On 2021-09-14 21:28:12, user Alberto wrote:

23 vaccinated individuals, samples collected 5.2 weeks (average) after the second dose of the vaccine. No information about age, health, etc... compared to 10 individuals infected one year prior to taking the blood samples and 7 infected less than 2 months prior to taking the blood samples. Again no information about age, health, etc...

Conclusion: "Hence, immune responses after vaccination are stronger compared to those<br /> after naturally occurring infection, pointing out the need of the vaccine to overcome the pandemic".

Isn't that conclusion going well over the possibilities of this study? When in real world studies with cohorts of > 25.000 individuals it has been proven that the immunity acquired from infection is vastly superior to that from vaccination, how should we take these results?

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

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

On 2021-09-16 07:33:29, user Chaos_14 wrote:

This study doesn't mention how many vaccinated vs unvaccinated people were tested.

"Notably, 68% of individuals infected despite vaccination tested positive with Ct <25, including at least 8 who were asymptomatic at the time of testing." (68% of what number?)

Since we know immune response, even with vaccines, decreases with age, it would be helpful to know the ages of the people in both the vaccinated and unvaccinated groups.

It would also be helpful to know the Ct in the samples of asymptomatic, <br /> unvaccinated people if there were any.

While it's beneficial to know that it's possible for infected vaccinated people to carry a viral load similar to infected unvaccinated people, this study left me with a lot of unanswered questions.

On 2021-09-16 13:24:58, user Theo Sanderson wrote:

The apparent pattern of back mutations at position 142 is an experimental artefact due to errors in some Delta sequences. It emerges from the fact that Delta has SNPs in the primer binding site for ARTIC amplicon 72 (in a previous ARTIC scheme) which often result in the failure to amplify this amplicon, containing the G/D 142 locus, from Delta samples. Small amounts of contamination from other genotypes (e.g. B.1.1.7) that are amplified normally at this location can then lead to an amplicon here (typically with reduced depth). This results in a final sequence which appears to have a back-mutation at this position, and phylogenetic analyses can tend to group such samples together on trees.

T95I is in this same amplicon.

It is likely that the Ct correlations observed here reflect the fact that the correct G142D call is much more likely to be detected despite the low efficiency of amplification for samples with higher viral loads.

On 2021-09-16 13:35:24, user David Brown wrote:

There is evidence that abdominal obesity in both humans and chickens is determined by the fatty acid profile of the diet; specifically, the linoleic acid content. Read pages 7-9 of this 2019 Master's Thesis. https://trace.tennessee.edu...<br /> For further comment regarding linoleic acid intake and vulnerability to COVID-19 complications, read these articles:<br /> https://www.medpagetoday.co...<br /> https://www.science.org/doi...

On 2021-09-17 17:04:42, user kdrl nakle wrote:

This would all be OK if we could rely on COVID reporting but we cannot. For example a continent of 1 billion people, Africa, on Wednesday reported 12,000+ cases while we have seropositivity in Kenya of 50%! Meaning, their numbers as reported, are a joke. India that reported some 33 million cases had more likely some 900 million cases. And similar things are happening throughout Asia, Latin America, and Eastern Europe. In other words, your statistics are a joke.

On 2021-09-19 12:46:53, user daan joubert wrote:

I rhink you are referring to the article entitled "Africa Dailye deaths.100k etc" showing the difference between the high incidence in the upper and lower parts of the continent compared to the equatorial region where Ivermectin is used against tropical parasites and there are few deaths. It seems to have been removed for some guessable reason.

On 2021-09-22 01:46:06, user jhick059 wrote:

Dear authors,

I believe your denominators (15,997 Moderna doses and 16,382 Pfizer doses) are off by more than a factor of 10.

Ottawa Public Health has 342,656 doses of Moderna and 485,178 doses of Pfizer between 2021-06-01 and 2021-07-31. Link: https://open.ottawa.ca/data...

You also state (pg. 6/20) that your data suggest a tenfold higher incidence than other papers estimating an incidence of 1/100,000. A tenfold higher incidence than 1/100,000 is 1/10,000, which is closer to the value you would obtain with the adjusted denominator.

Sincerely,<br /> Joseph Hickey

On 2021-09-22 03:14:20, user Norsksoul wrote:

It is a preprint article but they basically identified all vaccine recipients in Ottawa during the June 1 through July 31 study period. <br /> This was the denominator of the study group. <br /> Anyone from this study group that was admitted with Acute Myocarditis or Pericarditis within 1 month of a Moderna or Pfizer vaccine became the numerator. <br /> So 32 cases occurred in 32,379 vaccine recipients which comes out to a 1/1000 incidence. This study should be done in the 12-18 year old age range and the incidence would likely be even worse.<br /> But wait,....it gets even worse. <br /> That 1/1000 incidence is in a group of 32,000 men AND women. <br /> But out of 32 cases of myocarditis, 29 occurred in men. <br /> That’s 90%! <br /> They unfortunately don’t give the data on male/ female percentages in the study group denominator but if we assume a 50/50 split, then the male incidence is actually 29/16,189 or 1 in 558 males vaccinated. <br /> 1/558<br /> 1/558<br /> 1/558<br /> Let that sink in for a minute. <br /> This is reckless medical malpractice at its worst.

On 2021-09-23 06:52:46, user White Rabbit wrote:

There are several issues about the meta-analysis by Martinoli et al. for example they wrote they did a meta-regression in order to explain the the huge between-study heterogeneity affecting the results, but no meta-regression results appears anywhere. They observed a statistically significant publicaton bias ("We found an indication for publication bias (P=0.03)" ,page 10) a serious but unaddressed issue. Ther are also inconsistencies between the results and the conclusions, e.g. though they found that "Children and adults showed comparable SARS-CoV-2 positivity <br /> rates in most studies" (page 9)" the abstract reads "children are 43% less susceptible than adults".Furthermore in some tables and forest plots, they used as denominator the total of students and staff altogether instead of students only, to estimate the students incidence.

On 2021-09-23 15:49:33, user kdrl nakle wrote:

What is needed more is the distance between the shot and data collection. We need longer duration period for VE evaluation. Your time period is too short.

On 2021-09-23 18:14:58, user kdrl nakle wrote:

n-28, n=29, n=106 and no significant difference between 2.4x10^5 and 3x10^4? That is because your samples are small. I think that 8 fold increase would be significant if you got bigger samples.

On 2021-09-24 06:20:12, user Ella Lively wrote:

Where can I get the questionnaire for thus study please?

On 2021-09-28 15:09:49, user Tomas Maximus wrote:

Looks like the proportion of breakthroughs climbed dramatically as time went on, with breakthrough accounting for 17% of total new cases in July. Wonder what the August and September numbers showed.

On 2021-09-29 04:15:27, user Nikki wrote:

I work as an account Escalation Specialist/call center supervisor who takes over Escalated calls. I've never had issues with missing small details which are required to do my job. I caught covid in mid July, had a horrible experience with two weeks worth of severe vertigo, nausea, fever spikes, tons of phlegm, panic attacks. <br /> One month and a half after recovery, I've had 4 major fails which may ultimately end up costing me my job. <br /> My pcp, therapist, and boss appear to disregard this when I try to explain to them about the fogginess. <br /> As a very detail oriented person, I just don't miss those things.... never in my 15 years in callcenter experience.

On 2021-09-29 11:30:07, user kdrl nakle wrote:

Good example of making a paper focused on useless graphics instead on self-explanatory data.

On 2021-10-02 14:59:26, user Alberto wrote:

Thanks for the detailed report. I'd only like to ask about the last sentence included in the abstract: "The beneficial and protective effects of the COVID-19 vaccines far <br /> outweigh the low potential risk of neurologic and psychiatric reactions. Going through the paper I haven't seen anything that attempts to estimate these rinks vs. benefits in any way (let alone a systematic way, by age, risk of severe disease in case of COVID-19, etc...). It seems like a statement that's been added there arbitrarily and does not belong to a scientific paper that not actually evaluating any risks associated with the disease itself or the vaccine efficacy to prevent them.

On 2021-10-03 02:28:34, user OBS wrote:

How come this preprint (and the very recent publication of this in NEJM) both say 15 deaths vaccine vs. 14 deaths placebo, but the FDA briefing document for the booster shot (which summarizes the safety of the primary 2-dose series, see page 7), says 21 deaths vaccine vs. 17 deaths placebo?

https://www.fda.gov/media/1...

21 vs. 17 doesn't seem to be an update of the 15 vs. 14 result, since the booster FDA briefing document specifies March 13, 2021 as the data cutoff date corresponding to the 21 vs. 17 result, and that is the exact same cutoff date mentioned in this preprint / NEJM article. So why the discrepancy- what is going on here?

On 2021-10-03 07:19:18, user Ruth Berger wrote:

That age and male sex are major risk factors is well known; mortality associations with pandemic wave should not be reported without factoring in varying levels of underdiagnosis (to my knowledge, it was larger in the first wave than the second) and age-specific vaccination rates.

On 2021-10-03 16:42:16, user Luke Yaldo wrote:

The peer reviewed, published version of this article can be found here: https://link.springer.com/a...

On 2021-10-04 06:54:30, user kdrl nakle wrote:

Simple yet important result, meaning we should definitely know Cp (Ct) value after getting tested. The next thing would be to investigate transmissibility but that is obviously much harder research.

On 2021-10-04 12:12:28, user LG27 wrote:

Why was there no stratification by prior infection? It's not even mentioned in the limitations.

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

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

On 2021-10-16 12:57:40, user gerryz wrote:

Can anyone tell me if there is evidence infection symptoms are milder in vaccinated? Articles?

On 2021-10-05 10:08:23, user Samantha Hester wrote:

Members of the trans community are raising questions about your new exclusion criteria that eliminated people who self-identified as unicorns. Unicorns belong to the otherkin community and their responses could be in good faith.

Please review this post from a trans advocacy organization for more details:

https://www.facebook.com/pe...

On 2021-10-05 22:04:39, user Brooke wrote:

It would be useful to know what sorts of samples were used for PCR testing — were they nasal swabs or saliva?

On 2021-10-06 16:56:58, user zega wrote:

Baseline is 1/100000/365days https://www.myocarditisfoun... vast difference, they an be off by 8000times and still below...

On 2021-10-07 22:22:53, user Robyn Schofield wrote:

"As the devices do not meet medical device electrical safety standards (EN60601) they were operated at a distance of >=1.5metres from any patient." Can the authors please clarify - what wavelength the UV was operating at, and whether this device has been tested for ozone production / loss rates. I assume that the EN60601 requires ozone production to be tested for? If ozone is being produced (or destroyed to odd oxygen) that this would need testing before deployment in a medical setting. Ozone, a respiratory irritant gas, will easily travel more than 1.5m (so distance should not be seen as useful in setting safety protocols for electronic air cleaning devices in a medical setting).

Are hospital rooms with no ventilation in line with current infection prevention and control or hospital design / operational guidelines in the UK? In Australia this would be in breach of both our hospital design and operating guidelines which require a minimum of 6 ACH for all hospitals.

The effectiveness of UV with air high flow rates has to be questioned (because the exposure time for bio-aerosols is short) - are the authors able to separate the effectiveness of the filtration over the UV features? Most literature on this point shows that in real-world operation the HEPA provides 99.97% of the removal of bio-aerosols from air and the advantage of UV is untested / unproven (this is particularly true at 1000m3/h flow rates this device is operating at). I assume this device will be noisy >65dB - can this please be specified.

On 2021-10-11 18:40:32, user Andrew T Levin wrote:

Comment #1: Research in Context

1. Diamond Princess Cruise Ship. The manuscript makes no reference to any epidemiological analysis of this episode, which informed seminal assessments of the age-specific infection fatality rate (IFR) of COVID-19.[1-4] Nonetheless, that evidence is particularly relevant, because the cruise ship’s passengers included 1231 individuals ages 70+ who were not merely “community-dwelling” but healthy enough to embark on a multi-week grand tour of southeast Asia. Following extensive RT-PCR testing, 335 passengers ages 70+ were confirmed to have been infected with SARS-Cov-2, and 13 of those passengers died from COVID-19 – an IFR of about 4%. Moreover, the strong link to age is underscored by the even higher IFR of 8% for passengers ages 80+. Given the size of that sample (which meets the 1000+ threshold used here), this evidence should certainly be incorporated into this meta-analysis.

2. Comprehensive Tracing Programs. The manuscript makes no reference to countries that succeeded in containing the first wave of the pandemic in spring 2020 through systematic tracing and testing of all contacts of infected individuals.[5] Such evidence is particularly relevant here, because the virus was contained within the “community-dwelling” populations of those locations and never spread to any elderly care facilities. For example, in the case of New Zealand, there were 256 infections and 19 deaths among adults ages 70+ -- an IFR of about 7%.

3. Hospitalized Patients. The manuscript cites a single study (published in July 2020) that examined the association between comorbidities and mortality risk of COVID-19.[6] However, that study was not able to distinguish whether comorbidities were linked to greater prevalence (the probability of getting infected) or to a higher IFR (the risk of mortality conditional on infection). Unfortunately, the manuscript makes no reference to any subsequent studies on this issue. In particular, a large-scale study of U.K. BioBank participants found that measures of frailty were indeed associated with higher mortality rates in the overall panel but not linked to mortality within the subset of hospitalized COVID-19 patients.[7] In effect, the prevalence of COVID-19 was markedly higher among residents of U.K. nursing homes compared to individuals of similar age living in the community, but the IFR was not significantly different. Those findings directly contradict a key assertion made at the start of this manuscript.

4. Prior Meta-Analysis of Community-Dwelling Populations. The introduction of this manuscript neglects to mention that an existing meta-analysis study (published in Nature in November 2020) was specifically focused on assessing IFRs excluding deaths in nursing homes.[8] That study estimated the link between age and IFR using seroprevalence and fatality data for adults less than 65 years old, and then showed that the model predictiions were consistent with data on fatalities among community-dwelling adults ages 65+. Moreover, that study used seroprevalence data adjusted for assay characteristics, and the results were obtained using a rigorous Bayesian statistical model that incorporated random variations in the time lags between infection, seropositivity, and fatal outcomes – a striking contrast to this manuscript, which uses rudimentary assumptions to address those issues.

5. Other Meta-Analyses. The introduction of this manuscript briefly refers to two other meta-analysis studies of the link between age and IFR.[5, 9] However, the manuscript then asserts: “Importantly, the vast majority of seroprevalence studies include very few elderly people.” (p.5) That assertion is supported by a single citation to the SeroTracker database, which provides comprehensive coverage of all existing national, regional, and local seroprevalence studies across the globe.[10] However, this assertion is completely incorrect as a characterization of the preceding meta-analysis of age-specific IFRs. As indicated in Levin et al. (2020, figure 5), that meta-analysis study included seroprevalence data on older adults (including narrow brackets for ages 60-69, 65-74, 70-79, and 75-84 as well as open-ended brackets for ages 60+, 65+, 70+, 80+, and 85+) from nine national studies (Belgium, France, Hungary, Italy, Netherlands, Portugal, Spain, Sweden, and the U.K.) and eight regional locations (Ontario, Canada; Geneva, Switzerland; Connecticut, Indiana, Louisiana, Miami, Missouri, and San Francisco, USA).[5]

On 2021-10-14 17:12:39, user lbaustin wrote:

Has this been submitted to any of the MEDLINE indexed journals who publish on this topic?

On 2021-10-17 22:54:41, user Rob Reck wrote:

If appears that there is no differentiation given to to the amount of time that passed since a subject contracted CoVid19. Waning immunity is an issue that has been studied. Certainly more study would be a good thing. But there is enough current data to know that it does happen. People who have had CoVid19 do get re-infected.

Given the existence of even a small number of reinfections, the claim that a person who previously was infected with CoVid19 need not be vaccinated is not supported by this study.

On 2021-10-22 17:10:09, user Jeremy M Bartels wrote:

Can you clarify if Ct values decrease, does viral load go up or down?

On 2021-10-30 04:38:45, user Rn wrote:

The conclusions of this study stand in stark contrast to a report published today by the US CDC. https://www.cdc.gov/mmwr/vo...

Among COVID-19–like illness hospitalizations among adults aged >=18 years whose previous infection or vaccination occurred 90–179 days earlier, the adjusted odds of laboratory-confirmed COVID-19 among unvaccinated adults with previous SARS-CoV-2 infection were 5.49-fold higher than the odds among fully vaccinated recipients of an mRNA COVID-19 vaccine who had no previous documented infection (95% confidence interval = 2.75–10.99).

On 2021-12-06 14:07:04, user Steven Sampson wrote:

Why hasn't this been peer reviewed?

On 2021-08-27 15:48:14, user Kim wrote:

What about those who have never been vaccinated nor had any vaccines?

On 2021-08-29 21:38:00, user MANISH JOSHI wrote:

We must stop ignoring natural immunity - it’s now long overdue<br /> Manish Joshi, MD

This article by Gazit et al is another addition to a growing body of literature supporting the conclusion that natural immunity confers robust, durable, and high-level protection against COVID-19 (1-4). Yet some scientific journals, media outlets, and public policy messaging continue to cast doubt. That doubt has real-world consequences, particulary for resource limited countries. We would like to review available data.

Infection generates immunity. The “SIREN” study in the Lancet addressed the relationships between seropositivity in people with previous COVID-19 infection and subsequent risk of severe acute respiratory syndrome due to SARS-CoV-2 infection over the subsequent 7-12 months (1). Prior infection decreased risk of symptomatic re-infection by 93%. A large cohort study published in JAMA Internal Medicine looked at 3.2 million US patients and showed that the risk of infection was significantly lower (0.3%) in seropositive patients v/s those who are seronegative (3%) (2).

Perhaps even more important to the question of duration of immunity is a recent study that has demonstrated the presence of long-lived memory immune cells in those who have recovered from COVID-19 (3). This implies a prolonged (perhaps years) capacity to respond to new infection with new antibodies.

In contrast to this collective data demonstrating both adequate and long-lasting protection in those who have recovered from COVID-19, the duration of vaccine-induced immunity is not fully known- but breakthrough infections in Israel, Iceland and in the US suggests few months. Before CDC decided to stop collecting data on all breakthrough infections at the end of April, 2021, it reported >10,000 breakthrough infections (2 weeks after completion of vaccination) in the US, with a mortality of ~2% (5). Booster COVID vaccine recommendations have been already announced in Israel and in the US proving ineffectiveness within 6 months.

How should we use the collective data to prioritize vaccination? These new data support simple and logical concepts. The goal of vaccination is to generate memory cells that can recognize SARS-CoV-2 and rapidly generate neutralizing antibodies that either prevent or mitigate both infection and transmission. Those who have survived COVID-19 must almost by definition have mounted an effective immune response; it is not surprising that the evolving literature shows that prior infection decreases vulnerability. In our view, the data suggest that people confirmed to have been infected with SARS-CoV-2 may not need vaccination. We should not be debating the implications of prior infection; we should be debating how to confirm prior infection (6).

Manish Joshi, MD<br /> Thaddeus Bartter, MD<br /> Anita Joshi, BDS, MPH

1. Hall VJ, Foulkes S, Charlett A et al. SARS-CoV-2 infection rates of antibody-positive compared with antibody-negative health-care workers in England: large, multicentre, prospective cohort study (SIREN). Lancet. 2021
2. Harvey RA, Rassen JA, Kabelac CA, et al. Association of SARS-CoV-2 Seropositive Antibody Test With Risk of Future Infection. JAMA Intern Med.
3. Turner, J.S., Kim, W., Kalaidina, E. et al. SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Nature 2021
4. Wang, Z., Yang, X., Zhong, J. et al. Exposure to SARS-CoV-2 generates T-cell memory in the absence of a detectable viral infection. Nat Commun 12, 1724 (2021).
5. https://www.cdc.gov/mmwr/vo...
6. Kuehn BM. High-Income Countries Have Secured the Bulk of COVID-19 Vaccines. JAMA. 2021;325(7):612

On 2021-10-31 02:14:19, user Peter Jaji wrote:

So this shows vaccinated people spread the virus as much if not more then unvaccinated?<br /> Please enlighten

On 2022-12-12 15:51:53, user Koen van de Wetering wrote:

Dear Wera,

It is only now that I find out about your comment. I appreciate your input. Our manuscript has now been peer reviewed and was recently published in Analytical and Bioanalytical Chemistry. In the future I will more often check our preprints and incorporate comments like yours into our manuscripts. In case you still have questions about the assay to detect pyrophosphate, do not hesitate to contact me directly via email.

With kind regards,<br /> Koen van de Wetering

On 2022-09-22 09:16:27, user Wera Pustlauk wrote:

Dear authors,

thanks for the valuable effort to set up a new assay for the determination of PPi.

Regarding table III samples remained somewhat vague to me. Clarification in the table header including the unit of the determined PPi might be helpful. Calculation of the standard deviation in addition to the average would make the data more roboust and would establish a more substantial link to the variability discussed in the paragraph before. Moreover, time differences in the addition of EDTA to the CTAD tubes as discussed in the text should be clearly stated for each sample in the table III as well.

In addition, a hands on protocol (stored in a repository or as supplement) allowing the direct usage of the assay based on the optimized procedure would make the usage more accessible.

Best regards,<br /> Wera Pustlauk

On 2022-12-15 10:49:12, user Author wrote:

We would like to reply to a comment entitled “Japan preprint on myocarditis used inadequate methods to suggest COVID-19 vaccines cause more myocarditis deaths”: a review by Health Feedback (Editor: Ms. Flora Teoh). <br /> https://healthfeedback.org/...

We thank them for commenting on our paper. We understand their main points of criticism were three summarised as followings:

1. Comparison of pre-pandemic and post-pandemic rates of myocarditis death (their 2nd point)

2. No examination of history of myocarditis death and ignored COVID-19 as the cause (their 1st point)

3. Sample size was too small to discuss causality

1. comparison of pre-pandemic and post-pandemic rates of myocarditis death (their 2nd point)

Their 2nd point is based on the fundamental misunderstanding on the methods of our study. They erroneously stated "The authors' association of change in the risk of myocarditis death associated with COVID-19 vaccines was based on comparing pre-pandemic and post-pandemic rates of myocarditis death". <br /> We compared myocarditis mortality in the SARS-CoV-2 VACCINATED population with that of the 2017-2019 (pre-pandemic period: reference) population; we did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods.<br /> Because of the misunderstanding the fundamental methods of our study, the following criticism have no sense:<br /> “But this assumes that the only thing that changed between the two periods is the availability of the COVID-19 vaccines. It excludes, without justification, the possibility that COVID-19 itself could produce an increase in myocarditis deaths. No reason was given by the authors for excluding COVID-19 as a potential explanation, despite the fact that COVID-19 is a more likely explanation than COVID-19 vaccines for an increase.” “This is because we know—based on previous published studies—that COVID-19 is more likely to lead to cardiac complications than the vaccines [1,2]. Therefore, the alleged causal association rests on the assumption that only COVID-19 vaccines can explain the change in myocarditis mortality, which isn’t true.”<br /> However, we would like to comments on “COVID-19 is more likely to lead to cardiac complications than the vaccines” referring reports by Block et al [1], and Patone et al [2,3].<br /> It is important to consider following three points; vaccines are not given to dying persons and to persons with fever or other acute diseases. Hence vaccinated people are relatively healthier than the non-vaccinated (healthy vaccinee effect) [4]. Conversely, vulnerable persons (frail, suppressed immunity due to stress or sleep debt etc) are more likely to be infected with SARS-CoV-2 (vulnerability confounding bias: VCB) [5].

Patone et al. [1] stated in the discussion section as follows: “Of note, the estimated IRRs were consistently <1 in the pre-exposure period before vaccination. ---- This was expected because events are unlikely to happen shortly before vaccination (relatively healthy people are receiving the vaccine).” This is exactly the same as the healthy vaccinee effect [4] and it is the lowest at day 0 of vaccination [2]: for example, IRR of arrhythmia at day 0 of BNT162b2 vaccination was 0.33 (0.29 to 0.37) compared with 0.72(0.70 to 0.73) during -28 to -1 days before vaccination [2]. <br /> Paton et al [1] also discussed that the estimated IRRs were consistently >1 in the pre-risk period before a SARS-CoV-2–positive test. They thought that events are more likely to happen before a SARS-CoV-2–positive test (as a standard procedure, patients admitted to the hospital are tested for SARS-CoV-2). But they missed to discuss that IRRs on day 0 of vaccination are the most prominent (with 10 times more than that in the pre-risk period, because standard testing of SARS-CoV-2 is mostly done on the day of admission). Hence, constant IRR >1 during -28 to -1 days before vaccination may be another cause. It may be explained by the vulnerability confounding bias [5].<br /> We estimated the effect of vulnerable person’s susceptibility to infection (vulnerability confounding bias: VCB) from the pre-risk period (-28 to -1 days) of the SARS-CoV-2 test-positive group: 2.84 (1.89 to 4.28) for myocarditis and 4.82 (4.68 to 4.97) for arrhythmia. When applied these data for the index of VCB, VCB-adjusted IRRs are 3.44 (2.11 to 5.59) and 1.11 (1.07 to 1.16) which are similar to or less than the healthy vaccinee effect adjusted IRRs of myocarditis (3.97: 3.05 to 5.16) and arrythmia (2.70: 2.38 to 3.05) respectively [4].<br /> It is not possible to estimate the healthy-vaccinee effect and VCB directly from the report of Block et al [3], however, post-SARS-CoV-2 infection/post-vaccination myocarditis risk ratios may be less than 1.00 in almost half of those listed when above adjustments were applied.

2. No examination of history of myocarditis death and ignored COVID-19 as the cause (their 1st point)

This point is also derived from the fundamental misunderstanding on the methods of our study. We did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods BUT compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. <br /> Therefore, as a rule, deaths following SARS-CoV-2 infection were not included in this study. In fact, none had COVID-19 listed in the death cause column of cases included in this analysis.<br /> Moreover, in the MHWL list we referred; most deaths included brief medical history as well as the cause of death. We clearly stated that “these were myocarditis death cases reported by physicians as serious adverse reactions to the vaccine” in the Methods section.<br /> Furthermore, as we stated in the discussion section, myocarditis deaths in the 2017-2019 (reference) population were also based on a doctor's diagnosis, with no other medical history known. Mevorach et al [6] also analysed using the same methodology and already published as a peer reviewed paper.

3 Sample size is too small to discuss causality

This point is also derived from the fundamental misunderstanding on the methods of our study. We compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. Hence this sample size was enough to demonstrate increased myocarditis mortality rate ratio after vaccination.<br /> As we stated in the end of the discussion section and in supplemental Table S6, all of the Modified US Surgeon General criteria for causal were satisfied.

Sincerely,<br /> Watanabe and Hama.

References<br /> [1] Block JP, Boehmer TK, Forrest CB, et al. Cardiac Complications After SARS-CoV-2 Infection and mRNA COVID-19 Vaccination - PCORnet, United States, January 2021-January 2022. MMWR Morb Mortal Wkly Rep 2022; 71:517-23. DOI: http://dx.doi.org/10.15585/...<br /> [2] Patone M, Mei XW, Handunnetthi L, et al. Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex. Circulation. 2022; 146(10):743-54. doi:10.1161/CIRCULATIONAHA.122.059970<br /> [3] Patone M, Mei XW, Handunnetthi L, et al. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection. Nat Med. 2022; 28(2):410-22. doi:10.1038/s41591-021-01630-0<br /> [4] Hama R and Watanabe S. The risk of vaccination may be higher by considering “healthy vaccinee effect” Response to Husby et al: https://doi.org/10.1136/bmj... (Published 16 December 2021)<br /> Available at: https://www.bmj.com/content...<br /> (Accessed 30 November 2022)<br /> [5] Hama R and Watanabe S. Vulnerability confounding bias should be taken into account in assessing risk of post SARS-CoV-2 infection: an opposite concept of healthy-vaccinee effect (Under submission)<br /> [6] Mevorach D, Anis E, Cedar N, et al. Myocarditis after BNT162b2 mRNA Vaccine against Covid-19 in Israel. N Engl J Med. 2021; 385(23):2140-49. doi:10.1056/NEJMoa2109730

On 2020-12-01 03:41:09, user Padmaksha Roy wrote:

Hello authors, just curious to know if the github repo for this paper can be made publicly available. Currently the link mentioned does not have the code. Thanks!

On 2020-12-04 19:07:47, user lbaustin wrote:

Has this been submitted for publication yet?

On 2023-09-14 09:01:23, user Chris Iddon wrote:

CO2 measurements are taken for one occupied day in November 2022 and compared to PCR positive cases during the period Aug21 to Aug22. There is no data presented here to suggest that the single day CO2 reading is representative of the room ventilation during the whole period of Aug21 to Aug22. Also there doesn't appear to be any record of how many of the occupants were PCR positive prior to Aug21 and therefore have some level of prior immunity, nor how often the occupants are tested by PCR.

On 2020-12-28 04:33:58, user Igor Oscorbin wrote:

Interestingly, a strategy that has been commonly used to alter the capabilities of DNA polymerases, the addition of additional DNA- or RNA-binding domains, has yet to be applied to Bst DNAP.

It should be noted that the strategy has been applied at least once:<br /> https://academic.oup.com/na...<br /> Derivatives of Bst-like Gss-polymerase with improved processivity and inhibitor tolerance

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

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

On 2020-08-09 16:36:55, user David Leidner wrote:

Posted article says that data are fully available, but no link to the data is provided.

On 2021-09-22 20:35:49, user tooearly wrote:

What we don't know:How long is this effect? Months? Many months?<br /> Is MMR the best choice for LAV non specific immune boost? Would OPV not work even better? more details about the endpoints and how they were measured

On 2020-07-15 14:57:36, user Rhyothemis wrote:

The PDF 'heat map' figure is illegible.

On 2022-01-08 23:20:51, user Joshua wrote:

From the study: “1. The negative estimates in the final period arguably suggest different behaviour and/or exposure patterns in the vaccinated and unvaccinated cohorts causing underestimation of the VE. 2. This was likely the result of Omicron spreading rapidly initially through single (super-spreading) events causing many infections among young, vaccinated individuals.”

Let’s discuss the sentence I labeled 1.

1a) Is any data available which supports the author(s) hypothesis that the vaccinated cohort engaged in riskier behavior when compared to the unvaccinated? My anecdotal evidence from my lived experience with those in my circle is that the unvaccinated are living a much riskier life as it pertains to covid infection. But don’t take my word because that is not how science works, instead, consider this KFF survey of 1,527 adults aged 18+ conducted in July 2021 indicating the opposite reality: “ Majorities of vaccinated adults say news of the variants has made them more likely to wear a mask in public (62%) or avoid large gatherings (61%), while fewer unvaccinated adults say the same (37% and 40%, respectively).”

1b) Is there any explanation why this alleged confounding variable of riskier behavior by the vaccinated did NOT appear during the studies surrounding delta?

1c) Is there any explanation why this alleged confounding variable of riskier behavior by the vaccinated only appeared during the 91-150 days time period for the omicron variant?

Let’s discuss the sentence I labeled 2.

2) I found this statement in the Methods section of this study: “VE was calculated as 1-HR with HR (hazard ratio) estimated in a Cox regression model adjusted for age, sex and geographical region, and using calendar time as the underlying time scale.” That means the authors accounted and controlled for age, yet they claim age as a confounding variable. Talk about having your cake and eating it too!

On 2021-02-06 06:46:05, user kdrl nakle wrote:

Expected but good to know it is confirmed.

On 2021-02-09 11:51:56, user Robert van Loo wrote:

I cannot find over which period the sera were collected. Including that would greatly increase the value of the paper.

On 2020-08-30 16:23:17, user Martijn Weterings wrote:

Figure 3 shows two remarkable effects:

• 1) The observed curves (cumulative) have a sigmoid type of shape. There is at first an increase in the rate of growth but after some time there is a decline. This shape can have multiple reasons. Aside from the weather the most important reason in this case might probably be measures like social distancing.<br /> It is unclear how these alternative effects have been incorporated into the model and the calibration process. This is a tricky process because the possibility exist that the decline in cases is too much ascribed to the approaching of herd immunity, but it is only one of many reasons/effects to explain the shape of the curve.
• 2) The fitted curves with different lambda are extremely close to each other in the beginning, but in the extrapolated region they diverge a lot. There are multiple explanations of the current data, but with widely different outcomes. This makes the prediction based on current data an ill-conditioned problem and the results are not accurate and may be biased and influenced a lot by the assumptions. (The same point is made in reference 35 M Castro, S Ares, JA Cuesta, S Manrubia, Predictability: Can the turning point and end of an<br /> expanding epidemic be precisely forecast? arXiv: 2004.08842 (2020) The answer is: No, you can not forecast this accurately)

Instead of fitting to current epidemiological curves we should determine the epidemiological parameters more directly based on detailed data (e.g. based on databases with contact tracing information and tracing the tree of infection rather than basing estimates on an aggregated data like total deaths and total infections per day, which are also not so accurate). Estimates based on real life face-to-face networks help, but may not be sufficient to fill in the gap of information about epidemiological parameters.

In addition, while the model explains very well the effect of the heterogeneity in rates of infection among different people, it is still not a realistic model and lacks the nuance of spatial distribution and network structures which will have a remarkable effect on the curves as well including an early decrease of the growth rate (due to local saturation, increase of immunity). The downside of the simplicity is that the heterogeneity may become overestimated (in order to compensate for the lack of the other effects) and the predictions of the percentage to reach immunity may be underestimated.

Possibly the model may work well for cities. However in many European countries we see already second waves occuring in mostly different regions and different populations (also Australia gives a clear second wave which is even much stronger). <br /> The 1st wave was like a forest fire that has been mostly local and got actively extinguished (thanks to measures like working at home and travel restrictions). We should not confuse the decline as all the dry old trees being gone and as if the risk of fires is over now or relatively low. Those 1st wave fires were local and there may still be many patches that are able to catch fire.

On 2021-10-09 10:12:17, user Nick Turnock wrote:

How does your modelling take into account the incubation period. For instance Delta's reported increased viral load without epitope change may indicate immune response evasion by shortening the incubation period. ie increased population seropositivity may have driven a mutation which enables Delta to rapidly multiply, shed and infect new hosts in the short window before memory B cells start churning out antibodies.

On 2020-09-07 23:07:20, user Louis Rossouw wrote:

In the case if South Africa:<br /> * The reported deaths are very much undercounted.<br /> * The economist excess deaths figure includes drops in accidental deaths and other things. Please have a look at https://www.samrc.ac.za/rep... that tries to adjust for moving parts.

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 2022-01-26 15:10:58, user Siguna Mueller, PhD, PhD wrote:

Thank you for a very detailed study - looking into different questions (each of those relevant in their own right). I have one question re "time since vaccination." Fig 4 in the appendix provides the chart for up to 240 days. This seems to me that this only includes those you classify as "fully vaccinated." Such long time frame would not be possible for the booster vaccinated, as these were only made available recently. If so, your findings, based on the study design (by necessity) would only give you preliminary insights into rather short-term effects of boosters in this regard. Or am I missing something? Thank you.

On 2020-09-18 20:29:54, user David C. Norris, MD wrote:

This paper is fundamentally misconceived:

Biostatistically

This paper apparently arises out of the biostatistical perspective which presently dominates the design and analysis of dose-finding trials in oncology. Yet even by purely statistical standards, it suffers serious shortcomings. Most notably, it looks for an interaction (viz., dose-response) without first demonstrating or ensuring the existence of a main effect. Reference #153 in this paper (Hazim et al. 2020) reported a 5% median response rate in a systematic review of recent dose-finding trials. Would the authors venture to estimate what fraction of their 93 ‘analysis series’ employed a drug with a substantial therapeutic effect? Some indication might be found in what fraction of the treatments unequivocally demonstrated a therapeutic effect in subsequent phase 2 or 3 trials. Adashek et al. (2019) document a secular trend in overall response rate (ORR) observed in phase 1 trials which is “now almost 20%, or even higher (~42%) when a genomic biomarker is used for patient selection.”

Also arguably well within the purview of biostatistics would have been a decision-theoretic framing of phase 1 cancer trials. These trials may be understood as the earliest clinical steps in a learn-as-you-go (adaptive) drug-development process (Palmer 2002; Berry 2004). On such an understanding, aiming to treat early-phase participants at maximum tolerated doses (MTDs) in no way “dictates that an assumption is made … that higher doses are always more efficacious” (p. 4; italics in original). The authors’ use of “dictates” suggests they see something of logical necessity in this, and their further insertion of the logical quantifier “always” only exacerbates their overreach in formulating this central tenet of their study. Even the distinction between a logical assumption and a statistical prior gets lost in the shuffle. To remedy all this, the authors might consider attempting to state formally their understanding of the individual phase 1 trial participant’s decision-problem, complete with its essential uncertainties and some plausible utilities. (Within the community of investigators whom they address in the final paragraph of their Discussion, there is, I believe, broad agreement on the doctrine that these trials have therapeutic intent (Weber et al. 2016; Burris 2019). The authors would do well to take this patient-centered view as their starting point, as opposed to the dose-centered and unitary goal they proclaim at the end of their current Discussion.)

Furthermore, statistics is nothing if not a discipline for “mastering variation” (Senn 2016), and a paper that sets out to question the strict monotonicity of dose-efficacy ought also enquire as to the presence of inter-individual heterogeneity in dose-response. Note that such heterogeneity would tend to attenuate the maximum slope of a convex dose-response in aggregate.

Finally, the absence-of-evidence fallacy is widely appreciated among professional statisticians, yet seems to have been indulged liberally here without any safeguards such as are usually provided by power calculations.

Pharmacologically

Within statistics, there is a doctrine that statistical analysts should always engage ‘subject-matter experts’. But one sees in this paper no sign that any pharmacological concepts—let alone expertise—have been brought to bear on what would seem to be a pharmacological question. At a minimum, in any serious challenge to the ‘MTD heuristic’—as I have called it—one expects to find distinctions between on-target and off-target toxicities. In an analysis that invokes dose-response plateaus (whether these are conceived as approximate or absolute in this paper remains unclear), we ought to find discussion of receptor occupancy and saturation as underlying realistic mechanisms.

To some extent, a neglect of subject-matter knowledge may be embedded in the very form of the present analysis, which tries to deal with its question in aggregate (through statistical techniques such as standardization) rather than in its particulars.

Clinically

In the final paragraph of their Discussion, the authors proffer advice to clinical investigators. In light of the limitations—statistical, logical, subject-matter—catalogued above, this is premature and should be omitted. Any given phase 1 clinical investigator will be considering a candidate drug in its particulars, conditional on a great deal of preclinical data and perhaps even nontrivial PKPD and systems-pharmacology modeling. The authors acknowledge as much (p. 16), seeming to appreciate that they have conducted an unconditional analysis of highly conditioned decision-making. To investigators thus intimately engaged with pharmacologic particulars, the null conclusions from a marginal analysis such as this one can contribute little useful guidance. If it were proposed to submit this work for peer review in substantially its present form, only a statistical audience should be addressed—and then solely with a cautionary note that the finding of a dose-response interaction will not leap out at a statistician from a convenience sample of phase 1 studies in which a therapeutic main effect remains dubious and unexamined. The main lesson of this work is that statisticians ought to investigate questions of pharmacology in their particulars, and with recourse to subject-matter concepts and expertise.

References

Adashek, Jacob J., Patricia M. LoRusso, David S. Hong, and Razelle Kurzrock. 2019. “Phase I Trials as Valid Therapeutic Options for Patients with Cancer.” Nature Reviews Clinical Oncology, September. https://doi.org/10.1038/s41....

Berry, Donald A. 2004. “Bayesian Statistics and the Efficiency and Ethics of Clinical Trials.” Statistical Science 19 (1): 175–87. https://doi.org/10.1214/088....

Burris, Howard A. 2019. “Correcting the ASCO Position on Phase I Clinical Trials in Cancer.” Nature Reviews Clinical Oncology, December. https://doi.org/10.1038/s41....

Hazim, Antonious, Gordon Mills, Vinay Prasad, Alyson Haslam, and Emerson Y. Chen. 2020. “Relationship Between Response and Dose in Published, Contemporary Phase I Oncology Trials.” Journal of the National Comprehensive Cancer Network 18 (4): 428–33. https://doi.org/10.6004/jnc....

Palmer, C. R. 2002. “Ethics, Data-Dependent Designs, and the Strategy of Clinical Trials: Time to Start Learning-as-We-Go?” Statistical Methods in Medical Research 11 (5): 381–402. https://doi.org/10.1191/096....

Senn, Stephen. 2016. “Mastering Variation: Variance Components and Personalised Medicine.” Statistics in Medicine 35 (7): 966–77. https://doi.org/10.1002/sim....

Weber, Jeffrey S., Laura A. Levit, Peter C. Adamson, Suanna S. Bruinooge, Howard A. Burris, Michael A. Carducci, Adam P. Dicker, et al. 2016. “Reaffirming and Clarifying the American Society of Clinical Oncology’s Policy Statement on the Critical Role of Phase I Trials in Cancer Research and Treatment.” Journal of Clinical Oncology 35 (2): 139–40. https://doi.org/10.1200/JCO....

On 2021-10-25 17:06:17, user Lucy Carpenter wrote:

My first reaction is, why are you testing Nafamostat - a front-end, early stage antviral meant to block or inhibit TMPRSS-2 at the earliest entry and activation points for the virus - at the back-end, late-stage viral sequence of Covid19 pneumonia? By then, the host is typically so overrun with viral microbials that front-end antivirals will probably not make a measurable difference.

But that is not what the purpose is, for this particular component. Your methodology and assumptions, to me, appear flawed. For two reasons:

1. We all know the very high efficacy of children's natural, Innate Immunity against Covid. In the US, and worldwide. 99.7%+ (in the US it is a much higher figure) of all children worldwide easily fight off Covid. Minor symptoms if any; and less than a 1% death rate. In the US, per Hopkins and other figures, as of Oct. 7th, roughly 700 children - out of US population of 73 million children - had died of Covid19. We can all do the math on this. If we could 'bottle' the Innate Immunity function that is keeping almost every single child alive and well for almost two years it would be championed worldwide as a 'miracle cure.'

What makes the child's immunity different than adults or others? We all know this is a complex synergy - aminos, enzymes, different glyco protein structures, etc - but what stands out in immunity research is:<br /> A - children detect Covid much earlier, much earlier, broad-range viral pattern recognition, in the nasal passage (and throat, many children are still mouth breathers); <br /> B - children attack the virus much earlier - at the attachment and activation stage, well before viral replication. They recognize and start attacking the virus as soon as it enters their nose or throats; this limits the 'viral load' on their systems to a manageable level that is easily dispatched. Like stopping a hurricane when it just starts to form, off the coast of Africa; vs waiting for it to hit land in Florida. <br /> --- THIS IS WHY YOUR STUDY WITH NAFAMOSTAT APPEARS FLAWED: YOU ARE NOT MEASURING THE IMPACT OF NAFAMOSTAT ON STOPPING THE ENTRY OF COVID INTO THE HOST, AND LIMITING THE MICROBIAL LOAD BEFORE IT EVEN GETS TO REPLICATION STAGE -- WHAT THE REPURPOSING WAS DESIGNED TO ACHIEVE. <br /> Nafamostat was designed to be one of several elements attempting to emulate a child's Innate Immunity; and the child's very early blocking of ACE2 and TMPRSS-2 expression in the nasal and throat passages.

You are testing instead, the 'wrong' goal for this antiviral; the goal of Nafamostat was never to reduce inflammation or cytokine response -- if you want to test an antiviral for that, test a mega dose of Vitamin D IM or IV; or dexamethasone -- but to INHIBIT THE VIRAL ENTRY INTO THE HOST, BEFORE REPLICATION STAGE. By the time a human as Covid pneumonia, the microbial load is so extreme, it is time to shift gears to another type of antiviral response. (Concentrated D, btw, has excellent efficacy when combined with dexamethasone, against any viral or bacterial lung inflammatory response and infection.)

1. Children possess different types of membrane lining in their nasal mucosal for different reasons than what antivirals like Nafamostat would accomplish; but Nafamost is one of the guesses at how to emulate at least part of this upfront difference in immunity function with children: <br /> "SARS-CoV-2 copies, angiotensin-converting enzyme 2, and TMPRSS2 gene expression were similar in children and adults, but children displayed higher expression of genes associated with IFN signaling, NLRP3 inflammasome, and other innate pathways. Higher levels of IFN-?2, IFN-?, IP-10, IL-8, and IL-1? protein were detected in nasal fluid in children versus adults." (https://insight.jci.org/art... "https://insight.jci.org/articles/view/148694)") Other studies from August 2021 on spend more focus on the T cell concentration in children, vs adults, etc.

Bottom line: children have more, and better, general (broad-spectrum, not specialized) immunity and fighter cells in their nasal mucosal and this serves to support very early viral recognition -- broad-range, not specialized, as the vaccines wish to change this Innate Immunity in children - and viral attack mechanisms, from their natural, Innate Immune system, than adults. We cannot yet replicate this different concentration of Immunity cells in a child's nose, into a nose spray for adults. Much of it is genetic: God coded our systems so give children extra protection during their earliest years. *But that would be a good goal.<br /> _____<br /> So we lack a once-daily nasal spray for adults, which could coat our nasal passages with the same distribution and type of IFN-?2, IFN-?, IP-10, IL-8, and IL-1? protein and T cells and so on, encoded with extra-sensitive, broad-range viral recognition pattern recognition; that our children have. Until we get there, at least up-front, early stage broad-range antiviral components (not a complete pro-drug but a component of a complete antiviral) like nafamostat were intended or designed to 'emulate' specific 'functions' of what we know that children do naturally at the front end: 1 - recognize the virus and 2 - block it BEFORE replication stage by blocking or inhibiting Attraction, Attachment, and Activation.

So, did you test this singular component of a true, end-to-end broad-range antiviral therapeutic or cure, for Covid, at the right phase? Because Nafamostat again, is meant to inhibit the 3rd phase in viral development (activation); not to fight high microbial loads at the back end.

• Did you measure the microbial loads in your patients, before and after administration of Nafamosta? Did you then compare these microbial loads A - to each other, time phase; B - to microbial loads found in asymptomatic Covid patients, or children's nasal cells?
• Because Nafamostat is meant to be the most effective at the lowest microbial load point, which is what a child's immunity does; to prevent infection. It was never a contender for a strong infection status, pneumonia anti-inflammatory. There are antibiotics that assume an antirival function, that are very good choices for such a need. But Nafamostat is not one of them. It is like the difference in asthma inhalers: you would never use an Advair maintenance inhaler or leukotrine inhibitor, which limit inflammatory response by removing the conditions for such response, for an accute asthma attack. For that function, you would use a rescue inhaler like Albuterol. <br /> *Does that mean that Advair or leukotrine inhibitors do not work? They work very well. But if you tested Advair against an accute asthma attack, what would your results be? IN SUCH INSTANCE, YOU WOULD HAVE TESTED THE RIGHT DRUG AT THE WRONG PHASE IN A DISEASE LIFECYCLE. AND MEASURED THE WRONG THINGS. AND DONE DIS-SERVICE TO PATIENTS BY RULING OUT CAPABILILITES THEY DO NEED, BY CONCLUDING INCORRECTLY THEY DO NOT WORK. <br /> __________<br /> The only thing to test with Nafamostat, would have been efficacy against TMPRSS-2 expression: measurement of microbial loads before and after in patients not yet ill. Because that is what the real 'test' and question is: from the cathespan or ace 2 attachment phase, does Nafamostat reduce, and if so by what %, the activation phase of Covid19. <br /> Eg. X amount microbial load was presented for activation; <br /> Y amount exited activation successfully without Nafamostat<br /> Z amount exited activation or was measured, with Nafamostat

Conclusion: Nafamostat inhibited viral 'activation" and reduced microbial load by Nth %. <br /> If you do not fully understand the full life-cycle approach of the Innate Immune system against Covid, what is often termed the "molecular Covid-Host architecture," you will not be testing responses in the right way, at the right time, measuring the right results, etc.

And then you can go into all of your variants, temp differences (significant in the mutations of viral cells from corona to covid), ph, pre-existing conditions, by age, etc. *And, presumably this was all in silico and not human experimentation with very ill real people..<br /> __________

It is still not fully understood HOW children block these 3 critical phases of the Covid 19 lifecycle. And easily destroy it. (Other elements of the Innate Immunity, that my company has focused on, include destruction of the viral envelope - so that one is actually killing the virus, again before reproduction stage, not just 'fooling' it or sabotaging RNA, which opens the door to more and more deadly viral mutation or new strain development.) <br /> No one should have expected Nafamostat to have an 'anti-inflammatory' response anyway: what does cytokine or the proteins and genes expressed for that response, have to do with tmprss-2?

But it is so CRITICALLY IMPORTANT that researchers NOT DISCARD OR DISCREDIT VALUABLE ANTIVIRAL COMPONENTS, IN FAVOR OF THE VACCINES. OR FROM A PRO-VACCINE POINT OF VIEW. BECAUSE WE NEED FAST, AFFORDABLE TREATMENTS AND A CURE. You said that very well. And we need that new, fast, methodology in place for the next pathogen. But in my view, that methodology will mean embracing a component-based antiviral approach, and plug-and-play elements that tackle the sequential or concurrent viral lifecycle steps, either independently or in a cohesive therapeutic package. Much like we treat cancer. There are many drugs taken at once, typically. They each have different goals. We don't want to test for the wrong thing, and lose a valuable potential ally.

On 2021-07-29 07:51:21, user Portal Cedip wrote:

I am surprised that a country that was punished so badly by COVID-19, due to its nihilism, purely academic debates which misled the point even after recognizing that SARS-Cov-2, get the boy sick kills children and young people, but -you know Winston- their finest hour will not come until they get sick and die in numbers that do not even represent the TOTAL burden of the disease (just 6,340 boys, of which 700 got the PICU and oh, maybe 13 died, eventually more. Who cares? Just another non caucasic problem. Sense of safety for a far away condition that colonize, infects, make CYP get hospitalized, complicates 10% and kills with a lethality of 2% ONLY. I saw my pediatric unit got exhausted due to the large number of teleconferences with boys we could not hospitalize. The crisis was burning out or infecting our teams. We were under attack but the non-traslational sweatless sirs were complaining about us being hysterical and overplaying our hands with our small patients. And our government made the impossible. A country ranked 27 in Health Services got top 10 in number of cases and deaths / 100, 000. We did not see our boys dying in front of us. But got overwhelmed at all ages. Our 19 million people´s country got 130.000 CYP infected, Three thousand were hospitalized, half of them had a critical trajectory or came back from home with TIMPS. One hundred died. Eighteen had less than 1 yo. That´s crude data. Most of it occurred during the second wave, after we naivly thought we had gotten rid of the virus (Christmas 2020). But the virus gave itself a gift from England: the variant Delta, which seized the country for 4 additional months. Now is calmed again. You trust that it got surrended to vaccination, a plan that already involves more than 65% of the population. <br /> NO<br /> I do not.

My best wishes. With personal regards from the very south of the world,

Ricardo

On 2020-10-16 16:08:03, user COVIDscience wrote:

These data seem to contradict a previous study published in JCI by Yanqun Wang and colleagues, where increased IgG titers towards OC43 spike were associated with more severe disease outcome (https://doi.org/10.1172/JCI... "https://doi.org/10.1172/JCI138759)"). The time after infection at which the sera from the mild (outpatients) and severe (inpatients) cohorts were obtained is not specified in the study from Martin Dugas and colleagues. Potentially, these are not similar in the different patient groups.<br /> Given the complexly linked kinetics of antibody titers in COVID-19 patients towards SARS-CoV-2 and other coronaviruses (https://doi.org/10.1101/202... "https://doi.org/10.1101/2020.10.12.20211599)") this may change our perspective of these data.

On 2020-10-22 17:59:48, user Jeremy Rolls wrote:

I repeat below my comments on the earlier version but with an update on the numbers I referenced. London has continued recently to have much lower hospital deaths than its share of the population would suggest, even though if antibody data alone was used a judge of how many have been infected it would seem that 80+% were yet to be infected as against 90+% nationally. London has 15.95% of the population of England but since June 1st has only had 7.25% of the deaths. (The numbers were pretty consistent through June, July and August, rose in September but then haven fallen back in October mtd. I suspect the rise in September is because London was earlier than other regions in seeing the impact of whatever the reasons the general rise across Europe have been). This continues to tell me that antibody data is nor providing the full picture on whom has been infected or may have pre-existing immunity and that the low death numbers (both absolute and relative) in London are because the virus is naturally running out of people to infect.

A strategy of partial lockdown does not seem like a logical or proportionate response. Imperfect though it may be (but there is no perfect solution) the Gt Barrington concept of focused protection would seem a far more sensible way forward whilst allowing the rest of us to achieve herd immunity and get on with our lives.

Fascinating paper. Looking at the antibody data (such as there is any <br /> published here in the UK) about 18% of people in London have antibodies <br /> compared to about 8% nationally. On that basis alone 82% of Londoners <br /> may still get infected compared to 92% nationally - i.e. you would <br /> expect the mortality rate in London still to be pretty close to the <br /> national rate. Yet the hospital death stats for covid-19 in recent weeks<br /> shows London's rate consistently to be less than 40% of the national <br /> rate. Something else must, therefore, be going on - a) London is locking<br /> down better (unlikely), b) antibody immunity does not give the complete<br /> picture (possible given the data coming out of Sweden showing that for <br /> every person having antibodies two others have T-cell immunity) or c) <br /> there is a % of the population who have pre-existing resistance (from <br /> exposure to other corona-viruses) or are biologically incapable of <br /> getting infected. Ruling out a), a quick bit of maths shows about 75% of<br /> the population must fall into b) or c). So, on that basis, in London <br /> well over 90% have either been exposed to the virus or have pre-existing<br /> immunity and maybe 80-85% nationally. I suggest herd immunity has <br /> probably been achieved in London and is close in many other parts of the<br /> UK.

On 2020-10-26 13:59:51, user Chen Yanover wrote:

This preprint has been published in JMIR Public Health Surveillance here.

On 2020-10-28 18:00:00, user Tomas Hull wrote:

How is herd immunity tested?

On 2021-11-16 11:57:33, user disqus_aUdf6iYESf wrote:

This is an interesting study, and not an easy one to do. I congratulate the authors on their work.

I agree with the authors that the study is hypothesis generating.

A few questions/comments:

1) The authors describe no delay as being "score >2 SDs above the population mean". If no delay is the inverse of delay, I think this should be "a score higher than the cutoff for delay of 2 SD below population mean." A score >2 SD above population mean would include a very small proportion of children (about 2.5% in the population) of developmentally advanced children.

2) As the authors note, using a questionnaire (Age and Stages) by phone is not ideal for evaluation, and responses could be biased by parental knowledge of maternal SARS-CoV-2 infection.

3) The numbers with infection in the first trimester are small (only 5 children), but 4/5 (80%) had developmental delays, as compared to 6/20 in second trimester (30%), and 20/273 with infection in third trimester (7.3%). Those are striking differences, with a "dose-response" type pattern by trimester, but the numbers are small, so this study would need to be replicated by other groups, ideally with testing with the Bayley scales or other administered instrument.

4) A control group without SARS-CoV-2 infection would be important as an additional comparison group, and was not present. This would give a sense of whether in the population who responded and were assessed by phone questionnaire, the rate of developmental delay (score < mean - 2SD) was similar to that expected in the general population.

For all of these reasons, I think further studies are required to definitively state that maternal SARS-CoV-2 infection in the first or second trimester is associated with developmental delay, but this study provides preliminary data that this might be the case. It appears other studies in progress propose to prospectively address this question (e.g., PROUDEST study in Brazil), and such studies are required for a more definite answer as to whether SARS-CoV-2 infection early in pregnancy affects child neurodevelopment outcomes.

On 2020-11-09 00:43:51, user Antonio Gasparrini wrote:

The article is now published in the International Journal of Epidemiology.<br /> You can access the full-text for free at:<br /> https://academic.oup.com/ij...<br /> http://www.ag-myresearch.co...

On 2021-11-26 12:38:36, user Richard Hockey wrote:

It would be interesting to repeat this in other Australian cities that had very limited lockdown and very few Covid cases such as Brisbane or Perth.

On 2021-11-29 12:51:15, user Eleutherodactylus Sciagraphus wrote:

The ethical misconduct related to this work has also been covered by BMJ: https://www.bmj.com/content...

On 2021-11-29 13:12:04, user HarryT wrote:

Another research shows current vaccines induce excellent immunity against all variants, most likely include Omicron. Just get vaccinated.

On 2022-10-24 19:47:27, user Camille Sawosik wrote:

The main goal of this study was to create a new model in order to diagnose brain disorders as they are often complex and get misdiagnosed. It seems that the researchers here have created a base computer model in order to diagnose brain disorders. The main critique here, as even the researchers point out, is that the model needs greater development and study before it can be applied in a clinical setting. At this point in development of the model, I would say that this paper has moderate significance, but could provide a breakthrough in the field if further development occurs. For now, the brain samples looked at all came from a limited number at the NBB. In the future, perhaps applying this model in other populations would continue to develop its significance within the field. The large majority of this paper relied on methods based in computer design. Coming from someone with a smaller background in these methods, I would have liked to see greater description of what they did. For example, at one point the FuzzyWuzzy library is referenced, and it would have been helpful to include some explanation of what this is. At some points as well, I found that the methods were essentially repeated in the results. The concept was interesting, but it was often hard to follow exactly what was done as this study seemed much more programing and computing based. In some sections, as well, tables or figures were referenced, but then those tables did not exist within the paper. Moving forward, other researchers should definitely use this as a building block for the future in order to build off of and develop a more advanced model. The findings here are interesting and provide a good framework for future extrapolation of the model. I found this paper interesting and hope for future development to get this idea into a clinical setting!

On 2022-10-27 19:45:52, user Indi Trehan wrote:

This article has now been published in The Pediatric Infectious Disease Journal -- doi: 10.1097/INF.0000000000003740

On 2022-11-10 06:25:14, user David Hinds wrote:

This paper has been published: https://doi.org/10.1038/s41...

On 2022-11-17 03:39:53, user M. Cunningham wrote:

The FDA EUA specifies that Paxlovid's window of availability requires the patient to be within both 5 days of symptom onset and the first positive test, whichever comes first. Is this not the VA's protocol? I only saw the testing aspect mentioned. I would think that this would further narrow the margins of error (eg: confirming early treatment). Additionally, Paxlovid is nirmatrelvir and the co-drug ritonavir. Since the press is already reporting this the same as a peer-reviewed finding, IMHO it's important to correct these omissions so that the public is not confused about the use of this medication. But I am enthusiastic to re-read the study when it has been evaluated and reviewed! Thank you for your research.

On 2022-12-02 16:36:24, user Mark Czeisler wrote:

Note from the authors:

A revised version of this paper was published in Annals of Internal Medicine on 29 November 2022 following peer review. Below is a link to the article, along with the PubMed citation.

https://www.acpjournals.org...

Czeisler MÉ, Czeisler CA. Shifting Mortality Dynamics in the United States During the COVID-19 Pandemic as Measured by Years of Life Lost. Ann Intern Med. 2022 Nov 29. doi: 10.7326/M22-2226. Epub ahead of print. PMID: 36442062.

On 2022-12-13 18:27:20, user João Duarte wrote:

This article is now peer-reviewed and published in Frontiers in Neuroscience, here: https://www.frontiersin.org...

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

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

On 2023-02-07 14:43:14, user E Edlund wrote:

Interesting read. Have you seen this similar study, focused on stroke? https://pubmed.ncbi.nlm.nih...

On 2023-02-18 07:18:25, user Anikó Lovik wrote:

This preprint has now been published in an extended and revised version in PLOS One: https://doi.org/10.1371/jou...

On 2023-02-28 18:38:31, user Stéphanie C. Thébault wrote:

Link to published article:

PLoS One. 2023 Jan 12;18(1):e0278388. doi: 10.1371/journal.pone.0278388

On 2023-03-30 21:55:59, user Daniel wrote:

Can the authors please elaborate on the changes made, in particular the effect on TLR4 response?

On 2023-04-21 12:49:03, user antonia peros wrote:

I believe that the topic of your research is very important and current, however, I have several methodological objections. <br /> Although the authors pointed out the limitations, they made quite strong conclusions and recommendations despite too small, non-randomized sample and a cross-sectional design without a control group.<br /> The use of the used instruments is very questionable when it comes to recalling satisfaction, self-esteem, and reduction in depression from 6 months ago. I also think that the fact that the respondents were familiar with the purpose of the research could have contributed to the recall bias.<br /> An important factor in your research could be how long the subjects exercised, and you did not collect that data. What their target is in the research is also vaguely defined. I recommend including some more objective criteria for that.

On 2023-04-21 15:29:37, user Erin Jonaitis wrote:

This manuscript has now been published in Brain Communications. The final version is available at https://academic.oup.com/braincomms/article/5/2/fcad057/7070362.

On 2023-05-06 19:13:10, user Bret Nestor wrote:

Hello, If you are interested in a peer-reviewed subsequent work with a distilled message, please refer to our work in The Lancet Digital Health: https://doi.org/10.1016/S25...

On 2023-05-18 19:00:12, user Dave Fuller wrote:

Please add final peer-reviewed citation as:

Lin D, Wahid KA, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Cislo M, Murphy JD, Fuller CD, Gillespie EF. E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation. J Med Imaging (Bellingham). 2023 Feb;10(Suppl 1):S11903. doi: 10.1117/1.JMI.10.S1.S11903. Epub 2023 Feb 8. PMID: 36761036; PMCID: PMC9907021.

Thanks!!

CDF

On 2023-06-01 01:20:57, user Edmund Seto wrote:

This paper has been accepted for publication in the journal Science of the Total Environment under the title "Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings"

On 2023-07-15 08:38:27, user readthecoran wrote:

Thanks for this paper. Your result may bne related to variance shrinkage of quantile averaging relative to equal weight combination of distributions. See https://onlinelibrary.wiley... For right skewed distributions medians tend to be lower than means

On 2023-08-08 10:45:14, user Robin Walters wrote:

Now published:

https://doi.org/10.1016/j.x...

On 2023-09-14 11:31:45, user Indi Trehan wrote:

This has now been published: J Glob Health 2023 Jun 9;13:04065. doi: 10.7189/jogh.13.04065.

On 2023-09-20 17:48:48, user ASH wrote:

Why did the authors investigate the associations between poultry fecal matters and E.coli, instead of other more poultry-specific zoonosis, like Salmonella? E. coli is commonly found in the lower intestine of warm-blooded organisms, of which most are harmless...<br /> Why didn't the authors check the DHS data? Similar data can be found in the DHS data which is publicly available.

On 2023-11-04 15:16:53, user Clive Bates wrote:

Two problems here.

First is scalability. This doesn't sound like an intervention that would engage many veterans, nor does it seem likely to be affordable or practical at the scale necessary to achieve a turnaround in the aggregate burdens arising from smoking.

Tobacco-related deaths exceed those resulting from homicides, suicides, motor vehicle accidence, alcohol consumption, illicit substance use, and acquired immunodeficiency syndrome (AIDS), combined.

Almost all of that excess mortality is attributable to smoking not nicotine. Tobacco harm reduction approaches may deliver more and sooner - e.g. encouraging migration to smoke-free alternative forms of nicotine use such as vaping.

Second, it is quite possible that veterans with forms of PTSD are benefiting in some way from the functional and therapeutic properties of nicotine. Again, an approach to smoking cessation that does not demand nicotine cessation may achieve nearly all the health benefits of quitting smoking without demanding withdrawal from nicotine use.

The trial could at least consider an additional arm to assess the utility of encouraging vaping for smoking cessation. It might achieve more for less.

On 2023-11-13 10:04:59, user Theo Peterbroers wrote:

"The duration from the day of index vaccination to the day of the survey completion was a median of 595 days (Interquartile Range<br /> (IQR): 417 to 661 days; range: 40 to 1058 days)."<br /> That is at least one participant vaccinated before the start of the pandemic.<br /> EDIT Make that one person from early in the vaccine trials. How time flies.

On 2023-11-15 16:42:12, user jhick059 wrote:

This article was published in the peer-reviewed journal PLoS One on October 30, 2023 (citation below), but the medRxiv page has not yet been updated to reflect the PLoS One publication.

Citation: Hickey J, Rancourt DG (2023) Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities. PLoS ONE 18(10): e0293556. https://doi.org/10.1371/jou...

On 2023-11-27 21:08:47, user Judith Mowry wrote:

The recent paper on peripheral vasopressors by Yerke doi.org/10.1016/j.chest.202... is an important reference for your research. It is vital to note that they changed their protocol to add very specific protocols and rules regarding IV site inspection, defined who was responsible. Also note that an antecubital site (or any joint) is avoided to minimize movement and extravasation risk. I wish you success with your research.

On 2023-11-27 22:00:33, user Christos Proukakis wrote:

We note the results of the analysis, and look forward to submitting a detailed response soon.

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

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

On 2024-02-20 21:32:15, user Wally Wilson wrote:

It would be handy if the authors could get the abbreviations for Borderline Personality Disorder (BPD) and Bipolar Disorder (BD) corrected

On 2024-04-25 03:20:17, user Lena Palaniyappan wrote:

Very interesting work. We observed a similar 'amelioration' effect using a cross-sectional design a few years ago (Guo et al., 2016). Since then we made several cross-sectional and a few longitudinal observations supporting the possibility of compensation and reorganisation after first episode psychosis (Palaniyappan et al., 2019a; 2019b), including one with the largest untreated sample we could access at that time (Li et al., 2022). These observations compel us to spare more efforts to understand the compensatory processes in psychosis (Palaniyappan et al, 2017, Palaniyappan & Sukumar 2020, Palaniyappan, 2021; 2023).

Guo S, Palaniyappan L, Liddle PF, Feng J. Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study. Psychological medicine. 2016 Jul;46(10):2201-14.

Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychological Medicine. 2023 Jun;53(8):3500-10.

Palaniyappan L. Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neuroscience & Biobehavioral Reviews. 2017 Aug 1;79:1-3.

Palaniyappan L, Das TK, Winmill L, Hough M, James A, Palaniyappan L. Progressive post-onset reorganisation of MRI-derived cortical thickness in adolescents with schizophrenia. Schizophr Res. 2019a Jun 1;208:477-8.

Palaniyappan L, Hodgson O, Balain V, Iwabuchi S, Gowland P, Liddle P. Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study. Psychological Medicine. 2019b Feb;49(3):412-20.

Palaniyappan L, Sukumar N. Reconsidering brain tissue changes as a mechanistic focus for early intervention in psychiatry. Journal of psychiatry & neuroscience: JPN. 2020 Nov;45(6):373.

Palaniyappan L. The neuroscience of early intervention: Moving beyond our appeals to fear. Australian & New Zealand Journal of Psychiatry. 2021;55(10):942-943.

On 2024-04-26 17:02:43, user Gary Goldman wrote:

We broadened our analyses (of IMRs) to explore potential relationships between childhood vaccine doses and NMRs (neonatal mortality rates) and U5MRs (under age 5-year mortality rates). Using 2019 and 2021 data, 17 of 18 analyses (12 linear regressions and six ANOVA and Tukey-Kramer tests) achieved statistical significance and corroborated the trend reported in our original study, demonstrating that as developed nations require more vaccine doses for their young children, mortality rates worsen. Please see https://pubmed.ncbi.nlm.nih...

On 2024-04-27 18:42:07, user Kim Brumfield wrote:

Thank you to Dr. Myles and his team for doing this research. My daughter has 20 years of prescribed steroid use for eczema without informed consent of the risk of topical steroid addiction and withdrawal. The dermatologists she saw used step therapy which eventually resulted in tachyphylaxis. The topical steroids stopped working after reaching hichest potency and now she is suffering from "eczema on steroids" which is really topical steroid withdrawal. Please help us find the cure for this horrible iatrogenic disease.

On 2024-04-27 19:07:49, user Toby wrote:

So glad to see that this problem is being taken seriously. I have suffered from eczema all my life and from TSW as described here for many years. I hope this research will result in new treatments for this awful condition.

On 2024-04-27 19:50:44, user Eve Benson wrote:

I am starting my 8th year of TSW. The first five years were torture, the last few have been manageable. I can identify with every symptom listed in this study. I can also identify with the stress of seeing several "well-meaning" doctors whose only choice of treatment was putting me back on steroids, which I refused thanks to the education I received from online communities of thousands of people who were suffering like myself. More studies like this one are needed. TSW is real. Sufferers of TSW deserve appropriate medical care and care from practitioners who understand the disease and thus provide appropriate treatment. It is time for medical institutions to step up and address TSW.

On 2024-05-02 15:45:09, user Kelly Barta wrote:

This is such an important and groundbreaking study in its showing a differentiation between Atopic Dermatitis and Topical Steroid Withdrawal Syndrome, which has been one of the big debates within the medical community. Patients need and deserve acknowledgement and support from their health care providers, but understandably, this is unable to happen without the science to back up what we are seeing anecdotally in the eczema patient population.

More research is needed to determine how and when topical steroids are creating these dysfunctions in patients in order to better understand their proper use and prescribing guidelines. This research is CRITICAL to the over 31 million Americans living with eczema and over 300 million worldwide (not to mention the countless other dermatology patients), who are prescribed topical steroids to manage a skin condition.

On 2024-05-02 18:11:05, user Keith Robison wrote:

There is a great degree of interest in this preprint due to it being the first extended description of using the iCLR technology.

It would be very valuable to have details on how much Illumina short read data was generated from iCLR libraries and how much of that data contributed to the iCLR reads vs. what could not be used

It would also be valuable to report the read length distribution of the iCLR reads in greater detail - particularly since many interested parties cannot perform that analysis themselves on the clinical data sets

On 2024-05-28 13:49:19, user Jenn Ferris wrote:

This preprint is now published: https://www.neurology.org/d...

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

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

Dear Authors,

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

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

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

Best regards,

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

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

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

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

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

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

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

On 2024-12-03 21:03:36, user xPeer wrote:

Courtesy review from xPeerd.com

This manuscript introduces DeepEnsembleEncodeNet (DEEN), an innovative polygenic risk score (PRS) model integrating autoencoders and fully connected neural networks (FCNNs) to address limitations of existing PRS methods. By disentangling dimensionality reduction and predictive modeling, DEEN enables the capture of both linear and non-linear SNP effects, improving prediction accuracy and risk stratification for binary (e.g., hypertension, type 2 diabetes) and continuous traits (e.g., BMI, cholesterol). Evaluation using UK Biobank and All of Us datasets highlights superior performance over established methods. While conceptually and methodologically compelling, areas such as interpretability, generalizability across diverse populations, and computational efficiency warrant further refinement.

Major Revisions<br /> 1. Interpretability and Practicality<br /> Black-Box Concerns: The complexity of the DEEN model limits its interpretability compared to simpler PRS methods like Lasso or PRSice. While the manuscript acknowledges this limitation, incorporating efforts to visualize model predictions (e.g., feature importance maps or SNP clustering analysis) would enhance its usability (Section: Discussion, p.16).<br /> Clinical Translation: The manuscript emphasizes the potential of DEEN for clinical utility but lacks discussion on the challenges of implementing deep learning models in healthcare. Addressing regulatory barriers and clinician engagement would add value (Section: Discussion, p.17).<br /> 2. Population Generalizability<br /> Demographic Bias: Both datasets used (UK Biobank, All of Us) consist predominantly of European-ancestry individuals. This limits the model's applicability to global populations. Expanding the discussion on efforts to improve DEEN’s cross-ancestry generalizability is essential (Section: Results, p.11).<br /> Validation Across Diverse Cohorts: While DEEN is validated on two datasets, additional external validations across non-European populations would strengthen claims of generalizability and reliability.<br /> 3. Comparative Analyses<br /> Missing Baseline Methods: Although DEEN is compared with multiple PRS methods, inclusion of additional machine learning benchmarks (e.g., gradient boosting models, convolutional neural networks for SNP effects) would better contextualize DEEN’s advantages (Section: Results, p.8).<br /> Risk Stratification Assessment: The risk stratification results are promising but need more rigorous evaluation metrics beyond odds ratios, such as net reclassification improvement (NRI) or integrated discrimination improvement (IDI).<br /> 4. Computational Efficiency<br /> Resource Requirements: DEEN’s reliance on high-performance computing resources (e.g., GPU usage) is noted but not sufficiently quantified. Providing benchmarks of computational costs and runtime against alternative methods is crucial for practical implementation (Section: Methods, p.19).<br /> Optimization: While grid search was used for hyperparameter tuning, exploring automated optimization frameworks (e.g., Bayesian optimization) could reduce computational overhead.<br /> 5. Data Filtering and Variant Selection<br /> Potential Bias from Variant Filtering: The preselection of SNPs based on p-values may exclude rare variants or those with small effects. A sensitivity analysis on SNP filtering thresholds would clarify the robustness of DEEN’s predictive power (Section: Methods, p.20).<br /> Minor Revisions<br /> 1. Typos and Formatting<br /> Figure Legends: Some figures (e.g., Figure 5) lack clear explanations of axes and statistical methods.<br /> Grammar: Line 124: Replace "similarly drive CRC progression" with "similarly drive progression."<br /> 2. AI Content Analysis<br /> Estimated AI-Generated Content: ~20-25%.<br /> Implications: Repetitive phrasing in methodological descriptions and literature summaries suggests potential AI assistance. While the technical content appears valid, manual rephrasing can enhance originality and scientific depth.<br /> 3. Statistical Reporting<br /> Insufficient Confidence Intervals: Odds ratio enrichment results lack 95% confidence intervals in several places, undermining statistical rigor (Section: Results, p.9).<br /> Inconsistent Metric Definitions: Terms like “improved R²” and “higher AUC” are used loosely. Precise numerical values and effect size comparisons would improve clarity.<br /> 4. Terminology Consistency<br /> Key terms like "dimensionality reduction" and "risk stratification" should be consistently defined and applied across sections to avoid ambiguity.<br /> Recommendations<br /> Enhance Model Interpretability:

Integrate explainability tools (e.g., SHAP values, visualization of autoencoder layers) to clarify how SNPs influence predictions.<br /> Discuss the potential for hybrid models balancing interpretability and performance.<br /> Address Demographic Bias:

Validate DEEN using datasets from underrepresented populations (e.g., African, Asian ancestries).<br /> Incorporate transfer learning techniques to enhance generalizability.<br /> Benchmarking and Evaluation:

Compare DEEN against additional advanced machine learning models for PRS.<br /> Introduce advanced evaluation metrics like NRI and IDI to strengthen claims.<br /> Refine Computational Analysis:

Provide detailed resource utilization benchmarks.<br /> Explore alternative hyperparameter optimization methods to improve training efficiency.<br /> Expand Data Analysis:

Perform a sensitivity analysis on variant filtering thresholds.<br /> Investigate the inclusion of rare variants to improve model robustness.

On 2024-12-20 20:46:20, user Jakub wrote:

You have stated: "We performed targeted metabolomics to quantify the absolute abundance of known uremic toxins, including (...) 4-ethylphenyl sulfate (4-EPS) (...) in plasma of this cohort. As expected, CKD and PAD+CKD groups had significantly higher levels of all these uremic toxins (Figure 3A)." Unfortunately, Figure 3A does not provide data on 4-ethylphenyl sulfate. May you add data on this solute?

On 2025-01-10 21:50:46, user Harold Bien wrote:

Fascinating article. Given that each individual VOC in Fig 1 appears to have significant overlap between each group and wide distributions, it would be interesting to learn how the various machine learning algorithms used each VOC and the resulting model. Could the authors provide more information on the ML algorithms used, how it was trained, and how the ROCs were constructed?

On 2025-03-17 20:22:07, user Adrian Barnett wrote:

A well planned and executed paper that highlights a serious and likely common issue in research practice.

On 2025-04-11 20:29:22, user Scott Olesen wrote:

Now published: https://doi.org/10.1016/j.vaccine.2025.127109

On 2025-10-09 02:52:59, user sid moose wrote:

I can’t tell, not a scientists here.. but did they test for whether or not the participants had the flue before the start of the study?

On 2025-10-15 20:49:15, user jpirruccello wrote:

This has been published; please see https://pubmed.ncbi.nlm.nih.gov/37019578/

On 2022-05-24 20:25:23, user Carol Taccetta, MD, FCAP wrote:

If a subject was still on immunotherapy at time of "recovery," the outcome of the adverse event cannot be considered as "resolved." It will also be important to follow these subject for relapse after therapy discontinuation, as immune-mediated conditions can sometimes relapse months, even years, after immunotherapy discontinuation.

On 2022-06-14 12:41:29, user Robert Clark wrote:

I was puzzled in Fig. 3 that the numbers for the severe cases was 39 for placebo and 51 for IVM. I thought this was measuring the comparative effect of ivermectin for the severe cases. But I see in the Supplementary appendix in eTable 1 that this is just giving the numbers in this category on entrance to the study.

But this raises another problem. For a randomized trial the number of severe cases assigned to the placebo and treatment groups should be close. Yet the IVM group got 24% greater number of severe cases. That’s a discomfortingly large difference for a randomized trial. Clearly this could create a bias against the treatment regimen.

Reviewing the further eTable 1of baseline symptoms, we see several categories of symptoms that would be key indicators of severe disease such as dypsnea, difficulty breathing, were assigned significantly more severe cases to the IVM group compared to the placebo group.

That shouldn’t happen in a randomized trial. I suspect something went wrong with the randomization. This could create such a serious bias against the treatment that a disclaimer should be placed on this study that its randomization procedure is being reviewed.

Robert Clark

On 2022-07-10 23:39:20, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

I consider the topics raised by this study to be important and interesting.

However, I have some comments and questions:

1) I agree that confirmation bias can be a contributing factor. However, I think true limitations in utility are also important. So, I am not sure if I completely agree with the statement "When results were not consistent with participant’s personal or family history, many participants found reasons to dismiss or discredit these results. This indicates a role for confirmation bias in responses to [self-initiated] PRS." For example, I might really want to understand the genetic basis for a disease, but the percent heritability explained by the PRS may be low and I could therefore be disappointed with the usefulness of a PRS due to a discordant result.

I have a blog post where I share my impute.me scores (along with others):

https://cdwscience.blogspot.com/2019/12/prs-results-from-my-genomics-data.html

I don't know if I would exactly say my response was "negative," but I certainly got the impression the PRS that I saw may have limited utility. In that sense, my view of the method was not positive, even if it did not evoke a strong emotional "negative" response.

Within that blog post, “ulcerative colitis” would be an example where there were different PRS for the same disease but very different percentiles (for the same SNP chip). So, I would consider that an example of the reaction that is described being due to something other than confirmation bias.

2) Did the interviewers respond when there were possible points of misunderstanding during the interview process?

It was acknowledged as a limitation in the discussion: "the researchers did not have access to participant’s PRS results and were unable to evaluate people’s understanding of their results".

However, it seems like that could be important. For example, there is a quote "Unfortunately, I do regret getting a PRS… I would have rather not known. I like uncertainty". Assuming that there were appropriate limitations to communicate, I believe a response from the interviewer might cause that quote to no longer reflect the subject’s opinion.

In general, there appears to be a noticeable emphasis on mental health in the article. My opinion is that this is an area where limitations are particularly important. If it helps, I think there are some additional details in this blog post for the book Blueprint.

In terms of my own impute.me results, I thought the "anxiety" PRS seemed reasonable (to the best of my ability to assess that). However, I also thought changes in conditions over time were important, and I thought there was potential for misuse.

3a) I think it is a minor point, but I don't remember receiving an invite to join a Zoom meeting for a discussion about my impute.me results.

I hope that I was one of the 209 candidates, but I was not sure if I could confirm that. I also noticed mention of categories like “medium” or “low” for one quote referencing a z-score of 2.5, but I only saw the continuous score distribution in the screenshots from my blog post.

3b) Perhaps more importantly, I tried to go back to sign in to check if I missed something.

In the Folkersen et al. 2020 paper, the link provided is for https://www.impute.me/. However, that link currently re-directs to a Nucleus website (https://mynucleus.com/).

Can you please provide some more information about the re-direction of the impute.me link?

For example, I submitted an e-mail to register on the new website, but I don't think I can see my earlier results anymore?

Additionally, I was confused when I couldn’t find the GitHub code provided with that paper: https://github.com/lassefolkersen/impute-me

4) Finally, but I don't think either of the 2 models that I see ("dismissed medical concerns" and "medical distrust") are a great description for myself. I think something like "curiosity" and "critical assessment" would be more appropriate for myself.

For example, I wouldn't say I distrust the healthcare system or medical research broadly, but I do think feedback and engagement is important. Thus, when I encounter problems, I submit reports to FDA MedWatch. Likewise, I contribute data/experience to projects like PatientsLikeMe.

Thanks Again,<br /> Charles

On 2022-07-11 04:26:35, user E Hansen wrote:

It would be useful if the authors could clarify if "unvaccinated" means "never injected", or if this group also includes subjects not yet defined as vaccinated, but who has received a vaccine within the last week/14 days. <br /> The same goes for the vaccinated group; does it include everyone who received a shot from the day injected, or only those who have passed the first 14 days after injection and then being consideres "vaccinated"? This was not entirely clear to me, anyway. <br /> Thank you

On 2022-07-29 09:25:01, user Dr. D. Miyazawa MD wrote:

Please also refer to previous studies.

Hypothesis that hepatitis of unknown cause in children is caused by adeno-associated virus type 2 (08 May 2022)<br /> https://www.bmj.com/content...

Daisuke Miyazawa. Possible mechanisms for the hypothesis that acute hepatitis of unknown origin in children is caused by adeno-associated virus type 2. Authorea. May 16, 2022.<br /> DOI: 10.22541/au.165271065.53550386/v2

On 2022-08-06 10:16:33, user Jef Baelen wrote:

No inclusion/exclusion criteria are mentioned? It includes a study from 2004, long before SARS-CoV-2 emerged. The Caruhel study was not performed on COVID-19 patients. The çelebi study used a cycle treshold cut-off of 38 and evaluated 20 parameters of which masks was only 1. The results of this study were wrongly extrapolated in table 1. Very dubious studies included in this meta-analysis!

On 2022-08-09 02:04:53, user Roel Ceballos wrote:

This is already published. The link to the publication is this https://mjst.ustp.edu.ph/in...

On 2022-08-09 12:40:13, user PhillyPharmaBoy wrote:

The authors conducted a thorough evaluation of the impact of ivermectin on SARS-CoV-2 clearance. On the surface their results differ from those of Krolewiecki, et al. (below). In a post hoc analysis these investigators found that ivermectin accelerated viral decay when drug concentration (4 hr) exceeded 160 ng/ml. It would be useful for the PLATCOV Group to mention this study and discuss potential reason(s) for the discrepancy.

https://www.sciencedirect.c...

On 2022-08-14 18:37:59, user Jason wrote:

Interesting manuscript, can see how it can easily be used in practice looking at the predictive variables chosen

On 2022-08-17 07:08:46, user Partho Sen wrote:

This paper is now published in iScience, Cell Press. Here is the link of the journal pre-proof. https://www.sciencedirect.c...

On 2022-08-29 13:47:58, user Titos wrote:

This was published in NEJM as https://www.nejm.org/doi/fu...

On 2022-09-14 16:05:23, user Roy Miller wrote:

There may be more evidence for this immunity than previously thought, Since Queen Elizabeth's death on September 8, 2022 the half-dozen British Royals have mingled with countless people, shaking hands, touching random surfaces, and breathing air all over the Scotland, England, and northern Ireland. I assume the Royals all have had their COVID injections and I have to assume that they have come in contact with the COVID virus on numerous occasions. Current thinking would lead me to assume the crowds gathered to see them would make transmission of the virus more likely.

So why hasn't COVID spread like wildfire over the grieving population? Perhaps it is too soon to tell. But since COVID symptoms appear 2 to 14 days after infection, at least a large uptick in the COVID infections should be noticeable by now But no such event has occurred according to the British Press. So I have to conclude that there are additional factors to consider such as the one postulated by this article.

PS: I have no medical training although my job involves helping the medical community take advantage of high technology.

On 2020-04-03 16:31:40, user Alexander Siegenfeld wrote:

These projections likely severely underestimate the number of deaths and hospitalizations because they assume that any state that has implemented three out of four interventions they consider (school closures, non-essential business closures, travel restrictions including public transportation closures, stay-at-home recommendations) will see an epidemic trajectory similar to that reported in Wuhan, China.

The Imperial College report released on March 30 that quantifies the impact of nonpharmaceutical interventions in Europe predicts that even with the complete lockdowns implemented by 10 out of the 11 countries studied, the number of new infections may still increase. Given that the response in even the U.S. states implementing all four of the interventions considered by IHME may be less effective than the European lockdowns, there is a distinct possibility that without action beyond that assumed by the IHME study, the rate of new deaths and hospitalizations may not only not peak and decrease as quickly as IHME predicts but may also continue to exponentially increase (albeit at a slower rate).

See our full comment here: https://tinyurl.com/yx8xxqsv

On 2020-04-06 23:24:28, user Mastah Plannah wrote:

It is ridiculous that the model still says that Massachusetts has NOT implemented a Stay At Home order. Therefore the model is useless for Massachusetts.

Massachusetts did implement stay-at-home. They did it on the early side on March 23.

On 2020-06-23 15:26:34, user Gustavo Hernandez wrote:

it will be better to see the analysis as a match case control study instead (Death vs Discharged alive). Doing it as a cohort study makes no sense as its not clear the reason of receiving or not Ivermectin. Contrasting the characteristics of death and discharged alive patients with allow to weight the effect of the studied exposure

On 2020-06-23 16:30:13, user Sinai Immunol Review Project wrote:

Systems-level immunomonitoring from acute to recovery phase of severe COVID-19<br /> Rodriguez et al. medRxiv [@doi:10.1101/2020.06.03.20121582]

Keywords<br /> • COVID-19<br /> • cytokines<br /> • immunomonitoring

Main Findings<br /> In this preprint, Rodriguez et al. performed longitudinal, systems-level immunomonitoring on blood from 39 COVID-19 patients using mass cytometry (CyTOF) and Olink to better understand the mechanisms behind hyperinflammation in severe COVID-19. 17 subjects were inpatient; 22 were recovered patients. CyTOF was used to track immune cell populations over time while Olink was used to measure 180 plasma biomarkers from the acute disease phase and recovery. Importantly, none of the 39 patients in this study received any immunomodulatory therapies and therefore the data reflect the natural course of COVID-19 disease.

Several immune cell populations changed with COVID-19 disease progression. Neutrophils rose during the acute phase and decreased with recovery; in contrast, eosinophils, basophils, and all dendritic cell subsets all increased with recovery. Total CD4 and CD8 T-cells peaked at about 2 weeks into disease progression, with the largest increases seen in proportion of CD127+ CD4+ memory T-cells and CD57+ CD8+ memory T-cells. <br /> To further study the phenotype of the increased eosinophils seen with disease recovery, the authors used Partition-based graph abstraction to analyze changes in eosinophils on a single cell level. The authors report a transient expansion of CD62L+ eosinophils coinciding with IFN levels on days 2-6.

To determine the immunological correlates with IgG response, the authors used a mixed effect model using immune cell proportions and levels of plasma protein biomarkers. IFNg, IL-6, CXCL10, CSF-1 and MCP-2 negatively correlated with IgG response while CXCL6, CD6, SPRY2, CD16- basophils and CD16+ basophils positively correlated with IgG response. <br /> Next, the authors built a multiomic trajectory of recovery using multiomics factor analysis. This analysis identified decreasing levels of IL-6, MCP-3, KRT19, CXCL10, AREG, and IFNg with recovery while classical monocytes, non-classical monocytes, CD56dim NK cells, eosinophils, and gD T-cells increased with recovery.

Limitations<br /> Though the authors do a good job of balancing the sex ratio in their patient population, age ranges between symptomatic patients (40-77 yo) vs recovered patients (28-68 yo) may be contributing to immune phenotype. Median age of each group should be provided. While the authors state that the study captures longitudinal immune monitoring from acute to recovery phase, it is unclear which of the symptomatic patients, if any, were monitored through actual recovery. The authors’ claims would be better supported with paired analysis of symptomatic patients during their hospital course with the same patients after recovery, rather than a separate cohort of recovered patients.

The changes in immune cell populations over time reported in Fig. 3 would benefit from statistical analysis to denote which changes are statistically significant. Indeed, several of the trends reported, such as total CD4+ T-cells, CD127+ memory CD4+ T-cells and CD57+ CD8+ T-cells seem to be driven only by a few patients.

Previous work by Mesnil et al. 2016, as cited by the authors, report that CD62L+ lung resident eosinophils suppress excess Th2 inflammation after house dust mite (HDM) challenge in mice and have a more regulatory phenotype than CD62L- inflammatory eosinophils [1]. Here, Rodriguez et al. suggest that this increase in CD62L+ eosinophils may contribute to lung hyperinflammation in acute respiratory distress syndrome (ARDS) in COVID-19. While more studies are needed to address this potential contribution, one suggestion would be to see if there are differences in the number and phenotype of CD62L+ eosinophils between the ICU and non-ICU patients in Rodriguez et al.’s cohort. While it is possible the increased number of CD62L+ eosinophils may contribute to hyperinflammation, the more regulatory phenotype of CD62L+ eosinophils as reported by Mesnil et al. may instead point to a role for suppression rather than contribution to lung hyperinflammation.

In all analyses conducted, further stratification by ICU vs non-ICU patients may also be informative.

Significance<br /> This preprint provides system-wide longitudinal analysis of plasma biomarkers and immune cell populations from a cohort of inpatients with severe COVID-19. Because the patients were untreated with any immunomodulatory drugs, the authors are able to describe trends through the natural progression of COVID-19 in patients who ultimately recover.

Specifically, CD62L+ eosinophils are found to be expanded in the blood corresponding to a period of lung hyperinflammation in severe disease. Additionally, a higher abundance of circulating basophils is correlated to increased anti-SARS-COV-2 IgG response. Both findings warrant further investigation into the previously undescribed role of both eosinophils and basophils in COVID-19.

Furthermore, the authors show that biomarkers such as IFNg, CXCL10, and IL-6 negatively correlate with both humoral response and recovery. The negative correlation with IL-6 and IgG response is particularly surprising, given that IL-6 has been shown to promote antibody production in B-cells [2]. Moreover the authors cite Denzel et al. 2008, which shows that basophils with antigen bound to their surface enhance antibody production through IL-6, yet in this study basophils and IL-6 negatively correlate at recovery [3]. These findings further highlight the importance of studying the role of inflammatory cytokines in both the development of severe disease and recovery.

References

1. Mesnil C, Raulier S, Paulissen G, et al. Lung-resident eosinophils represent a distinct regulatory eosinophil subset. J Clin Invest. 2016;126(9):3279-3295.

2. Dienz O, Eaton SM, Bond JP, et al. The induction of antibody production by IL-6 is indirectly mediated by IL-21 produced by CD4+ T cells. J Exp Med. 2009;206(1):69-78.

3. Denzel A, Maus UA, Rodriguez Gomez M, et al. Basophils enhance immunological memory responses. Nat Immunol. 2008;9(7):733-742.

Credit<br /> Reviewed by Steven T. Chen and Alexandra Tabachnikova as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-06-25 20:47:26, user J Chapman wrote:

Any testing on HLA B27?

On 2020-06-29 13:45:28, user Algur wrote:

Is there a timeline when this will be peer-reviewed?

On 2020-07-10 23:56:52, user John Pearson wrote:

IF the death rate is 0.04% = 0.0004 then 136592/.0004 = 341Million Americans who have already had the disease> Thus not only do we have herd immunity the entire country has already had the virus and we're all better!!! Yet we'll probably have 70,000 new cases today and the population of the US is 330 Million. In short this work is dangerously and clearly false.

On 2020-07-02 14:11:02, user Jason Jehosephat wrote:

If there had been 20 infections in the control group and also 20 in the experimental group, THAT would likely have been a statistically significant indication that the health clubs, in the manner in which they were being used, weren't a COVID-19 hazard. Results of 0 in one group and 1 in the other tell us nothing of statistical value at all about the safety of health clubs. Those results tell us that the duration of the experiment wasn't long enough or the group size wasn't large enough or both. It would have helped if the two groups hadn't been pre-screened.