On 2025-02-16 01:25:45, user Muhammad Ali Khan wrote:

This article has been published in the Journal of American Medical Informatics Association: https://doi.org/10.1093/jamia/ocae325

On 2021-07-29 16:28:46, user aep wrote:

link to competing interest statement broken

On 2021-07-31 20:05:53, user I. Bokonon wrote:

Where are Figures S1A and S1B?

On 2021-08-01 21:16:57, user BiotechObserver wrote:

Ongoing for safety followup. The trial already met its primary endpoint for efficacy. There is no reason to continue it at that point and the participants would not have agreed to volunteer if it meant they'd have to wait 2 years to receive a vaccination if by chance they ended up in the placebo group. All trials get unblinded at the primary efficacy endpoint readout.

On 2021-07-10 05:28:04, user Cmharper wrote:

So would the likelihood of having or not having loss of taste and smell be a genre of mutation or an inheritable trait - if it is through inheritance, and if the referenced ‘p’ value is the likelihood of having loss of taste and smell, what is the difference in the ‘p’ value and the ‘q’ value for the recessive phenotype?

On 2020-04-12 13:04:20, user japhetk wrote:

I think BCG studies' conclusions came from spurious correlations regardless of BCG has an effect or not.<br /> Anyway, now data from South America and Africa keeps coming and although, it may depend on the methods of analyses, my analyses show already the number death 13 days after the 100th case, and whether BCG is currently done is no longer significantly associated without correcting anything (p = 0.291, ANOVA).And after the number of tourists, population,total GDP, temperature of March, ratio of 65 years or older are corrected the associations show get even weaker (P = 0.621, ANCOVA).Among these covariates, the number of tourists has a robust significant effect on the number of deaths 13 days after the 100th case (0.00016), and the ratio of 65 years or older and population have significant effects, too (P= 0.024, 0.05, respectively). Total GDP (not GDP per capita) and the number of tourists have a close relationship (r = 0.82). <br /> The date when the 100th case was detected show more robust relationship with the BCG policy (currently performed or not), but after the correction of abovementioned covariates, this association also became insignificant )(p= 0.167). But this kind of relationship with the date of 100th case is seen in the case of variables that are specifically associated with Western countries, such as the consumption of wine)(the consumption of wine per capita shows robust association with the date of the 100th case after correction of population (p = 0.0002, more wine, the faster the detection of 100th case). <br /> So, my guess is that this spurious correlation mainly came from the fact the countries which abandaned BCG policies are more developed and more popular from tourists (which increased the faster and more and multiple spread of the virus) and also show greater aging (which increased the risk) and also they locate in western countries which were confident of their medical system and which were away from Asia and which were less alert to this infectious disease from China. The habit of wearing mask, hug, handshake or religious ceremonies might affect, too. <br /> In the cruise ship Diamond Princess, Japanese who were put in the same ship with Westerners show greater mortality rate than Westerners. And in a lot of Western European countries, the risk population (elderly) has experiences of BCG (they are classified as "past BCG", but in fact most of risk populations are experienced with BCG). So, the BCG hypothesis is not consistent with these facts, either. <br /> I am not saying BCG doesn't work, I am saying you cannot conclude anything from these uncontrolled studies which lacks in numerous potential confounding variables. Just let's wait for results of RCTs.

Here's my data if I haven't made any mistakes.You can see the apparent little association with BCG policy and number of the death (13 days after the 100th case) as of 11th April.

O Iran 291<br /> X Spain 288<br /> O China 259<br /> X Italy 233<br /> O Turkey 214<br /> O Algeria 130<br /> X United Kingdom 103<br /> O Indonesia 102<br /> O Brazil 92<br /> X France 91<br /> X Netherlands 76<br /> X United States 69<br /> O Dominican Republic 68<br /> X Ecuador 62<br /> O Portugal 60<br /> O Morocco 59<br /> O Philippines 54<br /> O Ukraine 45<br /> O Iraq 42<br /> O South Korea 35<br /> X Switzerland 33<br /> O Argentina 31<br /> O Egypt 30<br /> O Panama 30<br /> O India 29<br /> O Mexico 29<br /> X Canada 27<br /> O Hungary 26<br /> O Honduras 24<br /> O Peru 24<br /> O Romania 24<br /> O Albania 22<br /> O Greece 22<br /> O Ireland 22<br /> O Tunisia 22<br /> X Luxembourg 22<br /> O Bosnia and Herzegovina 21<br /> X Belgium 21<br /> O Burkina Faso 19<br /> O Macedonia 17<br /> X Andorra 17<br /> O Colombia 16<br /> O Poland 16<br /> O Afghanistan 15<br /> O Cuba 15<br /> O Moldova 15<br /> O Pakistan 13<br /> X Denmark 13<br /> O Bulgaria 10<br /> O Malaysia 10<br /> O Russia 10<br /> X Lebanon 10<br /> X Sweden 10<br /> O Lithuania 9<br /> O Mauritius 9<br /> O Azerbaijan 8<br /> X Austria 8<br /> X Israel 8<br /> O Chile 7<br /> O Kazakhstan 7<br /> O Venezuela 7<br /> X Australia 7<br /> O Croatia 6<br /> O Ghana 6<br /> O Japan 6<br /> O Thailand 6<br /> X Czech Republic 6<br /> X Norway 6<br /> O Jordan 5<br /> O South Africa 5<br /> O Sri Lanka 5<br /> O Taiwan 5<br /> O United Arab Emirates 5<br /> X Germany 5<br /> X Slovenia 5<br /> O Saudi Arabia 4<br /> O Uruguay 4<br /> O Armenia 3<br /> O Cote d'Ivoire 3<br /> O Uzbekistan 3<br /> X Finland 3<br /> O Costa Rica 2<br /> O Oman 2<br /> O Senegal 2<br /> O Estonia 1<br /> X New Zealand 1<br /> O Cambodia 0<br /> O Kuwait 0<br /> O Latvia 0<br /> O Malta 0<br /> O Qatar 0<br /> O Singapore 0<br /> O Vietnam 0<br /> X Slovakia 0

On 2020-03-22 20:13:37, user Sinai Immunol Review Project wrote:

This study retrospectively evaluated clinical, laboratory, hematological, biochemical and immunologic data from 21 subjects admitted to the hospital in Wuhan, China (late December/January) with confirmed SARS-CoV-2 infection. The aim of the study was to compare ‘severe’ (n=11, ~64 years old) and ‘moderate’ (n=10, ~51 years old) COVID-19 cases. Disease severity was defined by patients’ blood oxygen level and respiratory output. They were classified as ‘severe’ if SpO2 93% or respiratory rates 30 per min.

In terms of the clinical laboratory measures, ‘severe’ patients had higher CRP and ferritin, alanine and aspartate aminotransferases, and lactate dehydrogenase but lower albumin concentrations.

The authors then compared plasma cytokine levels (ELISA) and immune cell populations (PBMCs, Flow Cytometry). ‘Severe’ cases had higher levels of IL-2R, IL-10, TNFa, and IL-6 (marginally significant). For the immune cell counts, ‘severe’ group had higher neutrophils, HLA-DR+ CD8 T cells and total B cells; and lower total lymphocytes, CD4 and CD8 T cells (except for HLA-DR+), CD45RA Tregs, and IFNy-expressing CD4 T cells. No significant differences were observed for IL-8, counts of NK cells, CD45+RO Tregs, IFNy-expressing CD8 T and NK cells.

Several potential limitations should be noted: 1) Blood samples were collected 2 days post hospital admission and no data on viral loads were available; 2) Most patients were administered medications (e.g. corticosteroids), which could have affected lymphocyte counts. Medications are briefly mentioned in the text of the manuscript; authors should include medications as part of Table 1. 3) ‘Severe’ cases were significantly older and 4/11 ‘severe’ patients died within 20 days. Authors should consider a sensitivity analysis of biomarkers with the adjustment for patients’ age.

Although the sample size was small, this paper presented a broad range of clinical, biochemical, and immunologic data on patients with COVID-19. One of the main findings is that SARS-CoV-2 may affect T lymphocytes, primarily CD4+ T cells, resulting in decreased IFNy production. Potentially, diminished T lymphocytes and elevated cytokines can serve as biomarkers of severity of COVID-19.

This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-13 16:56:55, user Brian Reed wrote:

This is an important contribution to the literature. I have a few questions, the answer to which might make it even more valuable and better able to evaluate the rates of transmission as an effect of age. I take these mostly from carefully table 3 and table 1, but also from the 'transmission characteristics' subsection of the results section.

Of the 1298 close contacts, it appears only 1155 actually had tests come back.

There is evidently substantial overlap between the household (HH) contacts and the meal contacts, which makes sense. Certainly the travel contacts in general are coming back much lower. The question is, where do the age groups stratify within these. I would imagine by far the close contacts with 0-9 and 10-19 are much more likely in the HH, as well as the "often" category" of contact frequency, as there is a much less likelihood of having co-travel and co-non household meals with kids for the substantial majority of your initial cases (except for maybe the 4 who were kids). I suspect this may skew the conclusion that the rates of transmission for these two younger age groups are similar to older groups. I suspect likewise that the substantial portion of the travel negatives are with older age groups. I think performing the age analysis, or at least presenting the data, for each age group for the HH, travel, meal, and contact frequency subgroups would be helpful. I am aware that the n for some of these subgroups might be too low to allow for a real analysis, but then that is part of my point...<br /> Also, there is a substantial gender difference in those infect among close contacts, with females having a far greater % infection rate (almost double!). Similarly to the age effects, stratifying this gender ratio by age as well as close contact type would be of benefit.

I hope you find my comments constructive and can address them. I still think children may be somewhat more resistant, although less so than before i read this preprint, and would like to know if I should adjust my view further still.

Thanks!

On 2020-03-08 13:41:12, user White Blabbit wrote:

Could be the vaccines. Could also be that the normal disadvantage of immune system naiveté is removed since the Novel virus has never been seen in earth before. That paves the way for children's naturally more robust bodies (otherwise) to have a superior ability to fend off the deleterious effects of the virus.

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 2020-08-23 16:11:32, user Ang wrote:

Hello there,<br /> below a question for someone with the right competence.

True the approach of this work is great, it might result that they are right or wrong we'll see, starting from the review outcomes. However a common person would ask: "Why can't we do a direct and conclusive experiment about transmissivity through aerosol?". A direct experiment is to put a never infected person in the same room with a SARS-CoV-2 ill person, without the physical possibility to exchange any particle between them except air/aerosol. 100 person would cover a good statistics in terms of age, gender, time of exposure and other characteristics of the volunteer. Is this possible? How can be that in the entire world we cannot find 100 voluntaries that are available for the following experiment. Why is this something not done yet?

On 2021-08-05 17:53:16, user Ultrafiltered wrote:

Not even peered reviewed and yet the authors and local news stations in Dane County are promoting and reporting on this paper. That sends a dangerous message to the public that anyone can write anything and its believable/credible. Seeing the other comments here on the fallibility of the PCR test, as CDC has called out and published authoritatively along with other FDA withdrawals of rapid assay test procedures, indicates mine and their comments are just as good as this paper. The authors should remember that information is like a match, it can burn a whole lot of people if left unchecked/ unqualified / uncontrolled.

On 2020-10-18 19:15:36, user Sam Wheeler wrote:

How recent is recent enough? Should one repeat the influenza vaccine for example every 2 months until covid-19 vaccine is available?

From the paper:<br /> "On average Covid-19 patients who received the inactivated trivalent influenza vaccine in 2020 – even if administered after the onset of SARS-CoV-2 infection-related symptoms – had significantly higher chances of surviving and less need for intensive hospital care than patients without recent influenza vaccination.<br /> If a long-lasting immunity had been the main mechanism of cross-protection, we should have also observed similarly protective effects for Covid-19 patients vaccinated in prior years, which was not the case in our analysis".

On 2021-01-21 13:24:54, user Miroslava Stancíková wrote:

Testing was not voluntary, it was in conflict with the Constitution of the Slovak Republic and the Charter of Fundamental Human Rights

On 2021-07-27 10:22:10, user Colin_Brown wrote:

Striking confirmation of 2020 OCT:<br /> "The use of Indomethacin… in hospitalised Covid-19 patients, was associated with marked symptomatic relief... There were no adverse effects. <br /> Indomethacin, instead of paracetamol, should be a preferred drug for the treatment of SARS-CoV-2”

On 2022-12-26 13:26:47, user y wang wrote:

You did not indicate the method of calculating the chi-squre.<br /> Actually, your method does not seem correct. <br /> One can google "Comparing Two Independent Population Proportions" and find the formula and calculator.<br /> Using the calculator, I found z=6.00, i.e., chi-squre=36 (not your 35.67).

On 2020-06-04 17:08:10, user Rob wrote:

Three points :

1) There's a strong prior that vitamin D against month should peak somewhere around August, as 3 of your 4 curves do. Can you explain why the curve for non-white females peaks in April/May? This looks wrong... so wrong that I think it needs to be investigated / explained before relying on this data.

2) The covid-19 infection dates are in March - May. Extrapolation of vitamin D levels measured later in the year to this period will in reality be subject to any individual differences in half-life. For example, your White curves appear to imply that men have a shorter half-life than women. This suggests that sex based adjustments might be a good idea. Presumably individual variation in half-life can also be expected. How much uncertainty would this imply for your adjusted values?

3) Your vitamin D levels are 10-14 years old. That is plenty of time for people to start taking supplements, move house, move city, change jobs, or even get old. Perhaps this is worth mentioning in your limitations section.

On 2021-05-25 20:30:08, user Marek J wrote:

You have wrong data in the mortality analysis for Niaee study. You swaped IVM for Control. According to the study, 4 deaths from 116 occured in IVM and 11/49 occured in Control.

Looking forward to your recalculation.

On 2021-05-25 16:46:56, user Ghatotpach Pilandi wrote:

Beltrane is not "moderate." They first had "moderate" in the title, then changed it to "Severe." The introduction still uses "moderate" but the inclusion criteria in the main text is "severe hypoxemic respiratory failure" plus more indicators of severity. Did tje meta study authors read the study?

On 2020-07-14 15:20:37, user Robert Kernodle wrote:

How do the authors sustain their claims in light of this:

https://vixra.org/pdf/2006....

On 2020-06-01 09:16:36, user ??? wrote:

Dear Colleague

I am Jaehun Jung, the corresponding author of the paper.

HIRA in Korea conducted a database update on May 15 that included 1,000 confirmed cases and over 150,000 controls. We will revise the manuscript based on a more detailed case definition and medication history.

Preliminary analysis showed that most of the drugs presented in our study did not show any statistically significant effects. If you are using our research results in systematic review or meta analysis, be sure to consider this.

Thank you

On 2021-10-05 22:59:35, user MrMemeinator wrote:

So you're seeing the same thing we all saw in the UK and Israel months ago. Color me surprised...

On 2020-09-24 06:47:06, user Subhajit Biswas wrote:

Pleased to see that other scientists are supporting with further evidences, the trend we had observed and reported as early as April 2020.

Based on non-overlap of dengue and COVID-19 global severity maps and evidences of SARS-CoV-2 serological cross-reactions with dengue, we proposed that immunization of susceptible populations in dengue non-endemic regions with available live-attenuated dengue vaccines may cue the anti-viral immune response to thwart COVID-19.

https://www.preprints.org/m...

Thanks to Dr Nicolelis and team from Brazil to provide further evidence to our original proposition that COVID-19 is less severe in highly dengue-endemic regions.

The chronology of this dengue covid conundrum, as it stands now, has been meticulously reported by The Print (https://www.msn.com/en-in/h... "https://www.msn.com/en-in/health/medical/dengue-antibodies-could-provide-immunity-against-covid-brazil-study-suggests/ar-BB19k0OB?li=AAggbRN)").

Amazing! Nature has its own ways of controlling parasite aggression! Antigenic correlation between a flavivirus and a coronavirus was unprecedented.

*Dengue vaccines could be tested in SARS-CoV-2 animal models and tried in dengue non-endemic countries*.

*Use in dengue-endemic countries may be problematic as such vaccination can elicit antibody-dependent enhancement of subsequent dengue infections*.

Our publications in this area to support our proposition: <br /> (1) COVID-19 Virus Infection and Transmission are Observably Less in Highly Dengue-Endemic Countries: Is Pre-Exposure to Dengue Virus Protective Against COVID-19 Severity and Mortality? Will the Reverse Scenario Be True? Clinical and Experimental Investigations, Volume 1(2): 2-5. <br /> (https://www.sciencereposito... "https://www.sciencerepository.org/covid-19-virus-infection-and-transmission-are-observably-less-in-highly_CEI-2020-2-105)")

2. Nath, H., Mallick, A., Roy, S., Sukla, S., & Biswas, S. (2020, June 19). Computational modelling predicts that Dengue virus antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?. <br /> https://osf.io/dutx4/

3. Dengue antibodies can cross-react with SARS-CoV-2 and vice versa-Antibody detection kits can give false-positive results for both viruses in regions where both COVID-19 and Dengue co-exist<br /> https://www.medrxiv.org/con...

On 2021-09-24 06:57:00, user gzuckier wrote:

Why would immune response to some viral protein which is not even expressed until after the infection has taken over the host cell be useful, compared to response to a target which is present before the virus attaches to the cell, and in addition is instrumental in the attachment process?

On 2022-02-17 02:03:18, user McKinley Weber wrote:

The jab alone is only considered enough because that the best protection you can get without ever having it. You can still gain protection from getting vaccinated after you get it.

On 2021-10-14 04:40:40, user gzuckier wrote:

Exactly.<br /> And the infected who would make up a large number of the reinfections because of chronic immune deficiency, for instance, are systematically removed from the study, because they died during the initial infection.<br /> Since these people are not dropped from the vaccination cohort, in that the vaccination did not kill them, this represents a systematic bias towards severe immune dysfunction in the vaccine group, which is definitely going to bias breakthrough rates.

On 2021-08-28 02:02:35, user Jonas Ferris wrote:

While it may be that natural immunity offers more protection than vaccine immunity, there seems to be some problems here:

How can you adjust for the issue that some in the previously infected group died, presumably those most susceptible to symptomatic infection while the vaccinated group likely has many of these most susceptible still in the group?

As the overall infection rate seems quite low (<2%) in the vaccinated group, though many multiples of the even lower numbers in the previously infected (and death screened) group (leading to sensationally high multiples of up to ’27-fold risk’) is it possible that many of these infected could have been deceased had they not been vaccinated?

I understand there are adjustments for comorbidities, but there is no real way to determine who would have died from a group with comorbidities yet they may not exist in the previously infected group.

Why are there so few people above 60 in the study (<5%) when this age groups is over 15% of the population over 16 and the very age group that is most likely to have serious symptomatic infection? How many went to the hospital from this group in both the vaccinated and previously infected groups?

Early seekers of vaccines were likely more at risk of death from Covid than those that were not as worried and didn’t (or couldn’t) get a vaccine in Jan/Feb.

Your two groups are basically those that were fearful of catching Covid and those that didn’t see it as much of a risk to them. These are groups that may have very different risks of testing positive for Covid even if they both received vaccines at the same time.

Those that received a vaccine after almost a year of watching out for the virus may have acted in a more risky fashion after getting vaccinated - the pendulum swung even further than the no vaccine group (who may not have known they were somewhat immune)?

Given these shortcomings, it seems like a more reasonable conclusion than natural immunity is 7 fold+ stronger than vaccine immunity after a few months, is that while both natural immunity and vaccine immunity offer similar substantial absolute protection from serious infection, for those in an age group already less likely to have serious infection, that has already made it through one infection without dying a significant population screening event of those most susceptible to serious Covid infection, symptomatic infection from Covid is less likely than for those that have self-identified as at risk and have been vaccinated for but not exposed to Covid.

As fears of wanning immunity may lead to over consumption of limited resources of Covid vaccines globally, a conclusion that is more likely to lead to the unvaccinated seeking vaccination while discouraging the already vaccinated to seek an aggressive booster timeline would be more appropriate as opposed to one that could rationalize seeking natural immunity and encourage frequent boosters to the previously vaccinated.

On 2021-09-18 21:17:19, user Mike wrote:

This study did an awesome job! One of the reason it didn't have the political bias rather letting the science flow instead of putting on a show to sway people in a particular direction to fit a particular narrative. I predict more studies will bare this out, that natural immunity is better than vaccines but it doesn't mean people shouldn't get vaccinated if they want to but shouldn't be forced! Israel is an example of weakness with the Pfizer vaccine, most likely the other have this weakness too. One of the weaknesses of this vaccine and others is the fact that it's the same, viruses change. This is why the delta variant spreads easier than previous versions.

Back in August almost 60 percent of the hospitalizations in Israel were people at least 60 years old and fully vaccinated! Israel's government a great believer in vaccinations will be going on their 4th booster! I don't see a high rate of re-infections of those who already had Covid along with hospitalizations, but I do believe Covid is here to stay and re-infections will become common and some people will have major health issues with it. People 18 or younger which total so far 74 million Covid infections, 362 have died due to Covid complications.

On 2021-08-29 05:35:10, user some_guy wrote:

Is there any variable control?

The first question that pops into my mind is: are we comparing the same populations?

People who were first vaccinated are disproportionately old people with weaker immune systems, more likely to be infected anyways. I wonder how much does this impact the results.

On 2021-10-15 12:41:54, user Mithat Temizer wrote:

Here is the question. Age is being treated in all models as a potential confounder. For a confounder age needs to be associated with COVID-19 outcome regardless of vaccination status AND age should be associated with vaccinations status regardless of COVID-19 infection. Both assumptions can be considered fulfilled in the model. Yet, the third assumption that age is not on the pathway between vaccination status and the COVID-19 infection/complication (such as hospitalization/death). In this case, we cannot confirm that age is not in the pathway. That is age is not a confounder. Could be an intermediate variable or more likely an effect modifier for the vaccine-induced/native infection-induced immunity against COVID-19 infection. In this case, why is age not considered in analyses as an effect modifier? I wonder whether the authors have checked for the effect size of vaccination (compared to natural immunity) in any of the 3 models, stratifying on age (such as those below 60 years versus those aged 60 years or more). Any comment on this?

On 2021-09-13 14:36:27, user Chadwick wrote:

It is incredibly odd that the study authors provide us copious odds ratios but never the number of participants in each condition with each outcome. It's absence is quite strange.

On 2021-08-28 15:08:52, user Drago Varsas wrote:

"Individuals who were both previously infected with SARS-CoV-2 and given a<br /> single dose of the vaccine gained additional protection against the <br /> Delta variant."Actually not. You get a narrow spectrum protection from the mRNA vaccines versus broad spectrum immunity via your innate immune system. The opposite is true. Covid vaccines weaken your innate immune system.

On 2021-09-30 19:29:51, user Jakob Heitz wrote:

Did you account for the previous infected who died or never recovered, because of long covid?

This reminds me of a study the navy did of war planes in world war 2. They found the majority of bullet holes in returned planes were in the wing tips and central body. Therefore, they decided to reinforce those parts. What they forgot was that the planes that got shot in the engines never returned to base.

On 2021-09-04 19:24:55, user melanoficus wrote:

Very encouraging results. I wish these investigators great success in their endeavours to find and implement beneficial treatment protocols that will save lives of those severely effected.

On 2020-04-20 11:52:48, user Yi-Hsuan Wu wrote:

A corrigendum should be made as the authors misused "Taiwan, China" instead of "Taiwan." That's not acceptable error a group of specialists would made.

On 2024-04-30 16:10:19, user Cameron Daniels wrote:

Hello! My name is Cameron Daniels and I am a Undergraduate Student at Sam Houston State University. As part of a class project we were told to pick a preprint and provide the authors with relevant comments that could help their study. I really enjoyed reading your study and learning about new data collection techniques. Here are my comments on your paper that could help you improve the paper:

* List the number of cycles for the first round of PCR in the patient samples. You did it for the in vitro experiment, but you must've forgotten about it in the patient one. The number of PCR cycles helps to be able to replicate your experiment and also see if too many cycles were run because the more cycles that occur leads to the more mistakes, chimeras, and faulty sample reads. Consider looking at this article to validate my response: Sze MA, Schloss PD.2019.The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. mSphere4:10.1128/msphere.00163-19.https://doi.org/10.1128/msphere.001...

* Specify filtering for chimeras and PCR errors, which is a must to ensure accurate data.

* List the number of sample reads for the ASV units. The sample reads will help understand your data better.

* Make sure to include coverage measures and do it before filtering of singletons. Calculating Good's Coverage or Chao1 helps to ensure that representative sampling occurred.

* Great introductory paragraphs but seems to have many long run-on sentences. Consider breaking up those sentences for readability.

* A preserving agent is mentioned numerous times as important to capsule function. What is this agent and what concentration?

* Fig4A visualizes beta diversity. Should also use an appropriate statistical test, such as ANOVA, too determine if the community compositions are significantly different with probiotic addition.

* The probiotic contains two specific strains, while the microbiome data is from V3-V4 sequencing, and so cannot identify at the species or strain level.

* An increase in Lactobacillus or Bifidobacterium is expected for the probiotic-treated patients. What would be more interesting is to analyze if certain other taxa are significantly increased or decreased in abundance by the probiotic strains, as the question of how those two strains affect the rest of the community is scientifically important and would enhance the literature.

* Fig5 and 6 show that stool sampling vs capsule are different. Authors also need to include analysis to show that capsule and endoscopy are not different, since capsules are proposed as a non-invasive alternative.

On 2020-04-26 18:36:10, user Christopher Rentsch wrote:

We believe that Magagnoli et al failed to correctly identify intubation occurring in hospitalized patients testing positive for COVID-19. They used CPT codes 31500, 94002, 94003, and E0463 and ICD-10 procedure codes indicative of assistance with respiratory ventilation, or extracorporeal membrane oxygenation (ECMO). We identified 5,906 COVID-19 patients treated in the Veterans Health Administration between March 1 and April 21, 2020. In addition to the above CPT codes, we identified intubation according to ICD-10 procedure codes for insertion of endotracheal airway, and respiratory ventilation, which were usually concordant. We cross-validated with medications typically used during intubation, such as neuromuscular blocking agents (e.g., succinylcholine, rocuronium) and short acting sedatives (e.g., propofol, midazolam). We also found these intubation codes most frequently in the context of intensive care. We did not find similar evidence of face validity for ventilation assistance codes. No instances of ECMO were found as this procedure is unlikely to be used in the Veterans Health Administration.

We classified 307/5,906 = 5.2% patients as intubated. Using the Magagnoli algorithm, only 96/5,906 = 1.6% patients were said to be intubated. Of these, 37 were classified based on ventilation assistance codes, not indicative of intubation.

List of ICD-10 Procedure codes used to identify intubation

Codes in both Magagnoli and Tate lists<br /> - Respiratory Ventilation (5A1935Z 5A1945Z 5A1955Z)

Codes in Magagnoli list, but not Tate list<br /> - Assistance With Respiratory Ventilation (5A09357 5A09358 5A09359 5A0935B 5A0935Z 5A09457 5A09458 5A09459 5A0945B 5A0945Z 5A09557 5A09558 5A09559 5A0955B 5A0955Z)<br /> - Extracorporeal Oxygenation, Membrane (5A1522F 5A1522G 5A1522H)

Codes in Tate list, but not Magagnoli list<br /> - Insertion of Endotracheal Airway Into Trachea (0BH13EZ 0BH17EZ 0BH18EZ)

Janet P. Tate (Janet.Tate2@va.gov)<br /> Christopher T. Rentsch (@DarthCTR)<br /> Joseph T. King Jr.<br /> Amy C. Justice

VA Connecticut Healthcare System<br /> West Haven, CT

On 2020-05-23 00:48:16, user Petrus wrote:

Good to know about Ambati, thanks. It is also no coincidence that much of the positive news around HCQ has routinely been omitted or suppressed, such as the recent decision in France to allow doctors to start prescribing the drug. How could that happen if it didn't work or was too dangerous? Dr. Didier Raoult brought the government to its knees with his research, and they had no choice. Big win for the French people.

On 2021-04-18 15:54:33, user ToGo wrote:

The maximum Ct to consider a positive SARS-CoV-2 infection is not 33, where do you have this erronous assumption from? There were some (bad) studies where samples delivering a Ct-value of about 30 were not infecting cell cultures, but real world infection rates show that even people which were tested for a Ct value of about 38 were infectious. The newes (yes, not peer-reviewed yet, but there are numerous other studies cited): https://www.medrxiv.org/con...

On 2021-06-14 03:42:27, user Jones Onigbinde wrote:

Hi. Great work. I do however have an issue with the way you injected political dichotomy into the COVID-19 outlook. In your DAG diagram you inferred that having a right wing populist idea somehow contribute to the spread of the disease but you do not mention the radical left wing rioting that occurred all around the world in the summer of 2020. You forgot that people who are right leaning are more likely to believe that the virus actually came out of a Wuhan lab which left leaning media, scientists, and politicians termed to be a conspiracy theory for the past one year. Now the theory is becoming more and more plausible. My advise is that you should keep politics out of science. Follow the evidence wherever it may leads, that's science. It appears to me that 2020 was the year science came to die because of the propensity of people like you to tailor science to politics.

On 2019-10-30 08:25:55, user Marema wrote:

This paper is done to investigate how acute financial problems affect undergraduate students' clinical learning.The study used qualitative method to explore their experiences. I here for further information.

On 2020-04-01 15:52:32, user Peter Hansen wrote:

Comments.<br /> This is a very noisy signal, because data are changing very much from week to week. Current country statistics are more influence by: <br /> 1) The time where COVID-19 began to spread in each country<br /> 2) The degree of preventive measures (e.g. compare Denmark and Sweden).<br /> So all figures in the paper might look very different 2 weeks from now.

Smaller comments.<br /> Would be nice to have a table with actual data for each country, rather than just highlighting selected countries in the figures. BTW.

Where is France in Figure 2? <br /> Number of deaths per capita in France is relatively large (higher than Denmark). <br /> The BCG was mandatory in France for school children between 1950 and 2007, and for healthcare professionals between 1947 and 2010. (Whereas Denmark stopped BCG in the mid 1980s).

On 2020-04-03 00:55:22, user ???? wrote:

What I felt strange was, in Japan, though the number of the infected persons have been increasing, the fatality rate is apparently low in comparison with the corresponding numbers in the U.S. and in the Europe (except Portugal, in which the BCG vaccination is mandatory, while the fatality rate in Spain, where the vaccination is NOT mandatory, has become around 60 times more than in Portugal).

I think the number of the infected persons in Japan must be much higher than the one reported so far (i.e., there must be a lot of actually infected people not diagnosed with the new coronavirus); however, it cannot explain the low fatality rate in Japan.

In addition, it's notable that those who passed away due to the virus in Japan (except the foreigners, who account for as much as around 30% of the infected persons in Japan) are almost limited to elderly persons, while the BCG vaccination became mandatory in 1940s and 70-year-old or older Japanese tend not to have taken the vaccination.

On 2021-12-07 07:25:37, user Atharva Gurav wrote:

How can I request for the data. I need it for research purpose.

On 2020-06-06 12:39:13, user OxImmuno Literature Initiative wrote:

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

On 2020-09-22 16:28:46, user Publicus wrote:

The data is highly polluted. There are cases of a motorcycle fatality being reported as a covid-19 related death. On county in Florida for a reporting period had 100% of their deaths reported as Covid-19. In the earlier days if you came in contact with a covid-19 positive person and then died your cause of death was listed as covid-19. When the government made allocations of money and resources based on covid-19 deaths the data became forever polluted.

On 2021-01-10 10:09:41, user Disqus wrote:

Gandini S et al. updated their previous preprint without, however, resolving the<br /> methodological problems, that is the errors already highlighted and the<br /> arbitrariness of most of the conclusions (see comments for the previous version<br /> here https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.12.16.20248134v1)").<br /> In particular in this second version the sample of public institutions increase from 81.6% to 97% of total, for a total of 7,376,698 students, thus it is not clear how on such large numbers one can hope to obtain significantly different or significantly more reliable results from such an update.

On the other hand Gandini et al. seem to have realized how their analyses suffer from the biases of an ecological study (page 13) though it is incomprehensible how the proposed additional analysis for the Veneto region only can significantly relieve the problem.

There are still also some gross errors here and there, e.g. although the authors have updated Table 8 by adding the (useless) absolute range of the number of tests per institution, the problem of standard deviations remains, certainly the result of a calculation error being compatible with negative values in the number of tests (e.g. 9-13 = -4 which would represent the lower limit for 1 standard deviation for the number of tests, see Student index case – Kindergarten row)

Finally, once again in spite of the medRxiv warning, Gandini et al. seem to consider it as a sort of personal press agency, a springboard to relaunch their studies without having to wait for the peer review, so much so that on the fb page of the first author (Gandini S.) a link to the article promptly appeared, the day after it was published on medRxiv

On 2020-04-29 19:47:21, user Frank Conijn wrote:

To the authors:

Thank you for this review, which is very useful.

However, I did find a small error. In the Flowchart you're listing the study by Molina et al, reference #24, as a non-randomized trial. That's incorrect, because in a non-randomized trial one is still supposed to compare one's experimental group with a comparable control group. Molina et al experimented with patients with severe covid, while the control group consisted of patients with light or mild covid from another study (mean symptom duration until treatment: 4 days). That's comparing apples and oranges. It should be listed as a short communication.

In table 3, you are describing it correctly.

On 2020-07-26 01:02:03, user JayTe wrote:

Relatively simple to calculate the herd immunity threshold. Take the CDC figure for the infection fatality rate multiply by Covid-19 deaths to determine the infected population. Divide the infection population by the total population and you get the implied herd immunity threshold. Overall for the US it is 15%. There are some states that have not reached that level. Mainly Florida, Texas and Arizona. Does that give you a clue as to what is happening in those states?

On 2020-07-08 15:26:34, user Abhijit Mallick wrote:

The paper is informative. Good analysis of data is observed. The paper is believed to explore scope of future research.

On 2020-08-18 09:41:35, user Subhajit Biswas wrote:

We are pleased to inform our readers that our discovery that Dengue sera can cross-react in SARS CoV-2 antibody tests has been further investigated and validated by a group of scientists from Israel.

They have more extensively probed and confirmed the cross-reactivity between Dengue antibodies and SARS CoV-2 antigen(s) and vice versa via lateral flow-based rapid tests and ELISA tests in a larger number of patient samples. Like ours, they have also used Dengue sera collected before September 2019, predating the outbreak of SARS CoV-2 in China.

Title of the publication: Potential antigenic cross-reactivity between SARS-CoV-2 and Dengue viruses

Journal: Clinical Infectious Diseases (Oxford Academic)

Link: https://doi.org/10.1093/cid...

The data reported in the aforesaid paper further supports our original idea of antigenic similarity between Dengue virus and SARS CoV-2 that forms the basis of the observed cross-reactivity between the two viruses.

On 2020-10-26 15:22:52, user Christopher Leffler wrote:

This paper is finally published--in the first and only journal we sent it to: https://www.ajtmh.org/conte...

On 2020-08-09 17:32:59, user ????? ????? wrote:

Where is the raw data?

On 2021-12-11 09:16:47, user degodified wrote:

This paper is, Im afraid, full of holes, There is no control group (why not?) and comparing local to national rates introduced bias. At the least it needs reproducing in a better trial. It is already being used by Quack doctors to scare people away from vaccines.

On 2020-08-19 21:36:05, user Daniel Gregory wrote:

Is the Interferon used in this study Traumakine?

On 2021-03-16 09:37:40, user Luandrie Potgieter wrote:

How can one get the data that was used to test the methods in this paper?

On 2025-09-15 13:27:23, user Maksim wrote:

The manuscript is now accepted for publication.<br /> DOI: 10.2174/0113816128412495250824132514<br /> Curr Pharm Des 2025

On 2021-09-13 13:36:20, user lbaustin wrote:

Has this article been submitted to a journal? This information needs to be disseminated.

On 2020-04-20 08:28:13, user adrian wrote:

Sorry I just don't buy this, this is a blatant example of cherry-picking. The paper states no-where how the regions were selected, and it is clear to anyone familiar with the data that in each country, 3 regions are specifically selected to demonstrate a clear relationship. Taking Italy as an example, there are several regions across the north with similar climates but very different growth rates of cases. Not to mention that the authors have not accounted for population correctly in their analysis. If I had reviewed this paper I would have recommended rejection.

On 2020-06-10 09:22:39, user alanarchibald wrote:

Am I correct in understanding that your definition of (European) travel in the context of this study is limited to travel by persons who are normally resident in the UK and that travel by visitors to Scotland/UK is not addressed, presumably as you did not have access to the necessary samples and medical records.

On 2021-10-05 13:49:34, user helene banoun wrote:

This study does not take into account infections between dose 1 and dose 2. Indeed, the vaccinated did not produce enough antibodies during this period and are therefore not considered as vaccinated; but as the vaccination takes place during an epidemic period, we cannot afford to write off the infections taking place during this crucial period.<br /> Indeed, ADE seems to cause post-vaccination outbreaks according to other studies (e.g., https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2021.05.27.21257583v1)")

On 2020-07-07 16:30:21, user Rosemary TATE wrote:

I took a quick look at this. Looks useful, but just a couple of comments from a statistician. 1. Spearman correlation coefficients are called rho - not r (well done for using Spearman). It's the coefficient that is important so no need to report the p-value as even a small correlation will be significant whit a large number of observations. Also, you are looking at the death rates, not the numbers so need to make this clear.

On 2021-06-29 08:20:33, user Max Pietsch wrote:

For the peer-reviewed updated version of this paper, please see: https://www.sciencedirect.c...

On 2020-07-22 20:52:39, user Stuart Hameroff wrote:

Congratulations. I think the ultrasound mechanism of action is on microtubules and the microtubule-organizing-center (MTOC) which get hijacked by the virus for trafficking, filopdium growth to infect other cells, and cytokine storm. Ultrasound is mechanical vibrations in megahertz. Microtubules have resonance frequencies in megahertz, and cell studies show ultrasound rearranges microtubules into optimal configurations. Let's treat the microtubules on which the virus depends, e.g. those in vagus nerve, spleen and other areas. Ultrasound is painless, safe, inexpensive and widely available.

On 2024-12-28 04:44:14, user xPeer wrote:

Courtesy review from xPeerd.com

The paper, "Machine Learning Approaches to Predict Alcohol Consumption from Biomarkers in the UK Biobank," evaluates five machine learning (ML) models to predict alcohol consumption (DPW) using biomarkers. The study leverages biomarkers and covariates from the UK Biobank to enhance prediction accuracy. The highest-performing model, XGBOOST, achieved an r² of 0.356. The research findings indicate that using biomarkers significantly improves the prediction of heavy drinking and other related phenotypes.

Potential Major Revisions:

1. Biomarker Selection Justification: While the paper discusses known biomarkers, it does not provide a detailed rationale for selecting the specific 338 predictors used. The study should offer more context or references explaining why these particular biomarkers were chosen and how they relate to alcohol consumption prediction comprehensively (pg. 4).

2. Ethical Considerations and Limitations: Although the study briefly mentions the ethical limitations concerning the UK's demographics, it could expound on this point, addressing how the findings might translate to diverse populations not represented in the UK Biobank dataset (pg. 16).

3. Model Generalizability: The study should provide more details on the applicability and generalizability of the model findings to different populations with genetic diversity and varying socio-economic backgrounds (pg. 17). It must address how the model could adapt or fail in non-European cohorts as the generalizability might vary.

Potential Minor Revisions:

1. Typographical and Minor Errors:
2. Consistency in the abbreviation of DPW (Drinks Per Week) is essential. There are minor inconsistencies throughout the manuscript that could be formatted uniformly (pg. 7, 14).

3. Clarity and readability can be enhanced by eliminating repeated phrases (e.g., "Alcohol Consumption prediction using biomarkers" is repeated frequently which might be condensed or varied).

4. Formatting Issues:

5. Figures and Tables: Ensure all figures and tables are referenced correctly in the text and positioned to avoid disrupting reading flow (p.13, Figures 3, 4, 8).

6. Supplementary Information: Cross-reference supplementary information more clearly within the text to aid readers in locating relevant data (e.g., Supplementary Table T3 and Figures S2).

7. AI Content Analysis:

8. There is no explicit indication of AI-generated content in this paper. However, the paper exhibits some areas of redundancy which can be indicative of AI-aided writing:
9. Assessed AI content reflects about 5% of the total document. These are sections that repeat information about statistical measures and known biological impacts without much nuanced discussion (e.g., discussion of model performances and the role of biomarkers) (pg. 10-11).
10. The epistemic impact of this AI-generated content is minimal and does not undermine the scientific integrity of the paper. It would benefit from a more nuanced discussion of the statistical results and implications.

Recommendations:

1. Improving Rationale and Discussion:
2. Strengthen the section discussing the selection of specific biomarkers with comprehensive explanations or references.

3. Expand on the implications of the model predictions, especially in clinical and public health contexts, to enhance readability and relevance.

4. Enhancing Generalizability:

5. Discuss in more detail how these predictive models could be adjusted or re-calibrated for non-European populations.

6. Provide more comprehensive demographic benefits and limitations to reinforce the findings' applicability and reliability.

7. Visual and Supplementary Data Clarity:

8. Organize figures and tables to enhance their impact without disrupting the flow.
9. Ensure all supplementary materials are accurately referenced and easy to locate within the text.

By addressing these major and minor revisions, the manuscript will achieve higher clarity, ethical robustness, and academic integrity while broadening its impacts across diverse populations and further grounding its findings within the literature.

On 2020-07-13 09:10:17, user OxImmuno Literature Initiative wrote:

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

On 2020-04-24 07:57:39, user Sinai Immunol Review Project wrote:

Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking <br /> Mahapatra et al. MedRXiv [@doi:10.1101/2020.04.05.20054254v1]

Keywords<br /> • Drug prediction<br /> • Machine learning<br /> • Docking

Main Findings<br /> The COVID-19 pandemic has ravaged hospitals: the disease can present severe complications (acute respiratory failure in particular), and yet no specific drug exists to date. Time being of the essence, it is therefore essential to explore drugs already on the market for other indications. These drugs, however, must be tested in COVID-19 patients and thus selection of limited candidates is important. The authors argue that an important step in accelerating the selection of promising drugs can be done in silico.<br /> The authors use machine learning (ML), training their algorithm on a dataset obtained from in vitro targeting of SARS Coronavirus 3C-like Protease with existing drugs. The trained algorithm was then used to screen drugs available in the Food and Drug Administration’s Drug Bank. Using the Drug Bank dataset, the authors also performed a docking study -a process used to predict in silico the orientation and conformation of a molecule when bound to its receptor. Since SARS-CoV-2 spike protein is considered to play an important role in infection by binding ACE-2, docking was also applied to study the stability of drug-spike protein complexes. The results of the ML and docking were aligned, and antiretroviral Saquinavir was identified as a potentially promising therapy for COVID-19.

Limitations<br /> The authors train their algorithm on SARS Coronavirus 3C-like Protease, as inhibitors of this protein should prevent the virus from replicating in the host. However, the authors note that the most promising target seems to be SARS-CoV-2 spike protein. Moreover, the training dataset is the result of in vitro studies, and may have limited relevance in vivo.<br /> Overall, preclinical studies and then potential clinical trials would need to be performed before administering this drug to COVID-19 patients though, admittedly, clinical validation of an existing drug could happen faster than the development of new drugs entirely. Saquinavir has been studied in vitro by Yamamoto et al.[1], and shown little promise in SARS-CoV-2 treatment so far.

Significance<br /> Repurposing of existing drugs is can be advantageous to develop treatment strategies. An in silico approach could help identify potential therapies, although they must be confirmed in clinical trials before being administered on a large scale.

References<br /> Yamamoto et al. Nelfinavir inhibits replication of severe acute respiratory syndrome coronavirus 2 in vitro. BioRXiV preprint, 2020

Credit<br /> Reviewed by Maria Kuksin as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-05 21:26:27, user AnneG wrote:

That is fantastic! A game changer that explains why kids have such low occurrence!

On 2021-06-11 17:30:08, user Beverly Hebert wrote:

Where has this study been submitted for publication?

On 2022-01-26 15:58:30, user Bruno Xavier wrote:

WRONG.

There is no information about COVID induced myocarditis here. The information is only about classic myocarditis, MIS-C myocarditis and Covid-19 vacine related myocarditis. You see no COVID induced myocarditis data.

Even if the amount of COVID induced myocarditis was specified here, we would need to know the number of vacinated vs not vacinated children in the population during the study. Only knowing that we could compare the rates of myocarditis in one and the other population.

On 2022-07-08 01:11:17, user Sun Yeop Lee wrote:

"(ii) the sclerostin reducing alleles of the genetic variants were associated with increased BMD level"<br /> what is the rationale for applying this criterion for choosing genetic IVs? Is this because it is already known fact taht lowering sclerostin increases BMD?

just wondering what are the variants that lowers the sclerostin but decrease BMD

On 2020-04-23 11:29:42, user Adam Chappell wrote:

this wasn't about counting deaths!

On 2020-04-20 07:16:34, user JustDoublechecking wrote:

Antibodies suppose to be detectable 11-12 days after onset of viral infection. If to take the study results at face value, it means 2.5-4.0 of population was actually sick by mid-March and on the day of the testing it was actually closer to 6% or more (even with very low viral doubling rate under quarantine). I saw no adjustment in the study as they compared directly to confirmed PRC tests on April 1st.

On 2020-04-27 10:41:12, user Lourenço wrote:

But isn't that a mortality rate comparable with that of seasonal flu? Or at least in the same order of magnitude?

On 2020-04-21 15:33:02, user ?????Ozymandias????? wrote:

I'm just an undergrad with no expertise, but based on Bayesian logic, when the sensitivity of a test isn't perfect, and given the low prevalence of a trait in a population, won't the test tend to elicit enough false-positives to cloud the results?

On 2021-12-04 13:02:08, user Kiwinka74 wrote:

Hi, I think at the very end of the paper there is a typo with the term primary infection, as it reads instead as 'primary reinfection'.

On 2020-05-25 08:23:11, user Chen Weihua wrote:

Now published in Clinical Chemistry doi: 10.1093/clinchem/hvaa102. Please cite this if you find our research useful.

On 2021-01-25 17:46:56, user Yin Aphinyanaphongs wrote:

who is the corresponding author?

On 2020-10-08 14:42:08, user Bob Martin wrote:

Doesn't your model need to take into account the dispersion factor of the negative binomial distribution of spreaders?

On 2021-05-25 20:39:22, user Luiz Henrique Oliveira wrote:

Nice paper, but you guys didn´t determine the % of efficience is for... is for light cases? moderate or serious cases? 28% for people above 80 years, that means that 72% of positive cases died when in serious cases? there´s a lot of miss informations that need to answer and in brazil a lot of midia is announcing this article as " the vaccine does not work"

On 2020-08-01 03:03:30, user Peter Lange wrote:

Thanks this is a really important paper. I wpuld like to see it in a high inpact journal. If I could make the following suggestions to enhance:<br /> - using STROBE checklist will improve the reporting and ensure no required points are missed. Many journals will require compliance with this for publication.<br /> - the abstract would be more compelling with the number of participants<br /> - the selection of participants could be better described - were these all the participants available in that period? Were 150 participants selected from the initial date?<br /> - though a retrospective observational study of routinely collected data nevertheless some journals will require a statement from the local ethics board - that full application was not required - to be obtained<br /> - the use of CFS in the regression analysis as an ordinal point scale gives a result just barely significant. The result may be strengthened by uniting categories 1-3 4-6 7-9 which is reasonable given numbers. Alternativelya dichotomous division at CFS 5. Just be careful to state this was post-hoc analysis. If not doing that stating the rsult more strongly "OR 1.25 (1.00-1.50) for mortality per point on the 9 point CFS" emphasises the importance and validity of the result.<br /> - where using medians to describe samples interquartile range can add additional information as to the distribution of the observations.

• i think there will be some interest internationally about the absence of mechanical ventilation in any participant. The use of respiratory support It would be informative to discuss why before the final section.

• the tables are pretty dense and hard to read. Some spacing would help.

All the best I hope to see this in print soon and consider an international follow-up

Peter Lange

On 2025-03-15 20:32:14, user Hani Molaie wrote:

The failure to critically address alternative factors such as economic sanctions and healthcare capacity further weakens the argument, as these variables likely play a significant role in shaping public health outcomes in addition to political populism.

On 2023-10-22 23:07:06, user CDSL JHSPH wrote:

Hello! Thank you for sharing your work with us. I believe that your work in identifying barriers of transitioning from acute care of substance use disorder (SUD) to community-based treatment is a big first step to making a change in providing impactful support to SUD patients. I wanted to start off with saying I think the title of the topic is well framed, it conceptualizes exactly what to expect in the paper including the research focus of transitions of SUD patients from acute-care settings to community-based setting, it also gives an insight to the methods and understanding that the paper will aim to categorize the strategies. There were a few comments and questions that I think may help the paper and my understanding of this paper.<br /> 1) The Abstract: I really like the breakdown structure of the abstract, it makes it easier to read. I do believe an extra line could be added to the background section of the abstract that indicates a direct connection of the research results to its direct use in the bigger issue. I think adding something like the sentence on Line 4, page 5 would help the reader make this connection. <br /> 2) Results and Figures: I felt as through a pie chart could be used to summarize a few things in this section. It would make it easier to read in a way and represent what portion if the category was taken from the whole picture. An example of this could be during the Additional IntervenntionC Components across Care Continuum. The Table is very helpful, but a graphic figure may help readers understand the results in a better way.<br /> 3) Discussion: The need for more literary review was repeated multiple times throughout the discussion and I was wondering if there was a way of indicating this limitation’s importance without the repetition of it. <br /> Overall, I really enjoyed reading this paper. It was well-written and easy to follow. I hope that this paper makes the effect it intends to, and I hope to follow up with future research in which these strategies, barriers and facilitators are put to the test. I think this is a great step to making a big difference in addiction medicine.

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 2020-08-29 18:55:11, user Freek Reedeker wrote:

Where and when will this study be published?

On 2024-11-19 17:19:24, user RMD wrote:

Did you also ask women which device they would prefer if they knew that Evalyn brush has the highest accuracy?

On 2021-09-10 19:11:30, user David Goldberg, MD, MSCE wrote:

Although the scientific question that is being address is an important one, I have concerns about the methodology used to adjudicate the outcome. In similar circumstances (e.g., the FDAs Mini-Sentinel Initiative), complex clinical outcomes like this (e.g., acute liver failure) were adjudicated independently by two experts, with a third person serving to break any ties. That seems not to have been done in this study, as there was only one cardiologist involved. Secondly, the clinical data to adjudicate the outcome of myocarditis seems to be insufficient in many cases. Although one could argue "this is the best data we have" sometimes that is not good enough. When the question is so important and politically charged, incomplete/invalid data is sometimes worse than no data. Unless the authors can have two-party adjudication with record review, and classification using standard techniques (e.g., definite vaccine-induced myocarditis, highly likely, probable, possible, not) then there are major methodological concerns with the outcome, and the overall validity of the study.

On 2021-08-16 15:59:43, user A. Jamie Saris wrote:

There are some excellent comments below that I will not rehash, but I agree that this pre-print "as is" would not survive peer review without some serious revisions. Unfortunately, as this site is Open Source, this "study" is appearing in a lot of anti-vaxx rants on social media (it's been cited twice to me on Twitter so far today). It would be a great help if there were some printed caveats on sites like this (especially around topics where pseudoscience to outright quackery is rife) to dissuade people from taking VERY provisional results (from a flawed study with a modest number of participants) as "settled" science "proving the effectiveness" of Ivermectin.

On 2021-11-20 21:08:59, user MICHAEL A WALLACH wrote:

Does the data include unvaccinated workers in the study who never tested positive for covid? I assume it does but was not addressed. How did they compare with the two groups compared in the study?

On 2025-09-29 22:21:03, user A.O. Akinrinade wrote:

Hello,

In Figure 2 (page 8), I believe it would be helpful to have a color legend showing these archetypes you've inferred. I think at least having the archetype number annotated would make it easier to connect the figure to the text.

Best,<br /> Ayomikun

On 2020-06-02 16:05:02, user Joseph Orr wrote:

Now published in NeuroImage: Clinical

On 2021-04-02 11:10:37, user Rudy Faelens wrote:

It's obvious why, in the first wave, GP's didn't get higher infection rates. The physical exam was replaced by a pure telephonic anamnesis, diagnosis, and therapy. Pure horror. Unethical. <br /> Definitely safe for the GP, but at a severe sometimes lethal cost of false diagnoses and wrong therapies.

On 2025-07-14 12:23:02, user Massimiliano Mazza wrote:

Yes, the paper has been recently published here:

https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2024.1488860/full

On 2021-07-19 03:08:20, user Miles Babbage wrote:

OK, authors, there are problems here. You don't have the sample numbers to make the claims you do, and your data do not bear it.

Interferon alpha effect you report here seems, from your own graphs, to be an artifact of changes between the first and the second dose, not between vaccine and lack of vaccine. I.e. you have a very minor shift up at t2, which makes the drop at t3 significant - but there is no actual effect between t1 and t3.

For TNFa data, the R848 seems to be based on one single patient who had a strong increase at t2 that declined in t3. The only significant observation that holds is the one with candida, which then brings up the problem of sampling (test enough things, and you'll get a result somewhere). You need to state your statistics much more clearly.

There is an possible trend here, but that trend a) needs to also be interpreted in the light of known post-viral effects on the innate immune system (such as e.g. those seen with post-influenza effects on bacterial resistance), and b) needs to stated as trend, not as a definite finding.

On 2022-01-28 20:26:41, user Hussein Turfe wrote:

Was there any relation found between those who had THC in their urine and coming into the ED stating that they had a suicidal ideation?

On 2022-02-22 02:12:34, user Juliet French wrote:

Nice paper. You may want to check out one of our papers. Moradi Marjaneh et al, Genome Biology 2020. PMID: 31910864. Some similarities between yours and ours.

On 2020-04-26 15:15:14, user Retelska wrote:

That's interesting and surely useful. it would be interesting to see a plot normalised by the number of infected persons, I don't know if you have this data. So I guess we would see that with flu 5% of seventy-years old or is hospitalized, whereas with covid, in addition to younger age, the proportion might be bigger. Also, about figure 1: Veterans group is certainly quite old. In Corea, hospitalized are very young, I suppose that much more young people were infected. I heard that infection spreads mostly between young, mobile people.

On 2021-08-30 14:40:54, user Nathan Johnson wrote:

Hi Sean, table 2 is the attention getting graph with the large drop but it mixes tests at all different ages so it's harder to read. It'd be better to see a graph by time for separate groups of 3 months old, 6 month old and 12 months old (or similar). Since table 4 shows "Overall, we note no significant reductions in development trends." taking out the older groups who didn't drop should make the drop in 2021 even more dramatic, no? Also if masking was used in first few months in children born prepandemic without a drop, could point more strongly to prenatal cause.

On 2021-06-18 23:08:13, user Number Six wrote:

A question on the definition of "infectious" people in your model.

Are you using positive test results and defining those as infectious people?

On 2020-07-17 11:16:47, user ADisquietingSuggestion wrote:

This paper puts the global burden of COVID-19 at 4.3 million YLL. The WHO tables on disease burden put this in context: all of the top 20 diseases by YLL are above 25 million. How would you characterize this comparison?

On 2022-01-13 17:30:19, user jetbundle wrote:

How were the dogs trained? Were they trained on the sweat of infected (symtomatic or asymptomatic?) people or on isolated viruses?

The authors should answer this. That makes the difference whether the dogs simply identify sick patients or whether it has anything to do with the virus.

On 2020-03-07 17:48:10, user fuyutao wrote:

Figure2 has Leucine and Serine codons swapped.

On 2020-05-06 06:16:09, user JL Segovia wrote:

Mike Bray, Craig Rayner, François Noël, David Jans, Kylie Wagstaff,<br /> Ivermectin and COVID-19: a report in Antiviral Research, widespread interest, an <br /> FDA warning, two letters to the editor and the authors' responses, Antiviral Research, 2020, http://www.sciencedirect.co...

"Ivermectin’s key direct target in mammalian cells <br /> is a not a viral component, but a host protein important in <br /> intracellular transport (Yang et al., 2020);<br /> the fact that it is a host-directed agent (HDA) is almost certainly the<br /> basis of its broad-spectrum activity against a number of different RNA <br /> viruses in vitro (Tay et al., 2013; Yang et al., 2020).<br /> The way a HDA can reduce viral load is by inhibiting a key cellular <br /> process that the virus hijacks to enhance infection by suppressing the <br /> host antiviral response. Reducing viral load by even a modest amount by <br /> using a HDA at low dose early in infection can be the key to enabling <br /> the body’s immune system to begin to mount the full antiviral response <br /> before the infection takes control."

On 2021-03-04 05:40:34, user J Sato wrote:

I read this preprint paper and would like to ask if the authors can share the data of BCG vaccinated individuals and unvaccinated individuals.

I understand that this research divides the individual into BCG primed and control primed.<br /> I guess there are BCG vaccinated and unvaccinated individuals among both BCG primed and control primed groups.<br /> I would like to divide the individual into four groups; BCG primed & BCG vaccinated, BCG primed & BCG unvaccinated, control primed & BCG vaccinated, control primed & BCG unvaccinated.<br /> Can you share the data of those?

Kind regards,<br /> Jun Sato

On 2021-06-14 00:01:49, user Lemarque wrote:

Hello, <br /> I have a doubt, it was not clear for me when the serological test was done. Looking for AZ numbers, its clear that the antibodies start to rise at week 3 or more, >15 days after vaccination with lower numbers in the timeframe 7 to 14 days (less than 50% had antibodies in this timeframe). The serological tests was made at the end of the timeframe or sorted during the timeframe (eg: 7 to 14 it was done on day 14, and 15 to 21 it was done on day 21)?

On 2020-05-21 19:50:26, user energyexpert wrote:

Peer Review has nothing to do with accuracy. For example, no peer-reviewer will replicate this study, or examine the actual data from this study.

Instead Peer Review is an assessment of acceptability.

On 2025-02-20 16:46:44, user Caleb Hartman wrote:

SN Table 5 and SN Table 6 appear to present the exact same mutational signature activities data for copy number signatures. The signature column labels for both tables appear to be decomposed COSMIC signature labels. I think SN Table 5 needs to contain de novo extracted copy number activities values. Would the authors be able to update this discrepancy?

On 2020-07-24 14:22:28, user Rosemary TATE wrote:

So where is the EQUATOR checklist that you say you have uploaded?

On 2019-07-04 23:42:29, user Guyguy wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

Thursday, July 4th, 2019

The epidemiological situation of the Ebola Virus Disease dated 3 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,382, of which 2,288 are confirmed and 94 are probable. In total, there were 1,606 deaths (1,512 confirmed and 94 probable) and 666 people healed.<br /> 420 suspected cases under investigation;<br /> 13 new confirmed cases, including 4 in Beni, 2 in Butembo, 2 in Katwa, 2 in Kalunguta, 1 in Mandima, 1 in Biena and 1 in Mabalako;<br /> 8 new confirmed cases deaths:<br /> 2 community deaths, including 1 in Butembo and 1 in Mandima;<br /> 6 deaths in Ebola Treatment Centers including 3 in Beni, 2 in Mabalako and 1 in Katwa;<br /> 11 people cured out of Ebola Treatment Center including 7 in Mabalako, 3 in Katwa and 1 in Beni. <br /> 128 Contaminated health workers: One health worker, vaccinated, is one of the new confirmed cases in Beni. The cumulative number of confirmed / probable cases among health workers is 128 (5% of all confirmed / probable cases) including 40 deaths.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo.

On 2025-02-24 23:45:15, user Stephen Goldstein wrote:

I am now pasting in the review done by Gemini 2.0 Flash Thinking Experimental. I have not edited it.

Prompt: Please conduct an extremely rigorous, journal-agnostic peer review of this manuscript.

Response:<br /> I have now thoroughly read the manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination" by Bhattacharjee et al., available at https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1 .

Here is an extremely rigorous, journal-agnostic peer review of this manuscript:<br /> Extremely Rigorous, Journal-Agnostic Peer Review of Manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination"<br /> Manuscript DOI: https://doi.org/10.1101/2025.02.18.25322379 <br /> Manuscript URL: https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1.full-text

Overall Assessment

This manuscript by Bhattacharjee et al. investigates the complex and highly sensitive question of immunological and antigenic signatures associated with chronic illnesses reported following COVID-19 vaccination. The authors employ a multi-faceted approach, combining clinical data from a prospective cohort study with deep immunophenotyping and autoantigen profiling. The study's aim is to identify potential immunological correlates of these post-vaccination chronic conditions, which is a crucial area of investigation given ongoing public health discussions and concerns.<br /> While the study addresses a highly relevant and important question, and utilizes advanced methodologies, this review finds that the manuscript in its current form presents significant limitations in its methodology, analysis, interpretation, and overall conclusions. From a rigorous, journal-agnostic perspective, the manuscript requires substantial revisions and further validation before it could be considered to provide robust and reliable scientific evidence. The current findings are preliminary and require cautious interpretation, and the manuscript overreaches in its conclusions and implications.

Major Strengths (Limited in Impact by Weaknesses)<br /> Addresses a Highly Relevant Question: Investigating potential immunological links to chronic illnesses post-COVID-19 vaccination is undeniably important for public health and vaccine confidence.

Prospective Cohort Design: The prospective cohort study design is a strength, allowing for systematic data collection and comparison between vaccinated individuals with and without reported chronic illnesses.

Multi-Omics Approach: The use of multi-parameter flow cytometry for immunophenotyping and autoantigen microarray profiling are advanced techniques that offer the potential to identify complex immunological signatures.

Clinical Data Integration: The study attempts to integrate clinical data (symptomology, diagnoses) with immunological data, which is essential for translational relevance.

Major Weaknesses and Critical Concerns (Requiring Substantial Revision)

Case Definition of "Chronic Illnesses": Critically Vague and Subjective. The definition of "chronic illnesses" post-vaccination is alarmingly vague and relies heavily on self-reported symptoms and broad diagnostic categories (e.g., "dysautonomia," "POTS," "fibromyalgia"). There is no clear, objective, and validated case definition used. This introduces significant subjectivity and potential for bias in patient selection and outcome assessment. The lack of specific inclusion/exclusion criteria for these "chronic illnesses" undermines the scientific rigor of the entire study. It is unclear if these are new-onset conditions post-vaccination, or exacerbations of pre-existing conditions, or simply background rates of these conditions temporally associated with vaccination.

Lack of Appropriate Control Groups: The study compares "Vaccinated individuals with chronic illness" to "Healthy vaccinated controls." This control group is insufficient. Crucially, there is no unvaccinated control group with similar chronic illnesses. Without this, it is impossible to determine if the identified immunological signatures are specific to post-vaccination illnesses, or are simply signatures associated with these illnesses in general, regardless of vaccination status. Furthermore, a "healthy unvaccinated" control group would be essential to assess baseline immunological differences related to vaccination itself.

Causality vs. Association: Fails to Establish Causality. The study is fundamentally correlational. It fails to provide any robust evidence for a causal link between vaccination and the reported chronic illnesses. The temporal association alone is insufficient to establish causality. The manuscript over-interprets correlational findings as suggestive of causal relationships, which is scientifically unsound. Confounding factors (e.g., pre-existing conditions, other exposures, reporting bias) are not adequately addressed or controlled for.<br /> Small Sample Size and Statistical Power: The sample sizes for the "chronic illness" groups are relatively small (e.g., n=15-20 per group in some analyses). Given the complexity of immunological data and the heterogeneity of "chronic illnesses," the study likely lacks sufficient statistical power to detect subtle but potentially meaningful differences, and is highly susceptible to false positives. The statistical analyses presented are descriptive and lack rigorous correction for multiple comparisons, further increasing the risk of spurious findings.

Over-Interpretation and Over-Generalization of Findings: The manuscript overstates the significance and generalizability of its findings. The conclusions are presented in a manner that suggests a stronger link between vaccination and chronic illnesses than is warranted by the limited and preliminary data. The discussion section speculates broadly on potential mechanisms without strong empirical support from the study itself. The manuscript risks contributing to vaccine hesitancy by presenting preliminary and correlational data as stronger evidence than it is.

Limited Validation and Reproducibility: The findings are not validated in an independent cohort. Without external validation, the reproducibility and generalizability of the identified immunological signatures are highly questionable. Given the small sample sizes and potential for batch effects in omics data, validation is absolutely essential.

Lack of Clinical Characterization and Phenotyping: Beyond broad symptom categories, the clinical characterization of the "chronic illness" groups is superficial. Detailed clinical phenotyping, including objective measures and validated diagnostic criteria, is lacking. This makes it difficult to interpret the clinical relevance of the identified immunological signatures. The manuscript does not adequately address the heterogeneity within these broad "chronic illness" categories.<br /> Potential for Reporting Bias and Selection Bias: Individuals who believe they have experienced adverse events post-vaccination may be more likely to participate in studies investigating vaccine-related illnesses. This introduces potential reporting bias and selection bias, which are not adequately addressed in the manuscript.

Ethical Considerations and Responsible Communication: Given the sensitive nature of the topic and the potential for misinterpretation, the manuscript needs to demonstrate a heightened awareness of ethical considerations and responsible scientific communication. The current manuscript, in its over-interpretation and lack of caveats, falls short in this regard. The potential for public misinterpretation and the fueling of vaccine hesitancy needs to be carefully considered and mitigated.

Recommendations for Substantial Revision (Minimum Requirements for Consideration in a Rigorous Journal)<br /> Develop and Implement Rigorous, Objective Case Definitions:

This is the most critical revision. The authors must develop and apply clear, objective, and validated case definitions for each "chronic illness" category. This requires moving beyond self-reported symptoms and broad diagnoses. Consult with relevant clinical specialists to establish specific inclusion and exclusion criteria based on objective clinical findings, validated diagnostic tools, and established disease classifications. Vague terms like "dysautonomia" and "POTS" are insufficient without precise operationalization.

Include Essential Control Groups: Absolutely essential revision. The study must include:<br /> Unvaccinated individuals with similar chronic illnesses: To determine if the immunological signatures are specific to post-vaccination illnesses or are general features of these conditions.

Healthy unvaccinated controls: To assess baseline immunological differences related to vaccination itself.<br /> Focus on Establishing Causality (or Acknowledge Purely Correlational Nature): The authors must either:<br /> Significantly temper their interpretations and conclusions to reflect the purely correlational nature of the study. Avoid any language that implies or suggests causality. Clearly state the limitations of correlational findings. <br /> OR<br /> Implement methodological approaches to strengthen causal inference. This would be extremely challenging in this observational study design, but might involve more sophisticated statistical methods to address confounding, mediation analysis, or focus on subgroups with stronger temporal associations. However, even with these, establishing robust causality will be very difficult.

Increase Sample Size and Statistical Power: Larger sample sizes are needed, particularly for the "chronic illness" groups, to improve statistical power and reduce the risk of false positives. This may require multi-center collaboration and a significant expansion of the study.

Rigorous Statistical Analysis and Correction for Multiple Comparisons: Implement more rigorous statistical analyses, including appropriate correction for multiple comparisons in omics data. Focus on robust statistical methods and validation of findings.

External Validation in Independent Cohort: Validation of the identified immunological signatures in an independent cohort is essential to assess reproducibility and generalizability. Without validation, the current findings are highly preliminary and should be interpreted with extreme caution.

Enhance Clinical Characterization and Phenotyping: Provide much more detailed and objective clinical characterization of the "chronic illness" groups. Include validated clinical assessments, objective measures, and explore heterogeneity within these groups.

Address Potential Biases: Acknowledge and discuss the potential for reporting bias and selection bias. Implement strategies to mitigate these biases where possible, or at least transparently discuss their potential influence on the findings.<br /> Responsible and Cautious Communication: The manuscript must be rewritten with a much more cautious and balanced tone. Avoid over-claiming, over-generalization, and any language that could be misconstrued as definitive proof of vaccine-induced chronic illnesses. Emphasize the preliminary and correlational nature of the findings, and the need for further rigorous research. Clearly discuss the limitations of the study and the potential for misinterpretation.

Recommendation for Publication<br /> In its current form, this manuscript is not suitable for publication in any rigorous peer-reviewed journal. It suffers from critical methodological flaws, significant limitations in its analysis and interpretation, and overreaches in its conclusions. Substantial revisions are absolutely necessary to address the major weaknesses outlined above.<br /> Without these extensive revisions, the manuscript risks contributing to misinformation and vaccine hesitancy without providing robust and reliable scientific evidence.<br /> If the authors are able to undertake the very significant revisions recommended, particularly regarding case definitions, control groups, causality, and validation, the manuscript might then be reconsidered for publication in a specialized immunology or clinical immunology journal, but even then, would require extremely careful and balanced framing of the findings. Publication in a very high-impact journal like Science or Nature remains highly unlikely given the fundamental limitations in the study design and the sensitive nature of the topic.

In conclusion, while the research question is important, the current manuscript requires a fundamental re-evaluation of its methodology, analysis, and interpretation to achieve scientific rigor and responsible communication of findings.

On 2020-04-22 09:23:55, user Dr Mubarak Muhamed khan wrote:

I keenly read this manuscript. My views. Following are the limitations before coming to conclusions <br /> 1. This is still not a published study in any top journals and must be taken back based on following points<br /> 2. Although a good write up, it’s retrospective study with hurried conclusions<br /> 3. Selection criteria is just based on hospitalised COVID19 patients. And at which stage drugs administered is not clear in all three groups<br /> 4. Outcome criteria is only either death or discharge. What happened to those who got discharged ? Whether there was any hastening in improvement due to these drugs? Whether there is shortening of duration due to drugs from COVID positive to negative?<br /> 5. Whether these drugs have been tried as prophylaxis? Or only used in hospitalised patients? <br /> 6. All patients included are with mean age at 70 and with many comorbidities <br /> 7. What dosages used for hcq and azythromicin ? How many days treatment given?<br /> 8. What type of pharmacovigilance noted for all groups?<br /> 9. Whether at anytime drugs discontinued due to side effects?<br /> 10. What side effects were obvious during the treatment period?<br /> 11. When patients succumbed to mechanical ventilation, how and what type of dosages of these drugs given?

*Although it is good retrospective study to know the effects of HCQ and HCQ+AZT in treatment of COVID 19 infected hospitalised patients.... it will be very much premature to conclude these drug’s role based on short experience and points raised above*

On 2020-04-22 12:07:15, user Thomas Aquinas wrote:

From the pdf:

""However, hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease, as assessed by baseline ventilatory status and metabolic and hematologic parameters. Thus, as expected, increased mortality was observed in patients treated with hydroxychloroquine, both with and without azithromycin."

The standard, successful, worldwide protocol is to begin the HCQ/Zithromax/zinc therapy at the onset of breathing difficulties, not when patients have been placed on ventilators. The current mortality rate for patients on ventilators is 50-80%.

On 2025-10-16 11:36:31, user schmitzboris wrote:

This data is well inline with our previous report in PCS<br /> 10.1038/s41598-023-42615-y

On 2020-12-23 13:11:08, user Sheena Ricarte wrote:

I want to emphasize on the basic human need for employment. Indeed, work gives people dignity, pride, and a sense that they are important since they are a component of an organization meant to serve a key purpose in society. As French Enlightenment writer and philosopher, Voltaire wisely remarked, "Work spares us from three evils: boredom, vice, and need."

These past few months, I found news articles about people committing suicide due to joblessness caused by the coronavirus or COVID-19 pandemic very common. The reports involved people in desperate scenarios from around the world: Canada, the United States, Thailand, India, the United Kingdom, South Korea, Japan, and so forth. This dismal reality shows that chronic unemployment caused by the COVID-19 pandemic is an international problem severely affecting mainly the most vulnerable and the indigents.

Workers whose jobs are on the brink of becoming irrelevant and obsolete due to technology revolutionizing the way we work and live worldwide are also susceptible to joblessness and severe mental health issues. I believe the articles and news reports I read highlight the strong correlation between the significance of JOBS, the apparent viciousness of the COVID-19 PANDEMIC, and the severity of SUICIDE committed by people unemployed for long periods and in abject poverty.

COVID-19 adversely impacted human survival. It immensely deprived the people around the world of their important need to earn a living, provide for their families' needs, basically survive, and live decently. I believe COVID-19 is a great and notorious human rights "violator." Moreover, I believe worklessness is lethal. Having a job basically translates to having food on the dining table and being able to pay one's essential household bills. People who are unemployed for a long period can surely get inconvenienced by its devastating effects on their overall wellness, quality of life, and family's survival.

I think one of the ways to resolve the COVID-19 and unemployment-related suicides is education with a different focus. The world's governments should stress to the people that we live in changing times and educate them on the things that matter the most today and possibly in the future. We live in a changing world and coexist with people from different generations and with different beliefs. As a millennial, I believe WORK and MONEY are LIFE.

Governments and schools worldwide should educate the people about the relevant life and workplace skills and jobs they believe would be important today and in the future. In this way, young people can equip themselves well skills-wise and become smart and adaptable. They can also find employment that can be future-proof. Schools should also emphasize offering helpful financial education and the significance of being financially stable, if not financially secure in the long run, to prevent people from suffering from poverty when they lose their jobs.

On 2024-10-22 02:23:08, user Olivia Piraino wrote:

I really enjoyed reading your paper. This study shows that when it comes to identifying duration-response correlations and determining the minimum effective duration (MED) in phase II trials, model-based techniques like MCP-Mod and FP1 consistently outperform traditional qualitative methods like the Dunnet test. Because these model-based techniques utilize flexible statistical models, they reduce bias and variation and are more accurate in calculating duration-response curves and the MED. But the study also points out drawbacks, like the possibility of underestimating the MED in cases with small sample sizes, which raises the possibility of bias and variability. Although model-based methods are more precise, their practical application may be limited due to their complexity and the requirement for meticulous control of confidence intervals.

After reading your paper, I wonder if this approach would work for other long-term treatments for diseases like HIV. Also, how would these model-based approaches perform using real-world medical patient data, which often includes complex medical conditions, comorbidities, and variations in patient adherence compared to the controlled clinical trial environment? Do you think this will enhance model flexibility or create more challenges?

Overall, I enjoyed your pre-print and look forward to seeing more of your work in the future.

On 2020-02-02 13:28:10, user lily absence wrote:

Dear authors:<br /> I want to know the details of the model, where can I find Methods Supplement?

On 2021-09-19 05:15:21, user Les Smith wrote:

The background rates between 2017 and 2019 are not a valid basis for comparison. The rates of GBS, Bell's Palsy, Neuralgic Amyotrophy, and other such disorders have been significantly suppressed during the pandemic by efforts such as masking and isolation.

On 2022-07-12 03:17:06, user Souradeep Chowdhury wrote:

The article has been accepted for publication; please find the link attached:

https://pubmed.ncbi.nlm.nih...

Please review and link to the published version. Regards

On 2021-03-18 09:12:13, user Bernhard Brodowicz wrote:

The summary in the last paragraph of discussion states: 'On the contrary, we found a significant, slightly increased risk of SARS-CoV-2 infection, which, however, was attenuated when taking account of older children in the same household.' The first paragraph of the discussion however is stating 'The risk of infection was amplified with increasing number of young children living in the household, but the overall association was attenuated when excluding households with older children.' and figure one, shows an increased risk associated with increasing number of children and increasing age of children.<br /> The wording in the summary (last paragraph of discussion) might be a bit misleading.

On 2021-03-26 21:03:47, user odevinyak wrote:

The authors should use Fisher's exact test on mortality data. This leads to p-value of 0.119. The overall mortality difference is non-significant.

On 2020-08-01 23:31:09, user Michael Verstraeten wrote:

I would like to add 2 comments on this article.

1. Specific governmental instructions to family doctors on testing, providing general care and hospitalisation criteria, affect also the randomness of the used samples. Patient with general complaints, but with a suspicion of Covid - 19, were refused or postponed for other needed care so that they were excluded from testing. In this way an unknown part of the population infected with Covid - 19 was excluded from your samples.

2. To state that "we are still far away from natural herd immunity" and "50 - 75 % of a population would need to have protective immunity (...) in order to achieve herd immunity mitigating subsequent waves of Covid - 19", you refer to 2 publications. One from D'Arienzo a.o., and the other from Liu.Y, a.o. Neither of them discuss the phenomenon of herd immunity. Both estimated in an early stage the R0 for Covid - 19. Taking in account your remark about eventual T-cell dependent immunity and your reference to the serology in Sweden, and considering the unknowns about Covid - 19, taking also in account the natural decrease of Rt in several countries before the confinements, there is no evidence that without confinement the overall infection rates in countries would ever reach 50 - 70 %, nor that herd immunity or other forms of immunity would not limit the overall infection rate for Covid - 19 to much lower percentages comparable to the historical infection rates for Influenza pandemics.

M. Verstraeten <br /> MBA

On 2020-11-28 05:22:37, user Samuel wrote:

Please post updated results!

On 2020-04-08 16:52:12, user Winton Gibbons wrote:

The data appear to be from middle of March. How does the analysis change, at least at a gross level, given what has happened over the intervening 3 weeks and counting?

It seems that perhaps even the lowest prevalence case is likely to be an overestimate, perhaps due to a) both local and statewide interventions, b) the specific, regional population structure and demographics, and c) additional hidden variables related to disease transmission.

On 2021-07-17 14:17:15, user killshot wrote:

Unless "efficacy" and "positivity" are more clearly defined, this is meaningless. Eg, using PCR amplification of 35 or greater for diagnosis then using an amplification of 24 for "transmission" would greatly favor vaccine-related prevention of transmission but would be faux data. Also, unless there is randomization for vitamin D levels -- shown to impair virulence if > 36 ng/mL -- the data is also meaningless.

On 2021-10-22 07:52:48, user Adrien MP wrote:

Hi,<br /> Could you make Tables 1 and 2 available in order one can take the full extent of the work presented here?<br /> Thanks

On 2021-08-24 01:52:14, user Raihan Farhad wrote:

Please answer the following, in the interest of academic integrity: <br /> 1. What is the effectiveness of masks used in your model? What number did you use? What type of mask? Worn in what way. I can only assume, given the absence of any experimental data regarding un-regulated masks stopping Covid aerosols, you either assumed the effectiveness of a mask or used someone else's assumption. Please divulge.

1. What is the assumption you made about % of kids having been exposed already? Covid has been around for a while now. If you assumed 0 previous exposure, that is unrealistic, but please state so clearly. If you assumed any other number, please explain how you came to that number and state that number.

2. What is the duration of infectiousness assumed in your model. According to science, someone infected is infectious for about 5 days. After that, even if he dies, he is not infectious. Please explain the temporal nature of infectiousness assumed in your model.

3. Please make your entire model / simulation software (all code) and all parameters, assumptions public.

On 2021-08-23 08:57:48, user Isatou Sarr wrote:

Hi,

What is the self ''clearance efficacy'' of the immune attack complex as a result of re-infection after vaccination and is there a need for medication to boost up the clearance cycle? What is the half-life of the vaccine induced antibodies/immune cells? Most vaccine studies are majorly focused on immune end-points with little on debris clearance and it is important to understand the dynamics of immune ''mop up'' as well as not only the longevity of the generated antibodies/immune cells but their subsequent efficacy upon initial encounter with antigens. It is also critical to understand the clonal expansion pathway of immune cells generated as a result of specific vaccination both on an individual basis and on the wider population.

Thank you.

On 2022-02-10 08:16:31, user Leonie Heron wrote:

Please note that the previous version of this living systematic review on medRxiv can be found here: https://doi.org/10.1101/202...

On 2021-04-15 16:36:59, user AYUSH YADAV wrote:

I want to ask about the validity of Data on Spectacle Wear, which you obtained from reference no. 20 of your article {Sheeladevi et. al.}, the analysis of Spectacle use was not done in that research paper; it was based on refractive errors, and also the predominant region covered by the reference 20 's study {Sheeladevi et. al.} was of Southern India, while your study takes into consideration a North Indian population, as I shall quote from the study, "Fifteen studies were included from South India, one each from Western and Central India, and one study covered 15 states across India" , I think you need a much better method to assess the Spectacle use in general population.

On 2020-07-17 15:30:31, user Kamran Kadkhoda wrote:

Great study! This has implications for vaccines; we already know people lose Abs in early convalescence... see : Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections...in Nature Med.

On 2022-03-27 21:18:37, user kpfleger wrote:

A 2nd, more recent, paper showing the delay and time course of the rise of 25(OH)D with oral D3 is "Effects of Cholecalciferol vs Calcifediol on Total and Free 25-Hydroxyvitamin D and Parathyroid Hormone" Shieh et al, 2017:<br /> https://pubmed.ncbi.nlm.nih...

Of note, the baseline 25(OH)D levels in this paper are nearly identical to those in the CORONAVIT trial. The dose here was 2400 IU/day, so the CORONAVIT 3200 arm is between this study's and the 125ug (5000 IU) arm of Heaney 2003 linked above, but both show a similar curve shape vs time, with the full rise in 25(OH)D level taking ~2 months and about 1/2 to 3/4 of the rise happening after ~1 month.

On 2024-01-27 21:34:52, user kasia bera wrote:

now published here: https://pubmed.ncbi.nlm.nih...

On 2021-12-15 06:52:55, user MD PhD wrote:

Although it's a small sample size still it would be worthwhile to know the antibody response to booster/third dose in 6 months vs 9 months group post-vaccination. Additionally whether these groups received first and second shots at 3-4 weeks or 7-8 weeks interval will offer pertinent information since this basic difference rendered more antibody response in the latter groups as per studies (the point being that boosters might turn out to an immediate requirement for the 3-4 weeks vaccination interval group while the 7-8 weeks interval group might potentially be able to put it off for a month or so in light of prior studies showing a robust antibody response with delayed vaccination)

On 2020-05-01 09:48:59, user Kasper Kepp wrote:

This paper on the state-of-the-art Danish blood-donor data finds a IFR = 0.08% for people between 0-69 years of age. The study is very important because the sampling bias from case fatality ratios (the iceberg effect of knowing almost all deaths but only the most symptomatic cases, i.e. missing the dark number) is largely removed.

By interpolation, the Danish population now has approximately 1.6% infection, corresponding to 100,000 people out of 6 million. The dark number stands at 12-fold the known cases (7-18).

Some minor sampling biases remain (people who are blood donors need to be healthy and may be socioeconomically skewed) but considering the wide blood donor representativeness in Denmark, I think all Danish researchers will agree that sampling bias must be small.

The IFR is also fully in line with the most representative data we have from Iceland (14% of population tested, 48000 tests), where the sampling bias is essentially eliminated, which stands at approximately 0.56% (10 deaths / 1799 cases as of May 1) and includes all the high-risk individuals >70 years. https://www.worldometers.in...

Compared to the Santa Clara study, which caried potential major sampling bias, this issue seems to be now largely removed. Consensus in Denmark is now emerging that the overall whole-population crude mortality of covid-19 is of the order of 0.25-0.6%, in excellent agreement with the Iceland data.

These two countries have not have their health care systems strained, making them the relevant data also for this reason for pinpointing the "real" mortality of covid-19 absent overmortality by capacity exhaustion as seen in some other countries.

Obviously, the fact that the IFR is 0.08% for the 0-69 year old has enormous implications for political decision making in Scandinavia, as it evidences that most of the population can build immunity at much reduced mortality than previously assumed.

On 2025-10-18 15:17:01, user Evolutionary Health Group wrote:

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

Here are our highlights:

The paper defines smoke-during-drought as a distinct exposure, building a single measurable target for preparedness from compound events.

National exposure counts computed are reproducible and dashboard-ready: ~3,630 county-months of drought, ~14,049 smoke-days, ~980 concurrent-days/year.

Results are policy-readable burdens for the contiguous U.S.: drought ~6,576 deaths/year (95% CI: 3,990-9,155), smoke ~10,465 (6,642-14,261), and concurrence +469 (256–682).

Reporting counts with CIs makes the outputs plug-and-play for briefs, staffing, and budgeting, and shows higher per-million impacts in high-SVI counties.

The concurrent added effect appears across causes (non-external, cardiovascular, respiratory, endocrine/metabolic, etc.), and for several categories it exceeds smoke-only. This supports joint operational triggers (when drought and smoke thresholds coincide) and targeted protections for vulnerable counties.

On 2020-05-14 16:32:24, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day. Specifically, your paper (DOI:10.1101/2020.02.15.20023457); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (1) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/d1D3uI-sEeqy...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2021-07-12 02:43:41, user Jennifer McDonald wrote:

Dr Conly et al, I read your pre-print with interest. I have a few clarifying questions.

First, given the demonstrated viral stability on hospital equipment/PPE, I am curious why there was no attempt to collect virus directly from used PPE. Does the pre-publication contain the full list of objects/equipment sampled, including those with negative results?

Second, can you elaborate on the sampling method used to obtain the positive cultures off hands? Were these samples collected at random or immediately following a cough into the hands? Regarding the hand transfer experiment, were the hands washed in culture medium immediately following the transfer? Was this experiment attempted with a longer duration between cough-handshake and handshake-culture?

Thank you for clarifying,<br /> Jennifer McDonald, MD, Ottawa

On 2020-08-06 21:57:31, user James Wyatt wrote:

It would seem that the same techniques could be used to predict the time when a vaccine will appear.

On 2020-03-24 19:51:57, user Aaron wrote:

From the Discussion: "Other studies have showed that high expression of ACE2 in the patients with hypertension, diabetes, and cardiovascular diseases might facilitate SARS-CoV-2 to enter the targeted cells in the respiratory system, and prolong the time of viral clearance [11, 31]."<br /> The references cited here do not show what the authors claim. Reference 11 is only a hypothesis that increased ACE2 expression in ACE inhibitor patients could lead to increased infection rates and increased case severity. These findings of ACE2 expression upregulation following ACE inhibition have only been demonstrated in rats and mice (the renin-angiotensin system is reviewed extensively in reference 31). Even if this phenomenon was shown to be identical in humans, there is no evidence showing that this upregulation leads to increased infection and/or increased case severity.

The authors go on to point out that age and incidence of hypertension, diabetes, etc. are difficult to untangle. This is a great point to make and it would be interesting if the authors can show that their cohort includes a significant population of younger patients with these comorbidities rather than the expected coincidence of older age and hypertension and/or diabetes. However, even if this is shown to be the case, an indication for hypertension or diabetes (type I or II) is not synonymous with ACE inhibitor use. Therefore, the authors should be mindful of any claims that equate hypertension or diabetes to ACE inhibitor use.

On 2022-11-08 15:26:46, user W_R_1 wrote: