On 2021-12-30 12:54:21, user madmathemagician wrote:

Lots of double tongue wording to imply evidence of fraud without even claiming evidence of fraud.

On 2021-11-02 23:21:24, user CptNerd wrote:

It's as bad as he Spanish flu to those that died from Covid...

On 2021-09-02 10:58:33, user yuk wrote:

You make a great point. The reason this is an issue, some people already had Covid and do not wish to see what the long term effects may be taking the vaccine for something they have very good immunity against.

I would say survivability from covid is now known. If you look at anyone under 18 that is healthy they are very rare. Obesity is the big one that is easy to deal with. Lose weight and you take out about over 70% of the victims.

Here is the Swedish government dashboard for Covid. Gives a great breakdown from a country many consider the control group that had a big covid party. Over 80 were old and the rest generally were fat. Big number is cases, medium number is hospitalization and small number is death.

https://experience.arcgis.c...

On 2021-10-28 12:44:20, user n0b0dy0fn0te wrote:

No. Peer-review is still a real thing, and critical. In fact, peer-review can take some time, especially when it concerns something like SARS-CoV-2, simply because of the sheer volume of papers being released on this topic.

It's also not that the study is merely 'not perfect', it is in fact deeply, deeply flawed, suffering from a severe shoddiness in experimental design. I explain why here:

https://www.medrxiv.org/con...

This is not a trivial problem. Meanwhile, the paper's conclusion if flatly contradicted by data from elsewhere, such as the UK Office of National Statistics.

More importantly, what's clear from the data overall is that ALL immunity wanes. This is not unusual, which is why, for example, if you've ever had a puncture injury, you probably got a tetanus booster if you hadn't already had one in the preceding few years.

What is clear is that acquired immunity doesn't give humoral immunity, while conferred immunity does.

On 2021-08-27 17:40:44, user David Wells wrote:

Table 1a shows that your 'vaccinated individuals' group exhibited higher rates of comorbidities. Comorbidities are therefore possibly correlated with vaccination. The model results show insignificant comorbidity effects, suggesting the possibility that your 'vaccination' effect is really (or partially) a case of stolen significance. Did you try removing the vaccination variable to find out if comorbidities then become significant? Or what if you matched on comorbidity rates, not just demographics?

On 2021-08-29 11:56:44, user Hilary wrote:

This is retrospective observational research, so causality isn’t the conclusion here. As mentioned before we don’t know health status of those observed. The journal does mention that the vaccine protects further for those previously infected. The greatest benefit was noticed for those infected at the beginning of the pandemic (Jan- Feb 2020).

On 2021-09-01 18:15:52, user Matthew Bartlett wrote:

How do you distinguish "previously infected" and "previously tested positive" (for being infected) as different?

On 2021-10-30 18:29:19, user Ben Veal wrote:

The CDC study compares people who went to hospital as a result of Covid-19 like symptoms, whereas the Israeli study compares people who were tested for Covid-19, and may or may not have gone to hospital. <br /> The data in the CDC study shows ~6x more vaccinated people went to hospital with Covid symptoms than previously infected people, but a lower percentage of the vaccinated ones actually had Covid-19. So perhaps the previously infected people are less likely to go to hospital in the first place when they don't actually have Covid-19, or to put it another way; vaccinated people are more likely to go to hospital with Covid-19 symptoms when they don't actually have Covid-19. <br /> If people who are more vigilant/paranoid are more likely to get vaccinated and also more likely to report into hospital, I think that could explain the difference in the results, but it's just a hypothesis.

On 2021-08-30 13:38:34, user Brunhilde wrote:

"many different reports" citation needed. A *peer-reviewed* CDC study from Kentucky (MMWR Aug 2021) reached the opposite conclusion. Note that this one is only a preprint.

On 2020-06-07 05:04:06, user Marm Kilpatrick wrote:

This is a very interesting study. There are several important details that aren't clear from the methods and data presented that would help in understanding the patterns:<br /> 1) When were household individuals tested for viral RNA? After the first person in the household became asymptomatic? If children are less likely to be symptomatic, they could be the primary case but not tested until after they infect their household member and that person develops symptoms which could be quite a long time after the child was infected (at the least, an average of 5.5 days after being infected). Given that sensitivity by swab decreases quickly with days since symptom onset, this by itself could lead to an underestimate of infection in children.<br /> 2) Are there data on viral loads? This would help greatly in supporting or refuting the potential infectiousness of children.<br /> Thank you,<br /> marm

On 2020-03-21 19:57:31, user KnowItAll wrote:

I am struggling to understand the labeling of the individual sequences in the tree. For France there are sequences such as hCoV-19/France/IDF0372/2020 and hCoV-19/France/IDF0372-isl/2020. IDF refers to Isle De France and I assume 0372 refers to a patient or sample number, so what does the -isl refer to, are these two sequences from the same sample? Same with hCoV-19/France/IDF0386-islP1/2020 and hCoV-19/France/IDF0386-islP3/2020

On 2020-04-02 23:06:44, user Zvi Herzig wrote:

The intro to this paper states:<br /> "through a follow-up survey, we found that none of our 80 SLE patients who took long-term oral HCQ had been confirmed to have SARS-CoV-2 infection or appeared to have related symptoms."

On the other hand, COVID-19 Global Rheumatology Alliance reports that "25% of patients who developed a COVID-19 were on HCQ at the time of diagnosis".

https://twitter.com/rheum_c...

On 2020-08-22 21:15:39, user Joserra wrote:

There is something I do not understand completely. It has to do with numbers. The autors state that they exclude deaths in nursign homes but estimated taht outside these institutions there have ben 19,228 laboratory-confirmed COVID-19 deaths and 24,778 excess all-cause<br /> deaths. But in reality the number of deceased in nursing home was 68% of the total laboratory confirmed deaths, that is 19,699, that leave the number confirmed deaths ourside nursing home in less than 9000 thousands and not 19.699. Consecuently IFR is about 0.3% similar to seriours flu outbreaks. The total death excess should no be appointed at the virus but to the lockdown and other measures taken by Spanish Goverment.

On 2020-04-23 18:52:50, user Deplorable Kev wrote:

So they tested for IgG antibodies in the population and found a high number of persons already had been exposed and likely immune. Interesting, but that depends on if their assay cross reacts with US Coronavirus and if so, then this is not correct. If their test is specific for COVID-19 antibodies then he is correct. PS: I believe it was here much earlier and we saw cases as early as mid November. Most of this early antibody testing has not been evaluated for cross reactivity yet.

On 2020-04-18 15:41:56, user Zev Waldman MD wrote:

I agree with other commenters that people who suspected prior Covid infection (or exposure) are more likely to seek antibody testing than those who did not. While participants were asked about prior symptoms, it is not clear what if anything was done with this information. It would also have been nice to ask about prior exposure concerns/risks, and report/use that information.

My other concern that has gone less discussed is their calculation of the case fatality rate. While they recognize that reported case numbers as of April 1 are an underestimate, it seems that they forget this skepticism when looking at reported deaths. They seem to take it as a given that 50 people died of Covid in the county as of April 10 as reported, and used this to project to deaths by April 22; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:

1. Reporting of deaths is well-known to be delayed - i.e., date of reporting does not equal date of death

2. People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths

3. The doubling time of deaths used to project to April 22 is also based on reported deaths; if reporting of deaths is delayed, the doubling time may appear slower than it actually was.

If their death estimate due to illness before April 1 is too low, their corresponding CFR would be an underestimate as well. (This would be exacerbated if their case estimate is too high due to self-selection into the study, as seems possible.) At the very least, some sort of uncertainty around the death estimate should be provider, which in turn would increase the uncertainty around the final CFR.

I know CFR wasn't the main focus on the article, but worry that, because these results support their prior beliefs, some readers may take the results at face value and push them to policymakers before they have been more widely vetted by the scientific community.

On 2020-04-18 21:58:35, user dakbio wrote:

What cross reactivity validation have you done against other Corona viruses and what did you use for an antigen (s)? Did you run a BLAST against all other known Corona virus sequences?

On 2020-04-23 19:04:31, user CP wrote:

Gov Cuomo of NY announced two hours ago (4/23) results of antibody study of 3,000 in 19 NY counties. Result showed average infection rate of 13.9% - higher in NYC, lower in rural areas.

On 2020-04-18 23:20:34, user dixon pinfold wrote:

You mean not overrun by serious illness, surely. The number of cases is what we're trying to find out.

On 2020-04-18 16:27:29, user spacecat56 wrote:

Very interesting, thank you. I see I am a bit "late to the party".

But, I'd like to suggest that the self-selection of participants would not be specific to prior COVID-19 infection (which is unknown) but rather on recent prior experience of "flu-like symptoms". Some subset of those, perhaps a majority, actually had influenza.

I'm not sure, though, how you might incorporate this into the analysis...

On 2020-04-23 17:13:56, user gmshedd wrote:

Infection fatality rate of 0.12% to 0.2% means ~100% of Queens and Bronx residents have been infected.<br /> https://uploads.disquscdn.c...

On 2020-04-21 20:29:57, user rerutled wrote:

The Santa Clara results calibrated the test using known gold-standard "negative for covid19" blood. They had 401 "pre-covid19" samples; of those, 2 resulted in "positives". Thus, the test produces a "false positive" rate of 2 out of 401.

They gave the test to 3330 people, and 50 of those tests returned positive. How many of them can be produced by the observed "false positives"?

On average, for a sample of size 3300, one expects approximately 3330*(2/401) = 16.6 false positive results. Is that statistically significantly different from the 50 detected? It depends on what the uncertainty in that "16.6" is. Playing fast and dirty, take the Gaussian uncertainty in that false positive rate to be fractionally, 1/sqrt(2) = 0.70. So the false positive rate is 16.6+/-11.7. Which means, the 50 detected is only (50-16.6)/11.7 = 2.9 sigma above the false positive rate.

Nobody in science should claim a detection which is only 2.9 sigma different from the false positive number. This result is not significantly different from NO DETECTION of positive anti-bodies.

It's even worse than that, though, because I used the Gaussian uncertainty; and as you know - since you have taken an undergraduate class in statistics and understand the binomial, poisson, and Gaussian distributions - the Gaussian approximation isn't accurate for this situation, and one should use the Poisson distribution, or better yet the binomial distribution; but, again, because of that undergraduate class, you remember that both the Poisson and binomial distributions have a broad wingy excess in the higher direction above the mode; which means, the significance of even the detection of ANY positive covid19 antibodies in the Santa Clara County results is even LESS significant than 2.9 sigma.

The authors of this result should answer this criticism, stating what they believe to the the probability of detecting 50 positive results among 3330 tested, given their measured false positive rate.

On 2020-04-17 22:25:59, user LCMB wrote:

So they test a bunch of very wealthy white women from Facebook and Palo Alto/Mt. View, and think that represents a real finding? Why was the socioeconomic characteristic not noted in this report? It goes without saying that this particular demographic likely was self-distancing and using precautionary measures, able to shelter at home, and had the financial means to stay home. If this many people tested positive for anti-bodies, imagine if a real cross-section had been tested. The numbers here are way lower than the actual prevalence of antibodies found in our community. Regardless if the researchers proclaim they were able to adjust/weight the under-represented demographics or not.

On 2020-07-05 00:22:50, user Philip De Groot wrote:

1.5 gray is the equivalent of 1,500 mSv. 100 mSv is the top of the low dose bracket. Are the authors claiming that an exposure of 1,500 mSv is safe, that it constitutes a low dose?

On 2021-01-22 03:19:14, user Roberto wrote:

Ok but, whats is this? More than 200 scientifics and academic articles saying HDC is effective for COVID-19? https://c19study.com

On 2020-04-06 01:21:26, user iggy wrote:

TLDR; Does Coronavirus lower the testosterone of those who survived it, long term? <br /> What percentage of men who had Covid-19 is affected by lowered testosterone? <br /> How much is it lowered?

On 2021-09-16 10:11:35, user kdrl nakle wrote:

The paper claims significant increase of virulence yet many epidemiologists in the US would claim that is not the case with any of the variants. Which is true?

On 2024-11-08 19:59:59, user Andre Boca Ribas Freitas wrote:

Important Observations on Underreported Chikungunya Mortality in Light of Global Burden Analysis

Dear Authors,

I thoroughly appreciated your recent preprint on the global burden of chikungunya and the potential benefits of vaccination. Your work provides critical insights into the widespread impact of this disease and emphasizes the significant potential of vaccine interventions.

However, I wanted to highlight a critical issue that our research and that of others in the field have identified: the substantial underreporting of chikungunya-related mortality across many regions. While chikungunya is often categorized as a non-fatal disease, a growing body of evidence reveals severe and sometimes fatal cases that frequently go unrecorded by epidemiological systems. Our recent studies in Brazil documented excess mortality rates from chikungunya far surpassing those officially reported, with mortality rates up to 60 times higher than recorded by standard surveillance systems?Freitas et al., 2024?. Additionally, studies like those by Mavalankar et al. (2008) in India and Beesoon et al. (2008) in Mauritius underscore the elevated mortality associated with chikungunya during epidemic outbreaks, further reinforcing this critical gap in mortality surveillance.<br /> This growing evidence highlights the critical need for increased investment in molecular diagnostics, integrated surveillance, and more comprehensive mortality tracking for chikungunya. These measures are essential for aligning public health responses with the true impact of the disease and ensuring the full scope of chikungunya’s burden is addressed.

Thank you for advancing this essential conversation. Through improved surveillance and research collaboration, we can work toward effective strategies to mitigate the severe impact of chikungunya globally.

Best regards,

Dr. André Ricardo Ribas Freitas<br /> Faculty of Medicine, São Leopoldo Mandic, Campinas-SP, Brasil

Freitas ARR, et al. Excess Mortality Associated with the 2023 Chikungunya Epidemic in Minas Gerais, Brazil. Front Trop Dis. 2024. doi: 10.3389/fitd.2024.1466207.

Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. Increased mortality rate associated with chikungunya epidemic, Ahmedabad, India. Emerg Infect Dis. 2008 Mar;14(3):412-5. doi: 10.3201/eid1403.070720. PMID: 18325255; PMCID: PMC2570824.

Beesoon S, Funkhouser E, Kotea N, Spielman A, Robich RM. Chikungunya fever, Mauritius, 2006. Emerg Infect Dis. 2008 Feb;14(2):337-8. doi: 10.3201/eid1402.071024. PMID: 18258136; PMCID: PMC2630048.

Manimunda SP, Mavalankar D, Bandyopadhyay T, Sugunan AP. Chikungunya epidemic-related mortality. Epidemiol Infect. 2011 Sep;139(9):1410-2. doi: 10.1017/S0950268810002542. Epub 2010 Nov 15. PMID: 21073766.

Freitas ARR, Donalisio MR, Alarcón-Elbal PM. Excess Mortality and Causes Associated with Chikungunya, Puerto Rico, 2014-2015. Emerg Infect Dis. 2018 Dec;24(12):2352-2355. doi: 10.3201/eid2412.170639. Epub 2018 Dec 17. PMID: 30277456; PMCID: PMC6256393.

Freitas ARR, Gérardin P, Kassar L, Donalisio MR. Excess deaths associated with the 2014 chikungunya epidemic in Jamaica. Pathog Glob Health. 2019 Feb;113(1):27-31. doi: 10.1080/20477724.2019.1574111. Epub 2019 Feb 4. PMID: 30714498; PMCID: PMC6427614.

On 2020-04-29 14:04:12, user JWL36 wrote:

Would someone mind posting the link to the GIthub source code when they find it?

On 2021-07-27 22:07:18, user ReviewNinja wrote:

Some remarks:<br /> - confidence intervals would be necessary when interpolating data from such small numbers<br /> - 90 days is a long period after a positive test for an acute event…<br /> - if you want to compare these numbers to vaccine-caused myo/pericarditis, you need to use the same method (same criteria and same codes) to determine these

On 2020-04-23 17:45:06, user Pedro de Almeida wrote:

How come that in this "paper" Reference 2 is claimed to be an example of studies showing poor results of the use of hydroxychloroquine, but when we go and look into it (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.22.20040758v3.full.pdf)"), it (itself) claims the exact opposite?????

On 2020-04-22 10:39:55, user Niall Toibin wrote:

***First Point***

Obviously the state of the patient and their progression may have influenced the decision to prescribe HC. To quote the paper

QUOTE<br /> baseline characteristics corresponding to clinical severity varied across the three groups of patients and could have influenced the non-randomized utilization of hydroxychloroquine and azithromycin<br /> UNQUOTE

This is the context in which the following has to be taken

QUOTE<br /> A total of 368 patients were evaluated. Rates of death in the HC, HC+AZ, and no HC groups were 27.8%, 22.1%, 11.4%, respectively.<br /> UNQUOTE

No media outlet should report the second quote without the first.

***Second Point***

The authors attempt to account for this obvious bias - the patient's state influencing the decision to use HC.

They compute propensity scores (for different clinical outcomes) for HC use and HC+AZ use based on all baseline characteristics.<br /> i.e. they attempt to look at people who are equally sick in each cohort and see if HC made a difference.

There is a problem with their attempt to account for these baseline characteristics (Age, BMI, pulse, breaths per minute, heart rate, blood pressure, blood count etc.)

Clearly we need to know patient's baseline characteristics at the start of treatment.<br /> (We don't know the dates on which the decisions were made to start HC treatments. We only know the dates of admission.)

If we don't know their medical states on the date of that decision we can't discount that HC was more likely to be tried on the desperate cases. This is the main issue the authors identify and are trying to overcome. Without which the study is meaningless.

But (page 21)<br /> QUOTE<br /> Patient demographic and clinical characteristics, including those associated with the Covid-19 disease severity, were evaluated ***at date of admission,***<br /> UNQUOTE

How the patients illnesses had progressed and what state they were in when it was decided to start them on HC neither we nor the authors have any idea.

On 2020-04-22 02:06:38, user David B Joyce wrote:

19 patient shifted fromNo HC to HC(7) or HC+AZ(12) after ventilation. Ventilation is obviously a sign of increasing severity and greater risk of death. If all of these ventilations resulted in death, then the pre ventilation treatment fatality statistics might look like 22%(HC), 13% (HC+Az) and 21% No HC. Need the data on individual outcomes. Also not impressed with the cohorts.

SPo2 >95: 63%(HC) 57.5%(HC+Az) 73.4%(No HC)<br /> BP> 159: 19.6% 9.7% 9.5%<br /> creatinine>5 17.5% 11.5% 7.6%

On 2021-01-19 14:10:34, user Curbina wrote:

It is very sobering to finally be presented with a study that gives statistical dimensions to what has been already suspected: that surviving COVID-19 is not the end of it, and that the sequelae can be life altering, and even lethal. May this open the eyes to those that insist that there’s no justification to strict measures, but above all, that those who oppose the measures finally realize that this is not a game and that the personal responsibility to stay healthy is also for the sake of keeping your personal sphere of relations healthy.

On 2021-01-24 19:40:01, user Han-Kwang Nienhuys wrote:

I have further analyzed the data in fig. 2; the odds ratios (frequency ratio B.1.1.7 / other) grow exponentially with daily growth factors between 1.06 and 1.09 between 6 weeks and 1 week before the of the data (only considering the UK regions where the error bars in Fig. 4 were reasonably small: EE, EMid, London, NEE, SEE, SWE, WMid). For this I need to assume that a fraction of the SGTF cases are 'false positive', since most regions show a constant SGTF rate in October, before taking off with exponential growth.

Also notable, genomic analysis in UK SEE, Denmark, Netherlands, and Portugal show consistently growth rates between 7 %/d and 9.4 %/d with only Denmark showing a slowdown (from 12 %/d to 7 %/d).

Also, one would expect the odds ratio to grow exponentially over time if there are just two competing variants, each with their own transmissibility or reproduction number. However, the other strains that make up everything else than B.1.1.7 are likely to have slightly different transmissibilities. Over time, one would expect the transmissibility to drift to higher values, also among those other strains. The fact that the odds ratio growth rate is decreasing does not necessarily mean that the B.1.1.7 is getting less infectious; rather, the mixture of other strains could be getting more infectious over time, just because the contributions of the less infectious ones in the mix gradually decreases.

Summarizing: I believe that 6 %/d is an estimate that is significantly too low.

For graphs of my analysis, please see https://twitter.com/hk_nien... .

On 2021-10-10 05:19:33, user kdrl nakle wrote:

I don't know about UK but in case of US this is useless since the mitigation measures are so heavily politicized in the US to the point of absurd actions. For example Alabama, does not even report outbreaks in schools any more and does not quarantine nor test exposed students.

On 2020-07-08 19:13:51, user Will Jones wrote:

Many countries have ramped up testing in recent weeks. Does this not make case data largely useless as an indicator of infection levels? More generally do the constant changes in testing regimes not undermine the usefulness of case data?

When I have analysed death data in various countries I have usually found a brief period of exponential growth, for a week or so. For example there is a brief period of exponential growth between 17-23 March in the death data for London hospitals (by date of death rather than report, using 7 day rolling average). How does this fit into the Gompertz function model - is it too short to 'count'?

On 2021-09-02 16:09:30, user MS Simon wrote:

How do you explain the results in India and Zimbabwe, or are these stories false or misleading:

https://medicalupdateonline...

https://www.citizensjournal...

On 2021-06-01 22:05:39, user st_publichealth wrote:

The article is interesting. A few questions to the authors. Did you pre-specify the definition of negative PCR?<br /> Did you compare changes in viral loads from the baseline? Since the median viral load was higher in the placebo arm, this might have affected the likelihood of viral clearance at day 6 and it should be accounted for.

On 2020-07-28 19:39:18, user Dude Dujmovic wrote:

People that are vaccinated are usually more careful about their health so direct causality here is non-existent.

On 2019-10-17 08:06:25, user Øystein Haaland wrote:

Paper published in PLOS ONE.

On 2021-07-13 02:15:24, user ?0 wrote:

Will note that absolute risk reduction between vaccinated and unvaccinated is 0.32%. The authors’ claims that the vaccines prevented death are not supported by the evidence provided, which is correlated in nature, not causative.

A good paper on this issue: https://www.ncbi.nlm.nih.go...

On 2022-09-30 04:17:40, user Thamir Alandijany wrote:

https://onlinelibrary.wiley...

On 2022-07-29 13:52:30, user Stuart MacGowan wrote:

This is great work! A few years ago I worked on something similar - mapping missense variants to Pfams and defining constrained positions https://doi.org/10.1101/127050 . We also saw enrichment of pathogenic variants at constrained positions. Great to see this area moving forward!

On 2021-11-15 06:45:38, user A Pharma person wrote:

Clearly you don’t have a clue about how FDA approval works if you are blaming the fda for this delay. There are regulations in place that govern vaccine approvals and they require a full 6 month safety followup for the patients on trial. Smh.

On 2021-08-22 14:03:14, user Herbert wrote:

There is strong incentive to upgrade your level of education, especially to the highest option available and perhaps to a lesser extent an MD because of the subject matter. There is much less incentive to downgrade your level of education. So I see no reason to put as much scrutiny on those values to begin with.

Additionally, I'd wager there would be less suspicion if the PhD values wouldn't stick out like a sore thumb. 1% would seem suspicious as well, although I agree with your point that less people would complain in that case. 5% to 15% is what I'd expect to see going by the other values. It is a form of confirmation bias, but partly informed by the data.

Also, the PhD holders make up a relatively small part of the respondents, so even if we'd assume a set percent of dishonest people that pretend to have a completely random level of education, it would matter more in that group.

On 2020-08-12 11:44:27, user My Opinion wrote:

In my opinion...this supports the explanation why certain facilities (e.g. nursing homes, prisons, cruise ships, church gatherings) experience large numbers of individuals who become infected....I have never believed that the primary mode of transmission was a cough or sneeze....in some prison facilities....we have seen 80% of the population inside the facility become infected, including prison guards....the virus spreads too efficiently to blame it on a cough or sneeze....for example, we know that small pox can be spread through exhaled respiration...this research appears to be the first published study to definitively prove COVID-10 can float in the air and infect people quite distant from the infectious source (17-feet)....this explains how large numbers of people can become infected quickly...it is in the air...Thomas Pliura, M.D., Le Roy, IL

On 2022-06-07 10:39:46, user M. M. Welling wrote:

For the 2 patients, both were vaccinated before the PET scans. The control patents were from 2019 thus uninfected and not vaccinated for COVID-19. Neuroinflammation can be initiated by the vaccination after liposomal transfer of the mRNA through the BBB. This needs to be discussed as well.issue

On 2021-08-15 00:21:45, user Covid Hospitalist wrote:

This abstract of this pre-publication is highly irresponsible. There is no clear delineation between 'infection' and 'illness'. This is going to be taken out of context as 'vaccine failure' by multiple groups and news media sources. The drop in prevention of 'infection' ei detectable virus on PCR is important. AND without the data showing that it is still exceptionally effective at preventing hospitalization, is reckless. The authors need to fill in the rest of the blank... they quote the ability of the vaccine to decrease illness/hospitalization from the wild-type "wuhan" strain EUAs in the intro, but then completely leave it out of the results portion of the abstract??? How many antivaxxers/news media are actually scrolling down to table 7 to see that the rate of covid death for pfizer was 0/38,000(n rounded) and moderna 1/36000(n rounded). Seriously irresponsible headline grabbing abstract.

On 2022-01-22 07:07:07, user JanLotvall wrote:

Vaccine equity is certainly important, but does this data really support the conclusion that vaccination rates explain difference in COVID mortality?. If you use the January 2021 rCFR numbers as a baseline, it was 1.83 (95% CI: 1.24-2.43) in highly vaccinated countries, and in rest of the world it was 2.32 (95% CI: 1.86-2.79). This suggests that other factors than vaccination may explain presumed differences in mortality between the different countries, presumably quality of health care, and perhaps other public health variables in the different countries. Tobacco smoking is potentially one factor that could explains differences in trends between countries.

On 2020-07-20 15:23:57, user Gerald Williams wrote:

I have followed your work since April NBC story.

You said you followed the Zelenko protocol (HCQ+Zn+Azithromycin) and just added Ivermectin in order to get the great results.

In a prior version of your article I recall reading that you had HCQ & Azithromycin in a large % of the Ivermectin group as well as in some of the "usual care" group. You never mention critical Zinc in your article. If I was reviewing, I would want to know the details even if they may make the study more "messy". Your results are already very significant in that adding Ivermectin to the Zelenko cocktail extends efficacy to many days later.

There is no need at this time to kill placebo group by having a DB RCT.<br /> What is needed is to repeat protocol where the the control group exactly follows the Zelenko protocol (now in preprint or via Yale's Dr. Risch review in Am. J. Epidemiology) vs the Zelenko protocol + Ivermectin. You should reference.

It is not just you, but every article on treatment should document # of days since first symptoms and # of days since hospitalization (if former isn't known).

The current hypothesis is that Zelenko protocol works great up to 5 days of symptoms and sometimes up to 7 days. Your addition of Ivermectin extends that window into many days after hospital admission, It is no coincidence that in the less severe group you did not find significance because the Zelenko protocol was adequate (except some patients didn't receive full Zelenko protocol.

Your protocol makes Remdesivir obsolete, dangerous and too expensive.

You can follow me and others on Twitter to continue discussion,.<br /> It is shameful that Big Pharma is suppressing this very important finding.

Ideally the Zelenko protocol should be followed at first signs of symptoms and if fails, around day 6, can add Ivermectin to cocktail and repeat (in outpatient or hospital setting).<br /> As you know, Doxycycline (doesn't interact w/HCQ) can be substituted for Azithromycin, so no need for outpatient cardiac monitoring. Perhaps even use the 4 drug cocktail in outpatient setting from start, if continues to prove safe.

On 2021-08-11 09:43:54, user Sebastian wrote:

Could you please add some other vaccine that "reprogram" the immune-system like BCG or MMR (compare Netea et al. 2020)? You present (indirect) the reprogramming as a new effect of mRNA vaccines, what isn't exact enought in my opinion.

On 2021-08-18 19:00:42, user FABIO LIPIANI wrote:

very interesting, the immune system undergoes a very evident reprogramming of which no long-term effects are known,

On 2020-10-14 05:33:25, user Gennadi Glinsky wrote:

Different interpretation of these analyses suggest that preexisting T cells cross-reactive against SARS-CoV-2 are more likely to affect diseases severity because high levels of pre-existing immunity in uninfected individuals appears associated with lower mortality (https://www.bmj.com/content... ). The significant direct impact on the innate herd immunity against COVID-19 and effect on populations’ susceptibility to the infection seems less likely because no association was observed between levels of preexisting immunity and prevalence of the infection.

On 2020-05-03 18:38:20, user nomad monkey wrote:

In this chart, the circle colors indicate the BCG vaccine policy for different countries. The circle locations are plotted according to the the median age and % urbanization of the population. The circle size is proportional to covid-19 deaths per capita. As we have all noticed, the older and more urbanized populations tend to have greater per capita covid deaths (i.e. bigger circles). But notice how huge the circles are for the countries that don't do universal BCG (green & pink circles), as compared to similarly aged & urbanized northern Asian countries that have universal BCG policies (blue circles).

Ecuador strongly supports the the BCG theory, as Ecuador has comparatively high per capita covid deaths for a relatively young and less urbanized population. And Ecuador (pink circle) doesn't do universal BCG vaccination like the rest of South Amercia (blue circles).

https://uploads.disquscdn.c...

On 2021-05-26 03:59:44, user Steve Kirsch wrote:

Why hasn't this paper been retracted yet?

They reversed the numbers for the Niaee study which was pivotal to their conclusion. See this tweet from CovidAnalysis for details on the switch. There is also a video from Niaee himself attesting to the fact ivermectin works.

When you use the correct data, it shows ivermectin works. No surprise.

On 2021-08-01 21:38:38, user Dan Jones wrote:

Should we be concerned that deaths were 14 among the vaccinated and 15 among the unvaccinated placebo group?

On 2022-01-03 01:52:37, user Mike Austin wrote:

Looking for Table S4 referenced in this document? The link to Supplementary material is hidden on the first four tabs of this page. You can find it here: https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.supplementary-material

On 2021-10-18 22:45:59, user Sir Henry wrote:

Table S3 of the Supplementary Materials shows 262 "severe" adverse events for the vaccine, compared to 150 for the placebo. The difference of 112 is too large to be a statistical fluke (p < 0.001) and is a multiple of the number of "severe COVID-19" cases (30) for the placebo (Table S6). In terms of "severe" outcomes (COVID-19 or adverse events), the vaccine appears significantly more dangerous than the placebo over the four month observation period.

On 2020-08-14 20:29:53, user Liisa Laine wrote:

Thank you for your feedback. We will be updating the manuscript shortly.

On 2020-04-08 18:01:50, user Terry McInnis wrote:

What are LabCorp and Quest using? Are they using the same 9 probes on their PCR tests?

On 2024-05-10 00:42:15, user Jess Keller wrote:

Thank you for conducting this important research. I have been going through TSW for 2.5 years and the lack of understanding or consensus within the medical community has made it extremely difficult. We desperately need more research like this in order to start to understand who gets TSW, why and what the treatment options there are.

On 2024-04-27 16:09:01, user Alicia F. wrote:

As someone who is 19 months into TSW, this research is SO important. It helps us get one step closer to finding a treatment and understanding how our bodies are affected by topical steroids.

On 2020-08-09 20:47:59, user med sci wrote:

Perhaps this topic "Seroprevalence of COVID-19 in Niger State" may be modified to reflect that the study was conducted only on small population of Niger state.

On 2021-01-23 12:27:23, user Martin wrote:

Testing was forced and not voluntary. No ethical guidelines were followed. Slovak citizens were forced to go on testing sites to get tested otherwise they would get fine and can not go to job. Even prime minister Igor Matovic (who made people go to testing by force) said few weeks after testing in national TV, that testing was not voluntary.

There is a lot of sources online, even on Youtube (in Slovak language).

In these days, another forced testing is in progress with more severe penalties for people who not attend. For example - who did not attend this testing, from 27th January cant even go out to the nature alone (with no people around). Violation = 1000 eur fine (averege monthly salary in our country).

This goverment totally ignores rule of law and basic principles of law state.

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

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

On 2020-09-16 21:36:10, user Qunfeng Dong wrote:

An updated version of this manuscript is now accepted for publication at JAMIA (Journal of the American Medical Informatics Association)

On 2021-09-17 17:37:04, user kdrl nakle wrote:

Separate two different vaccines you have and then rewrite paper with separate conclusions, not just giving tables. You cannot jam them altogether, tables are not enough.

On 2021-06-23 21:55:50, user David Wiseman PhD wrote:

Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.

Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..

As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.

You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).

To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.

Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.

So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).

Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.

Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.

We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).

Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).

Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.

Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.

_________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.

With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”

We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.

On 2020-08-13 00:49:59, user Jesse Baker wrote:

Regarding a passage in this MedRxiv post (July 21, paragraph 3 with citation to reference #15), “Additionally, recent clusters of COVID-19 cases linked to a…restaurant in Wuhan are suggestive of airborne transmission.”

Although the index case having lunch on Jan. 24 was from Wuhan, the restaurant was in Guangzhou. Indeed, its location far from Wuhan so early in the spread of Covid increased Guangzhou CDC’s confidence that the other patrons were infected by the index case and no one else.

On 2020-08-24 06:26:34, user Stan Himes wrote:

For COVID-19 you should include co-morbidity data, without this key information (which may be contained in full article) the data presented is worthless.

On 2020-06-24 18:16:30, user Gerard Cangelosi wrote:

Nice study, and a very valuable addition. I collaborated on one of the previous studies you cited (Tu YP et al, 2020). May I suggest an alternative explanation for the difference between your findings and ours? You used all-purpose flock swabs, and we used foam swabs. These differences aren't trivial (e.g. see https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.28.20083055v1)"). I would urge you to note this possibility in your manuscript. Thank you!<br /> Jerry

On 2020-04-30 13:54:19, user Charles R. Twardy wrote:

Nice. As A. Kumar notes in Twitter, this is productive engagement rather than just critique.

Note: The result in the abstract & discussion seems to combine two estimates from the results section: the [0.27%, 1.72%] unweighted estimate and the [0.49%, 3.21%] using the original authors' re-weighting. Not sure if this is copy/paste error or deliberately using widest range.

On 2020-06-12 04:50:15, user Paul_Vaucher wrote:

Dear authors,

Thank you for this interesting article of major interest. I find the process and research question to be most relevant. I however have a few questions that remain open to understand how the study could come to the conclusion that aerosols and surfaces were not important vectors of covid-19.

1. What is the external validity of the results for making inferences over infectiousity on the entire period people could be carriers of the disease? In this study, most participants had already been in quarantine for 5 days. Repeated sampling has shown viral load to be optimal in the upper airway system 2 days before and 2 days after symptoms appear. Viral load from nasal and throat swabs drop to a rate where viral culture becomes difficult from 8 days onwards. Most of the study participants were probably beyond that point and were therefore not expected to be very infectious in the first place. If existant, infection through secondary contact and aerosols are however more likely when viral loads are high. It therefore seems difficult from the collected data to infer that household infection through these vectors are unlikely at all times.<br /> 2) When comparing risks from different surface types, how do authors justify the use of chi2 statistics with a sample smaller than 200 and all positive cells with less than 5 cases? In this condition, type 2 errors are very high and this test should not be used under this condition. The number of positif tests are too low to be able to answer the question of whether different surface types are more or less potential vectors of the disease.<br /> 3) Statistical inference assumes independence between measures. This is clearly not the case as a median of 9 samples were taken from each household. Statistical methods should therefore account for these clustering effects. However, the sample size is probably too small for this and a pure descriptive approach without inference could be more relevant.<br /> 4) Could we have any indication on viral load from throat swabs in household cases? If their viral loads were low, we wouldn’t then expect contamination to happen anyway. In two of your 21 housholds, there were apparently not a single case with a positive PCR. This might suggest viral loads to have been too low for any form of infection to have occurred in these households. It seems important to document to what extent each household had at least one person who could infect the air and surfaces.<br /> 5) Likewise, to document risks of infecting the air, were any samples from direct breathing taken from cases Within each household? This seems important as we would theoretically not expect ambient aerosols to be present in aerosols if viruses were difficult to find from air breathed out from cases.

This study investigates an important question. I am however not convinced the method used truly answers the question as the public seems to understand it. Their is indeed room for misinterpretation and for the public to consider contact and air contamination not to occur at any time.

To avoid any overinterpretation, it seems important to clarify that this study only tests risks of air and surface contacts days after people have been placed in quarantine when we don’t suspect them to be very infectious anymore.

On 2020-10-23 05:06:05, user Robert Clark wrote:

This is another paper where positive effects of HCQ are left out of the conclusions the paper reports. In the Table 2, the line for mortality at 28 days shows a cut by a factor of 0.54 on HCQ. The difference is not at the standard 0.05 significance level, with a p-value of 0.22. However this does not mean the result is false. It could just as well be the sample size is not large enough for the significance to reach the 0.05 level.

Too often this is overlooked in medical studies. For instance a significance level of 0.05 means there is 5% chance that the difference is just by chance. Or said another way there is a 95% chance that the difference is not by chance alone, meaning the difference is a real effect.

But by the same token a statistical significance of 0.22, i.e., the p-value being 0.22, means there is a 78% chance that it is a real effect. In other words in probability terms it’s more likely than not to be a real effect.<br /> {There are several online calculators of, for example, the Fishers Exact test of statistical significance, such as here: https://www.graphpad.com/qu...}

Yet, often when a result does not reach the 0.05 significance level, it is common, and mistakenly, reported as the result being proven wrong.

In this regard it must be remembered that these calculated levels of statistical significance are dependent on the sample size. For instance with the mortality rates for the HCQ and non-HCQ cases the very same as in this study but at a large enough sample size the statistical significance could be at the 0.05 level. This is especially important in a study such as this one where The originally planned on number of subjects had to be greatly reduced because of a reduced number of cases of the illness.

Another aspect of this Table 2 becomes apparent from unwrapping the data. The study uses what is called a “composite endpoint”, or “composite outcome”. This means two subcases are combined into one. In this study, the cases of “invasively mechanically ventilated”, i.e., intubated, and “deaths” are combined, called the “Primary outcome” in the Table 2.

But the number of deaths specifically on invasive mechanical ventilation is an important number to find out. This is because the mortality rates for that category have been so high. So, the RECOVERY trial for example counted it as a breakthrough when dexamethasone cut deaths in that category by 30%.

In this study, the “Primary outcome” is the union of the two sets, “invasively mechanically ventilated” and “deaths”. What we want though is the number of those ventilated patients who died, the intersection of the two sets.

Use the formula |A ? B| = |A| + |B| – |A ? B|, which simply means the number in the union is found by adding the numbers in the two sets minus the number in the overlap.

We want the number in the intersection though so we’ll turn it around to get:

|A ? B| = |A| + |B| – |A ? B|

For HCQ:<br /> |ventilated?deaths| = |ventilated| + |deaths| – |ventilated?deaths| = 3 + 6 – 9 = 0. So 0 deaths out of 3 patients on invasive ventilation on HCQ.

But for non-HCQ:<br /> |ventilated?deaths| = 4 + 11 – 12 = 3, so the number of deaths on invasive ventilation not taking HCQ was 3 out of 4.

The numbers are too small to draw firm conclusions though. It is unfortunate that the study could not be completed with the originally planned number of cases.

One last fact left out of the conclusions of the paper that supports benefits of HCQ:

Figure 2. Analysis of outcomes in predefined subgroups.<br /> For analysis of the primary outcome in the subgroup of patients receiving azithromycin at randomization, the relative risk could not be calculated because the primary endpoint occurred in 0 of 10 patients who received both azithromycin and hydroxychloroquine compared to 3 of<br /> 11 patients who received azithromycin and the placebo.

???????

Robert Clark

On 2024-05-02 18:05:18, user Keith Robison wrote:

Using mutagenesis of DNA to break up repeats in sequencing has an intellectual history that is not captured in this version and really should be - e.g. <br /> Unlocking hidden genomic sequence from NAR in 2004 and Facilitated sequence counting and assembly by template mutagenesis in PNAS in 2014 represent two different research groups; there may be others

On 2020-04-11 05:44:42, user Serge wrote:

Please be aware and advised that the situation is still developing in many jurisdictions and <br /> the information in the paper represents only a snapshot in time. Only <br /> following the developments over certain period can provide more <br /> confident background for further statistical studies.

On 2020-10-16 19:04:34, user rick wrote:

The results contradict those published in NEJM for remdesivir. This trial is slightly larger, but the NEJM study was better described, and more homogeneous in its methods. It's unclear to me how the two patient populations compare, although some comments here suggest that, in this trial, they tended to be fairly sick. I would also like to see more work on this drug given early. By the time a person is hospitalized, their immune response to the virus may be more important than viral load, and antivirals can't do anything about that.

On 2020-05-26 10:38:48, user OxImmuno Literature Initiative wrote:

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

On 2020-07-24 04:38:59, user Dr Prachi Sinkar wrote:

How are you sure it was not against COVID? if you are negative when you are tested for COVID but exposed to it before or after - still Ab against COVID possible?

On 2022-01-12 20:21:08, user Mike B wrote:

Do we know it's VOC? Dogs detect Parkinson's using same technique, perhaps from minor expression of misfolded protein. Discrimination and scent memory by dogs is much more complex than we know. <br /> Implies we could build a molecular filter to mimic dog's nose. That however, is elusive.<br /> Fantastic study. Much ??? to working dogs, especially Belgian Malinois!

On 2021-08-10 22:41:16, user Ashley Derrick wrote:

I had covid in May of 2020. I had many long covid issues for months after - but the biggest issue was chest (heart and lung) pain. The symptoms included shortness of breath, burning sensation in the chest, difficulty getting a deep breath and feeling as if I was having a heart attack when more active (exercising). I went to both a heart specialist and a pulmonary specialist and nothing ever was found to be "wrong". The heart doctor was convinced it was inflammation in and around my lungs and heart that caused this. After 9 months, it went away (shortly before my first vaccine). I felt "normal" for about 4.5 months, but two weeks ago, the chest and heart issues came back. Same feelings. I am a fit 49 year old woman. Wondering if there are any studies that see symptoms going away and then months later coming back. Thanks -

On 2021-08-11 10:34:14, user Apriyano Oscar wrote:

I am sorry, I am just a layman. I want to ask about the 1.8% tested positive (608 people). Does it mean that the effectiveness of the Pfizer vaccine in this study is 98.2% ? And is this also the same as what is called as 'efficacy' ?

On 2020-07-18 11:46:21, user Benjamin Kirkup wrote:

Could you address the observations [ie. https://www.medrxiv.org/con...] of viral loads up to 10^11 viruses per ml saliva?

On 2020-05-26 06:06:22, user Stanley wrote:

Modeling is far from accurate. Modeling predicted 2.2 million deaths in this country.

On 2020-05-18 13:48:34, user OxImmuno Literature Initiative wrote:

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

On 2021-08-19 17:26:48, user Pasco Fearon wrote:

Great you have the CDI data! And yes, the modelling could be a bit complicated with potential interactions I agree, but maybe not impossible. And you might still see a main effect in a regression discontinuity analysis - I think that would be worth trying, although you'd need to think carefully about how to specify it. With an administration effect I guess you'd expect a step-down pattern and no linear trend after pandemic measures came in, whereas in a 'developmental impact' model you'd expect to see 'softer' discontinuity, with a likely linear trend setting in after the pandemic measures came in. Don't know if there's enough data there to tease that all apart of course!

On 2020-06-27 15:36:00, user Dr SK Gupta wrote:

Researchers have rightly pointed out that plasma works in Non intubated patients and not so in Intubated patients meaning thereby.. plasma therapy if given late in course of disease may not be effective. Intubated patients obviously meant more sick and those with advanced disease who failed to respond to nasal oxygen inhalation, High flow nasal oxygen and CPAP... <br /> (Authors may please clarify the criteria of intubation if different.)<br /> Findings are in line with the observation that once the cytokine storm has done the damage, Neutralizing antibodies in plasma may not be able to reverse it. Cytosorb or ECMO is the only modality left for such patients apart from usual care.

Good interesting finding was effectively of use of plasma with titres 1:320 while most of the studies are done with titres 1:640.<br /> Study has the limitation of using retrospective cohort as control arm. Prospective selection for inclusion in Plasma arm and retrospective selection in control arm both can increase observer bias.

Nevertheless it is important be because it may not be practically possible to take consent of sick patient to be included into placebo group depriving him of so much advertised benefits of plasma therapy.

On 2021-06-04 11:45:26, user Stephen Smith wrote:

Thanks, Robert. These data shocked me and I was one of the few ID docs using the higher doses of HCQ and AZM. The data suggest that AZM is needed to improve survival. However, when we looked at all the pts who received >3g HCQ, those who also received >1g AZM, on average, received more HCQ and than those who didn't. BTW, I have no idea why Dr. Raoult added AZM to the HCQ regimen. But at the time, those were the only clinical data available. So, we used that regimen. We then realized that the weights of pts varied enormously. So, we started aiming for 80 mg/kg cumulative dose of HCQ (6,000 mg HCQ divided by 75 kg or average adult weight). But overall, the data strongly suggest that AZM co-administration is needed to achieve this success in increased survival.

On 2020-05-05 22:07:15, user Katri Jalava wrote:

I would be quite cautious about shielding. If possible to isolate to an island or similar, then ok, but within the community, I think that is a major challenge. I think it may only create highly vulnerable clusters within the population which then in turn increase the force of infection substantially. One of the main thing to be done in EU is to break these chains of transmission within care homes by e.g. excluding exchange of staff between units, removal of symptomatic from the care homes to isolation units and preventing staff from working even if a suspect case within their household. It may be that this outbreak is much driven by care home clusters.

On 2021-06-17 11:47:05, user Jay wrote:

What are the doses or the duration of the treatment with glucocorticoids<br /> of this study? I'm in a middle of a prednisona treatment I will get the<br /> Pfizer vaccine in a few days. I have a cycle of 50mg x3days, 30mg <br /> x3days, 15mgx3 days and 5mg x2months and I want to know if it's better <br /> now (15mg) or next week with 5mg but more time with the treatment.

On 2021-01-26 07:48:38, user Oliver Kumpf wrote:

This is an interesting study. Regarding some analyses I would be interested in the distribution of organ dysfunction. Were vasopressor-free days and pulmonary-support-free days equally distributed in the therapy vs control cohorts? What were the age groups who profited most? Younger patients were much more likely to survive as is represented in the suplementary material. The Kaplan-Meier curves were without statiscal analysis. Was there a statistically significant difference between pooled IL-6Ra treated patients and controls? What is the number needed to treat. This therapy is expensive and especially in countries with restricted ressources it would almost be impossible to use such treatment.

On 2021-01-21 15:04:47, user CB Bass wrote:

Been saying this for 9 months but Ignored by all MSM outlets. Our published study found that the culprit in the cytokine storm and Covid severity is IL-6. Guess what else? Your gut bacteria- specifically Bifidobacterium regulate IL-6. This is why we are not seeing severe cases of covid in children. They have much higher concentrations of Bifidobacterium in their guts than adults do and it down regulates IL-6 which is pro inflammatory, while up regulates interferon and IL-10 which are anti inflammatory.

Also a study coming out of Hong Kong university last week not only confirmed what our Initial study and discovery showed, it found that patients with Covid severity had deficiencies in Bifidobacterium.

Here is a summary of our study if you’d like to read more on how IL-6 plays a major role in Covid severity in high risk individuals.

https://www.worldhealth.net...

On 2021-09-03 13:01:15, user David B wrote:

Wouldn't Figure 4 and Suppl. Table 2 suggest that for Biontech/Pfizer 19-29 days between doses should be preferred over 30-44 days for people aged 50-64 years, if longer periods between doses aren't possible? Seems to me that there is something in the data, some confounding variable that leads to lower VE in that group. If this is not due to some uncontrolled underlying factor, then 3 weeks between doses should be the preferred option over 6 weeks, if you want to stay within the range suggested by the manufacturer.

On 2021-02-16 20:52:40, user Chris Cappa wrote:

Very interesting study. Interesting to see that exercise doesn't appear to increase the smaller particles but does the larger particles. In any case, two factors you might consider in revision. First is the differential dilution that will occur between different activities. Breathing and talking expiratory airflow rates differ substantially from coughing, from the various ventilatory therapies, and importantly from the OPC. Thus, there will be different levels of dilution associated with each activity that you might factor in to facilitate comparison between activities. It doesn't appear this was done (although I could be wrong). Or, at least note that this likely had an influence. The second issue relates to the comparability between the different activities. For example, talking was continuous whereas coughing was just 6 times in a minute. If a person had (for example) been asked to cough twice as often the number of particles measured would have doubled. Or, if there were more breaks in speech the number of particles would have differed. You might consider normalizing to per second of activity to allow for greater comparability.

On 2020-10-29 16:33:54, user Najmul Haider wrote:

Dear Readers-- the original article of this Preprint is Published as a Peer review article at "BMJ Global Health". You can download the published article from here: https://gh.bmj.com/content/...

On 2022-01-04 18:56:26, user Thomas Barlow wrote:

Did you account for pre-existing T-Cell immunity (pre-2020) which was known in 30 to 50% of the population in year 2020?<br /> Did you account for the fact that most people have had covid (most without even noticing) and will have developed T-Cell immunity naturally? How did you control for that? It's not a static measure and is more expressed and less expressed at different times of month, year.

Studies on INFECTION FATALITY RATE (IFR) - peer-reviewed studies [Studies conducted long before any vaccine] :

(notice the one confirmed for publication by the W.H.O. in September 2020 [published Oct. 2020] - A 0.23% IFR...about the same as flu).

MARCH 2021<br /> “the available evidence suggests average global IFR of ~0.15%”<br /> https://onlinelibrary.wiley...

FEB. 2021<br /> “The infection fatality rate for both the Bureau of Prisons and U.S. was 0.7%. Among institutions that tested >=85% of inmates, the combined infection fatality rate was 0.8%”<br /> https://www.ncbi.nlm.nih.go...

JAN 2021<br /> “The overall non-institutionalized IFR was 0.26%.”<br /> - https://www.acpjournals.org...

DEC 2020<br /> “This rate varied from place to place, with a lower range of 0.17% and a highest estimate of 1.7%.”<br /> https://www.sciencedirect.c...

DEC. 2020<br /> “Results show a fatality ratio of about 0.9%, which is lower than previous findings.”<br /> https://www.mdpi.com/1660-4...

NOV. 2020<br /> “The overall infection fatality risk was 0.8%”<br /> https://www.bmj.com/content...

NOV 2020<br /> “In the United States, COVID-19 now kills about 0.6% of people infected with the virus, compared with around 0.9% early in the pandemic, IHME Director Dr. Christopher Murray told Reuters.”<br /> https://www.reuters.com/art...

NOV. 2020<br /> “The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community.”<br /> https://www.nature.com/arti...

OCT 2020<br /> “We know that antibody tests are not perfect, and there may be a considerable number of people who do not mount a detectable antibody response to SARS-CoV-2. However, even when this uncertainty is taken into account, we still find that COVID-19 has a high fatality rate - on the order of 1% for a typical high-income country.”<br /> https://www.imperial.ac.uk/...

SEPT 2020<br /> The W.H.O. posted a heavily peer-reviewed & critiqued study from May 2020, showing the deaths per cases are 0.23% overall, and going up to 0.5% in the worst hit cities. 0.05% for under 70s - The W.H.O. reviewed it again, then published it in September:<br /> - https://www.who.int/bulleti...<br /> - https://apps.who.int/iris/h...

AUG 2020<br /> The medical journal 'Nature' had an analysis and stated that:<br /> "This result was used to calculate an overall IFR for England of 0.9%”<br /> https://www.nature.com/arti...<br /> ________________

On 2020-10-20 14:30:05, user Martin Dugas wrote:

In our paper serum was collected at the first patient contact (see material and methods). We used a validated commercial test kit to measure OC43 and HKU1 antibodies. We assessed 60 patients in three groups, age and gender matched. Yanqun Wang analyzed 12 severely ill and 11 mildly ill patients.

On 2020-10-14 16:22:00, user Yevhen Pylypenko wrote:

And then — https://www.medrxiv.org/con... :-|

On 2020-04-30 13:35:27, user John Lambiase wrote:

This has greater implications than just covud 19. It could effect most "enveloped" respiratory pathogens. The antimicrobial processes signalled by vitamin D are absolutely fascinating. They trigger multiple facets of immunity.

On 2021-06-30 18:55:18, user Medini A wrote:

Hello! Thank you for flagging this typo; yes, this is meant to be March 2021. The text will be updated shortly in the next version.

-Medini

On 2025-05-16 17:44:24, user Adil Muneer wrote:

Published version of this article titled "Development of a brain-penetrant G9a methylase inhibitor to target Alzheimer’s disease-associated proteopathology" is now available: https://www.nature.com/articles/s41467-025-59128-z

On 2020-08-13 03:24:12, user sjh007 wrote:

As a health care professional I have read this interesting article and find this approach to be extremely beneficial. Time is of the essence, as we all know.. I hope that the proper time and effort are given to review this so that there won't be any delays in getting a "good thing" into the public stream.

On 2025-07-30 13:21:44, user Abebe Almu Fola wrote:

the preprint has been published PMID: 40681807 PMCID: PMC12274420 DOI: 10.1038/s43856-025-01008-0. Can you help me to do connection between the preprint and the published version

On 2020-08-05 20:03:26, user Shi ZHAO wrote:

The peer-reviewed version is published in BMJ Open (DOI: 10.1136/bmjopen-2019-035308), access via: https://bmjopen.bmj.com/con...

On 2021-05-16 08:22:03, user Kohsuke Imai wrote:

Yang Y, Shen C, Li J, Yuan J, Wei J, Huang F, Wang F, Li G, Li Y, Xing L, Peng L, Yang M, Cao M, Zheng H, Wu W, Zou R, Li D, Xu Z, Wang H, Zhang M, Zhang Z, Gao GF, Jiang C, Liu L, Liu Y. Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J Allergy Clin Immunol. 2020 Jul;146(1):119-127.e4. doi: 10.1016/j.jaci.2020.04.027. Epub 2020 Apr 29. PMID: 32360286; PMCID: PMC7189843.

On 2021-06-15 18:59:56, user Quixander wrote:

Do you happen to know of a good primer for laypeople warning them of the pitfalls of diving into the world of scientific studies? I have learned a good number of caveats along the way (the most important being that this is not a layperson’s game), but not before being misled and manipulated a few times by agendas that misrepresented or mischaracterized the science. If there isn’t a one-pager on this, there should be, because (mis)interpreting scientific studies seems to have become a national pastime.

On 2021-08-13 16:42:49, user Dr. Jon wrote:

Isn't it pretty normal to assume those who have recovered from a disease are unlikely to get the same disease again?<br /> Why is this a controversy?

On 2021-02-28 12:37:41, user micro dentist wrote:

Many thanks for your effort. Very useful data, yet requires cautious interpretation.<br /> It is important not to aggrandise conclusions when the sample population is skewed due to disproportionate under-representation.

Such an aggrandisement potentially occurs here:<br /> “The observation that the seroprevalence amongst dental practice receptionists, who have no direct patient contact, was comparable to the general population, supports the hypothesis that occupational risk arose from close exposure to patients.’

Whilst in comparison to 16% of clinical staff 6% of receptionists were seropositive, it is important to also acknowledge that 21.6% of practice managers (also non-clinical) were seropositive.

Where significant conclusions may be derived through occupational comparisons, the effect of disproportionality should also be independently validated through careful examination of the internal validity of any inferred conclusions.

Here this would show lack of consistency with the derived conclusion. Should there still be a requirement a desire for an assumption, it may be worth considering combining any smaller similar samples (such as receptionists and practice managers in this case). In this study such combined group would show a seropositivity of 12.2% (n=131).

Through erroneously overlooking disproportionate occupational representation, there is the real potential of developing ludicrous conclusions: the most obvious being that seroprevalence is related to the amount of occupational administrative paperwork completed by each member of the team: practice managers>dentists>receptionists.

Clearly such a conclusion is neither desirable or valid.

On 2021-06-05 13:34:28, user Gowtham Kumar wrote:

We were trying to access the scans available in Oasis-1 dataset but we were unable to open the scans because the extensions of the files are .nifti.img , if you have any idea to open this please help us.

On 2022-01-05 19:42:22, user Christopher Hickie wrote:

Sorry, your paper is not valid. Omicron not dominant in US until week of Christmas so your 2-week sampling ending on Christmas week is way too premature. Please retract your preprint and wait several months to do this analysis.

On 2020-07-02 23:29:56, user Sergey wrote:

This pre-print does not have a Methods section- how did the medRxiv QC miss this?

On 2021-08-03 11:51:04, user cinnamon50 wrote:

Don't we have to know that testing occurs the same for vaxxed and unvaxxed people ?<br /> am I missing something there in how the selection for testing occurs ?<br /> what if unvaxxed people are selected for testing differently ?

thanks

On 2021-08-13 17:47:57, user Leper wrote:

The problem with studies in "extremely high" vaccination rate locations, is that they include any "herd immunity" effect in both the vaccinated and unvaccinated samples. This makes the comparison between vaccinated and non-vaccinated trickier

On 2021-07-14 13:52:21, user djconnel wrote:

Cases in San Francisco have gone from a 7-day average of 10, to over 70 in just 20 days, with positivity rates from 0.5% up to 3.2%, which for a serial interval of 5 days, corresponds to an R of exp(5*ln(6.4)/20) = 1.6 (using positivity). Your algorithm needs to be tuned if it's yielding < 1.1. Plot of rolling 7-day case totals.

On 2021-02-10 19:35:03, user philippeboucher wrote:

Any link or conflict of interest?

On 2021-07-30 15:16:55, user Resia Pretorius wrote:

This paper does not look at the presence/detection of spike protein in circulation before or after vaccination. The paper only describes the action of spike protein when it is added to blood in the lab, as is the case e.g.during acute COVID-19 infection.

On 2020-12-21 00:47:06, user Nikunj Bhagat wrote:

This paper has now been published by NeuroImage:Clinical and readers can access it at https://doi.org/10.1016/j.n...

On 2020-07-05 18:52:28, user Kamran Kadkhoda wrote:

Neigher Abbott nor the authors used well pedigreed serum samples from patients recently infected with common coronaviruses . this means the specificity arm of the study is very well biased and essentially hundred percent is neither mathematically nor statistically possible.

On 2020-04-19 16:51:41, user Sinai Immunol Review Project wrote:

Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications

Fan Wu et al.; medRxiv 2020.03.30.20047365; doi:https://doi.org/10.1101/202...

Keywords

• Neutralizing antibodies<br /> • SARS-CoV-2<br /> • pseudotype neutralization assay

Main findings

In this study, plasma obtained from 175 convalescent patients with laboratory-confirmed mild COVID-19 was screened for SARS-CoV-2-specific neutralizing antibodies (nABs) by pseudotype-lentiviral-vector-based neutralization assay as well as for binding antibodies (Abs) against SARS-CoV-2 RBD, S1 and S2 proteins by ELISA. Kinetics of neutralizing and binding Ab titers were assessed during the acute and convalescent phase in the context of patient age as well as in relation to clinical markers of inflammation (CRP and lymphocyte count at the time of hospitalization). Across all age groups, SARS-CoV-2-specific nAbs titers were low within the first 10 days of symptom onset, peaked between days 10-15, and persisted for at least two weeks post discharge. In contrast to spike protein binding Abs, nAbs were not cross-reactive to SARS-CoV-1. Moreover, nAb titers moderately correlated with the amount of spike protein binding antibodies. Both neutralizing and binding Ab titers varied across patient subsets of all ages, but were significantly higher in middle-aged (40-59 yrs) and elderly (60-85) vs. younger patients (15-39 yrs). However, plasma nAb titers were found to be below detection level in 5.7% (10/175) of patients, i.e. a small number of patients recovered without developing a robust nAb response. Conversely, 1.14% (2/175) of patients had substantially higher titers than the rest. Notably, in addition to patient age, nAb titers correlated moderately with serum CRP levels but were inversely related to lymphocyte count on admission. In summary, the authors show that patients with clinically mild COVID-19 disease mount a strong humoral response against the SARS-CoV-2 spike protein. Compared to younger patients, middle-aged and elderly patients had both higher neutralizing and binding Ab titers, accompanied by increased CRP levels and lower lymphocyte counts. These patients are usually considered at higher risk of severe disease. Therefore, robust neutralizing and binding Ab responses may be particularly important for recovery in this patient subset. Conversely, patients who failed to produce high nAb/binding Ab titers against spike protein did not progress to severe disease, indicating that binding Abs against other viral epitopes as well as cellular immune responses are equally important.

Limitations

This study provides valuable information on the kinetics of spike protein-specific nAb as well as binding Ab titers in a cohort of convalescent mild COVID-19 patients of all ages. However, similar studies enrolling larger patient numbers, including those diagnosed with moderate and severe disease as well as survivors and non-survivors, especially in the elderly group (to rule out potential bias for more favorable outcome), are warranted for reliable assumptions on the potentially protective role of Abs and nAbs in COVID-19. Moreover, longitudinal observation beyond the acute and convalescent phase in addition to stringent clinical and immunological characterization is urgently needed. <br /> In their study, Wu et al. did not measure binding Abs against non-S viral proteins, which are also induced in COVID-19 and therefore could have added valuable diagnostic information with regard to patients who seemingly failed to mount both binding and neutralizing Ab responses against the SARS-CoV-2 spike protein. Likewise, while this study excluded cross-reactivity of nAbs against SARS-CoV-1, no other coronaviruses were tested. Of additional note, neutralizing activity of plasma Abs was only assessed by pseudotype neutralization assay, not against live SARS-CoV-2. Generally, while these are widely used and reproducible assays, in vitro neutralization of pseudotyped viruses does not necessarily translate to effective protection against the respective live virus in vivo (cf. review by Burton, D. Antibodies, viruses and vaccines. Nat Rev Immunol 2, 706–713 (2002)). Further studies are therefore needed to assess the specificity and neutralizing characteristics of these Abs to test whether they could be candidates for prophylactic and therapeutic interventions. In this context, setting arbitrary cut-off values (ID50<500 vs. a detection limit of ID50 < 40) and thus classifying up to 30% of patients in this study as “weak” responders does not take into account our currently limited knowledge regarding protective capacity of these nAbs and should therefore have been avoided by the authors.

Significance

This preprint is arguably the first report on neutralizing and binding Ab titers in a larger cohort of mild COVID-19 patients. Assessing Ab titers in these patients is not only important in order to confirm whether mild COVID-19 elicits robust nAb responses, but also adds further information regarding the use of plasma from mild disease patients for convalescent plasma therapy as well as vaccine design in general. Future studies will need to address now whether the nAb responses generated in mild disease will be protective or (functionally) different from nAbs generated in moderate and severe disease. The findings in this study are therefore of great relevance and should be further explored in ongoing research on potential coronavirus therapies and prevention strategies.

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 2020-11-19 15:54:00, user Lorenzu Borsche wrote:

Hello, this sentence:

Subsequently, we calculated sample size assuming a 50% between-group difference in hospital length of stay (considering 7 days as a median time of stay, with an expected variability of 9 days).

to me is not quite clear: do you mean, that you preset a desired length of stay to 7 days and the grouped the data so that both groups fit these 7 days? Thus did you mean a 50:50 distribution wrt the 7 days? If so, this cannot be done without distorting the data. If not, please explain, TIA Lorenz Borsche

On 2021-08-03 00:44:35, user practiCalfMRI wrote:

Any chance you could assess hematocrit levels in the COVID group before/after? While I wouldn't expect any significant changes in Hct for the controls, the WBC count for the post-infection group could be significantly increased, with RBC count decreased accordingly. (Pretty standard post-viral infection effect, esp. w/ serious disease.) If Hct is indeed lower, I would then wonder if there might have been a compensatory boost in local CBV which, by virtue of the different blood and tissue T1s, happens to manifest as patchy changes in apparent GM volume.

On 2021-09-12 06:33:29, user kdrl nakle wrote:

Very interesting, so mixing is the way to go. I want to see now J&J with AZ.

On 2021-12-08 20:43:25, user Peka Bali wrote:

page 15 of the full text report reads: "Symptom probability time courses for participants with confirmed COVID-19 (n=1020, RT-PCR, antigen, or antibody tests) overlapped significantly with probability estimates from the whole population (Figure 7), except for “changes in sense of smell/taste."

How does this coincide with the conclusion of the report on the first page?!

"Conclusions. Patients with Long COVID report prolonged multisystem involvement and significant disability. Most had not returned to previous levels of work by 6 months. Many patients are not recovered by 7 months, and continue to experience significant symptom burden. "

I am simply flabbergasted by this pseudo-scientific conclusion, not to mention giving it a collective name!<br /> If anything, the only rational conclusion that can be drawn is that other than the altered sense of taste/smell there is NO correlation or causation whatsoever between Covid-19 and the other 65 symptoms as described!<br /> The insinuation as posed above in the publication, stating negative PCR and antibody tests as "suspected cases" is just absurd. What do you base this assumption on?!

This report makes no sense: when you have a control group and baptizing your control group to "suspected cases" to justify the conclusion, which is that these symptoms are Covid related when they are clearly not.

On 2021-04-26 04:10:49, user ????' ???? ??? wrote:

Tables S4-S7 show coefficients for both male and female. How come? One should be the reference.

On 2020-06-06 13:59:32, user Nayo57 wrote:

While the result of this interesting and meaningful analysis may be statistically correct: a reduction of R of 0.04 with 10% mobility reduction does not explain the vast reductions from R = 3-4 at outbreak to below 1. A rough analysis of WHO reported case data and Google mobility data gave a similar result e.g. for time to reach R<1 or cumulative cases per population, measures one would expect to be impacted by effective social distancing. The best conclusion may be that mobility index as provided is not a suitable measure to assess or guide policies to contain COVID-19: Fig1 (Germany: increase in mobiilty index while R stays <1), Fig. 2(USA: decrease in mobility by further increase in R) and the scatter in Fig 3 support this view.

The interpretation would rather be that BEHAVIOUR during mobility activities matter much more than the QUANTITY of mobility. Alternatively, more focussed indexes (restaurants/bars; cinemas/theaters..) may be worth while to examine if they could be useful.

On 2023-10-06 12:33:05, user Ashok Palaniappan wrote:

An advanced version post peer-review is now available [open-access]:<br /> Muthamilselvan S, Palaniappan A. CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers. PeerJ. 2023 Sep 27;11:e15912. doi: 10.7717/peerj.15912. PMID: 37786580; PMCID: PMC10541812.

On 2021-08-26 05:47:36, user MarcoBonechi wrote:

You assume 2-3 students being infected at the beginning. Out of 500 students. 2.5/500=0.005 i.e. 500 cases per 100k.<br /> That's 10x actual Aug-2021 US rate at 46 (https://www.nytimes.com/int... "https://www.nytimes.com/interactive/2021/world/covid-cases.html)").<br /> 18x the CA rate of 26.

Your study has <10% chance of happening?

Please explain.

You should redo the study using several scenarios using randomized chances of a student being positive from outside.

Then also randomize symptoms, as symptomatic cases will stop spreading or be caught altogether before reaching school.

Then also randomize mask failure rate, badly worn masks, ineffective masks etc..

Finally add testing with weekly or twice-weekly universal antigen with their success rate.

You got to put more work!

On 2022-01-29 14:26:08, user Alberto wrote:

Thank you for this study. It's important to have this kind of study in a country like Greece where mortality has been very high in 2021 (compared to other European and worldwide countries and compared to itself in 2020) because we can appreciate the difference between the reality observed and the projected modeling based on the data that is available about vaccination status. The resulting model, which is incompatible with the reality observed worldwide, is a good measurement of the quality of the data available. I hope this can be looked at in more <br /> detail by more people thanks to this study.

On 2024-08-14 07:26:35, user Christina Dahm wrote:

Hi there!<br /> Since posting here, our paper has been published. Could you add this information to the preprint listing? Thanks!<br /> 10.1007/s00394-023-03090-3<br /> bw Christina

On 2020-06-24 20:42:23, user Ece Demirbas wrote:

TzanckNet will be a useful method in clinical applications by not only providing high accuracy, sensitivity and specificity but also lowering the cost of diagnosis for erosive-vesiculobullous diseases. Congratulations to the authors for such promising research. Ece Demirbas ,MD

On 2021-02-25 15:12:43, user jubel wrote:

"It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms." – do these 80% refer only to infected people who were hospitalized, or are mild cases (no hospitalization) included? That would be very important to know.

On 2021-08-21 04:36:50, user Fergal Daly wrote:

This applies linear regression to cumulative cases against NPI scores. It does not specify any model that justifies this. Simple models suggest a linear relationship between NPI scores and estimated R_t or log(case-growth). No model would suggest a linear relationship between these two. In the simplest example, if NPIs bring R_t below 0.9 it leads to very few cumulative deaths, with no much difference between very strict and less-strict, as long as R_t is < 0.9. Conversely, all NPI that leave R_t above 1.1 , lead to explosive growth and very similar large numbers of cumulative deaths. The relationship is highly non-linear and applying linear regression has no justification. The statistically significant outcome must be either chance or systematic result of the mis-specification.

On 2021-08-27 04:39:32, user William Brooks wrote:

The authors use cumulative deaths from June 2020 but don't explain why they omit deaths before June 2020 (i.e., the <br /> whole first wave). Since many of the deaths during the omitted period <br /> occurred in states with strict NPIs such as Maryland (Fig.1a), this probably biases the results in favor of stricter states since they would have had smaller susceptible populations after the first wave than other states. Another study got around this problem by excluding northeastern states from the main analysis of the summer wave and including them in the analysis of the autumn/winter wave [1]. Because different NPIs were introduced/lifted at different times in different states, it would be interesting to see how consistent the correlation between NPI strictness and cases/deaths is during different waves.

Also, Fig. 3a shows that case trajectories are clearly effected by geography, so rather than directly compare two states with different NPI strictness from different regions (Maryland and Tennessee), it might be more informative to compare two states with different NPI strictness from the same region (e.g., Louisiana and Florida).

[1] https://escipub.com/Article...

On 2025-06-26 07:45:58, user Stephen Jones wrote:

Now fully available as of June 26, 2025 at<br /> https://doi.org/10.1162/IMAG.a.76

On 2021-03-17 20:32:20, user Alessia Visconti wrote:

Now published in the British Journal of Dermatology (https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/full/10.1111/bjd.19807)")

On 2021-08-03 21:39:28, user Fruit Gal 522 wrote:

Judging from how long ago this was published...June 1, and it's now Aug. 3, it's taking excessively long for this to be peer reviewed. Sources from a google search say it should take 3-4 weeks to review.

On 2021-08-17 21:21:24, user Harold Saive wrote:

There is no evidence that masks provide mitigation

On 2021-01-09 09:39:25, user Dr. Sebastian Boegel wrote:

This is a wonderful study. Congratulations. I am very honoured that you used my tool, seq2HLA. As seq2HLA also output HLA gene (and allele) expression (normalized to RPKM and the coounts), i am wondering why you additionally used AltHapAlignR for obtaining read counts for HLA genes. Did you experience any issues with seq2HLA? If yes, i am happy to help. <br /> All the best for you and keep up the great work,

Sebastian

On 2020-04-11 15:25:20, user Sinai Immunol Review Project wrote:

Title: Level of IL-6 predicts respiratory failure in hospitalized symptomatic COVID-19 patients

Summary:<br /> As hospitals around the world are being overwhelmed due the COVID-19 pandemic, it is of utmost urgency to identify biomarkers which would accurately predict patient outcome at an early stage. Severe COVID-19 complications often include acute respiratory distress syndrome.<br /> The authors analyse clinical and laboratory findings in 40 COVID-19 patients: 13 required mechanical ventilation, and 27 did not. Age, comorbidities, radiological findings, respiratory rate or qSofa score (mortality prediction score upon sepsis) did not discriminate between<br /> both groups. However, all patients requiring intubation were male, and the pulse as well as several laboratory parameters correlated with the risk of respiratory failure. The strongest correlation was found to be with serological IL-6. Elevated IL-6 was found to be associated with the risk of respiratory failure by a factor of 22.

Critical analysis:<br /> The value of 80pg/mL of IL-6 was identified as decisive, as 92% of patients who reached this cut-off required intubation 0-4 days afterwards. The study must be extended to a larger cohort, however, to perhaps identify a more precise cut-off.<br /> Moreover, the causality between IL-6 and respiratory distress remains unclear. Understanding the mechanisms behind these correlations could pinpoint new therapies to prevent respiratory failure.

Relevance to current epidemic:<br /> Foreseeing respiratory distress at an early stage in admitted COVID-19 patients could allow hospitals to better manage their resources, both human and material.

By Maria Kuksin

On 2020-07-14 03:14:19, user Robert Kernodle wrote:

I hope a statistical expert looks at this, because I suspect significant flaws in methodology that do not justify the conclusions. Based on physics, fluid dynamics -- the extension of these basic principles to the structure of woven cloth masks, in relation to infectious aerosols -- this supposed statistical study does not seem to hold up to reality.

On 2020-05-10 04:11:31, user JM V wrote:

Not taking shot noise of the 7 deaths into account. If the expected deaths would have been 14.6, the two sigma plausibilty range would have been 7 to 22. The error bounds in this paper do not reflect this.

On 2021-03-03 01:17:29, user Dawn Christine Khan wrote:

I am a covid survivor, and said the same. 95 symptoms was incomplete. <br /> I had 150. This is the most comprehensive Long Haul research I have seen. I recommended it for CDC/NIH publication. Community NEEDS this!! May I receive a text or spreadsheet list of symptoms and categorization used? for more information http://www.linkedin.com/in/...

On 2022-01-24 21:23:22, user KBNJ wrote:

Am I wrong in thinking it's entirely possible that the negative effectiveness of the vaccine for recovered people is real (biological), and not behavioral? Our bodies don't have unlimited immune resources. If vaccines induce an immune response that is less effective than recovered immunity at fighting an evolved variant (which various studies have shown at least for Delta), I would think this would be expected, and not a surprise.

On 2020-04-10 11:48:59, user Srinivasa Kakkilaya wrote:

It's a very interesting analysis which should show the way forward in this crisis. If I'm allowed, I am posting a brief analysis that I did the day before, with data collected from various official sources and publications. It's here below:

Corona Virus Disease (COVID) 2019: Comparison of Cases in India and Abroad

Summary:

The trends of COVID 19 infections, complications and mortality are similar in almost all the countries, including India.

Risk of developing severe disease and death is higher in those aged 60+ years, and particularly in those with modern diseases such as hypertension, diabetes and coronary artery disease.

In India, 8.5% of the population is aged 60+ years, and 4-11% of the population aged less than 40 years is afflicted with hypertension and diabetes, and these are vulnerable to severe COVID 19.

The common factor for increased risk of severe COVID 19 is the presence of the so called metabolic syndrome at any age, old or young. These disorders are related to consumption of sugars and sweets, fruit juices, sweetened beverages, processed and fast foods, fried foods etc., and also alcohol consumption, and smoking. Avoiding these will be helpful in combating COVID 19.

COVID 19 remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are likely to have very mild or no symptoms.

Details:

India has already recorded about 5500 cases and more than 160 deaths due to COVID 19. The following analysis is based on the scientific and media reports published so far from India and elsewhere.

Corona Virus Infections - Age Distribution:

India:

47% of infections in age <40 years<br /> 34% in age 40-60 years<br /> 19% in age >60 years.

Wuhan, China:

27.2% in the age 0-39 years<br /> 41.6% in 40-59 years<br /> 31.2% in >60 years

It's almost identical in India and China and it correlates with the age distribution of population.

China:

<60 years - 82% of the population, 69% of infections

60 years - 18% of the population, 31% of the infections.

India:

<60 years - 91.5% of the population, 83% of the infections

60 years - 8.5% of the population, 19% of the infections

The higher percentage of infections in the elderly is likely due to more prominent symptoms than the younger population and hence presentation to the hospitals in more numbers.

COVID 19 Deaths: Age Distribution and Risk Factors

India

63% of deaths in those 60+ years of age 30% in those aged 40-60 <br /> 7% in those below 40 years

Average age of victims - 60 years

Average Case Fatality Rate -2.7%<br /> 0.4% for those below 40 years<br /> 2.4% for 40-60 years<br /> 8.9% for those above 60 years

86% had pre-existing conditions<br /> 17% had more than three diseases<br /> 40% had two<br /> 35% had one<br /> 56% had diabetes<br /> 47% had hypertension<br /> 20% had lung disease<br /> 16% had heart disease with diabetes and/or hypertension.

This pattern is also comparable with other countries.

China

81% deaths in age 60+ years<br /> 16.4% in 40-60 years<br /> 2.6% in 10-40 years<br /> 0 in <10 years

The average case fatality rate 2.3%;<br /> 0.2% for those below 40<br /> 0.85% for 40-60<br /> 8.8% for those above 60 years<br /> (14.8% in patients above 80 years)

Italy

95% deaths in age 60+ years<br /> 4.7% in 40-60 years<br /> 0.27% in 0-40 years

99.2% had one or more pre-existing diseases (75% had high blood pressure, 35% had diabetes and 33% had coronary heart disease)

United States (of the first 1150 deaths)

89.9% in 55 years and above<br /> 9.4% in 35-54 years<br /> 0.7% in 0-34 years

UK (of 750 deaths)

69% aged above 75+ years<br /> 96% had pre-existing conditions

These details clearly show that in all the countries, the case fatality of COVID 19 has shown direct correlation with age of the patients and with age-related diseases such as hypertension, diabetes and coronary artery disease and that the mortality was higher in men compared to women.

In India, 63% of deaths occurred in those above 60 years of age, and 30% deaths occurred in those aged 40-60. Considering the fact that 86-90% of the deaths occurred in those who had pre-existing diseases, the higher number of deaths in the 40-60 years age group seen in India is attributable to younger onset of these diseases in Indians. In India, the overall prevalence of hypertension is about 30%, and about 11% in the age group of 40 years or lesser. Type 2 Diabetes has an overall prevalence of 16-19%, whereas in the young, it is about 4-8%. These diseases, coupled with consumption of alcohol and tobacco, increase the risk for COVID 19 complications in those aged above 60 and also in those who are younger. Otherwise, COVID remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are very likely to have very mild or no symptoms.

If I may add, it appears that the deaths are directly related to metabolic syndrome linked disorders and the 33 cases that apparently had no identifiable cause in NY in your series might have had other problems of metabolic syndrome such as hypertriglyceridemia or premature balding etc., all of which are linked to hyperinflammatory state.<br /> Thank you again for the interesting and path breaking effort!