On 2021-12-18 02:12:44, user Peachyjenniekag21 wrote:

The main concern i have over the implications of this report are how this could impact conditions and protocols of prison inmates and populations. An unfortunate reflex seems to be rigid and stringent focus on isolation efforts as opposed to abundant supply of safe and effective treatment in congregate settings...

On 2021-11-29 10:58:19, user Andy Bloch wrote:

This study had just 13 unvaccinated participants with no known prior SARS-CoV-2 infections. To say that it was "underpowered" is an understatement. It's incorrect to conclude "we found no statistically significant difference." Add this paper to the long list of articles that mistakenly interpret statistical significance. The authors and reviewers should read this comment: Scientists rise up against statistical significance.

On 2021-12-23 21:35:13, user Dan Ostermayer wrote:

is the negative VE for 91-150 days a typo?

On 2022-01-12 11:43:46, user kdrl nakle wrote:

There is nothing in this paper worth beyond what is already expected. The numerical predictions will likely be erroneous. I have no idea why would anybody want to write the stuff like this that wil be outdated in two weeks time.

On 2022-01-14 18:11:36, user Maxwell Neal wrote:

A revised version of this article has been published by the journal npj Vaccines and is available at https://doi.org/10.1038/s41...

On 2021-10-16 13:53:34, user Sam Smith wrote:

Thanks for the great study, but when will you publish results what happens if one takes Sputnik light as a booster? I am only interested in boosters that give >90% protection against delta, because in Israel 3 doses of Pfizer gives >90% protection.

On 2021-10-20 08:13:57, user ClearSkys wrote:

"...the vaccination coverage rate is inversely correlated to the mutation frequency of the SARS-CoV-2 delta variant"

Correlation != causation

Motives are questionable especially when the authors then go on to recommend the public health policy based solely on the correlation.

On 2021-10-29 12:26:39, user Yehonatan Knoll wrote:

Good study of a bad question. <br /> Ten months into the vaxx campaign, why is there no similar, comprehensive study following vaxxed and unvaxed, *starting with the date of vaccination rather than that of infection*. This is the only pertinent question, now that a biannual booster is required of the vaxxed.

On 2021-10-29 17:15:41, user kdrl nakle wrote:

This is a very troubling report as we really need to differentiate "regular" deaths from the ones that are consequence of (and caused by) vaccination. This report is vague on that and it is going to be used against vaccination, no doubt.

On 2021-10-30 13:29:02, user ??? wrote:

Currently under revision.

On 2021-11-07 19:02:49, user Sook Wah Yee wrote:

I see quite a few metabolites that I was interested previously found by Long T. et al. metabolomic GWAS (performed in >97% female) (https://doi.org/10.1038/ng.... "https://doi.org/10.1038/ng.3809)") not listed as significant in this male only mGWAS. This is interesting and wonder if there are gender specific mGWAS and whether comparison have been done?

On 2021-11-14 13:53:40, user Marc Middleton wrote:

I don't even have to read the whole (not yet peer-reviewed and thus questionable) article to see that the conclusion, which anti-lockdownists like to draw from it, is faulty. It's already stated in the abstract that "efficient infection surveillance and voluntary compliance make full lockdowns unnecessary". People of the studied population obiously had enough common sense to contrain their contacts, which OF COURSE reduces viral spreading without the need for lockdowns! But as we have seen in several countries, not all people are as smart as the Danish...

On 2021-11-15 05:09:26, user John Davies wrote:

Might be good to make it extra clear in plain English that these are background rates - not actual vaccine side effects, as they appeared to me at first glance.

Other lay people might use these data to perpetrate the antivax argument.

On 2021-11-16 02:36:21, user Peter Renzland wrote:

The last sentence in the "Results" seems difficult to reconcile with the first sentence in the "Conclusions":

We observed no difference in the LoS for patients not admitted to ICU, nor odds of in-hospital death between vaccinated and unvaccinated patients.<br /> vs.<br /> Vaccinated patients hospitalised with COVID-19 in Norway have a shorter LoS and lower odds of ICU admission than unvaccinated patients.

On 2021-11-16 17:30:31, user Marcelo Sauaf wrote:

Authors using ONLY the term "faster" about the viral cleareance while this "faster" meant mere 2 DAYS less than unvaccinated evidentiate their POLITICAL bias on the subject. Why don't they QUANTIFY in the conclusion the "faster" was mere 2 days less than unvaxxed - AND that the contagious phase (PCR ct = 25) is tipically up to 9 days ??

On 2021-11-16 17:54:49, user C D wrote:

Why do we keep thinking herd immunity can be achieved for every strain? Isn't it normal to have new strains that people aren't immune to yearly? Iran's covid cases have currently plummeted, what happens if they stay there? Does that mean herd immunity has been achieved?

On 2021-11-17 05:34:19, user V Deepak Bamola wrote:

This preprint is under review at 'Experimental Biology and Medicine'<br /> @medrxivpreprint:Effect of Bacillus coagulans Unique IS-2 in Inflammatory Bowel Disease (IBD): A Randomized Controlled Trial https://t.co/hH32BrOdYT #medRxiv

On 2021-11-17 10:29:38, user David P. Herrmann wrote:

This article was peer-reviewed and published in the journal Hearing Research.<br /> https://doi.org/10.1016/j.h...

On 2021-11-19 20:32:12, user Corky Gardner wrote:

https://www.bloomberg.com/n...

On 2021-11-20 09:36:30, user Amador Goodridge wrote:

Great ongoing work of Amanda et al bringing to the light of scientific evidence the dramatic situation of migrants. While looking forward findings and results of this study, hope this warning help Panama together with other agencies continue to reinforce POC,<br /> & clinical diagnosis as well as on-site treatment strategy in order to assure the public health. Congrats!

On 2021-11-20 16:38:50, user Jeffrey Chan wrote:

This study has been published on Frontiers of Cardiovascular Medicine: http://dx.doi.org/10.3389/f...

On 2021-11-22 18:02:48, user Timeisrelative wrote:

This is an excellent paper. I have a few minor comments related to the word choice and clarity that I hope are helpful to you.

1)The uses of the words "rate" and "rate of change" are problematic in this context. I think it would be more clear to use different words. A "rate" usually describes how much of something happens over a specified unit of time. So the "rates of change in antibody titres during 3-6 months" might be about 10%/per month. Your metric is defined as:

rate of change = [(Ab titre 6 months after the 2nd dose - Ab<br /> titre 3 months after the 2nd dose [12]) / Ab titre 3 months after the 2nd dose] × 100 (%)

I believe this metric would be better described as simply the "change" or "percentage change" instead of the "rate of change" since it doesn't have a unit of time in it's denominator. This phrase "rate of change" occurs at many times throughout the paper and I believe they all should be replaced with "change" or "percentage change".

2) I was confused by the meaning of this line near the end of the results section:

because the Ab titres 3–6 months after vaccination were significantly higher in women than in men.

If I correctly assumed your intention, I think this line could be written more clearly as: "because the Ab titres were significantly higher in women than in men at both 3 months and 6 months after vaccination"

3) I think it would be helpful to specify in the table headings and in the chart axes labels whether the measured titres were 3 months or 6 months post vaccination. This information is in the paper and the caption of the figures, but it would be clearer if, for example, the headings of tables 1 and 2 were "Ab titre at 6 months, median (IQR), U/mL" and the x-axis label of figure 2b was changed similarly.

On 2021-11-24 00:22:39, user Nik Kolb wrote:

Could you please double check if the German vaccination data in ECDC are handled correctly for your calculations? The burden is unexpectedly high.<br /> It might be that the lack of more detailed age groups than 3 categories (<18 years, 18-64, 65+) resulted in a wrong attribution of the vaccine coverage. I could not find a method how you "interpolated" the vaccine coverage by age group, but Supplementary Figure S1 suggests that it does not really reflect the true vaccine coverage in each age group. While the true coverage sadly is unknown in Germany, a telephone survey among german speaking participants conducted by RKI given some hint about the true coverage: https://www.rki.de/DE/Conte...

On 2021-11-25 15:30:42, user kdrl nakle wrote:

When you write nonsense like this:<br /> ***<br /> The rate of detected reinfection after two doses of vaccine was 1.35 (95% CI 1.02 to 1.78) times higher in those vaccinated before first infection than in those unvaccinated at first infection.<br /> ***<br /> in your abstract then I know it is not worth reading any further.

On 2021-11-29 13:08:12, user TheBigWakaWaka wrote:

There's something that needs explanation.

In table S2, the raw <br /> ratios of unvaccinated cases over unvaccinated person-time (45% vs 55%, <br /> single dose vaccinated cases over single dose person-time (24% vs 19%), <br /> and double dose vaccinated cases over double dose person-time (30% vs <br /> 26%) are pretty close.

Nevertheless, the Cox coefficients indicate<br /> a strong difference. This means that very strong confounding effects <br /> are at play here: this would need commenting. Usually such a strong <br /> difference between "corrected" effects and raw effects indicates a <br /> weakness in the study, that should at least be commented.Based <br /> upon the raw ratios one would think there's no effect of extra <br /> vaccination ; based upon the Cox coefficients, there's a very strong <br /> effect.

On 2021-11-29 13:59:27, user Samer wrote:

The article is published in Frontiers in Immunology https://www.frontiersin.org...<br /> https://doi.org/10.3389/fim...

On 2021-12-03 01:31:09, user Alex Johnson wrote:

This analysis did not address infection after vaccination, which we know is happening with Omicron. I'd like to see the rate of reinfection compared with the rate of breakthrough infection, before I get too excited about reinfection.

On 2023-01-02 11:42:16, user Lance wrote:

It seems that the authors indulged in the pharma-friendly practice of starting the clock on exposure 7 days post-exposure:

"Individuals were considered bivalent vaccinated 7 days after receipt of a single dose of the bivalent COVID-19 vaccine... Curves for the non-vaccinated state were based on data while the bivalent vaccination status of subjects remained “non-vaccinated”. Curves for the bivalent vaccinated state were based on data from the date the bivalent vaccination status changed to “vaccinated”. "

This is particularly egregious given the potential for these vaccines to increase infection risk in the period immediately following vaccination. What little VE is reported here for the bivalent could itself be an illusion, disappearing upon proper treatment of the data.

On 2021-12-04 00:21:53, user TaShelby wrote:

These results are important and are consistent with findings in “Quantitative SARS-CoV-2 viral-load curves in paired saliva and nasal swabs inform appropriate respiratory sampling site and analytical test sensitivity required for earliest viral detection.” Doi:10.1101/2021.04.02.21254771. See https://www.medrxiv.org/con...

On 2021-05-28 18:07:40, user Craig Austin wrote:

Nobody wears masks properly, except professional staff in a clinical setting , nobody. Viruses didn't change sizes, mask' s pore size didn't change only human behavior changed.

On 2020-12-08 13:43:42, user Iveta Nagyova wrote:

Might be interested to read also our article published in The BMJ Opinion „Could Slovakia’s mass testing programme work in England?“ https://blogs.bmj.com/bmj/2...

On 2021-01-21 18:33:05, user Calogero wrote:

Slovak people were forced to participate to the testing under threat of losing their jobs. One months after testing we were and now still are among the countries with higher deaths rate pro capite in the world. People had to wait per hours outside in severe november weather to be tested and after that wait inside for the results risking to be infected. During the weeks after testing the number of daily pcr tests was significantly lowered, that is reason why there were less new covid cases after testing. And despite whole scientific and medical community is contrary to the wide-testing, it is to be repeated next week, same conditions, not tested not allowed to go to the work, risking unexcused absence standing on the words of minister of labour, without any financial compensation. Unbelievable but true. (sorry for my english)

On 2021-01-23 13:21:08, user Dušan wrote:

"All authors declare that they have no conflicts of interest"

Have a look at Jarcuška's organisation Euromedpro which is sponsored by GSK and Pfizer.

On 2021-09-14 13:39:06, user Henri van Werkhoven wrote:

Dear colleagues,

With interest did we read this manuscript which fueled a lively discussion during our journal club of the department of infectious diseases epidemiology at the University Medical Center Utrecht. The authors address a relevant research question. If there is a substantial difference in the risk of SARS-CoV-2 infections between previously infected and vaccinated individuals – as suggested - this may have consequences for social distancing, testing recommendations, and for projections of the impact of vaccination on future COVID-19 trends. However, we have several concerns regarding generalizability, selection bias, information bias, and confounding that we would like to address. We focus our discussion on model 1: the comparison of the fully vaccinated non-infected group (group 1) to the infected non-vaccinated group (group 2).

In regard to generalizability:<br /> - Due to the matching process, only 4% of the available data is used (i.e. for model 1 only 32430/736559) and as a consequence the study population is fairly younger (with expectedly less comorbidity) than the source population (i.e. vaccinated individuals, infected individuals). Therefore, the study population may not be representative of this source population which severely limits the external validity of results for all vaccinated/infected people.<br /> - Naturally, subjects who died due to previous SARS-CoV-2 infection were not included in the study. Yet, without information on morbidity and mortality and contribution to the spread of SARS-CoV-2 from the primary infection, the results of the study are not informative for the question whether people without previous SARS-CoV-2 infection should be vaccinated or await natural infection. <br /> - All three study groups – vaccinated or infected at baseline (28th of February) – were established upon future information (no infection, no additional vaccination after June 1, 2021), which severely limits the use of the results for today’s decision making.

In regard to selection bias:<br /> - People with a SARS-CoV-2 infection between February 28, 2021 and June 1, 2021, or those who received a first (infected group) or third vaccine (vaccinated group) between February 28, 2021 and August 14, 2021 were excluded from this study. Thus the study population of group 2 consists of previously infected people that do not take the opportunity to receive a booster vaccine, which may well be the less vulnerable people with a lower baseline risk of getting infected/hospitalized. This would bias the estimate in favor of the infected group.<br /> - Similarly, though at a smaller scale, people who died from COVID were not included in the analysis. This decreases the vulnerability of the infected group for secondary infections and/or hospitalization. This too would bias the estimate in favor of the infected group.

In regard to information bias:<br /> - A difference in willingness to test between the vaccinated and previously infected group can result in biased estimates. Vaccinated people may be more on guard in regard to COVID-19 symptoms (especially if they adhere less to regulations because they are vaccinated) and will be tested more frequently. This can bias the estimate, again in favor of the infected group. However, this form of bias should not have affected the outcome hospitalization due to COVID-19, for which differences had the same direction. Yet, the number of those endpoints was low, limiting statistical power.

In regard to confounding:<br /> - The authors acknowledge absence of information about health behavior, such as social distancing and masking. If the vaccinated group would adhere less to these preventive measures due to a sense of safety, this would also bias the estimates in favor of the infected group.<br /> - A potential important aspect is the young average age (36 years) of the study population. As they were all fully vaccinated before February 28th, we thought that a large proportion may have been health care workers, who have a higher chance of exposure to SARS-CoV-2, and thus infection after vaccination. This would also bias the estimate in favor of the infected group.

We have scrutinized the paper in search of the fatal flaw; the one major methodological limitation that could explain the extreme effect in favor of the infected group, as reported. We conclude that it is not there, as we don’t think that any of the above biases can explain all of the effect. However, we did found several weaknesses that each have the potential to yield a modest bias, all in the same direction. Five modest biases may yield a large effect estimate. We, therefore, consider the question whether natural immunity provides better protection than full vaccination with Pfizer/BioNTech’s COVID vaccine remains unanswered.

The authors (Annemarijn de Boer, Valentijn Schweitzer, Marc Bonten and Henri van Werkhoven, all at University Medical Center Utrecht) acknowledge all other journal club participants for their time dedicated to discussing the paper.

On 2020-12-27 02:14:14, user valley_nomad wrote:

What is the definition of mortality rate in this study? The numbers seem to be way too high if it is CFR (case fatality rate).

On 2021-12-21 21:03:38, user Mike B wrote:

Fantastic early news on boosting to increase circulating antibodies to provide Omicron protection. I hope we see a matching case study to correlate clinical data. Although the author declared the limitations regarding waning, it is critical to determine the waning pattern of boosted response.<br /> Taking "likely to be similar" as a starting point, the data appears to show significant loss of circulating antibodies 6 months post vaccination. This a critcal clinical issue in the USA because the high number of elderly/institutional vaxxed early in 2021 and subsequently boosted in Aug/Sept timeframe to enhance protection against Delta. For this highly risk population, boosted protection may already have significantly waned leaving less than expected protection just as Omicron begins to dominate. Without data on waning attached, the study may set false expectations of protection and open questions on continued booster use. <br /> One way to ameliorate the issue is to extend the study, collect samples pre and post a 4th dose at 50 and 100 micrograms. Thus will settle discussion and improve application towards clinical use.

On 2020-07-31 13:04:08, user Anne Wyllie wrote:

Since the OGD-510 has been shown to be detrimental to SARS-CoV-2 RNA,how does the buffer in the 610 compare?

On 2021-08-18 15:53:47, user K Meijer wrote:

Does this assume the previously infected peeson still has antibodies left? What about someone who had Covid Beta variant middle December 2020, tested positive for IgG antibodies late in January with lab test, but showed hardly any antibodies remaining with rapid antibody test early August 2021?

On 2021-08-19 13:20:53, user Libby wrote:

Thank you for this important study.

On 2020-07-04 15:08:27, user Robert van Dijk wrote:

Interesting paper! I have a few questions/comments. <br /> - is the model really just a ResNet-50 with the final classification layer fine tuned? Sounds amazing haha! <br /> - if you’re looking to apply the model in real clinical practice I think it’s good to think about how it would fit in the workflow. I think it’s already great that it does not output a diagnosis, but actually the step before it. Transparancy is very important especially in the clinic, so I could still expect that they want the model to explain it’s own decision as well. Does it allow for highlighting (using bounding boxes) the cells it has identified? <br /> - from what I have learned sensitivity is often more important than specificity in a clinical setting, but that differs of course per specialisation. So perhaps fine tuning towards that may be beneficial<br /> - great that you mention limitations of the model. Think that’s going to be essential especially with regard to specific cell types.

On 2021-01-17 01:45:40, user Oguzhan Alagoz wrote:

AN updated version of this paper is published by Annals of Internal Medicine:<br /> https://www.acpjournals.org...

Full updated citation is:<br /> Alagoz, O., Sethi, A. K., Patterson, B. W., Churpek, M., & Safdar, N. Effect of Timing of and Adherence to Social Distancing Measures on COVID-19 Burden in the United States: A Simulation Modeling Approach. Annals of internal medicine, M20-4096.

On 2020-08-14 13:42:47, user Nikita Michaels wrote:

From the paper: "Because the amount of virus present in the samples was low and thus unsuitable for common next-generation sequencing approaches, Sanger sequencing based on a gene-walking approach with over-lapping primers was used to obtain the virus<br /> sequence." Probably any air sample would have led to the same results given the "right" primers. But they did not use another sample of "non-contaminated" air to perform the same test so the results are without any value. Terrible, how these studies without peer review end up influencing public regulations.

On 2020-08-14 15:40:32, user Ricky Turgeon PharmD wrote:

This article has generated some discussion on Twitter, including a thread where I provide some comments. https://twitter.com/Ricky_T...

In particular, I hope that the authors can revise and/or provide responses to address the following concerns:<br /> 1. Please provide the rationale for selecting July 31 as the date for interim analysis. Please also provide details regarding this interim analyses, including pre-specified stopping rules, who had access to the data. Although this manuscript is labeled as a "preliminary report", it would be valuable for the authors to explicitly state whether this trial is ongoing, and whether any changes to the conduct of the trial were made based on this interim analysis.

1. In version 1 of the article on this site, the Methods section had a sentence that stated "No concealment mechanism was implemented". This was subsequently removed in version 2 yesterday. Please clarify what is meant by this. Did the authors mean to imply that allocation concealment was not performed, or was this an erroneous statement intended to describe the unblinded nature of the study? Please also describe the process for treatment allocation and how allocation concealment was maintained.

2. The authors describe a change in the primary outcome in terms of timing of CRP measurements. However, I note that the clinicaltrials.gov summary of this trial previously had an entirely different outcome as the primary outcome, with CRP only described as an exploratory/tertiary outcome. The authors should describe the timing and rationale for switching the outcome from a clinical one (need for supplemental oxygen in the first 15 days post-randomization) to the inflammatory biomarker CRP.

3. Despite changing the timing of CRP measurements, data on this modified primary outcome of CRP was missing in a large proportion of patients at day 5, and in the majority of patients at day 8. Further details should be provided regarding the reason for missing data, how this was handled in their analyses, and how this should temper conclusions.

4. Finally, performing an interim analysis and disseminating their results in the midst of an open-label trial with subjective endpoints can pose challenges to maintaining impartiality. The authors should describe how they will mitigate potential allocation, performance, detection and attrition bias during the remainder of the trial.

I hope that the authors will seriously consider these comments.

Sincerely,<br /> - Ricky Turgeon

On 2020-08-17 22:56:26, user Irene Tosetti wrote:

identical to the French test (already for sale in Europe) called Easycov, a saliva rt-lamp. Good job.

On 2021-09-27 21:43:38, user Xem wrote:

Where does it say why it was withdrawn?

On 2020-07-15 11:28:56, user One bird one cup wrote:

The CDC quotes a current best estimate for planning purposes as .0065. I'm not sure if that's just for the US. The above study is the only study they list that contributes to that estimate. I'm sure I'm missing something here but I just don't understand the numbers. I will look closer, though.

On 2020-08-25 08:12:24, user Bart Rijnders wrote:

The analysis is done with days after diagnosis (I presume the positive PCR this is) and not days since start of symptoms and the most important variable. This means that the diagnosis can thus be made somewhere between >14 days preceding hospital admission (if the test was done by a GP of testing venue) but can also be several days after hospital admission (e.g. when the first PCR is false negative but the second is positive)

Important bias may happen when patients who get tested easily / earlier on in the disease course while still outside the hospital also can get hospitalized easier (e.g. good health insurance). These patients will be overrepresented in the "treatment within 3 days after diagnosis" group. I do not see how this was (and can) be accounted for.

The study should therefore analyse the treatment effect in function of symptom duration at time of plasma transfusion as well and how this relates to a possible therapeutic effect of plasma. Hope this will be possible

On 2021-02-09 05:10:52, user David Brewer wrote:

What honey was used in the HNS study?

On 2020-12-15 23:58:58, user Samantha Braun wrote:

What was used as the placebo?

On 2021-02-09 09:47:38, user Alex wrote:

Hi guys, interesting paper. I’m curious as to how you justified a change in well-being pre-during when baseline data was collected during the peak?

On 2021-10-05 13:12:28, user Benjamin Paradis wrote:

Interesting. Are we seeing similar results with studies that use other methodologies than self-reporting?

On 2020-09-07 03:47:09, user Stephen D wrote:

Your conclusion is faulty. Note that nothing in your data implies that anything should be made "mandatory". Your modeling might imply that if everyone wore a mask 24/7, the effect would be to reduce the probability of a certain increase in infections. But it cannot in principle have any implication as regards legal responses by governments. This is not science. Your conclusion is framed using political and ethical concepts implied in the term "mandatory" that are in principle not amenable to science, and cannot be inferred from data or models.

On 2020-09-09 11:10:26, user Andrew Broadbent wrote:

Draft comment by – T Andrew Broadbent CES Economic & Social Research info@ces.org.uk

Overview<br /> This important paper claims to transcend the large number of ‘conventional’ epidemiological ‘SEIR’ models (Susceptible, Exposed, Infectious, or Recovered) of the current pandemic. Its fascination lies in its attempt to ‘compare SEIR models of immune status’ and derive results more directly from the data available.<br /> It uses ‘Bayesian inference’ on data from 10 countries from 25 Jan to 20 June 2020 to estimate the daily proportion of people in each country who are (i) not exposed to infection(ii) not susceptible even though exposed (iii) not infectious even when susceptible. These sub-populations are what the authors call ‘dark matter’. It concludes that many more of the population are ‘effectively immune’ than generally understood, and so the second wave can be indefinitely postponed or suppressed without successive lockdowns as concluded from some of the conventional models.<br /> The immediate issues and queries seem to be :<br /> (i) Suppression is said to depend on an effective Track and trace system, as with some ‘conventional’ models. The practical policy implications are thus not very different from studies which suggest maintaining restrictions until infection is very low, so as to enable managing with track and trace, without needing to reimpose universal lockdown.<br /> (ii) The proportion of the population isolating/shielding dominates the results (eg Germany) . This is social behaviour and government policy, not ‘dark matter’ in the way susceptibility and infectibility may be, being more biologically determined.<br /> (iii) The parameters in the model are estimated within limits determined from external sources This may include the predominant ‘effective population’ parameter – the population who are not shielding.<br /> (iv) ‘Effective herd immunity’ is a somewhat troubling term – given overtones of ‘let the old die’ in some policy discussions of ‘herd immunity’.<br /> (v) It claims to incorporate all the different data collection biases in different countries, such as testing people with or without infection. Would it be worth including countries with early success in suppressing infection – Taiwan, S Korea, China, New Zealand etc?<br /> ‘Effective’ herd immunity?<br /> It references other studies which also look at ‘heterogeneity’ of the population, where the first wave either kills or makes immune the more susceptible population – so that a second wave necessarily involves a less susceptible population and will tend to be lower than the first wave, other things being equal.<br /> It concludes that ‘effective herd immunity’ following the first wave of infection is much higher than suggested by the proportion of people who have been infected and recovered and may now be immune – ‘seroprevalence’ . This is now in the range 5-7% in the UK.<br /> The term ‘herd immunity’ prompts wariness following the UK government’s early discussions which were interpreted as contemplating 60-80% of the population becoming infected with 500K-1 million deaths. ‘Culling the old and infirm’ was one interpretation. The paper concludes that having less than 20% of the population infected and recovered could be enough to dampen a second wave.<br /> The second wave - a ten-fold reduction in infection and death?<br /> A main claim of the paper is that the second wave could be postponed indefinitely, or if not, have a factor of 10 fewer infections and deaths than predicted by some SEIR models, (deaths peaking at 30-100 per day in the UK, compared with 1000 per day at the peak of the first wave) .<br /> But this projection, has in common with the conventional models, a heavy reliance on an effective FTTIS (‘Find, Test, Trace, Isolate, Support’) system in order to isolate those infected or exposed to infection. But the paper suggests that only 25% efficacy of FTTIS is needed, compared to the present official target of 80%.(?)<br /> Dark matter – very high?<br /> ‘Dark matter’ seems a very high proportion of the population. From one illustration (figure 2) dark matter results in under 20% of the population being infected. Almost 50% of the total population are not exposed (shielding/sequestered), so that only half the populations is ‘effective’ in the epidemic. Of those who are, 50% are not susceptible, and of those who are susceptible 50% are not infectious. <br /> The proportion of the total population which is non exposed (self isolating, shielding, sequestered) would seem to be very dependent on people’s behaviour and on government instructions, and thus on the social context and time lapse of the pandemic. The other components of ‘dark matter ‘ - susceptibility and infectibility – seem more biologically determined, not so subject to behaviour and social and policy context.<br /> Data – why not include countries with greater success in suppressing the first wave?<br /> Although the FTTIS is said to be enough to limit or suppress the second wave without a ‘lockdown’, the Bayesian inference was conducted on countries, many of whom who were in some kind of lockdown for at least part of the period. They are the 10 countries with high death rates.<br /> The data is from USA, UK, Canada, Spain, France, Italy, Belgium, Germany, Mexico, and Brazil . It would have been interesting to include countries which largely succeeded suppressing the virus in the first wave, with either very short sharp lockdowns, or early interventions of intense FTTIS, namely Taiwan, South Korea, China, Hong Kong, Singapore, New Zealand. <br /> Many model parameters are influenced from outside the model. (?)<br /> There ar 25 parameters listed in the model, and their levels and potential variation – are apparently influenced by external empirical studies outside the model, and are listed as ‘priors’. This apparently influences the final estimated parameters after the model has been run. (?)<br /> Parameters include the effective population, the probability of going out, social distancing threshold, critical care capacity threshold (per capita), Infection, proportion of non-infectious cases, effective number of contacts, effective number of contacts: work, transmission strength, infected period , infectious period , proportion of non infectious people etc. etc.<br /> Rich findings – country by country results – ‘effective population’ dominates?<br /> The paper suggests that only Spain, and Brazil don’t exhibit the heterogeneity embodied in the model – in that their whole population seems to participate in the epidemic – their ‘effective population’ is equal to the whole population, with almost no one isolating, or shielding.<br /> The country comparisons involve changing the input parameters, so as to eliminate each component of heterogeneity in turn. The parameters - effective population, non susceptibility, social distancing threshold, decreasing seropositivy are each removed in turn.<br /> Germany and Canada have by far the smallest proportion of ‘effective population’ due to their high levels of shielding – this seems to determines their relatively good performance and low level of deaths, - it would be useful to learn more about how far this parameter is set ‘prior’ to the model.<br /> There is much less variation in the proportion of the effective population susceptible to infection – from ~67% in Spain (operating on an effective population almost equal to the whole population) , to ~47% in Canada.<br /> Similarly there is low variation in the proportion of susceptible people who are non infectious – from ~60% in Canada and Italy and to ~45% in Germany, France , and USA .<br /> Some more details<br /> The claim is that the analysis can incorporate all kinds of real world fuzziness in the data - by modelling latent variables such as the bias towards testing people with or without infection or, the time-dependent capacity for testing. ‘Everything that matters —in terms of the latent (hidden) causes of the data—can be installed in the model, including lockdown, self-isolation and other processes that underwrite viral transmission’.<br /> This is a ‘LIST’ model with four factors (Location, Infection, Symptoms and Testing). It models the probability of people being in different states, and produces two outputs – positive cases, and deaths .<br /> The states in each factor are::<br /> Location – Home, Work , Hospital, Isolated, Removed<br /> Infection – Susceptible, Infected, Infectious, Sero negative, Seropositive<br /> Symptoms – Health, Symptoms, Severe, Deceased<br /> Testing – Untested, Waiting, Negative, Positive<br /> Each individual in the population has to be in one state, and only one state, within each of the four factors.

On 2020-09-13 19:32:19, user Qunfeng Dong wrote:

An updated version of this manuscript is now accepted for publication by Journal of the American Medical Informatics Association Open Access on Sep, 13, 2020.

On 2021-02-26 11:58:57, user Magda Bucholc wrote:

https://doi.org/10.1002/trc...

On 2021-08-30 11:33:43, user KTHPIO7 wrote:

Do the results account for the faulty data based on PCRs of >40 ct?

On 2021-02-27 06:50:50, user Suriati Jamalludin wrote:

good exploration. i'm digging the source that online learning during emergency response are facing the problem mental health disruption among student. therefore the learning continuity is not easy when student in a bad condition of mental health. how do i use the reference as a support. may i have the reference format this article for citation?

On 2021-02-28 16:23:44, user Larisa Tereshchenko wrote:

Now published in the Journal of Innovations in Cardiac Rhythm Management: https://www.innovationsincr...

On 2021-09-01 02:33:45, user Andrea Boggan wrote:

"Survival of the Flattest." This new variant will wait it's turn until Delta is through delivering its blow, and when Delta is done, it and the others waiting in the wings will step forward and compete for fuel.

On 2021-03-17 10:01:33, user Bernhard Brodowicz wrote:

As the aim of this study was to determine the prevalence of SARS-CoV-2 it is essential to define the parameters when a test is rated as SARS-CoV-2 positive or negative (as a qualitative analytical test result, ct-value cutoff, handling of different results for viral targets…). Neither the paper itself, nor in the supplementary data, gives an evidence about how positive and negative test results were delimited. Especially as different analytical setups were used, validation data of the different RT-qPCR setups should be reported, discussed and comparability should be shown. When reporting quantitative analytical results (viral loads in children and adults via ct-values), the method should be validated for its quantitative purpose (including standardization). Especially when using different analytical setups this is crucial to assure the reporting of valid and comparable results. Looking on ct-values given in Supplementary Figure 2b the results from Graz (first round), which used a FDA authorized and (also for pool samples qualitative) validated diagnostic kit, showed comparable results for both targeted genes (E and ORF1a/b) and suggests robust positive results for 9 samples in pupils. For other assays the human housekeeping gene RPP30 (RP2) was used as sample control. In clinical diagnosis it might be useful (specially to reduce the risk of false negatives) to also report test results as positive when only one viral target is detected or RPP30 (RP2) was absent, but only when covered by method validation results. However, in a study, where different analytical setups were used, it should be further investigated and discussed, when viral target N2 and ORF1b were report as positive results also with high ct-values (> 40) and in the absence of RPP30 (as it is suggested by Supplementary Figure 2b). This could question the validity of the analytical setup used in this study and is calling for the presentation of the validation parameters of the different RT-qPCR setups to interpret comparable results.

On 2021-07-27 12:56:21, user James Jarvie wrote:

This paper is referred to as evidence that vaccine is superior to naturally acquired immunity. However, the paper appears to me to suggest that vaccine is as-good-as naturally acquired immunity (using naturally acquired immunity as the benchmark).

On 2020-10-14 02:40:05, user Robert Stephens wrote:

Could it be that a more recent HCoV infection increases the likelihood of the dysfunctional 'back boost'. If such is the case then perhaps this partially explains the lower second wave CFRs seen in many European countries. Maybe the Sars-CoV-2 mitigating behaviours (distancing/ masks etc) have also reduced the incidence of HCoV infections in the preceding 6 months - thereby reducing the frequency and amplitude of the back boost.

Dr Robert Stephens MB BS FACD

On 2021-07-29 01:05:33, user Luke Zabotka wrote:

Article published: https://doi.org/10.1016/j.m...

On 2021-03-30 12:09:03, user jgas wrote:

Has there been furtheer follow-up beyond 72 hrs?<br /> This data would really help to clarify possible mechanism of serious adverse effects emerging with the roll-out of the Oxford adenovirus vector vaccine to frontline workers across Europe and the safety of use of these adenovirus vectors per se.

On 2021-07-31 00:05:48, user Arthur wrote:

Check these stats from results section.<br /> The percentages of white, black and Latino participants do not add up to 100%.

Please check before publishing , these are some things that makes a publication lose credibility.

Participants were 49% female, 82% White, 10% Black/African American, and 26% Hispanic/Latinx; median age was 51 years.

Then

Of vaccinated participants, 58% had >=2 months follow-up post-dose 2, 49% were female, 86% were White, 4.6% were Black/African American, and 12% were Hispanic/Latinx.

On 2021-04-09 12:49:29, user Francesco Pilolli wrote:

In spite of the difficulties encountered by other studies evaluating the efficacy of therapies in outpatients, this work describes an impressive statistically significant reduction in the hospitalization rate.<br /> This study reports a share of hospitalised patients in the “recommended” cohort (2,2%) similar to the one described in the placebo groups of the Pfitzer-Biontech (2,5% https://www.fda.gov/media/1... ) and Moderna (3,3% https://www.nejm.org/doi/fu... ) vaccine studies between symptomatic cases (these studies did not exclude hospitalised cases at the onset), but it describes an impressive 14,4% share of hospitalised cases in the “control” group.<br /> This rate is much higher than the one described in the placebo group in the Bamlanivimab and Etesevimab study in high-risk outpatients (7% https://www.fda.gov/media/1... ) and in the COLCORONA trial again in the placebo group of high-risk outpatients (5,8% https://www.medrxiv.org/con... ).<br /> It’s peculiar that this study (carried out on general population and excluding severe cases at onset) describes a hospitalization rate in the control group much higher than that observed in other studies in high-risk patients, considering also hospitalisation at onset.<br /> I think that the majority of the difference of the hospitalisation ratio between “recommended” and control group could be explained by the choice of selecting 88 out of 90 cases of the control group from people infected in the first wave in the province of Bergamo, one of the most severely hit zones in Italy.<br /> The control group required swab or serological positivity but the swab test capacity was limited in Italy during the first wave and it is very unlikely that all the symptomatic people underwent a serological test. During the first wave many symptomatic people were at home without having undergone any swab. The limited test capacity causes a high underestimation of paucisymptomatic and mild cases resulting in a high rate between hospitalisation and tested cases.<br /> In fact the Italian ratio between hospitalisation and tested cases from March to May 2020 (the same infection period of 88 out of 90 patients of the control cohort) was 36,4% compared to the 7,8% observed from October 2020 to January 2021 https://www.epicentro.iss.i...<br /> This difference was much higher for the province of Bergamo. Data about daily hospitalisation by province are not public but we know the cases ( https://lab24.ilsole24ore.c... and deaths ( https://www.istat.it/it/fil... ) by province until December 2020. While the ratio between Italian cases and deaths was 14,8% from March to May 2020, it falls to 2,2% from October to December, in the same periods the rate deaths/cases in the province of Bergamo was 23,4% (almost one patient with positive swab every four died in the first wave) and 1,5% (less than the Italian average).<br /> Therefore, it is very likely that the majority of the difference in the hospitalisation rate between the “recommended” (from the second wave) and the “control” cohort (from the first wave in the most hit zone in Italy) is explained by the different historical moments which were characterised by a large difference in test capacity and many symptomatic people at home without getting tested during the first wave.

On 2021-08-03 10:01:20, user Alan Yoshioka, PhD wrote:

There are several numerical discrepancies and questions about methods that should be resolved before any conclusions can be drawn from the study.

When was it decided to exclude patients whose RT-PCR results had a cycle threshold value >35 in the first two consecutive [tests]? When was it decided to adjust the Kaplan–Meier analysis for symptom onset?

Please reconcile the discrepancy between the "mild" in study title and the "mild to moderate" in the description of the mandate of the isolation hotels. The inclusion criteria do not appear to specify the severity of disease, which would apparently then depend on the admission criteria of the hotels.

In Table 1, stated percentages of patients who are male do not match raw numbers of 69/89 for all patients and 36/47 for ivermectin, respectively; instead (corresponding to females accounting for 21.6% in the abstract) 78.4% = 69/88, and 78.3% = 36/46.

The abstract says 16.8% were asymptomatic at baseline, which does not complement the 80.9% symptomatic in Table 1, nor the 69 symptomatic patients in Figure 3. Perhaps I am missing something, but it is not clear why 37 and 35 symptomatic patients in Table 1 do not match the numbers of subjects at risk, 36 and 33, on Day 0 in Figure 3.

Table 2 presents results from RT-PCR testing at days 4 to 10. Day 2 is said to have been added to the protocol along with Day 4, but no explanation is given for why data from Days 2, 12, and 14 are not also shown in the table.

I'm not a specialist in lab tests, but I'm afraid I am having trouble understanding the post hoc analysis based on a convenience sample of 16 samples on Day 0. Does Table S2 mean there were then 26 samples taken on Day 2?

I am mildly puzzled by the alignment of the dots in Figure 2: most appear to lie on a grid, but a few sets of points are slightly raised or lowered. Is this a normal occurrence?

On 2022-06-22 18:31:49, user Elisabeth Bik wrote:

I have serious concerns about the data integrity of this paper, in particular about Figures 2 and 3. Some constellations of data points in these images appear to be duplicated within or across panels, and the lowest/highest values of the X axis (which should be the same across the four panels within a figure) appear to unexpectedly vary. This paper has been published in Sleep Science in 2020, under DOI: 10.5935/1984-0063.20190133 and I have posted my detailed concerns on PubPeer at https://pubpeer.com/publica...<br /> The PubPeer entry lists other concerns about this paper as well, including concerns about the ethical approval process and the p values in Table 1, raised by PubPeer user 'Meliosma donnellsmithii'.

On 2021-11-15 13:29:28, user Gary Porter wrote:

How long does this reduction in transmissability last?

On 2024-02-26 17:17:08, user Ciarán McInerney wrote:

Please, justify why<br /> statistical significance of individual values in your omnibus PheWAS protocol<br /> warrants an indication of predictive performance? Firstly, looking at main<br /> effects in an omnibus assessment commits the Table 2 fallacy (doi: 10.1093/aje/kws412).<br /> Secondly, the p-value associated with an odds ratio is a statistic related to<br /> the validity of the parameter estimate in a hypothetical null world. It can and<br /> should only be used for making statements about the model used to estimate the<br /> parameter of interest (in your case, the odds ratio). It has nothing to do with<br /> the quantifying the association. Thirdly, why do you select features based on the<br /> p-value but not the magnitude or direction of the association statistic to<br /> which it refers? A feature with a very large magnitude might be clinically<br /> meaningful for many patients, regardless of how spread the distribution of that<br /> feature’s values are.

On 2024-04-09 15:52:16, user Tarachopoiós wrote:

This looks like an intersting analysis. One question was around the correlation of predictors to TTFT. So growth rate requires time-series data to be estimated did you account for the time taken to estimate growth rate? One option would have been to use a joint longituinal time to event model to account for immortal time bias. How did you account for immortal time bias in your analysis i.e. the fact that tumour growth rate isn't known at your time zero? Or is it known? That wasn't clear in the methods.

On 2024-04-27 19:47:53, user Rebecca L. Roop wrote:

Thank you for the work and dedication to create this article for publication. I suffered through TSW for 24 months after only using various classes of topical steroids for 12 months. That period of my life was absolute hell.

On 2024-04-28 13:06:50, user Gina Dee wrote:

I’m so happy and relieved to see research being done to better understand TSW. My daughter suffered through TSW starting at the age of 2 and it was a nightmare. We had to struggle through with minimal support from doctors. I hope this study and further studies help to lessen the occurrence and better treat the condition.

On 2024-05-01 03:00:07, user Bernadette wrote:

Firstly my thanks to each one of you. This paper gives me hope that diagnostic and treatment guidelines for ‘TSW’ can be developed. I have a H/O of 70+ years of skin problems. Having struggled for 4 years with skin rashes which present to me, and to a Sydney based GP with an interest in TSW, as being consistent with TSW, I have experienced the frustration of presenting to dermatologists who say TSW is not an ‘accepted’ skin condition even though I have experienced and photographed my red sleeve, elephant skin on my ankles and wrists, non stop oozing on my face, neck and ears, non stop skin flaking, hair loss, heat, pain and intense itching. I have experienced the isolation and depression too often associated with this condition. Recently I have focused on managing heavy staph and fungal concentrations and have seen significant improvements. So I’m hoping this paper will act as a catalyst for a comprehensive focus on TSW so that we, those affected, can access medical expertise without running the gauntlet of being dismissed and belittled. Again. Thank you.

On 2024-05-08 14:58:49, user mira wrote:

Thank you for carrying out this research. This preprint needs to be published so people and the medical world can finally stop gaslighting us and telling us it's "just eczema". I have been suffering for years with TSA and after quitting steroids my life has been terribly changed because of TSW. We are so overlooked, desperate for knowledge and solutions, we are a suffering community.

On 2024-05-09 14:39:59, user Ana Angel wrote:

Very important piece of research for the thousands of us suffering from this condition. More research is needed! <br /> I stopped all forms of steroids 4 years ago. I’m now much much better, but still flaring on my elbow creases. We need treatments to shorten these lo g recovery times

On 2024-08-13 07:20:46, user Christine Markwalter wrote:

The peer-reviewed version of this article can be found here: https://doi.org/10.1038/s41467-024-49080-9

On 2024-08-13 07:22:59, user Christine Markwalter wrote:

The peer-reviewed version of this article can be found here: https://doi.org/10.1371/journal.ppat.1011442

On 2024-08-15 15:39:08, user Vanessa Merker wrote:

The final version of this article has been peer-reviewed and published online.

Citation: Chou C, Wiredu SO, Von Imhof L, et al. Health services interventions to improve the quality of care in rare disease: a scoping review. Therapeutic Advances in Rare Disease. 2024;5. doi:10.1177/26330040241262810

Available open-access at: https://journals.sagepub.com/doi/10.1177/26330040241262810

On 2024-10-19 15:28:14, user Steve Laurie wrote:

Great work - congratulations. Let's hope WGS becomes standard of care in NICUs some day soon.

Just wanted to let you know that you have duplication of text in the Methods in the current version, lines 144-153 and 153-162.<br /> I also doubt that citation 67 at the end of the paragraph is the one you meant to cite.

On 2024-10-27 08:26:34, user Mohsen Ghanbari wrote:

This preprint has been published recently:

A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data.Genome Biology. 2024 Oc t 21;25(1):276. doi: 10.1186/s13059-024-03420-6.

On 2024-11-04 08:17:51, user Oren Miron wrote:

Short video summarizing the findings: https://www.youtube.com/watch?v=NFVkE_nwSEk

On 2024-12-03 21:07:38, user xPeer wrote:

Courtesy review from xPeerd.com

This manuscript investigates the genetic underpinnings of gene expression noise (variability in mRNA expression) and its contributions to complex trait variation. By leveraging single-cell transcriptomics from 1.23 million peripheral blood cells across 981 individuals, the study identifies expression noise quantitative trait loci (enQTLs) in seven immune cell types. Key findings include distinct enQTLs independent of traditional expression QTLs (eQTLs), with implications for hematopoietic traits and autoimmune diseases. This comprehensive analysis highlights gene expression noise as an overlooked molecular trait impacting genetic variation in complex traits.

Strengths include the integration of large-scale single-cell data, robust methodological frameworks, and a novel focus on noise QTLs. However, the work’s translational potential and certain mechanistic aspects require refinement.

Major Revisions<br /> 1. Mechanistic Depth<br /> Limited Exploration of Noise Regulation Mechanisms:

While the authors identify enQTLs enriched in chromatin marks (e.g., H3K27ac, H3K4me3), the functional pathways connecting these marks to noise modulation are underexplored (Section: Functional Enrichment, p.8). Including mechanistic validation, such as CRISPR perturbation experiments targeting key SNPs, would enhance understanding.<br /> The interplay between noise and transcriptional bursting models (e.g., initiation frequency vs. burst size) remains superficially addressed. Expanded quantitative modeling of burst kinetics could better explain noise-associated traits (Section: Discussion, p.9).<br /> Post-Transcriptional Contributions:

The discussion briefly mentions mRNA stability but does not evaluate post-transcriptional regulation’s role in noise. Experimental validation, such as ribosome profiling or RNA decay assays, could substantiate these claims.<br /> 2. Population Diversity and Generalizability<br /> Limited Ancestral Representation:

The cohort comprises Northern European ancestry individuals, limiting the generalizability of enQTL findings. Noise might vary due to ancestry-specific SNP frequencies or regulatory architectures (Section: Methods, p.3). Validation in diverse populations is critical for ensuring broad applicability.<br /> Cell-Type Specificity:

Some findings, such as HVGs shared across cell types (e.g., HLA genes), require validation in other tissues or disease models. Cell-specific functional assays could strengthen the biological relevance of these findings.<br /> 3. Statistical and Computational Robustness<br /> Unexplained Variance in enQTL Effects:

While enQTLs explain certain GWAS loci, the authors do not quantify the proportion of unexplained variance attributable to unaccounted mechanisms (Section: GWAS Colocalization, p.10). Comparative analysis with polygenic risk scores or partitioning heritability methods would contextualize enQTL contributions.<br /> Colocalization Analysis Limitations:

Colocalization methods prioritize high-probability overlaps (PP.H4 > 0.7), but alternative loci with moderate probabilities (e.g., PP.H4 > 0.5) might merit inclusion. Revisiting loci with expanded statistical thresholds could yield additional insights.<br /> 4. Functional Insights<br /> Overemphasis on Chromatin Features:

While the enQTL analysis emphasizes chromatin states, the link to noise-specific regulatory dynamics is unclear. Functional experiments, such as live-cell imaging of noise dynamics in specific chromatin contexts, would substantiate claims (Section: Functional Enrichment, p.8).<br /> Underexplored Relationship Between enQTLs and Disease:

The finding that autoimmune risk variants correlate with attenuated noise is intriguing but not mechanistically explained (Section: Discussion, p.9). Immune activation studies in enQTL-defined contexts could clarify whether lower noise promotes immune tolerance or other phenotypes.<br /> Minor Revisions<br /> 1. AI Content Analysis<br /> Estimated AI-Generated Content: ~15-20%.<br /> Stylistic Observations: Repetitive phrasing (e.g., “highlighting noise as an important mediator”) and predictable transitions suggest AI-assisted drafting in some sections.<br /> Epistemic Impact: Minimal; technical content is original, but editing for stylistic variation is recommended.<br /> 2. Figures and Data Presentation<br /> Figure Annotation:<br /> Figures (e.g., Figures 3-5) lack precise legends detailing axes, significance thresholds, and methodological descriptions.<br /> Supplemental figures require clearer integration into the narrative (e.g., referencing HVG enrichments in Figure S2).<br /> Data Accessibility:<br /> Raw data from single-cell noise calculations and SNP annotations should be made available as supplementary files for reproducibility.<br /> 3. Terminology Consistency<br /> Inconsistent Definitions:

Terms like “expression noise” and “transcriptional variability” are used interchangeably but should be clearly defined early in the manuscript.<br /> Confusing Use of Abbreviations:

HVG, enQTL, and eQTL acronyms require standardized introduction and consistent usage across sections.<br /> 4. Citations and References<br /> Key Omissions:<br /> Recent advances in single-cell variability analysis (e.g., newer methods beyond tensorQTL) are underrepresented. Including citations for innovative noise quantification approaches (e.g., scVI) would modernize the references.<br /> Recommendations<br /> Mechanistic Studies:

Employ experimental tools (e.g., CRISPRi/a, live-cell reporters) to validate enQTL roles in noise dynamics.<br /> Integrate transcriptional bursting models to elucidate enQTL regulatory mechanisms.<br /> Enhance Population Scope:

Expand cohort analysis to include non-European populations.<br /> Incorporate ancestry-aware computational models to assess demographic variability in enQTLs.<br /> Data Presentation Improvements:

Add supplemental raw data files for transparency.<br /> Expand figure annotations and connect supplemental content to main findings.<br /> Expand GWAS Interpretation:

Investigate enQTL roles in non-immune traits to broaden the study’s impact.<br /> Compare enQTL contributions with existing functional annotations (e.g., enhancers, transcription factor binding sites).

On 2024-12-11 16:30:22, user Andrew Hagen wrote:

This preprint has now been published in its final form as:

Hagen AC, Tracy BL and Stephens JA (2024)<br /> Altered neural recruitment during single and<br /> dual tasks in athletes with repeat concussion.<br /> Front. Hum. Neurosci. 18:1515514.<br /> doi: 10.3389/fnhum.2024.1515514

On 2024-12-15 08:46:43, user Ujváry István wrote:

Note the correct chemical name:<br /> bis(2,2,6,6-tetramethyl-4-piperidinyl) sebacate

(BTMPS is a piperidine derivative; it is not a pyridine derivative!)

On 2024-12-23 02:34:26, user IA Signore wrote:

Now published as Signore, I. A., Donoso, G., Bocchieri, P., Tobar-Calfucoy, E. A., Yáñez, C. E., Carvajal-Silva, L., ... & Colombo, A. (2024). The Chilean COVID-19 Genomics Network Biorepository: A Resource for Multi-Omics Studies of COVID-19 and Long COVID in a Latin American Population. Genes, 15(11), 1352.

On 2025-01-02 09:48:12, user Teresa Ramírez García wrote:

Dear Dr. Witt: We have read with great interest your article published in preprint format. In this article, an aspect that we consider confusing is mentioned in relation to our work [1]. We refer to the authors' assertion that “in the FCSRT only 4 words need to be learned in three learning trials, whereas the VLMT requires learning of 15 words in 5 trials” [2].

In this regard, we would like to point out that the FCRST requires you to effectively memorise 16 words spread across 3 trials, not just 4 words, as stated in the original paper by the author who developed the exam. Because it aligns with the original test scales by age and cognitive reserve of the patients in the Spanish population, this test can also be explained in the Neuronorma project, which we use in Spain. Since we discovered a baremation based on the Spanish population [3], this is also the reason why this test is typically utilised in Spain.

1.- Serrano-Castro PJ, Ramírez-García T, Cabezudo-Garcia P, Garcia-Martin G, De La Parra J. Effect of Cenobamate on Cognition in Patients with Drug-Resistant Epilepsy with Focal Onset Seizures: An Exploratory Study. CNS Drugs. 2024 Feb;38(2):141-151. doi: 10.1007/s40263-024-01063-6. Epub 2024 Jan 24. PMID: 38265735; PMCID: PMC10881647.

2.- Witt JA, Badr M, Surges R, von Wrede R, Helmstaedter C. Negative Impact of Cenobamate on Cognition: Dose-Dependent and Independent Effects medRxiv 2024.12.23.24319533; doi: https://doi.org/10.1101/2024.12.23.2431953

3.- Peña-Casanova J, Gramunt-Fombuena N, Quiñones-Ubeda S, et al. Spanish Multicenter Normative Studies (NEURONORMA Project): norms for the Rey-Osterrieth complex figure (copy and memory), and free and cued selective reminding test. Arch Clin Neuropsychol. 2009;24(4):371-393. doi:10.1093/arclin/acp041

Ramirez-Garcia T and Serrano-Castro PJ.

Hospital Regional Universitario de Málaga.

Instituto de Investigacion Biomedica de Málaga (IBIMA-Plataforma Bionand).

On 2025-01-16 06:08:40, user xPeer wrote:

Courtesy review from xPeerd.com

Summary:<br /> The manuscript titled "Typhinder: Rapid, low-cost colorimetric detection of Salmonella Typhi bacteriophages for environmental surveillance" presents a novel colorimetric assay designed to detect Salmonella Typhi (S. Typhi) bacteriophages in environmental water samples. This study primarily focuses on areas with poor sanitation infrastructure, including regions in Brazil, Côte d’Ivoire, Nepal, and Niger, demonstrating high sensitivity and specificity of the assay. The work indicates potential applications in public health surveillance, particularly in resource-limited settings, by providing a cost-efficient method (approximately $2.40 per sample) that does not require sophisticated equipment.

Potential Major Revisions:

1. Validation and Methodological Robustness:<br /> One key concern is the validation of the colorimetric assay only against the double agar overlay method. More comprehensive testing against additional molecular techniques like PCR/qPCR, which are considered gold standards for pathogen detection, is essential to determine the assay's accuracy and reliability under diverse environmental conditions. This gap was acknowledged in the discussion section.

2. Sample Diversity and Detection Limit:<br /> The study demonstrates that the detection limit is 28 PFU/mL, which although sensitive, may need further optimization to ensure applicability in environments with even lower pathogen concentrations. Additionally, the research did not provide adequate comparative data from different environmental contexts, such as varying water sources with potential inhibitors like antibiotics, which could affect assay reliability.

3. Comprehensive Data Analysis:<br /> The study's reliance on environmental surveillance data lacks integration with epidemiological data and molecular-based assessments of typhoid burden. Correlating phage detection with rates of clinical typhoid fever incidents would offer stronger evidence of the assay's utility in public health management. Future studies should aim to establish these correlations more explicitly.

Potential Minor Revisions:

Typographic and Grammatical Errors:<br /> 1. Page 2, Line 1: "particularly in low-resource settings with inadequate sanitation." - Repetition of the phrase "particularly in low-resource settings", consider rephrasing for clarity.<br /> 2. Page 6, Line 3: "require precise data on where typhoid is most prevalent, yet current surveillance methods are expensive and limited in scope..." - The sentence structure could be improved for readability.<br /> 3. Page 9, Line 5: "Antimicrobial resistance among S. Typhi strains poses serious challenges to effective treatment and may lead to higher mortality..." - Consider rephrasing for clarity.

Formatting Issues:<br /> The figures and tables should be better integrated into the text for improved readability. For example, citing Table 1 and Figure 2 explicitly within the corresponding discussion for context will aid readers' understanding.

AI Content Analysis:<br /> - Estimated AI-generated content: Given the extensive detail and specific nature of the subject, it is estimated that the manuscript has less than 5% AI-generated content.<br /> - Highlighted AI-detected sections: The introductory summary and some instances of repetitive phrasing suggest possible AI involvement.<br /> - Epistemic impact: Minimal as the core research contributions and data seem original and substantive.

Recommendations:

1. Enhanced Validation:<br /> Incorporate a broader range of validation techniques, particularly molecular methods like qPCR, to establish the assay's robustness across different environmental samples and contexts.

2. Addressing Limitations:<br /> Include detection methods for concurrent fecal contamination to provide contextual data, enhancing the reliability of typhoid phage detection results as environmental indicators.

3. Future Studies:<br /> Focus future research on correlating phage presence with clinical incidence of typhoid fever, and explore structural analysis of phage-host interactions. This will substantiate the assay's efficacy in public health interventions and policy-making.

Overall, the manuscript provides a promising tool for typhoid fever surveillance in low-resource settings, with significant public health implications. Addressing the detailed critiques will strengthen the manuscript and its potential impact.

On 2025-02-05 20:08:07, user Daniel Corcos wrote:

Gotzsche and Jorgensen claim to have found a high level of overdiagnosis after mammography screening. However, the method they use does not allow them to distinguish between cancers related to overdiagnosis and those caused by X-rays. Yet, when measuring the delay in the appearance of excess cancers, it becomes clear that, in addition to the excess corresponding to the lead time due to detection, there is a significant excess of delayed-onset cancers, which are therefore caused by X-rays ( https://www.biorxiv.org/content/10.1101/238527v1.full ; Corcos D & Bleyer, NEJM, 2020). These cancers explain the failure of screening at decreasing breast cancer mortality observed at 13 years by the authors.

On 2025-02-10 12:39:18, user MINGXIN LIU wrote:

This preprint has been published in International Journal of Medical Informatics and can be accessed at: " https://doi.org/10.1016/j.ijmedinf.2024.105673 ."

The title of the published version has been changed to "Evaluating the Effectiveness of advanced large language models in medical Knowledge: A Comparative study using Japanese national medical examination". Readers are encouraged to refer to the published version for the final peer-reviewed content.

On 2025-02-12 20:00:36, user Aron Troen wrote:

Review Part III

Results and Discussion<br /> Quantity of food trucked in: No source is cited for the figure of a pre-war baseline of 150-180 food-transporting trucks per day. This number is inconsistent with Israeli and UN sources. According to a document published in June by the Food Security Cluster, only 23% of UN recorded incoming goods to Gaza (not including fuel) before 7 October were food or food production inputs ( https://fscluster.org/sites/default/files/2024-06/Gaza%20imports%20and%20food%20availability%2015_may_V2%202.pdf) "https://fscluster.org/sites/default/files/2024-06/Gaza%20imports%20and%20food%20availability%2015_may_V2%202.pdf)") . If one is to rely on those UN statistics, the pre-war monthly average of trucks carrying food into Gaza was 2,288 (an average of approximately 100 trucks per working day in a normal month). Another UN source is the OCHA online Gaza crossings dashboard according to which during Jan-Sep 2023 a total 27,434 trucks carrying food entered Gaza, representing a monthly average of 3,048 trucks. <br /> The comparison in Figure 1 between the mean daily number of trucks for each week during the war with the "pre-war number of food-carrying trucks" per working day is highly misleading since it assumes that the number of working days remained steady. The distortion is significant because between 21 October and 5 May the crossings were open almost every day, as opposed to the 5-day work week in the period before the war. The following chart shows the monthly figures of UNRWA and COGAT compared to the monthly pre-war average of 2,288 trucks carrying food.

Compare it with Figure 1 from the article, which tells an entirely different story for the same period (blue columns represent trucks carrying food) in which is all but one week at the end of April the number of trucks carrying food was below the pre-war average:

Contribution of different food sources [to the northern and southern regions] (Table 1 & Figure 4)<br /> The result and discussion devote substantial attention to the relative distribution of food between the northern and southern regions. The governates designated as North and South Gaza are not explicitly defined. The only explanation for how the author determined the distribution of food deliveries between Northern and southern-central Gaza is as follows:<br /> "Until Israel re-opened the northern Erez and Erez West crossings, trucks had to leave south-central Gaza to resupply the north. We reconstructed the number of these trucks over time based on published information and data shared by WFP. As no data on content were available, we simulated their caloric equivalent by repeatedly sampling from the empirical distribution of calories per truck obtained from the UNRWA dataset (see below and Figure S1, Annex). The remaining trucked food was attributed to the south-central region."

The breakdown of that amount between northern and central-southern Gaza is based on an incomplete dataset (Commodities Received.xlsx) that appears to be missing the bulk of supplies by the private sector, appearing in the COGAT data ( https://gaza-aid-data.gov.il/main/) "https://gaza-aid-data.gov.il/main/)") , and which provided a significant share of supplies to the north. The dataset shows that during January and February 84 trucks were delivered to the north (according to the Logistics cluster). According to the same file, during March and April there only 20 private sector trucks delivered aid to the north. However, according to COGAT, deliveries to the north at that time were carried out mostly by the private sector, which are not fully covered by UN data. The flow of aid within Gaza and its regional distribution is difficult to ascertain. Media sources have provided conflicting reports from different sources. But they underscore the need to clarify precisely how the study assigned the regional food supply. For example, a story by the Associated Press from February 28 2024, reported that the UN had not been involved in aid deliveries to the North that month. According to one of COGAT's reports, during the first half of March they "facilitated over 150 aid trucks to the north" ( https://gaza-aid-data.gov.il/media/qtvbs5u0/humanitarian-situation-in-gaza-cogat-assessment-mar-15.pdf) "https://gaza-aid-data.gov.il/media/qtvbs5u0/humanitarian-situation-in-gaza-cogat-assessment-mar-15.pdf)") . In addition, COGAT claimed in a tweet from March 25 that UNRWA had not submitted a single request for delivering food to northern Gaza in six weeks ( https://x.com/cogatonline/status/1772316633605812511) "https://x.com/cogatonline/status/1772316633605812511)") . Thus, the methodology for determining the distribution of aid between northern and southern-central Gaza appears to be flawed since it almost entirely disregards aid deliveries by the private sector, which had a significant share of the total deliveries to the north during that period. Findings and conclusions that are contingent on this issue cannot be fully evaluated until this is corrected.

Main findings

The authors insinuate that the shortfall in the adequacy of food aid is solely due to intentional Israeli actions. For a subtle example of this the authors write that “Patterns in the diversity and caloric value of food trucked-in suggest that humanitarian actors may not have optimised the selection of what aid was allowed into Gaza.”. The food diversity findings suggest the humanitarian actors, who are responsible for deciding what is supplied to Gaza may not have optimized the selection of the aid. However, the use of the word “allowed” insinuates that the fault for this lies with Israel. The correct word should be “delivered”. Israel is responsible under international law for facilitating the entry of humanitarian aid. It is not responsible for selecting, procuring or delivering the aid. The fact that there was a considerable decline in food availability the first months of the war should not be surprising. Israel did not initiate the war, and should not be expected to have in place the logistics capacity for providing food to over 2 million conflict-affected people immediately after a strategic surprise attack. These major efforts, facilitated by the international community acting together with Israel, eventually yielded results as demonstrated by the study’s findings (eg. “a steep increase in food availability occurred from late April 2024, coinciding with the reopening of crossings into northern Gaza, and by June acute malnutrition prevalence appeared to be relatively low…”. [As noted above, “reopening” is a misleading term for the conversion of the Hamas-damaged Erez crossing from a pedestrian to a trucking terminal].

Similarly, one might ask why the Hamas failed to prepare for the needs of the Gazan civilian population under its governance, while it demonstrably prepared meticulously for the attack that was intended to provoke retaliation.

The authors seem intent to find Israel alone at fault, to encourage political pressure on Israel. They criticize “operations to deliver food via air or sea [as] cost-inefficient and a poor substitute for diplomatic pressure to merely reopen crossings”, stating in passing that “the 230M USD cost of the JLOTS operation [43] was higher than the entire humanitarian aid budget for the Central African Republic in 2024”. A back of the envelope calculation examining this assertion, and using WFP statements that their “emergency response [in Gaza] requires USD 740 million to provide support for up to 1.1 million people monthly” ( https://www.un.org/unispal/wp-content/uploads/2024/04/WFP-Palestine-Emergency-Response-External-Situation-Report-18-23-April-2024.pdf) "https://www.un.org/unispal/wp-content/uploads/2024/04/WFP-Palestine-Emergency-Response-External-Situation-Report-18-23-April-2024.pdf)") , shows that USD 740 per 1.1 persons monthly translates to 22.4 dollars per person per day. This means that the cost of the air-dropped food was only 29% higher than the delivery of land-based humanitarian food-aid. Thus, an equally plausible alternative interpretation of the resource expenditure might be that the air and sea operations, involving cooperation of USA, Jordanian, Israel and other Arab militaries to assist the Palestinian civilian population, could be considered a valuable attempt to circumvent the challenges to land-based humanitarian aid-operations during fierce fighting between Hamas and the IDF, as well as a means of exerting diplomatic pressure on the combatants. The policy implications and cost effectiveness of political pressure to increase food influx via land crossings are not obvious.

Comparing the resources allocated by the international community to the Palestinian population versus the long list of other pressing humanitarian crises, out of proper concern for emergency-affected civilian populations, is indeed a vexed question. Clearly, a critical and balanced discussion of this issue is beyond the scope of this paper. However, if one insists on raising this important question, one might also question the efficiency of the billions of dollars donated to Gaza over the past decade by the international community, including from UNRWA, and how the funds, which were intended for civil and humanitarian development, were misappropriated by Hamas for a massive military buildup to the attack including the construction of hundreds of kilometers of military tunnels and the stockpiling tens of thousands of rockets and launchers, embedding them in their civilian population ( https://www.wsj.com/world/middle-east/hamas-gaza-humanitarian-aid-diverted-cf356c48; https://govextra.gov.il/unrwa/unrwa/#:~:text=Update%206%2F8%2F24%3A,massacre%20are%20credible%20and%20true; https://www.nytimes.com/2024/12/08/world/middleeast/hamas-unrwa-schools.html?unlocked_article_code=1.f04.lcW3.n2kj8akEfM-M&smid=nytcore-ios-share&referringSource=articleShare; https://www.atlanticcouncil.org/blogs/new-atlanticist/how-to-reform-unrwa-to-improve-palestinian-lives-and-israeli-security/) "https://www.atlanticcouncil.org/blogs/new-atlanticist/how-to-reform-unrwa-to-improve-palestinian-lives-and-israeli-security/)") .

Limitations

The authors acknowledge several of the more obvious limitations and assumptions described above. However, they minimize or arbitrarily dismiss these weaknesses and proceed to make tendentious interpretations in support of their preferred policy implications. For example, they write that they relied heavily on a single UNRWA dataset “which appears highly complete and well-curated” without explaining how they make that subjective and unsupported assertion. The authors are demonstrably aware of the controversy and limitations of the data, yet they feign ignorance and avoid placing the data in the context of the known controversy writing that the data “may be biased by systematic under- or over-reporting UNKOWN TO US”. This knowingly downplays and misrepresents the CERTAIN under-reporting of UNRWA trucking data which the official disclaimer states clearly on the online dashboard and in the dataset that they provide for review: “We [UNRWA] are unable to provide comprehensive monitoring of cargo for the following reasons: i) safety and security concerns, which continue to prevent UN staff from maintaining constant presence at Kerem Shalom, therefore severely impacting our ability to cross-reference UN cargo, and record data from INGO, Red Cross and commercial trucks, and ii) delays and/or denials in approvals for UN to retrieve, count and move UN humanitarian aid from Kerem Shalom to other parts of the Gaza Strip, which mean that we are unable to fully verify all trucks which have transited the land crossings. We will resume presentation of comprehensive data once the situation at the crossing allows.” Similarly, the acknowledgement of “considerable uncertainty about population denominators” does not logically lead to the conclusion that this would “…have only marginally affected our estimates”.

Policy Implications

The conclusion of the article makes politicized recommendations that are disconnected from the findings. The authors’ recommendation to “reinstate UNRWA’s role as an independent and experienced on-the-field monitor” is unsupported, and the summary dismissal and evaluation of COGAT data as “not of sufficient quality to guide decision-making”, reflects bias rather than a balanced analysis. Considerations relating to the role that international actors can and should play is determined by far more complex factors that are the partial shipping data analyzed here.

The claim that Israel, “as the de facto occupying power”, did not ensure sufficient food availability to Gaza (while acknowledging the relatively short period of deficiency), vastly oversimplifies the complex dynamics of the conflict and the multifaceted factors affecting food availability. This claim appears intended to promote the use of the study as “evidence” supporting “forensic efforts” (in the courts) to prove allegations that “Israel deliberately has starved Gaza’s population”, presenting as fact a disputed interpretation of Israeli combat operations in Gaza as constituting occupation, and hence its obligations under international law, while ignoring weighty arguments to the contrary. This view also ignores corresponding obligations of Hamas as the governing power in Gaza, and the role of international humanitarian actors. The legal questions on this point are far beyond the scope of this review, but there is no basis in the data provided to make this claim – it is simply presented as an unsubstantiated assertion. In order to evaluate the morality, legitimacy or legality of the Israeli military strategy in response to the Hamas attacks and terror infrastructure, including its impact on food availability, it is necessary to examine and understand the strategy challenges in conditions of military asymmetry, the large-scale use of human shields to protect Hamas forces, and urban warfare as exist in Gaza. The authors of this article appear to be unaware of this central dimension in the issues they are claiming to address. Given the slanted narrative, the selective and biased use of data and their interpretation, and the far-reaching and unsupported conclusions, it is difficult to escape the impression that this study is aimed at providing a prosecution with ostensibly credible academic findings, rather than advancing open-ended research in support of humanitarian efforts.

Timely and reliable data are crucial to address the critical needs of the war-affected civilian population of Gaza. There is no doubt that data “on the civilian impacts of the war in Gaza”, and “situational awareness on food security in Gaza” are “important to inform appropriate humanitarian response”. It is also undoubtedly true that “humanitarian actors should review whether there is adequate coordination and technical expertise in place to ensure that what food is allowed into Gaza is both calorically efficient and diverse enough to maintain the best-possible diet, especially for population groups most vulnerable to malnutrition”. How a retrospective simulation of the food supply informs “situational awareness” is less obvious. Slanted, simplistic and politicized framing of the findings that ignore complexity, place the onus on Israel alone, and overlook the role of Hamas, the agency of Palestinian civil society, and the responsibility and obligations of the international community, do not advance scholarly discourse, nor will it strengthen the cooperation that is urgently needed to strengthen humanitarian efforts to benefit the civilians of Gaza.

On 2025-02-16 02:16:55, user Michael Pazianas, MD wrote:

Low BMD can be a common finding in both osteoporosis and renal osteodystrophy—two distinct histological diagnoses with distinct pathophysiology. While a low BMD and a T-score below -2.5 are often used to define osteoporosis, this finding does not necessarily indicate an osteoporotic etiology. Non-osteoporotic causes should be considered.

In this study, the authors included patients with CKD who were diagnosed with osteopenia or osteoporosis based solely on BMD measurements, rather than bone biopsy findings. However, low BMD in these patients could stem from other forms of renal osteodystrophy, such as adynamic bone disease or osteomalacia, rather than true osteoporosis.

"Given this premise, a more accurate and clinically relevant title might be: 'Low BMD Prevalence in Cardiovascular Kidney Metabolic Syndrome: Implications for Mortality.' The current title promotes an overly simplified approach that risks making the already challenging task of successfully managing these patients—particularly those with CKD—an even more distant prospect. This concern is especially relevant because antiresorptive therapies, commonly prescribed for osteoporosis, are contraindicated in adynamic bone disease, a pathology prevalent in CKD, as well as in osteomalacia."

On 2025-02-21 05:08:41, user Evan Stanbury wrote:

The paper refers to "a chronic debilitating condition after COVID-19 vaccination, often referred to as Post-Vaccination Syndrome" (which it calls PVS). This should not be confused with a common chronic debilitating condition after viral infection, often referred to as Post-Viral Syndrome (also PVS). This paper could confuse many, so it would be better to call the sick cohort something different from "PVS".

On 2025-02-22 17:25:17, user Shawn M wrote:

The study's questionnaire has significant design flaws. The main issue is how the questions are worded - they repeatedly ask about 'health conditions that you have had as a result of vaccine injury.' This phrasing assumes vaccines caused these health problems before even asking the question. It's like asking 'When did you stop stealing?' instead of 'Have you ever stolen anything?'<br /> This problematic wording can influence how people respond in two ways. First, it might lead people to automatically connect their health issues to vaccines without considering other possible causes. Second, by focusing only on vaccine-related problems, the questionnaire misses important information about people's overall health that could explain their symptoms.<br /> These issues make it difficult to trust the study's findings because we can't tell if the health problems reported were actually caused by vaccines or if they happened for other reasons that weren't explored.

On 2025-02-23 19:55:05, user Jan Páleš wrote:

Hello, I cant find any Ees formula in the reference 10...

On 2025-03-07 03:42:49, user mehrdad alemi wrote:

The COVID-19 pandemic posed unprecedented challenges for countries worldwide. Despite international sanctions, Iran managed to respond effectively to this crisis by relying on its domestic capacities.

Among the actions taken by Iranian scientists, researchers, and physicians:

1. Production of Domestic Vaccines: Iran became one of the countries producing COVID-19 vaccines by developing domestic vaccines such as Noora.

2. Expansion of Diagnostic and Treatment Capacity: The development of diagnostic kits, the increase in the number of equipped laboratories, and the production of medical equipment, including ventilators, contributed to better crisis management.

3. Healthcare System Management: The establishment of field hospitals, the strengthening of medical infrastructure, and the implementation of health restrictions at critical times played a significant role in reducing infection and mortality rates.

4. Research and Innovation: The publication of reputable scientific articles and the conduction of clinical studies on Iranian vaccines strengthened Iran’s scientific standing in this field.

On 2025-03-10 13:27:10, user Feng Gu wrote:

Hi, Please check the published version here: https://www.doi.org/10.1038/s41746-025-01501-9

On 2025-03-30 09:52:42, user Isatou Sarr wrote:

Over time, immunity from both vaccination and previous infection can decrease, leading to an increased risk of breakthrough infections. This phenomenon is particularly noticeable as the immune response fades, and the virus continues to evolve.This waning immunity presents a challenge for public health strategies that rely heavily on initial vaccination or infection-induced protection. Boosters become crucial in reinforcing the immune system and restoring protective antibody levels, especially for vulnerable populations such as the elderly or those with underlying health conditions. Moreover, the emergence of new variants, often with mutations that allow them to evade existing immunity, further complicates the picture. These variants can spread more easily and cause illness in individuals who were previously protected, necessitating ongoing adaptation of vaccines and preventative measures to keep pace with viral evolution. Continuous monitoring of variant spread, vaccine effectiveness, and the duration of immunity are essential for informed decision-making and effective mitigation strategies.

On 2025-04-09 12:24:29, user Breck wrote:

My colleague wants to know why you didn’t mention any testing for the flu. Did you test for the flu?

On 2025-04-10 22:33:05, user Will wrote:

My first comment should be: <br /> I noticed that Table 2 mentions Covid 19 under "Abbreviations" but in the actual table there is no Covid 19 variable. Could you clarify that please?

On 2025-06-04 21:03:24, user Meg McSorley wrote:

The unadjusted risk estimates are exactly the same as the adjusted, down to the confidence intervals and p-values?

Where is table 1, comparing baseline characteristics of the comparison groups (vaccinated and unvaccinated)? This would inform which covariates should be included in the model. This is actually the most important table because the vaccinated likely do have different characteristics than the unvaccinated that would affect the risk estimates.

How was influenza ascertained? Self-report? Employee Health testing? Why aren’t raw numbers reported?

Is there a reference for the Vaccine Efficacy calculation? Is there a statistical rationale for this calculation?

On 2025-04-23 20:04:44, user Maiwenn wrote:

Hi - this article is now available in the WHO Bulletin: https://pmc.ncbi.nlm.nih.gov/articles/PMC11978406/pdf/bullworldhealthorgan-103-4-BLT.24.292167.pdf <br /> DOI: 10.2471/BLT.24.292167

On 2025-04-30 07:16:59, user Frédéric Derbré wrote:

This preprint is now published on Cell Reports : https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00219-0?uuid=uuid%3A08f5e5ab-1371-444d-b8cf-ba64672613f3 Is it possible to inform the readers on Medrxiv ?

On 2025-05-13 14:50:33, user Jasmohan Bajaj wrote:

this is now published in Clinical and Translational Gastroenterology https://journals.lww.com/ctg/abstract/9900/a_national_survey_shows_a_dilemma_towards_sbp.397.aspx

On 2025-05-28 23:11:26, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( http://evoheal.github.io/) "http://evoheal.github.io/)") really enjoyed this paper. Here are our highlights.

Investment in ensemble forecasts resulted in better calibration and less variable predictions

Work bridged the gap between theory and policy; builds infrastructure for future data integration

Climate zone analysis balanced the desire for model generalizability, the need for sufficient historical data to characterize each zone, and the risks of unrealistically grouping diverse regions together.

Limitations of individual models including the provision of confidence intervals was honestly presented

On 2025-06-04 05:27:15, user Sira Jam Munira wrote:

This paper is now published in the “Journal of public health research” and the link-

https://doi.org/10.1177/22799036251337641

On 2025-06-04 17:34:59, user Sarah Jorgensen wrote:

Questions for the authors: <br /> From the results, 11 children started GAHT within 12 months of GnRHa initiation and another 20 within 12-24 months (total 31, 31/94 (33%)), yet in the discussion, "more than half of the participants had initiated gender-affirming hormones over the 24-month follow up period." Could the authors resolve this apparent discrepancy?

59 patients were assessed at 24 months. If 31 were not assessed because they started GAHT, there still appears to be 4 children unaccounted for. Were they lost to follow-up? What was their status at last assessment?

Details on psychiatric medications at baseline and initiated during follow-up would be of interest and could be considered for inclusion as time-varying covariates in models.

Given that 4-9 years have elapsed since GnRHa initiation, why was this analysis limited to 24 months follow-up? At the very least it would be of interest to know vital status and how many ultimately went on to receive GAHT versus desisted.

On 2025-06-07 15:03:33, user Nicolai Krekiehn wrote:

The git link does not work for me. I would like to test an use your model. Who is the contact?

On 2025-06-24 11:20:26, user Christopher Hickie wrote:

Could Tracy Beth Høeg please show evidence of reported affiliations with Sloan MIT and UCSF Emergency Medicine as I am not finding any listing for her for either.

On 2025-08-28 17:40:53, user gzuckier wrote:

Just a typo of some sort, I assume, but<br /> "-0.85 (95% Cl: [-0.48 --0.37]) for dose 3" can't be correct.

On a related note, however, I can't avoid a nagging suspicion of bias from the fact that some of the estimates used to support <br /> "concerning evidence of a higher-than-expected fetal loss rate" <br /> are not statistically significant is missing from the paper and must be intuited by the reader, as in <br /> "1.9 (95% CI: 0.39-3.42]) for dose 3";<br /> particularly when it's specifically noted with respect to estimates involving COVID infections <br /> "all the 95% CIs of the respective observed-to-expected differences included 0 (Table S8)."

The main findings, however, do not have this problem.

My humble suggestion is to include these other findings as "suggestive but not reaching statistical significance."

Or perhaps, since the results given for week 14 seem to be significant but are diluted by nonsignificant later results, that should be pointed out?

On 2025-06-26 00:25:38, user Pegah Khosravi wrote:

We are pleased to share that this preprint has now been peer-reviewed and published in Biological Methods and Protocols. The final version of the article is available at: https://doi.org/10.1093/biomethods/bpaf032

On 2025-07-01 18:15:13, user Yogev wrote:

This manuscript is now published in a peer reviewed journal, and can be found in: https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1593221/full

On 2025-07-14 22:06:03, user Pedro Rocha wrote:

This work has been published in its entirety in a peer-reviewed journal. The published version is available at: https://journal.unoeste.br/index.php/cv/article/view/5016/3709 , doi: 10.5747/cv.2024.v16.v379

On 2025-07-15 16:59:57, user zlmark wrote:

The preprint relies on survey data that shows clear evidence of sampling protocol violations, including improper household selection, failure to screen for residency, and geographic deviations confirmed by GPS data.

In several cases, inconsistencies appear to have been retroactively edited to align with protocol.

Data from two teams—Gaza9 and Gaza3—raise particular concern, with demographic anomalies and mortality figures that suggest possible manipulation or fabrication.

These issues compromise the representativeness of the sample and call into question the reliability of the resulting estimates.

A full analysis of these issues is available here: <br /> https://markzlochin.substack.com/p/design-vs-execution-in-gaza-mortality

On 2025-07-29 02:44:01, user Qiwen Yang wrote:

The article has been published at: https://doi.org/10.1093/ehjqcco/qcaf064

On 2025-08-08 09:31:31, user David Fournier wrote:

Dear authors, commenting on the recent Nat. comm. release, did you actually studied the direct connection in the samples from encode ad brains you studied between histone modifications and actual expansions? i dont see a plot of histone modifications versus repeat expansions directly plotted from the same individual. Did you check that? Thanks.

On 2025-08-11 18:12:37, user Aluoch Naomi Roosevelt wrote:

A good paper

On 2025-12-04 07:03:00, user Paul Olale wrote:

Very insightful with very viable best practices worth emulating in future cholera response expeditions as well.

On 2025-08-15 08:47:16, user Jouke- Jan Hottenga wrote:

Nice paper!

Population stratification severly influences HWE, which is known as the Wahlund effect. Hence, also the much larger SNP removal in mixed populations.

Imputation and phasing software in general assume HWE. Which might a) be a reason to apply HWE beforehand and b) will thus likely result in all markers also being in HWE post imputation.

With kind regards, Jouke

On 2025-08-20 13:21:16, user Anthony Clanton wrote:

Thank you for the opportunity to comment on your well-constructed manuscript. We appreciate the authors’ efforts to advance the STARD-IONM framework and promote rigor in reporting diagnostic accuracy for IONM. We would like to highlight the importance of clarifying partial recovery scenarios within the STARD-IONM framework. While the manuscript provides valuable discussion on reversible and irreversible signal changes, it does not explicitly define or address partial recovery—cases in which IONM signals improve but do not return to baseline. These common scenarios are clinically relevant and may reflect incomplete injury or partial mitigation. To improve clarity and consistency, we suggest considering the following additions:

• Clearly define “partial recovery” and distinguish it from full recovery and persistent deterioration.<br /> • Include guidance on how to classify and report partial recovery in diagnostic accuracy studies, particularly when calculating sensitivity, specificity, and predictive values.<br /> • Provide illustrative examples or decision frameworks to support consistent interpretation and reduce bias in outcome classification.

We would also like to emphasize that the STARD-IONM checklist does not currently call for authors to specify which muscles, nerves, or anatomical structures were included in the intraoperative monitoring plan, nor does it recommend reporting which signals changed and then recovered or failed to recover. While item 10a under “Test Methods” may implicitly suggest this level of detail, making this expectation explicit would be beneficial. Such information is frequently absent in studies evaluating IONM, yet it is essential for interpreting outcomes and ensuring reproducibility.

Thank you again for your commitment to transparency and community engagement. These additions could further strengthen the STARD-IONM framework and help ensure it serves the entire research community effectively.

Kent Rice, Kevin McCarthy, Anthony Clanton & Adam Doan

On 2025-09-21 16:07:08, user practiCalfMRI wrote:

The authors should address the issue of brain/CSF motion as a potential source of error in the MISL difference images.

On 2025-10-02 15:15:56, user Soumik Mandal wrote:

This preprint has been published at: https://www.nature.com/articles/s41746-025-01972-w

On 2025-10-05 04:11:20, user Mohammad Golam Kibria, PhD wrote:

The GitHub code link doesn't work.

On 2025-10-07 12:53:16, user Ronald wrote:

It an interesting paper to read

On 2025-10-07 13:01:22, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( https://evoheal.github.io/ ) really enjoyed this paper.

Here are our highlights:

Asks what role the microenvironent (including antibiotic administration) plays in shaping the phenotypic traits of S. aureus.

Shows increase in MRSA associated with coexistant pseudomons infection and ciprofloxacin.

Monoinfected (SA only) cases showed increased pigment and biofilm production. Coinfected (SA and pseudomonas) cases showed reduce pigment and biofilm production. Two of these phenotypic shifts coincided with ABX treatment, and these patients had significantly more MRSA infections

On 2025-11-11 03:39:57, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.

Here are our highlights:

As wastewater surveillance expands, not all systems will have the same infrastructure or sampling practice. Adjusting for flow allows standardization across plants of different sizes or environmental conditions.

Cost is a main barrier to sustained wastewater surveillance. This paper demonstrates that reducing sampling still yields stable real-time trends in the reproduction number, R, suggesting that wastewater surveillance can scale well without major budget requirements.

This model does not require clinical data for calibration, which demonstrates that wastewater surveillance can be a primary, rather than a secondary, disease monitoring system capable of tracking transmission even when clinical surveillance is poor.

The publication of a real-time reproduction number dashboard helps public health officials track seasonal waves without having to interpret raw wastewater concentrations, bridging scientific output and real-world action.

On 2020-04-04 18:33:11, user Jhansi Dan wrote:

Do anyone have data from last saturday/sunday for Virginia state. I remember seeing the peak date as April 28th and it shows May 20 now. I deduce flattening of curve. Please share the graphs. I wish they have archives for past data to compare.<br /> Thank You

On 2020-04-08 15:59:31, user Vee_Kay wrote:

Why have they dropped individual state numbers in the IHME projections? Instead they go to other countries that is of little interest to US....

On 2020-04-06 12:58:13, user Maria wrote:

A very accurate study, which explains the high rate of spread of Sars-CoV2. I would repeat it in dark and cold rooms, keeping air samples also in the dark, since UV light and heat damage the virus. This could reveal why only nude RNA is found.

On 2020-07-06 22:03:04, user Charles White wrote:

Full data and release for peer review? RNA detection only or did you culture in human epithelial cells?

On 2020-06-26 22:10:08, user kpfleger wrote:

On what date did the VDD protocol (table 1) commence? Is it possible to analyze COVID-19 outcomes (fatality, ventilator need, ITU admission, etc.) by baseline 25OHD on admission for before vs. after the VDD protocol started, as they did in the Singapore study: https://www.medrxiv.org/con... (which perhaps you should also cite BTW)? Or was 25OHD status not assessed for COVID-19 patients before the VDD protocol began?

On 2020-06-30 14:23:56, user John H Abeles wrote:

Estimates ( Stanford U ) for herd immunity are at around 40% or less ( Ionnaidis; Levitt)

On 2020-07-03 18:28:19, user Mark Pollington wrote:

Heterogeneous is clearly an important factor in determining herd immunity. However, in the developed counties discussed in this paper surely this will have been masked by the introduction of various non-pharmaceutical interventions.<br /> I was therefore fascinated to see how this problem could be tackled.

However, the equations outlining susceptibility do not appear to have been followed up to fit the parameters to data. Indeed, the discussion simply alludes to the authors fitting CVs which are an order of magnitude less than the susceptibility values used in the main paper!

Given the lack of evidence, then, why are arbitrarily susceptibility factors as high as 4 used? Why publish graphs which are so far removed from reasonable expectations? Unless politically motivated?

Clearly further research needs to be done to establish reasonable susceptibility factors, but I can't see any effective proposals in the paper. Computationaly intensive data fitting exercises with the inherent uncertainties in the data are certainly not the way to go!

On 2020-07-05 11:49:14, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-04-14 17:31:09, user Troy D wrote:

Why arent they using Zinc?