without the science to back up what we are seeing anecdotally in the eczema patient population.
More research is needed to determine how and when topical steroids are creating these dysfunctions in patients in order to better understand their proper use and prescribing guidelines. This research is CRITICAL to the over 31 million Americans living with eczema and over 300 million worldwide (not to mention the countless other dermatology patients), who are prescribed topical steroids to manage a skin condition.
On 2024-11-15 06:07:16, user Nina T ang wrote:
May I have access to the FFA dataset please?
On 2020-02-13 21:52:14, user reuns wrote:
I deduce from "case 13: no viral RNAs were detected until the fourth upper respiratory samples" that in the graphic U means negative
On 2020-03-25 19:31:48, user Charles Haas wrote:
My concern with their disinfection experiments is that there is no indication that they neutralized the disinfectant prior to culturing. This is an absolute necessity.
On 2020-04-22 20:47:32, user John Stevens wrote:
Hope this is widely understood - each tiny bump in efficiency makes differences in daily KPIs
On 2022-12-24 03:16:24, user ArCH wrote:
Published version<br /> https://www.frontiersin.org...
On 2020-05-28 08:18:17, user Philippe Brouqui wrote:
The paper of KIM et all reports on the response to treatment of hydroxychloroquine or<br /> lopinavir-ritonavir with or without antibiotic in a retrospective cohort study<br /> with comparison with standard of care. The evaluation has been carried out on<br /> moderate case only and no death were reported. They assume that HCQ and ATB is superior to both SCO and LR plus ATB in time to viral clearance, length of hospital<br /> stay, duration of fever and cough. Adverse event been significantly different<br /> from SOC but not different between the two arms of treatment and only<br /> mild. <br /> Methodology: Is appropriate for the aim<br /> -The study is relevant to the aims: treat patient early (non-severe disease) at the time to diagnosis to avoid complication and death<br /> - The study is relevant toward bringing new data in time to outbreak by using repurposing of drugs HCQ and LR <br /> -The study is relevant toward bias related to heterogeneity of care as a single center study.<br /> - Classification of patients referred to NIH Guidelines (NEWS Score) as mild, moderate and severe COVID<br /> - Definition of negative PCR and viral clearance is in adequation to previous published literature.<br /> -Treatment was done within the range of recommended dosage ; HCQ 200mg/ twice a day as well as for lopinavir/ritonavir 200/50 mg, however higher doses are generally used for HCQ (600mg/d). For antibiotics there were given as recommended
Ethics
-Data were collected through the patient medical file of the hospital blinded and using patient dataprotection
Outcome measures<br /> -Correspond to aims delay between treatment initiation and viral clearance, discharge from hospital and symptoms resolution
Statistical analysis
This is classical analysis methods relevant to aim
Confounding bias/ Limitation:<br /> -Those relative to retrospective study but well a posteriori controlled <br /> -As a retrospective study of the 358 patients (270) 173 mild and 97 moderate covid-19 cases were analyzable for completion of treatment and data availability. <br /> - 3/270 patients were still ongoing treatment at time of release (1%)<br /> - 97 moderate Covid-19 patients were categorized HCQ ATB SOC (22) LR ATB Soc (35), SoC (40) and analyzed for treatment and outcomes<br /> -A posteriori comparative analysis shows that the two groups HCQ and LR were identical in terms of comorbidity and other known factors that may weigh on the outcome.<br /> -Comparison with the Soc group alone showed that this last was less severely ill<br /> (significantly less pneumonia), and factor associated with poor outcomes, such<br /> as low lymphocytes count, and elevated CRP were associated with the treated<br /> groups. Interestingly dyspnea was more prevalent in this SOC group but we know<br /> that absence of dyspnea (silent hypoxemia) is rather than dyspnea linked to<br /> outcome. This suggest that if a difference in treatment exist it will probably<br /> be under evaluated. <br /> - Retinopathy to HCQ as never been reported in such short treatment and HCQ in serum returns to negative in 15 days after end of treatment (unpublished) <br /> The limitation of the study as been well reported
Interpretation of results: adequate except comparison LR/LR and ATB<br /> Time to clearance are shorter particularly in the HCQ and ATB group for which time to PCR > 35CTis 12 days in what is published elsewhere<br /> -Cough resolve significantly better in the HCQ ATB and fever in the two treated groups compare to SOC.
Adverse events were more frequent but only mild
The subgroup analysis LR versus LR ATB should be interpreted carefully as we don’t know interval time to onset of the LR only arm which may interfere with viral clearance of this subgroup.
Conclusion<br /> This study appropriately show that HCQ & ATB is better than LR & ATB than to SOC<br /> to shorten viral clearance, resolution of symptoms, and to shorten hospitalization duration in moderate form of COVID-19. The role of ATB alone to shorten viral clearance is overestimated
Note : P BROUQUI , has no conflict of interest with the industry concerning this review<br /> but has already published study supporting the efficiency of HCQ and azithromycin in COVID-19.
On 2020-04-23 05:41:01, user FrezzaLab wrote:
Is it possible that some of these kids had already been infected, before the lock down, and passed it onto relatives?
On 2020-04-26 13:59:26, user Italian_in_london wrote:
There is an important element of this research widely mentioned in Italian TV interviews and on Italian generalist press: the isolation of all positive cases caused a sharp drop (60%) in intensive care cases, as if the high viral load of people exposed to multiple contacts with positive cases is the main cause as to why some people end up I intensive care. I am interested to understand why information allegedly coming from this research does not seem to appear here. It is obvious what the implications are for medical staff being asked to go back to work despite being still contagious.
On 2021-12-05 11:00:28, user Professor Ritual wrote:
I actually like the modelling idea, a noble effort - but as things move fast the data used in the study is now outdated. Please remodel for the current data: 71% senior breakthrus and 50% adult breakthrus.
On 2020-04-07 01:06:33, user Cristian Reyes P. wrote:
In Chile BCG vaccine has been mandatory since 1949. Everybody is vaccinated. Over 90% of the population. You can study us. We still have the lowest mortality in the region.
On 2023-05-30 13:27:21, user Nils Yang wrote:
This paper has been published in Journal of Child Psychology and Psychiatry, see http://doi.org/10.1111/jcpp...
On 2020-07-24 16:43:50, user Kamran Kadkhoda wrote:
The correlate of protection is not inferred this way it is typically inferred through prospective vaccine trials in SARS-CoV-2-native volunteers.
On 2020-04-08 01:57:26, user Eliot Abrams wrote:
This just fits a gaussian curve. Absurd. Among other reasons, there is a second wave as soon as the current shelter in place restrictions are lifted.
On 2020-07-27 03:48:45, user Hagai Perets wrote:
A potential explanation for such dynamics could be related to co-infectio, see <br /> https://arxiv.org/abs/2007....
On 2021-12-22 14:03:31, user Simone Davies wrote:
I had these symptoms (vibrations for months, muscle twitching for days) after my all 3 of my vaccine shots. I had not had covid before I was vaccinated. Would be interested to know if this is true in others?
On 2023-11-07 14:15:21, user Nils Yang wrote:
This preprint has been published at Sleep: https://doi.org/10.1093/sle...
On 2020-06-09 17:41:58, user Hamid Reza Marateb wrote:
This is a hospital-based cohort whose results could not be generalized to the population. Moreover, these patients usually have commorbidity, and thus avoid smoking. Here are justifications.
On 2020-08-08 06:57:06, user Dr-Beesan Maraqa wrote:
Thank you for this study. I am struggling to find studies assessed the associations between stress and demographic factors, job title, and relation to social life.
On 2022-01-01 05:17:59, user Ardiana wrote:
N501Y and E484K signals high spread ability and original vaccine antibody evasion. But there were many variants like this and that couldn't beat Delta.
On 2020-04-15 11:57:26, user Renato Prandina wrote:
Duration and extent of immune protection will be critical to the novel betacoronavirus SARS-CoV-2 and will unfold in coming years. Some speculative scenarios...
On 2020-04-16 06:06:53, user Hellbound Reaper wrote:
SARS-CoV-2 is the virus name..COVID-19 is the disease name..you can't catch a disease. :P
On 2020-04-16 12:20:10, user Marlowe Fox wrote:
The tests on the efficacy of HCQ are confounded by multiple variables, including comorbidities, symptom onset, prescription drugs (RAAS inhibitors appear to play a key role in viral intensity), and testosterone/estrogen level, to name only a few.
Geneticists, epidemiologists, and other scientists have long used casual diagrams to clearly show variables that may potentially confound their results (1). The Wuhan study at the very least would need to account for the following:
HCQ <— comorbidities —> recovery<br /> HCQ <— symptom onset —> recovery<br /> HCQ <— drug prescriptions —> recovery
Adjusting for the confounding variable would essentially smooth out the flow of information between the treatment (HCQ) and the outcome (recovery), allowing for the inference of causal effects.
Assuming observable data is not available to adjust for confounding variables, a casual mechanism (mediator) could smooth out the flow of information from the treatment to the outcome (so long as the mediator is not influenced by confounder).
Luckily, multiple in vitro studies have been performed. One study posits that HCQ lowers endosomal pH which ultimately inhibits COVID from binding to ACE 2 and decreasing viral intensity (3).
HCQ —> endosomal pH —>glycosylation of COVID cellular receptor —> ACE 2 binding —> viral intensity —> acute lung injury
Another in-silico study posits that HCQ blocks specific protein sites on the host ACE2 cell, thereby thwarting its attempt to infect it and preventing the cytokine storm (over-reaction of the lymphatic system) that some posit is responsible for Acute Lung Injury (3). So here we have an entirely different causal mechanism:
HCQ —> BRD-2 receptor sites —> cytokine storm —> acute lung injury
Despite these problems, some believe that the p-values obviate the need to control for potentially lurking variables. However, they are subject to myriad influences, known as p-hacking. Whether it is the number of tests performed or the number of comparisons made, it increases the chance of finding a statistically significant p-value (4). Three professional statisticians co-authored a paper reviewing the validity of the Wuhan study (5). There were several issues with the data upon which the two significant p-values were based.
I suppose there is also a pragmatic argument: The p-values, along with existing studies and reports, are sufficient enough evidence to offset any concern for lurking variables in these urgent times. In other words, how much evidence is sufficient to warrant large scale roll-out of a low-cost treatment that may have a beneficial effect, from saving individuals who would have otherwise died to curbing its spread?
The consequences of large roll-out: manufacturing, scaling, distribution chains, and so forth could result in a tremendous diversion of resources. How many pharmaceutical manufacturers even have the capacity to roll out production of this magnitude? What if they all start scaling their labor to produce this particular drug. You can’t just put this genie back into the bottle. Not to mention the scientific energy/intellectual capital that would go to proving or disproving this proposed treatment. And why? Because scientific evidence demanded it? No because a tortured p-value and unpublished/unsubstantiated anecdotal evidence caught the attention of some in the media, and it has been over-popularized as a panacea. What about the risk that HCQ is not an effective treatment despite large investments in cash and resources that have been invested? Do you think the wheels of capitalism turn so easily? Investors will want a return and if that means continually touting an ineffective drug through spurious science, they will continue to do so. What about individuals taking HCQ as a prophylactic, believing themselves to be protected against COVID? Or COVID+ individuals taking HCQ and believing themselves to be cured? Or individuals who think: Well, if I get it—I’ll just take HCQ and be fine. This would increase the spread of COVID. From my perspective, the ignorance to viral transmission and the required precautions is widespread. This is just one more reason not to acquiesce to the new social norms of wearing face masks, social distancing, and abiding by shelter-in-place rules. Here, I think an understanding of cognitive psychology is important to anticipate the future behavior of a society in which a cheap and easy-to-manufacture cure is published in the media.
To sum up, HCQ's efficacy is not sufficiently proven to warrant a widespread roll-out, because it could result in several downstream consequences, from the diversion of resources (both manufacturing capabilities and intellectual capital) to increasing the risk threshold of individuals--who spurious believe in an easy and cheap treatment--thereby increasing the infection rate. One of two things needs to happen. Clinical trials that properly adjust for all potential comorbidities. Or the discovery of a causal mechanism (in vivo), which would obviate the need to control/adjust for confounders. For me, this would tip the utilitarian scales in regard to the potential benefits versus the risks.
References
On 2025-04-02 10:00:44, user Md Shahed Morshed wrote:
The published version can be found here: https://doi.org/10.3329/jacedb.v3i2.78642
On 2020-08-31 13:17:21, user Kamran Kadkhoda wrote:
Great work! This suggests the specificity of the Euroimmun assay is around 33%!
On 2019-10-30 08:25:55, user Marema wrote:
This paper is done to investigate how acute financial problems affect undergraduate students' clinical learning.The study used qualitative method to explore their experiences. I here for further information.
On 2020-05-06 06:53:53, user Hossein Mirzaei wrote:
Dear Georg<br /> i cant find table 1 in your article, Which you refereed to that for Main characteristics of patient.<br /> can you help me to find that?<br /> Thanks<br /> Hossein
On 2020-02-17 08:52:17, user Ellie_K wrote:
Once this paper has been peer-reviewed, could someone post here in the comments where (i.e. in which scholarly journal) it is published? Thank you!
On 2020-02-24 21:00:31, user Alexei Vazquez wrote:
As predicted https://arxiv.org/abs/cond-...
On 2020-03-04 13:30:42, user Bìtao Qiu wrote:
I think there are some wrongly put numbers on Table 3 (page 35), e.g. n = 59 for Immunodeficiency patients. It should be 3 according to the abstract and n = 3 on page 36.
On 2020-09-19 14:10:12, user kdrl nakle wrote:
Given the low infection rates you most likely have many false positives and the adjustment is probably of uncertain quality. Basically, it is not very accurate to use serosurveys in low infection areas.
On 2020-03-12 14:56:17, user Reza Amini wrote:
This is great job! I've done something similar to this earlier but I have DATASET shortage.<br /> "imreza.ir" .<br /> This was really surprising
On 2020-03-24 19:18:13, user Luis Cabrera wrote:
In the Extended Data 2, there is another standard curve, instead of "Primer, reporter molecules, target gene fragments, and guide RNAs used in this study. "
On 2020-03-26 15:11:11, user Sinai Immunol Review Project wrote:
Study description: Plasma cytokine analysis (48 cytokines) was performed on COVID-19 patient plasma samples, who were sub-stratified as severe (N=34), moderate (N=19), and compared to healthy controls (N=8). Patients were monitored for up to 24 days after illness onset: viral load (qRT-PCR), cytokine (multiplex on subset of patients), lab tests, and epidemiological/clinical characteristics of patients were reported.
Key Findings:<br /> • Many elevated cytokines with COVID-19 onset compared to healthy controls <br /> (IFNy, IL-1Ra, IL-2Ra, IL-6, IL-10, IL-18, HGF, MCP-3, MIG, M-CSF, G-CSF, MIG-1a, and IP-10).<br /> • IP-10, IL-1Ra, and MCP-3 (esp. together) were associated with disease severity and fatal outcome. <br /> • IP-10 was correlated to patient viral load (r=0.3006, p=0.0075).<br /> • IP-10, IL-1Ra, and MCP-3 were correlated to loss of lung function (PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury) with MCP-3 being the most correlated (r=0.4104 p<0.0001 and r=0.5107 p<0.0001 respectively).<br /> • Viral load (Lower Ct Value from qRT-PCR) was associated with upregulated IP-10 only (not IL-1Ra or MCP-3) and was mildly correlated with decreased lung function: PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury).<br /> • Lymphopenia (decreased CD4 and CD8 T cells) and increased neutrophil correlated w/ severe patients.<br /> • Complications were associated with COVID severity (ARDS, hepatic insufficiency, renal insufficiency).
Importance: Outline of pathological time course (implicating innate immunity esp.) and identification key cytokines associated with disease severity and prognosis (+ comorbidities). Anti-IP-10 as a possible therapeutic intervention (ex: Eldelumab).
Critical Analysis: Collection time of clinical data and lab results not reported directly (likely 4 days (2,6) after illness onset), making it very difficult to determine if cytokines were predictive of patient outcome or reflective of patient compensatory immune response (likely the latter). Small N for cytokine analysis (N=2 fatal and N=5 severe/critical, and N=7 moderate or discharged). Viral treatment strategy not clearly outlined.
On 2020-05-18 12:43:08, user Sinai Immunol Review Project wrote:
Long period dynamics of viral load and antibodies for SARS-CoV-2 infection: an observational cohort study<br /> Huang et al. medRxiv [@doi.org/10.1101/2020.04.22.20071258]<br /> Main Findings<br /> The presence of serum IgM and IgG against SARS-CoV2 has been shown in several studies, however, a limited number of studies have shown the longitudinal relationship between viral RNA levels and antibody titers. This retrospective, observational study evaluated the dynamics of viral RNA, IgM and IgG specific for SARS-CoV2 proteins in patients with confirmed SARS-CoV-2 pneumonia over an 8-week period. <br /> Throat swabs, sputum, stool and blood samples from 33 patients with laboratory confirmed SARS-CoV-2 pneumonia were collected to analyze viral load and specific IgM and IgG against spike protein (S), spike protein receptor binding domain (RBD), and nucleocapsid (N). The demographics of the patients showed that 24 had respiratory symptom, two had symptoms in both the respiratory and the gastrointestinal tracts, one had gastrointestinal symptoms and six were asymptomatic. Chest CT revealed 27 patients had bilateral infiltrates and six had unilateral infiltrates. All the patients received antiviral treatment and atomized interferon during hospitalization. <br /> While viral load in throat swabs and sputum was higher at the symptom onset and undetectable by three weeks and five weeks respectively, viral load in stool started low but remained detectable for more than five weeks in many patients. The viral loads in sputum declined significantly slower compare to throat, so that the patients were divided into two groups based on load in sputum: short-persistence (viral RNA undetectable within 22 days, n=17) and long-persistence (viral RNA persists more than 22 days, n=16). The relationship between the persistence of sputum viral RNA and antibodies showed that short-persistence group had higher anti-S IgM, anti-RBD IgM and anti-RBD IgG levels compare to long-persistence group suggesting a potential protection by anti-RBD antibodies. The length of time from symptom onset to hospital admission was also associated with SARS-CoV-2 viral clearance. In addition, the higher seropositive rate for anti-S and anti-RBD IgM was seen in long-persistence patients. They suggest that delayed admission to the hospital resulted in higher seropositive and longer infection in patients with COVID-19.<br /> Limitations<br /> The separation of ‘low persistence’ vs ‘high persistence’ groups seems quite arbitrary, as only virus RNA levels for in sputum were considered, and viral loads in throat swabs and stool were not considered (these were no significant difference between the low vs high groups. The manuscript needs better and more detailed description of methods and figure legends. The same color codes for each patient could be used in figure 1, so that the readers could see if there was a trend in viral load between specimens in a same patient, and graphics for each patient will be useful to understand viral dinamics in the three types of samples for each person. The graphs on figure 5 seemed to correspond to one time point data, but there was no explanation which time point was used. In the results section, it was not clear which figures or tables were related to the text. The correlation between severity, viral load and persistency, and antibody titers could be analyzed.<br /> Significance<br /> It is important to understand the relationship between viral loads, disease progression and viral-specific antibodies in COVID-19 disease. More studies are necessary.
Reviewed by Miyo Ota as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-10-30 23:23:26, user Leonidas Palaiodimos wrote:
This article has been reviewed by peers and published at Hormones-the International Journal of Endocrinology and Metabolism
On 2020-03-31 16:17:23, user Johannes Opsahl Ferstad wrote:
Link to tool: https://surf.stanford.edu/c...
On 2020-04-22 04:36:02, user Paul Hue wrote:
Has Covid19 been truly isolated? Have its purported surface proteins been linked to genetic sequences in recovered genetic material from a true isolation?
On 2020-04-22 12:09:29, user Three-legged Crow wrote:
Where’s zinc mentioned in this study?
On 2020-04-23 17:39:53, user Patrick wrote:
"Rates of ventilation in the HC, HC+AZ, and no HC groups were 13.3%, 6.9%, 14.1%, respectively."
"The risk of ventilation was similar in the HC group [...] and in the HC+AZ group [...], compared to the no HC group."
Is there a mistake in one of these sentences, or am I reading this right?
On 2020-04-24 20:55:11, user wangkon936 wrote:
The issue with a "retrospective" study is that populations cannot be randomized. It is clear just looking at the data that the vitals and the biochemistry of the hydroxlchloroquine ("HC") or hydroxlchloroquine and azithromycin group ("HC + AZ") was inferior vs. the non HC or HC + AZ group. In other words the HC and HC + AZ groups were significantly unhealthier vs. the the non HC or HC + AZ groups. A randomized true clinical trial would have filtered this bias out, but a retrospective study structurally solidified this bias in. Thus, this study has a structurally solid bias that render's its ultimate conclusions suspect and of limited use.
On 2020-04-25 08:48:51, user Huan Mo wrote:
Of course the patients who were treated with hydroxychloroquine were sicker at the baseline. The no-hydroxychlroquie patients have tons of missing values in basic labs and apparently they are mostly mild and outpatients.
This study is like saying pneumonia treated in tertiary medical center has worse outcome than treated in an outpatient clinic.
On 2020-11-18 21:50:14, user Hamid Merchant wrote:
Very interesting findings. Can you post the Ct values of all individual patients at different time points in a table as a supplementary data file please?
On 2022-10-24 19:47:27, user Camille Sawosik wrote:
The main goal of this study was to create a new model in order to diagnose brain disorders as they are often complex and get misdiagnosed. It seems that the researchers here have created a base computer model in order to diagnose brain disorders. The main critique here, as even the researchers point out, is that the model needs greater development and study before it can be applied in a clinical setting. At this point in development of the model, I would say that this paper has moderate significance, but could provide a breakthrough in the field if further development occurs. For now, the brain samples looked at all came from a limited number at the NBB. In the future, perhaps applying this model in other populations would continue to develop its significance within the field. The large majority of this paper relied on methods based in computer design. Coming from someone with a smaller background in these methods, I would have liked to see greater description of what they did. For example, at one point the FuzzyWuzzy library is referenced, and it would have been helpful to include some explanation of what this is. At some points as well, I found that the methods were essentially repeated in the results. The concept was interesting, but it was often hard to follow exactly what was done as this study seemed much more programing and computing based. In some sections, as well, tables or figures were referenced, but then those tables did not exist within the paper. Moving forward, other researchers should definitely use this as a building block for the future in order to build off of and develop a more advanced model. The findings here are interesting and provide a good framework for future extrapolation of the model. I found this paper interesting and hope for future development to get this idea into a clinical setting!
On 2022-10-27 19:45:52, user Indi Trehan wrote:
This article has now been published in The Pediatric Infectious Disease Journal -- doi: 10.1097/INF.0000000000003740
On 2022-11-10 06:25:14, user David Hinds wrote:
This paper has been published: https://doi.org/10.1038/s41...
On 2022-11-17 03:39:53, user M. Cunningham wrote:
The FDA EUA specifies that Paxlovid's window of availability requires the patient to be within both 5 days of symptom onset and the first positive test, whichever comes first. Is this not the VA's protocol? I only saw the testing aspect mentioned. I would think that this would further narrow the margins of error (eg: confirming early treatment). Additionally, Paxlovid is nirmatrelvir and the co-drug ritonavir. Since the press is already reporting this the same as a peer-reviewed finding, IMHO it's important to correct these omissions so that the public is not confused about the use of this medication. But I am enthusiastic to re-read the study when it has been evaluated and reviewed! Thank you for your research.
On 2022-12-02 16:36:24, user Mark Czeisler wrote:
Note from the authors:
A revised version of this paper was published in Annals of Internal Medicine on 29 November 2022 following peer review. Below is a link to the article, along with the PubMed citation.
https://www.acpjournals.org...
Czeisler MÉ, Czeisler CA. Shifting Mortality Dynamics in the United States During the COVID-19 Pandemic as Measured by Years of Life Lost. Ann Intern Med. 2022 Nov 29. doi: 10.7326/M22-2226. Epub ahead of print. PMID: 36442062.
On 2022-12-13 18:27:20, user João Duarte wrote:
This article is now peer-reviewed and published in Frontiers in Neuroscience, here: https://www.frontiersin.org...
On 2022-12-26 13:26:47, user y wang wrote:
You did not indicate the method of calculating the chi-squre.<br /> Actually, your method does not seem correct. <br /> One can google "Comparing Two Independent Population Proportions" and find the formula and calculator.<br /> Using the calculator, I found z=6.00, i.e., chi-squre=36 (not your 35.67).
On 2023-02-07 14:43:14, user E Edlund wrote:
Interesting read. Have you seen this similar study, focused on stroke? https://pubmed.ncbi.nlm.nih...
On 2023-02-18 07:18:25, user Anikó Lovik wrote:
This preprint has now been published in an extended and revised version in PLOS One: https://doi.org/10.1371/jou...
On 2023-02-28 18:38:31, user Stéphanie C. Thébault wrote:
Link to published article:
PLoS One. 2023 Jan 12;18(1):e0278388. doi: 10.1371/journal.pone.0278388
On 2023-03-30 21:55:59, user Daniel wrote:
Can the authors please elaborate on the changes made, in particular the effect on TLR4 response?
On 2023-04-21 12:49:03, user antonia peros wrote:
I believe that the topic of your research is very important and current, however, I have several methodological objections. <br /> Although the authors pointed out the limitations, they made quite strong conclusions and recommendations despite too small, non-randomized sample and a cross-sectional design without a control group.<br /> The use of the used instruments is very questionable when it comes to recalling satisfaction, self-esteem, and reduction in depression from 6 months ago. I also think that the fact that the respondents were familiar with the purpose of the research could have contributed to the recall bias.<br /> An important factor in your research could be how long the subjects exercised, and you did not collect that data. What their target is in the research is also vaguely defined. I recommend including some more objective criteria for that.
On 2023-04-21 15:29:37, user Erin Jonaitis wrote:
This manuscript has now been published in Brain Communications. The final version is available at https://academic.oup.com/braincomms/article/5/2/fcad057/7070362.
On 2023-05-06 19:13:10, user Bret Nestor wrote:
Hello, If you are interested in a peer-reviewed subsequent work with a distilled message, please refer to our work in The Lancet Digital Health: https://doi.org/10.1016/S25...
On 2023-05-18 19:00:12, user Dave Fuller wrote:
Please add final peer-reviewed citation as:
Lin D, Wahid KA, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Cislo M, Murphy JD, Fuller CD, Gillespie EF. E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation. J Med Imaging (Bellingham). 2023 Feb;10(Suppl 1):S11903. doi: 10.1117/1.JMI.10.S1.S11903. Epub 2023 Feb 8. PMID: 36761036; PMCID: PMC9907021.
Thanks!!
CDF
On 2023-06-01 01:20:57, user Edmund Seto wrote:
This paper has been accepted for publication in the journal Science of the Total Environment under the title "Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings"
On 2023-07-15 08:38:27, user readthecoran wrote:
Thanks for this paper. Your result may bne related to variance shrinkage of quantile averaging relative to equal weight combination of distributions. See https://onlinelibrary.wiley... For right skewed distributions medians tend to be lower than means
On 2023-08-08 10:45:14, user Robin Walters wrote:
Now published:
On 2023-09-14 11:31:45, user Indi Trehan wrote:
This has now been published: J Glob Health 2023 Jun 9;13:04065. doi: 10.7189/jogh.13.04065.
On 2023-09-20 17:48:48, user ASH wrote:
Why did the authors investigate the associations between poultry fecal matters and E.coli, instead of other more poultry-specific zoonosis, like Salmonella? E. coli is commonly found in the lower intestine of warm-blooded organisms, of which most are harmless...<br /> Why didn't the authors check the DHS data? Similar data can be found in the DHS data which is publicly available.
On 2023-11-04 15:16:53, user Clive Bates wrote:
Two problems here.
First is scalability. This doesn't sound like an intervention that would engage many veterans, nor does it seem likely to be affordable or practical at the scale necessary to achieve a turnaround in the aggregate burdens arising from smoking.
Tobacco-related deaths exceed those resulting from homicides, suicides, motor vehicle accidence, alcohol consumption, illicit substance use, and acquired immunodeficiency syndrome (AIDS), combined.
Almost all of that excess mortality is attributable to smoking not nicotine. Tobacco harm reduction approaches may deliver more and sooner - e.g. encouraging migration to smoke-free alternative forms of nicotine use such as vaping.
Second, it is quite possible that veterans with forms of PTSD are benefiting in some way from the functional and therapeutic properties of nicotine. Again, an approach to smoking cessation that does not demand nicotine cessation may achieve nearly all the health benefits of quitting smoking without demanding withdrawal from nicotine use.
The trial could at least consider an additional arm to assess the utility of encouraging vaping for smoking cessation. It might achieve more for less.
On 2023-11-13 10:04:59, user Theo Peterbroers wrote:
"The duration from the day of index vaccination to the day of the survey completion was a median of 595 days (Interquartile Range<br /> (IQR): 417 to 661 days; range: 40 to 1058 days)."<br /> That is at least one participant vaccinated before the start of the pandemic.<br /> EDIT Make that one person from early in the vaccine trials. How time flies.
On 2023-11-15 16:42:12, user jhick059 wrote:
This article was published in the peer-reviewed journal PLoS One on October 30, 2023 (citation below), but the medRxiv page has not yet been updated to reflect the PLoS One publication.
Citation: Hickey J, Rancourt DG (2023) Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities. PLoS ONE 18(10): e0293556. https://doi.org/10.1371/jou...
On 2023-11-27 21:08:47, user Judith Mowry wrote:
The recent paper on peripheral vasopressors by Yerke doi.org/10.1016/j.chest.202... is an important reference for your research. It is vital to note that they changed their protocol to add very specific protocols and rules regarding IV site inspection, defined who was responsible. Also note that an antecubital site (or any joint) is avoided to minimize movement and extravasation risk. I wish you success with your research.
On 2023-11-27 22:00:33, user Christos Proukakis wrote:
We note the results of the analysis, and look forward to submitting a detailed response soon.
On 2024-01-27 21:34:52, user kasia bera wrote:
now published here: https://pubmed.ncbi.nlm.nih...
On 2024-02-20 21:32:15, user Wally Wilson wrote:
It would be handy if the authors could get the abbreviations for Borderline Personality Disorder (BPD) and Bipolar Disorder (BD) corrected
On 2024-04-25 03:20:17, user Lena Palaniyappan wrote:
Very interesting work. We observed a similar 'amelioration' effect using a cross-sectional design a few years ago (Guo et al., 2016). Since then we made several cross-sectional and a few longitudinal observations supporting the possibility of compensation and reorganisation after first episode psychosis (Palaniyappan et al., 2019a; 2019b), including one with the largest untreated sample we could access at that time (Li et al., 2022). These observations compel us to spare more efforts to understand the compensatory processes in psychosis (Palaniyappan et al, 2017, Palaniyappan & Sukumar 2020, Palaniyappan, 2021; 2023).
Guo S, Palaniyappan L, Liddle PF, Feng J. Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study. Psychological medicine. 2016 Jul;46(10):2201-14.
Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychological Medicine. 2023 Jun;53(8):3500-10.
Palaniyappan L. Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neuroscience & Biobehavioral Reviews. 2017 Aug 1;79:1-3.
Palaniyappan L, Das TK, Winmill L, Hough M, James A, Palaniyappan L. Progressive post-onset reorganisation of MRI-derived cortical thickness in adolescents with schizophrenia. Schizophr Res. 2019a Jun 1;208:477-8.
Palaniyappan L, Hodgson O, Balain V, Iwabuchi S, Gowland P, Liddle P. Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study. Psychological Medicine. 2019b Feb;49(3):412-20.
Palaniyappan L, Sukumar N. Reconsidering brain tissue changes as a mechanistic focus for early intervention in psychiatry. Journal of psychiatry & neuroscience: JPN. 2020 Nov;45(6):373.
Palaniyappan L. The neuroscience of early intervention: Moving beyond our appeals to fear. Australian & New Zealand Journal of Psychiatry. 2021;55(10):942-943.
On 2024-04-26 17:02:43, user Gary Goldman wrote:
We broadened our analyses (of IMRs) to explore potential relationships between childhood vaccine doses and NMRs (neonatal mortality rates) and U5MRs (under age 5-year mortality rates). Using 2019 and 2021 data, 17 of 18 analyses (12 linear regressions and six ANOVA and Tukey-Kramer tests) achieved statistical significance and corroborated the trend reported in our original study, demonstrating that as developed nations require more vaccine doses for their young children, mortality rates worsen. Please see https://pubmed.ncbi.nlm.nih...
On 2024-05-02 18:11:05, user Keith Robison wrote:
There is a great degree of interest in this preprint due to it being the first extended description of using the iCLR technology.
It would be very valuable to have details on how much Illumina short read data was generated from iCLR libraries and how much of that data contributed to the iCLR reads vs. what could not be used
It would also be valuable to report the read length distribution of the iCLR reads in greater detail - particularly since many interested parties cannot perform that analysis themselves on the clinical data sets
On 2024-05-28 13:49:19, user Jenn Ferris wrote:
This preprint is now published: https://www.neurology.org/d...
On 2024-11-08 19:59:59, user Andre Boca Ribas Freitas wrote:
Important Observations on Underreported Chikungunya Mortality in Light of Global Burden Analysis
Dear Authors,
I thoroughly appreciated your recent preprint on the global burden of chikungunya and the potential benefits of vaccination. Your work provides critical insights into the widespread impact of this disease and emphasizes the significant potential of vaccine interventions.
However, I wanted to highlight a critical issue that our research and that of others in the field have identified: the substantial underreporting of chikungunya-related mortality across many regions. While chikungunya is often categorized as a non-fatal disease, a growing body of evidence reveals severe and sometimes fatal cases that frequently go unrecorded by epidemiological systems. Our recent studies in Brazil documented excess mortality rates from chikungunya far surpassing those officially reported, with mortality rates up to 60 times higher than recorded by standard surveillance systems?Freitas et al., 2024?. Additionally, studies like those by Mavalankar et al. (2008) in India and Beesoon et al. (2008) in Mauritius underscore the elevated mortality associated with chikungunya during epidemic outbreaks, further reinforcing this critical gap in mortality surveillance.<br /> This growing evidence highlights the critical need for increased investment in molecular diagnostics, integrated surveillance, and more comprehensive mortality tracking for chikungunya. These measures are essential for aligning public health responses with the true impact of the disease and ensuring the full scope of chikungunya’s burden is addressed.
Thank you for advancing this essential conversation. Through improved surveillance and research collaboration, we can work toward effective strategies to mitigate the severe impact of chikungunya globally.
Best regards,
Dr. André Ricardo Ribas Freitas<br /> Faculty of Medicine, São Leopoldo Mandic, Campinas-SP, Brasil
Freitas ARR, et al. Excess Mortality Associated with the 2023 Chikungunya Epidemic in Minas Gerais, Brazil. Front Trop Dis. 2024. doi: 10.3389/fitd.2024.1466207.
Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. Increased mortality rate associated with chikungunya epidemic, Ahmedabad, India. Emerg Infect Dis. 2008 Mar;14(3):412-5. doi: 10.3201/eid1403.070720. PMID: 18325255; PMCID: PMC2570824.
Beesoon S, Funkhouser E, Kotea N, Spielman A, Robich RM. Chikungunya fever, Mauritius, 2006. Emerg Infect Dis. 2008 Feb;14(2):337-8. doi: 10.3201/eid1402.071024. PMID: 18258136; PMCID: PMC2630048.
Manimunda SP, Mavalankar D, Bandyopadhyay T, Sugunan AP. Chikungunya epidemic-related mortality. Epidemiol Infect. 2011 Sep;139(9):1410-2. doi: 10.1017/S0950268810002542. Epub 2010 Nov 15. PMID: 21073766.
Freitas ARR, Donalisio MR, Alarcón-Elbal PM. Excess Mortality and Causes Associated with Chikungunya, Puerto Rico, 2014-2015. Emerg Infect Dis. 2018 Dec;24(12):2352-2355. doi: 10.3201/eid2412.170639. Epub 2018 Dec 17. PMID: 30277456; PMCID: PMC6256393.
Freitas ARR, Gérardin P, Kassar L, Donalisio MR. Excess deaths associated with the 2014 chikungunya epidemic in Jamaica. Pathog Glob Health. 2019 Feb;113(1):27-31. doi: 10.1080/20477724.2019.1574111. Epub 2019 Feb 4. PMID: 30714498; PMCID: PMC6427614.
On 2024-12-03 21:03:36, user xPeer wrote:
Courtesy review from xPeerd.com
This manuscript introduces DeepEnsembleEncodeNet (DEEN), an innovative polygenic risk score (PRS) model integrating autoencoders and fully connected neural networks (FCNNs) to address limitations of existing PRS methods. By disentangling dimensionality reduction and predictive modeling, DEEN enables the capture of both linear and non-linear SNP effects, improving prediction accuracy and risk stratification for binary (e.g., hypertension, type 2 diabetes) and continuous traits (e.g., BMI, cholesterol). Evaluation using UK Biobank and All of Us datasets highlights superior performance over established methods. While conceptually and methodologically compelling, areas such as interpretability, generalizability across diverse populations, and computational efficiency warrant further refinement.
Major Revisions<br /> 1. Interpretability and Practicality<br /> Black-Box Concerns: The complexity of the DEEN model limits its interpretability compared to simpler PRS methods like Lasso or PRSice. While the manuscript acknowledges this limitation, incorporating efforts to visualize model predictions (e.g., feature importance maps or SNP clustering analysis) would enhance its usability (Section: Discussion, p.16).<br /> Clinical Translation: The manuscript emphasizes the potential of DEEN for clinical utility but lacks discussion on the challenges of implementing deep learning models in healthcare. Addressing regulatory barriers and clinician engagement would add value (Section: Discussion, p.17).<br /> 2. Population Generalizability<br /> Demographic Bias: Both datasets used (UK Biobank, All of Us) consist predominantly of European-ancestry individuals. This limits the model's applicability to global populations. Expanding the discussion on efforts to improve DEEN’s cross-ancestry generalizability is essential (Section: Results, p.11).<br /> Validation Across Diverse Cohorts: While DEEN is validated on two datasets, additional external validations across non-European populations would strengthen claims of generalizability and reliability.<br /> 3. Comparative Analyses<br /> Missing Baseline Methods: Although DEEN is compared with multiple PRS methods, inclusion of additional machine learning benchmarks (e.g., gradient boosting models, convolutional neural networks for SNP effects) would better contextualize DEEN’s advantages (Section: Results, p.8).<br /> Risk Stratification Assessment: The risk stratification results are promising but need more rigorous evaluation metrics beyond odds ratios, such as net reclassification improvement (NRI) or integrated discrimination improvement (IDI).<br /> 4. Computational Efficiency<br /> Resource Requirements: DEEN’s reliance on high-performance computing resources (e.g., GPU usage) is noted but not sufficiently quantified. Providing benchmarks of computational costs and runtime against alternative methods is crucial for practical implementation (Section: Methods, p.19).<br /> Optimization: While grid search was used for hyperparameter tuning, exploring automated optimization frameworks (e.g., Bayesian optimization) could reduce computational overhead.<br /> 5. Data Filtering and Variant Selection<br /> Potential Bias from Variant Filtering: The preselection of SNPs based on p-values may exclude rare variants or those with small effects. A sensitivity analysis on SNP filtering thresholds would clarify the robustness of DEEN’s predictive power (Section: Methods, p.20).<br /> Minor Revisions<br /> 1. Typos and Formatting<br /> Figure Legends: Some figures (e.g., Figure 5) lack clear explanations of axes and statistical methods.<br /> Grammar: Line 124: Replace "similarly drive CRC progression" with "similarly drive progression."<br /> 2. AI Content Analysis<br /> Estimated AI-Generated Content: ~20-25%.<br /> Implications: Repetitive phrasing in methodological descriptions and literature summaries suggests potential AI assistance. While the technical content appears valid, manual rephrasing can enhance originality and scientific depth.<br /> 3. Statistical Reporting<br /> Insufficient Confidence Intervals: Odds ratio enrichment results lack 95% confidence intervals in several places, undermining statistical rigor (Section: Results, p.9).<br /> Inconsistent Metric Definitions: Terms like “improved R²” and “higher AUC” are used loosely. Precise numerical values and effect size comparisons would improve clarity.<br /> 4. Terminology Consistency<br /> Key terms like "dimensionality reduction" and "risk stratification" should be consistently defined and applied across sections to avoid ambiguity.<br /> Recommendations<br /> Enhance Model Interpretability:
Integrate explainability tools (e.g., SHAP values, visualization of autoencoder layers) to clarify how SNPs influence predictions.<br /> Discuss the potential for hybrid models balancing interpretability and performance.<br /> Address Demographic Bias:
Validate DEEN using datasets from underrepresented populations (e.g., African, Asian ancestries).<br /> Incorporate transfer learning techniques to enhance generalizability.<br /> Benchmarking and Evaluation:
Compare DEEN against additional advanced machine learning models for PRS.<br /> Introduce advanced evaluation metrics like NRI and IDI to strengthen claims.<br /> Refine Computational Analysis:
Provide detailed resource utilization benchmarks.<br /> Explore alternative hyperparameter optimization methods to improve training efficiency.<br /> Expand Data Analysis:
Perform a sensitivity analysis on variant filtering thresholds.<br /> Investigate the inclusion of rare variants to improve model robustness.
On 2024-12-20 20:46:20, user Jakub wrote:
You have stated: "We performed targeted metabolomics to quantify the absolute abundance of known uremic toxins, including (...) 4-ethylphenyl sulfate (4-EPS) (...) in plasma of this cohort. As expected, CKD and PAD+CKD groups had significantly higher levels of all these uremic toxins (Figure 3A)." Unfortunately, Figure 3A does not provide data on 4-ethylphenyl sulfate. May you add data on this solute?
On 2025-01-10 21:50:46, user Harold Bien wrote:
Fascinating article. Given that each individual VOC in Fig 1 appears to have significant overlap between each group and wide distributions, it would be interesting to learn how the various machine learning algorithms used each VOC and the resulting model. Could the authors provide more information on the ML algorithms used, how it was trained, and how the ROCs were constructed?
On 2025-03-17 20:22:07, user Adrian Barnett wrote:
A well planned and executed paper that highlights a serious and likely common issue in research practice.
On 2025-04-11 20:29:22, user Scott Olesen wrote:
Now published: https://doi.org/10.1016/j.vaccine.2025.127109
On 2025-10-09 02:52:59, user sid moose wrote:
I can’t tell, not a scientists here.. but did they test for whether or not the participants had the flue before the start of the study?
On 2025-10-15 20:49:15, user jpirruccello wrote:
This has been published; please see https://pubmed.ncbi.nlm.nih.gov/37019578/
On 2022-05-24 20:25:23, user Carol Taccetta, MD, FCAP wrote:
If a subject was still on immunotherapy at time of "recovery," the outcome of the adverse event cannot be considered as "resolved." It will also be important to follow these subject for relapse after therapy discontinuation, as immune-mediated conditions can sometimes relapse months, even years, after immunotherapy discontinuation.
On 2022-07-10 23:39:20, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint.
I consider the topics raised by this study to be important and interesting.
However, I have some comments and questions:
1) I agree that confirmation bias can be a contributing factor. However, I think true limitations in utility are also important. So, I am not sure if I completely agree with the statement "When results were not consistent with participant’s personal or family history, many participants found reasons to dismiss or discredit these results. This indicates a role for confirmation bias in responses to [self-initiated] PRS." For example, I might really want to understand the genetic basis for a disease, but the percent heritability explained by the PRS may be low and I could therefore be disappointed with the usefulness of a PRS due to a discordant result.
I have a blog post where I share my impute.me scores (along with others):
https://cdwscience.blogspot.com/2019/12/prs-results-from-my-genomics-data.html
I don't know if I would exactly say my response was "negative," but I certainly got the impression the PRS that I saw may have limited utility. In that sense, my view of the method was not positive, even if it did not evoke a strong emotional "negative" response.
Within that blog post, “ulcerative colitis” would be an example where there were different PRS for the same disease but very different percentiles (for the same SNP chip). So, I would consider that an example of the reaction that is described being due to something other than confirmation bias.
2) Did the interviewers respond when there were possible points of misunderstanding during the interview process?
It was acknowledged as a limitation in the discussion: "the researchers did not have access to participant’s PRS results and were unable to evaluate people’s understanding of their results".
However, it seems like that could be important. For example, there is a quote "Unfortunately, I do regret getting a PRS… I would have rather not known. I like uncertainty". Assuming that there were appropriate limitations to communicate, I believe a response from the interviewer might cause that quote to no longer reflect the subject’s opinion.
In general, there appears to be a noticeable emphasis on mental health in the article. My opinion is that this is an area where limitations are particularly important. If it helps, I think there are some additional details in this blog post for the book Blueprint.
In terms of my own impute.me results, I thought the "anxiety" PRS seemed reasonable (to the best of my ability to assess that). However, I also thought changes in conditions over time were important, and I thought there was potential for misuse.
3a) I think it is a minor point, but I don't remember receiving an invite to join a Zoom meeting for a discussion about my impute.me results.
I hope that I was one of the 209 candidates, but I was not sure if I could confirm that. I also noticed mention of categories like “medium” or “low” for one quote referencing a z-score of 2.5, but I only saw the continuous score distribution in the screenshots from my blog post.
3b) Perhaps more importantly, I tried to go back to sign in to check if I missed something.
In the Folkersen et al. 2020 paper, the link provided is for https://www.impute.me/. However, that link currently re-directs to a Nucleus website (https://mynucleus.com/).
Can you please provide some more information about the re-direction of the impute.me link?
For example, I submitted an e-mail to register on the new website, but I don't think I can see my earlier results anymore?
Additionally, I was confused when I couldn’t find the GitHub code provided with that paper: https://github.com/lassefolkersen/impute-me
4) Finally, but I don't think either of the 2 models that I see ("dismissed medical concerns" and "medical distrust") are a great description for myself. I think something like "curiosity" and "critical assessment" would be more appropriate for myself.
For example, I wouldn't say I distrust the healthcare system or medical research broadly, but I do think feedback and engagement is important. Thus, when I encounter problems, I submit reports to FDA MedWatch. Likewise, I contribute data/experience to projects like PatientsLikeMe.
Thanks Again,<br /> Charles
On 2022-07-11 04:26:35, user E Hansen wrote:
It would be useful if the authors could clarify if "unvaccinated" means "never injected", or if this group also includes subjects not yet defined as vaccinated, but who has received a vaccine within the last week/14 days. <br /> The same goes for the vaccinated group; does it include everyone who received a shot from the day injected, or only those who have passed the first 14 days after injection and then being consideres "vaccinated"? This was not entirely clear to me, anyway. <br /> Thank you
On 2022-07-29 09:25:01, user Dr. D. Miyazawa MD wrote:
Please also refer to previous studies.
Hypothesis that hepatitis of unknown cause in children is caused by adeno-associated virus type 2 (08 May 2022)<br /> https://www.bmj.com/content...
Daisuke Miyazawa. Possible mechanisms for the hypothesis that acute hepatitis of unknown origin in children is caused by adeno-associated virus type 2. Authorea. May 16, 2022.<br /> DOI: 10.22541/au.165271065.53550386/v2
On 2022-08-09 02:04:53, user Roel Ceballos wrote:
This is already published. The link to the publication is this https://mjst.ustp.edu.ph/in...
On 2022-08-09 12:40:13, user PhillyPharmaBoy wrote:
The authors conducted a thorough evaluation of the impact of ivermectin on SARS-CoV-2 clearance. On the surface their results differ from those of Krolewiecki, et al. (below). In a post hoc analysis these investigators found that ivermectin accelerated viral decay when drug concentration (4 hr) exceeded 160 ng/ml. It would be useful for the PLATCOV Group to mention this study and discuss potential reason(s) for the discrepancy.
On 2022-08-14 18:37:59, user Jason wrote:
Interesting manuscript, can see how it can easily be used in practice looking at the predictive variables chosen
On 2022-08-17 07:08:46, user Partho Sen wrote:
This paper is now published in iScience, Cell Press. Here is the link of the journal pre-proof. https://www.sciencedirect.c...
On 2022-08-29 13:47:58, user Titos wrote:
This was published in NEJM as https://www.nejm.org/doi/fu...
On 2022-09-14 16:05:23, user Roy Miller wrote:
There may be more evidence for this immunity than previously thought, Since Queen Elizabeth's death on September 8, 2022 the half-dozen British Royals have mingled with countless people, shaking hands, touching random surfaces, and breathing air all over the Scotland, England, and northern Ireland. I assume the Royals all have had their COVID injections and I have to assume that they have come in contact with the COVID virus on numerous occasions. Current thinking would lead me to assume the crowds gathered to see them would make transmission of the virus more likely.
So why hasn't COVID spread like wildfire over the grieving population? Perhaps it is too soon to tell. But since COVID symptoms appear 2 to 14 days after infection, at least a large uptick in the COVID infections should be noticeable by now But no such event has occurred according to the British Press. So I have to conclude that there are additional factors to consider such as the one postulated by this article.
PS: I have no medical training although my job involves helping the medical community take advantage of high technology.
On 2022-09-22 09:16:27, user Wera Pustlauk wrote:
Dear authors,
thanks for the valuable effort to set up a new assay for the determination of PPi.
Regarding table III samples remained somewhat vague to me. Clarification in the table header including the unit of the determined PPi might be helpful. Calculation of the standard deviation in addition to the average would make the data more roboust and would establish a more substantial link to the variability discussed in the paragraph before. Moreover, time differences in the addition of EDTA to the CTAD tubes as discussed in the text should be clearly stated for each sample in the table III as well.
In addition, a hands on protocol (stored in a repository or as supplement) allowing the direct usage of the assay based on the optimized procedure would make the usage more accessible.
Best regards,<br /> Wera Pustlauk
On 2020-04-24 13:43:14, user Anand wrote:
Congratulations for leading the systematic review team. Great effort highlighting long term outcomes and rehabilitation needs.
On 2020-04-24 15:03:58, user Iba net wrote:
I am Masum Billa from Bangladesh. Now situation going to volcano speed, we don't know what will be happen in the next but our government cannot control all of us meanwhile how is it possible to serve them. We are trying our best to serve them in camp. If anybody wants research or visit Rohinga Camp. Please contact me billaasia@gmail.com
On 2020-04-24 16:08:53, user Rajendra Kings Rayudoo wrote:
To<br /> Prof.Dankmar Boehning<br /> I'm.from india and IAM f glad to hear a realistic approach to realtime infectious cases including asymptomatic and presymptomatic.and interested to know how it works<br /> Please explain how can I calculate the cases in india through your model .<br /> Regards <br /> Rajendra
On 2020-04-25 22:54:50, user wbgrant wrote:
This open access article supports the present modeling study and should be cited in the final version:<br /> Low Temperature and Low UV Indexes Correlated with Peaks of Influenza Virus Activity in Northern Europe during 2010?2018.<br /> Ianevski A, Zusinaite E, Shtaida N, Kallio-Kokko H, Valkonen M, Kantele A, Telling K, Lutsar I, Letjuka P, Metelitsa N, Oksenych V, Dumpis U, Vitkauskiene A, Stašaitis K, Öhrmalm C, Bondeson K, Bergqvist A, Cox RJ, Tenson T, Merits A, Kainov DE.<br /> Viruses. 2019 Mar 1;11(3). pii: E207. doi: 10.3390/v11030207.<br /> http://www.mdpi.com/resolve...
On 2020-04-25 23:09:56, user wbgrant wrote:
One more publication in support<br /> Environmental predictors of seasonal influenza epidemics across temperate and tropical climates.<br /> Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, Viboud C.<br /> PLoS Pathog. 2013 Mar;9(3):e1003194. doi: 10.1371/journal.ppat.1003194. Epub 2013 Mar 7. Erratum in: PLoS Pathog. 2013 Nov;9(11). doi:10.1371/annotation/df689228-603f-4a40-bfbf-a38b13f88147.
On 2020-04-27 04:52:01, user Krishna Undela wrote:
It is the first information on knowledge and beliefs of general public of India on COVID-19. In this article we can understand the false beliefs / myths circulating among the general public of India about transmission of novel coronavirus and prevention and treatment of COVID-19.
On 2020-04-27 10:34:04, user Elena Sharova wrote:
They don't randomize health care workers to find an effect in comparable conditions (hospital, patients management, patient burden). Jan21 to Feb23 - incubation period before pneumonia detection is approximately 1-2 weeks. So how to compare pneumonias in the 1st week in control group with abcence in test group starting interferon - regurding the 1st week test group DO NOT infect a 1-2 week ago, not during the research. Are these groups equal?
On 2020-04-27 16:44:44, user Dr. Amy wrote:
Obesity increases the density and upregulates ACE2 receptors. I couldn't figure out how women were protected given the higher incidence of obesity, but a Japanese researcher in this area shared this with me (which I do not fully understand.) "ACE2 is not only an entry receptor for the SARS-CoV-2 virus, but also protects against the pathogenic effects of RAS and the ACE/AngII/AT1R axis. I believe the balance between ACE/AngII/AT1R axis and ACE2/Ang1-7/MasR axis is important. Higher expression of ACE2 in old female rats than male represents protective effects. That's why women would have lower morbidity and mortality. This is my speculation."
On 2020-04-27 19:52:19, user eldhose poulose wrote:
You mentioned that you colllected the airline data, I see only airline data for the year 2017, February. can you provide the latest data? Also can you give more details on the data that you used for the study?
On 2020-04-27 21:36:26, user Marc Bevand wrote:
Page 2: NYDOC should be NYDOH
On 2020-04-27 23:05:18, user Jink wrote:
Hope the higher Ct's are due to low yield from filters. Having a synthetic standard/control would have justified the Ct numbers. Think about it if expanding the sample size.
On 2020-04-30 02:42:13, user Tyler Chen wrote:
I appreciate the authors’ urgency in addressing SARS-CoV-2 decontamination for reuse of N95 filtering facepiece respirators (FFRs). In the spirit of that urgency and health impacts, I note two concerns with the current preprint that could accidentally cause confusion: (1) The paper claims N95 filtration is preserved after microwave-generated steam, whereas the test listed in the methods was a TSI quantitative fit test, which is primarily designed to test fit, not necessarily filtration. (2) The paper’s claim of a universally accessible N95 decontamination protocol may accidentally overstate the N95 models for which this protocol is verified. N95 models vary widely in their construction and resistance to steam heat, so any models other than the one used in this experiment will likely require thorough testing before this method is applied.
I would suggest that the authors make the following changes:<br /> (1) Clarify whether or not filtration is verified at larger particle sizes and charge (e.g. 0.26 microns, uncharged). If filtration is not yet verified at larger particle sizes, this test may be important to verify N95 performance following microwave steam treatment.<br /> To provide some background: The TSI 8026 Particle Generator generates 0.04 micron particles [1] that are intended to be readily filtered by the N95, and are assumed to only enter the N95 through gaps in the face seal and not through the mask material itself [2]. It is possible for N95 FFRs to pass quantitative fit tests while still failing filtration tests at different particle sizes--one study “observed [protection factors] <100 even for subjects who passed fit testing (fit factor > 100)” [3]. Therefore, fit testing using the 8026 particle generator does not imply that N95 filtration is necessarily preserved at larger particle sizes which are most relevant for filtration effectiveness in the SARS-CoV-2 pandemic, especially given the fact that the decontamination treatment may shift what particle size is most penetrating for the N95. Recent non-peer-reviewed research shows N95 material suffering a decrease in filtration of 0.26 micron particles after 4-5 cycles of 10min stovetop steam treatment [4], though it is unclear from this manuscript if the MGS treatment did not reach this limit, or that the limit was not observed due to the different particle size used. Therefore, testing for quantitative fit should perhaps be supplemented by filtration tests at larger particle diameters (whether from this study or by citing others), especially when a decontamination process is involved such as steam heat that has an unknown effect on the most penetrating particle size for the N95. Given the potential for widespread implementation of this protocol it seems important that this point be clarified.
(2) Secondly, it may be important to notify readers that the performance of the N95 model in this paper likely cannot be generalized to all N95 models without further testing. N95 models vary widely in construction and resistance to steam, and each model should be individually verified to maintain both fit and filtration by this protocol before use. This is supported by the fact that N95 models can vary widely with respect to the most penetrating particle size, and each country may only have access to certain N95 models [3,4,5]. Furthermore, there is literature evidence that the mask performance in response to steam and heat also varies across N95 models (see the table of results in Appendix B https://www.n95decon.org/fi... "https://www.n95decon.org/files/heat-humidity-technical-report)"). Therefore, it is important to clarify in the text that this method is not yet universally-validated -- N95 fit has only been verified for the 3M 1860 molded N95 in particular, and other models are likely to have significantly different behavior. Independent verification of both fit and filtration may be needed for other N95 models.
On 2020-04-30 04:49:44, user Aaron wrote:
It would be a useful addition to show the % positive of tests performed in the same range as Fig 1C. If a greater proportion of tests are coming back positive following April 7, that could suggest that testing is occurring more and more frequently among those most likely to be positive. This is problematic as it can obscure mild cases that may not appear to be positives. In general, this information would help contextualize what types of cases are being captured by these data in Wisconsin, mostly severe cases, or mild and severe cases alike.