On 2021-01-07 14:34:51, user Meerwind7 wrote:
Households with single parents are also considered as socially deprived in other contexts. For infection rates, it might be of benefit if there is no second adult that could bring infections to the family. It was a pity if the effect was not taken into account. I also do not know if the social deprivation was evaluated for each individual child and its infection risks, or just for the schools and the aggregat of their pupils overall.
On 2021-01-08 21:53:07, user Marek J wrote:
Your study says that methylglyoxal contained in honey cause immunostimulatory or pro-inflammatory action via stimulating the production of immunological mediators, also IL-1?. This study https://www.nature.com/arti...<br /> Shows that low levels of IL-1 are linked to lower mortality.<br /> Is it then safe to recommend using honey rich for MGO, e.g. Manuka honey?
On 2021-01-09 18:28:12, user Alex Backer wrote:
This study appears to have been used to Curative to justify their methodology, yet the false positive and false negative rates in the FDA report are incredibly high. Look at the numbers: https://www.linkedin.com/pu...
On 2021-01-10 12:16:07, user Hans Binder wrote:
The paper is published under<br /> https://www.mdpi.com/1999-4...
On 2021-01-10 19:47:40, user Wayne Griff wrote:
21 days after the 1st dose of the Pfizer Vaccine, patients have 1/5th the viral neutralizing power of Convalescent Plasma. In contrast, 7 days after the 2nd dose, patients have 2-4 times the neutralizing power of Convalescent Plasma. NEJM Also, the 47% effectiveness rate is only up to 3 weeks. It's definitely going to be less at 6, 9, or 12 weeks.<br /> Giving only 1 vaccination is a waste of a vaccination. It provides little, if any immunity.
On 2021-01-12 20:36:18, user Michael Meyer wrote:
Can someone tell me if this study has completed the peer-review process prior to publication in the Journal of Infectious Diseases? If not, is it currently in that process?
On 2021-01-14 00:01:25, user Emmanuel Aluko wrote:
Will such a study be done on older cohorts to show the age-dependent efficacy of Ivermectin on reducing mortality, for mortality is seen mainly in older patients with associated co-morbidities? Days to negative tests on such cohorts would also provide a better basis for accepting Ivermectin as a worthy COVID-19 therapeutic.
On 2021-01-15 12:19:29, user Kit Byatt wrote:
Given that:<br /> 1. Excess mortality for a year can't be known for several weeks into the following year <br /> 2. Especially in a year with Christmas & New Year's Day on Fridays, and a pandemic impeding the bureaucracy of collecting & collating mortality data - particularly in cases where the Coroner [or equivalent] is involved)<br /> 3. Different countries have exhibited different patterns (or none) of winter excess mortality peaks (as shown in Figure 1c)
a) Is it not premature to undertake a comparison before all the data are in (or alternatively, at least title as 'Preliminary observations on...'?
b) Might not the association end up significantly higher, the same, or lower, then?
On 2021-01-27 10:12:04, user Elias Hasle wrote:
some countries being hardly hit while others to date are almost unaffected
Readers will tend to read this as "barely hit", the opposite of the intended meaning. "hit hardly" would be less ambiguous.
On 2021-01-17 06:42:44, user Seri Ratu Putri Binti Baharom wrote:
Hi, where can I see the survey form?
On 2021-01-18 11:40:13, user Kit Byatt wrote:
Was there a sex difference in LOS?
We know there are age differences in LOS (median & variance increasing with age). What were the LOSs for age brackets (e.g. each decade)?
Given the very skew LOS distributions in hospital in-patients usually, and especially here, mightn't it be helpful to put the inter-quartile range for the median LOS in 'Outcomes' para 2?
Could you calculate the 'decay' in in-patient numbers (i.e. the equation for the 'current number of inpatients' curve from time zero, for 1000 pts with the LOS distribution you know)? This could be invaluable for modelling bed occupancy.
It is difficult to know the percentage figure for each bar in the symptom combinations and ICU admission matrix. Either putting the number over/in each column (as in Figure 15 Top), or at least showing minor tick marks at 0.01 intervals would greatly improve the clarity of this chart.
On 2021-01-21 13:24:54, user Miroslava Stancíková wrote:
Testing was not voluntary, it was in conflict with the Constitution of the Slovak Republic and the Charter of Fundamental Human Rights
On 2021-01-25 15:10:24, user Chip Hughes wrote:
Thank you all for this great research. It will really help OSHA target enforcement to the highest risk sectors and job classifications so that we can hope to reduce deaths among frontline workers with the highest level of COVID19 exposures on the job. Chip Hughes, USDOL-OSHA DAS
On 2021-02-02 18:17:42, user Robert Enger wrote:
The #1 candidate is "cook". Frequently that person stands in proximity to the grill, which is a high air flow location, due to the high power exhaust fan system in the range-hood over the grill. This acts as a funnel, sucking air from the kitchen (and surrounding areas back to the various points of makeup-air ingress). The cook is thus "downstream" from numerous co-workers in the kitchen, and from persons in other locations between the makeup-air ingress points and the range hood (potentially all the restaurant patrons, if interior dining resumes).
This should sound familiar. Recall the studies of remote distance Covid transmission studied in Korea and China. In those studies, high air flow from air conditioner output ports carried virus from infected individuals to victims many feet removed from the infected party. In this case, the cook near the range-hood is "downstream" from potentially significant numbers of individuals (depending on the location of makeup-air ingress points, etc).
If this line of reasoning is found to be sound, then providing a direct ingress path for outside fresh air to enter into the kitchen "may" reduce the exposure of the cook (and nearby kitchen staff).
On 2021-01-30 20:33:49, user Michael Höhle wrote:
Dear authors,
thanks for posting this very relevant manuscript! Please consider having a look at the statistical methodology of the two following papers, which are very closely related to your work, as they also use an imputation + nowcast + backprojection approach:
Nowcasting the COVID-19 pandemic in Bavaria:<br /> https://onlinelibrary.wiley...
Analysis of the early Covid-19 epidemic curve in Germany by<br /> regression models with change points:<br /> https://www.medrxiv.org/con...
Best regards,<br /> Michael Höhle
On 2021-02-04 19:21:10, user Eli Rosenberg wrote:
We note that the final form of this article was published in JAMA Network Open on February 3, 2021:
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2775827
We thank the medRxiv community for your interest in our work.
Eli Rosenberg<br /> Associate Professor<br /> Department of Epidemiology and Biostatistics<br /> University at Albany School of Public Health, State University of New York
On 2021-02-22 19:53:12, user Alexander Porter wrote:
What does: '8,041 individuals received two doses of a COVID-19 vaccine and were at risk for infection at least 36 days after their first dose.' mean? Were these individuals exposed to SARS-CoV-2 directly every day?
Were all PCR methods run with the same cycle threshold before and after administration?
On 2021-04-15 21:01:27, user CY Liu wrote:
Did you control batch effect, position effect, covariates in the data analysis? I could not see it in the paper.
On 2020-10-30 18:25:19, user gatwood wrote:
I suspect there could be a strong corellation between vaccination status<br /> and following a strict adherence to all COVID anti-infection <br /> guidelines, PPE etc... Experienced and medically trained Drs and nurses<br /> more likely have been vaccinated and also are more likely to follow PPE<br /> wearing and careful anti-infection routines. Support staff (food <br /> service, assistants and claening staff) with less formal medical <br /> training and understanding of infection are probably less likely to be <br /> vacinnated and also may be less likely to carefully employ all technical<br /> anti-infection measures. Would this account for the vaccinated folks <br /> having less COVID infection?
On 2020-11-06 05:07:01, user Schneide Fu wrote:
Not sure they got the dosage correctly, as Nitazoxanide has a half life of 2.9 hrs. Should have been on divided doses, several times a day.
On 2020-11-06 14:33:43, user Dr. Thiagarajan wrote:
Timely need article and very interesting. We need to understand the challenges faced by dialysis professionals during this COVID 19. I congratulate Dr. Ravi Kumar for this timely article. Looking for this complete manuscript.
On 2021-08-10 22:41:16, user Ashley Derrick wrote:
I had covid in May of 2020. I had many long covid issues for months after - but the biggest issue was chest (heart and lung) pain. The symptoms included shortness of breath, burning sensation in the chest, difficulty getting a deep breath and feeling as if I was having a heart attack when more active (exercising). I went to both a heart specialist and a pulmonary specialist and nothing ever was found to be "wrong". The heart doctor was convinced it was inflammation in and around my lungs and heart that caused this. After 9 months, it went away (shortly before my first vaccine). I felt "normal" for about 4.5 months, but two weeks ago, the chest and heart issues came back. Same feelings. I am a fit 49 year old woman. Wondering if there are any studies that see symptoms going away and then months later coming back. Thanks -
On 2021-08-12 06:11:36, user Gabriele Pizzino wrote:
The study brings up relevant insights.<br /> I agree with Richard, uniformity of testing frequency is a potential confounding factor that should be taken into account.
Also, you may have a selection bias generated by vaccination policies, and timeline.<br /> I’m gonna use Italy to give you the idea: Pfizer was rolled out earlier, and in a much more massive way, than Moderna. The same was true for the AstraZeneca one.<br /> The Italian government gave priority to high-risk workers (especially health workers, and whoever was working into medical facilities or nursing homes), and to high-risk individuals (so elderly people, and/or people with severe underlying conditions).<br /> At the time, the choice was between Pfizer and AstraZeneca; considering Pfizer showed a better efficacy, and it was better perceived in terms of safety profile by the public, the very very large majority of those high-risk categories received the Pfizer shots.
That is a textbook-level selection bias, which could potentially affect the results in a significant way.<br /> I live in Italy, so I followed the vaccination process here much more closely; I don’t know if the same dynamics I described stand true also for the US, but I think it is worth to take a look and check it.
On 2021-08-12 19:49:26, user Steven Ramirez wrote:
From the preprint:
"Date of vaccination (bucketed). For a given individual in the mRNA-1273 cohort who<br /> received their first vaccine dose on a given date, only individuals in the BNT162b2<br /> cohort who were vaccinated on the same date or within two weeks after that date were<br /> considered for matching. This match helps to ensure that matched individuals reach their<br /> date of full vaccination (14 days after the second dose) on approximately the same date."
This seemingly addresses my point and effectively controls for diminution over time.
Perhaps an explicit sentence in the preprint would help clarify this point.
On 2021-08-12 17:18:46, user Iñigo Ximeno wrote:
Although the observation is very interesting, it does not lead to the conclusion that it is the most prudent thing to continue with indiscriminate vaccination in the middle of a pandemic when the vaccine does not prevent the transmission of the pathogen.
It is not relevant how many mutations are detected but the importance of them. It is obvious that among people with some immunity the mutations for which the antibodies can neutralize the virus will not be spreaded and therefore those will not be detected. However the mutations that can evade the immunity, even if they are few, will be more virulent and letal.
On 2021-08-14 05:50:28, user Brad Mellen wrote:
I assume the 167 infected people tracked in the study were previously vaccinated. Is it possible that antibody mitigated viral enhancement played a role in the increased viral loads? A study done by Wen Shi Lee et al in the Nature Microbiology volume 5, pages 1185–1191 (2020) noted the potential dangers of Covid 19 vaccinations could increase viral loads and ultimately increase the spread of Covid 19.
On 2021-08-15 14:53:19, user Johannes Hambura wrote:
The study authors found that the prevalence of mutations is higher in B cell epitopes than in T cell epitopes. They infer that vaccines that rely primarily on T cell immunity should confer protection more durable against the worrisome variants of SARS-CoV-2.<br /> It would be logical and consistent to assess this T cell immunity in unvaccinated and recovered Covid-19 patients, in order to better coordinate the strategy towards collective immunity.
The authors report, based only on 47 cases studied, that unvaccinated patients share significantly more genomic mutational similarities with the variants of concern than patients with a breakthrough infection.<br /> This finding is interesting, but it requires statistically significant evaluation and verification.<br /> Especially since<br /> - in vitro, the selection pressure imposed by the antibodies, induced by the vaccines, has led to the emergence of new variants of SARS-CovV-2 (1);<br /> - in vivo, the case of a severe and prolonged form of infection by SARS-CoV-2, treated with antibodies taken from convalescents, favored the appearance of a variant. The variants decreased when the treatment with the injected antibodies was stopped and reappeared with new doses of the injected antibodies. In the absence of the antibodies, the wild strains again became the majority (2).
In addition, the following findings by the authors tend to favor selective pressure exerted by vaccinations:<br /> - the diversity of SARS-CoV-2 lines decreases at country level with an increased rate of mass vaccination (negatively correlated with the increase in the rate of mass vaccination in the countries analyzed);<br /> - The decline in lineage diversity is coupled with the increased dominance of variants of concern;<br /> - vaccine breakthrough patients harbor viruses with significantly lower diversity compared to unvaccinated COVID-19 patients.
The question still remains open, whether vaccination should not be limited to only vulnerable people in the world population, in analogy with a risk calculator for the population proposed by American scientists (3).
On 2021-08-18 17:39:03, user John Baron wrote:
The first sentence states that this is a study of individuals who underwent SARS-CoV-2 testing, presumably (though not stated) during the study period. Unless there was population testing surveillance in place, there must have been selection factors (e.g. COVID exposure or symptoms) prompting testing. This would bias estimates of infection rates upward, though the bias is unlikely to differ between vaccine groups and may not differ much between vaccinated and unvaccinated groups. Tthe relative infection measures might be OK.
In the "final" paper, it would be important to account for the usual cohort issues: censoring, movement out of the health care system, etc. Also, the first paragraph of the Methods section should include unvaccinated individuals.
On 2021-08-19 02:48:38, user Sheila Halpenny wrote:
I assume everyone responding here will use the same “logic” to apply to every other poll taken.
On 2021-08-09 21:26:26, user CdSS wrote:
Where can I get the reasons why PhDs answered "probaly not/definitely not"?
On 2021-08-21 17:14:03, user Alan wrote:
How will increased vaccination make a difference if the efficacy is as is being reported less than 50%? Once one is infected it does not appear to reduce one's odds of being hospitalized. So if the efficacy approaches zero, there would be zero benefit to being vaccinated, with the currently available vaccines.
On 2021-08-23 13:04:39, user Eyal Oren wrote:
I don't see any mention of vaccination status. Were these patients vaccinated or unvaccinated? Also the study would be stronger if comparison done to other patients in your catchment area vs CDC data (I believe that dataset is across US and not limited to Georgia?).
On 2021-08-24 09:17:07, user Martin Steppan wrote:
A fundamental methodological caveat of this article is the date / season of sample collection. Breakthrough infection samples were collected in the sprjng-summer period of 2021 only (apr-jul), whereas unvaccinated samples seem to be predominantly from fall / winter 2020 (apr-dec). It has been shown in many studies that sars-cov-2 virions are temperature-sensitive and less active / infectious in warm environments. Hence, the results of this manuscript may reflect a seasonal pattern in infectivity. The authors may want to control for this statistically by either (1) using a matched design of samples from similar dates; (2) include historical temperature data for the Netherlands as a covariate / proxy for this likely bias. Due to this reason, the analyses in their current form do not rule out this bias, which casts doubt on the authors' implicit hypothesis that vaccination status moderates the link between viral load and infectiousness.
On 2021-08-26 11:54:58, user Gubbedefekt wrote:
Can someone confirm: what difference did it make if the natural immunity was acquired from a delta variant infection compared with one from another variant? Did the study look at this?
In any case the comparison looks devastating for Pfizer and other vaccine producers.
On 2021-08-26 18:20:25, user thestreetlawyer1 wrote:
Love to see this peer reviewed. Been really trying to determine what is the best move for me. Hard to put all the dogma and peer pressure aside (from both sides). I'm curious how this data reckons with u.s data on hospitalizations, seems most of our hosp cases are unvaccinated.
On 2021-08-28 19:52:31, user Catriona wrote:
“ individuals who were previously infected with SARS-CoV-2 and given a single dose of the BNT162b2 vaccine gained additional protection against the Delta variant.”
I’m confused. Didn’t your study show no significant difference for symptomatic infections in previously infected individuals with or without a single vaccination dose? Or did I get that wrong? The table says the previously infected and vaccinated odds ratio for symptomatic infection is 0.65 but confidence intervals were 0.34-1.25 and P value was 0.194.
In which case, exactly what were they protected against? Is there some sort of clinical significance regarding a positive PCR in the absence of any symptoms? Or are you implying that individuals can have atypical symptoms that can cause them health problems but aren’t recorded as symptoms? The study didn’t look at infectivity.
Are you claiming the vaccinated individuals were healthier in some way because they had fewer positive PCRs? Or are you claiming they did not pass on the Delta variant as often? Or was there something else that you meant by “additional protection against the Delta variant?”
Would love some clarification on these issues.
On 2021-08-27 11:08:39, user Lakesha Scout wrote:
Most people do not understand how their immune system works, and fall back upon the inept press as their inept narratives. Antibodies do not continue long term, as such a condition would in fact prove disastrous.
Long term immunity relies upon the creation of memory .. specifically memory B and T cells. These are the cells which identify later reemergence of a pathogen (such as SARS-CoV-2) in the body and mount a successful and rapid response to its demise.
There are two pathways to "Active" immunity (infection, and inoculation) <br /> Also there is "Passive" immunity which occurs through such things as a blood plasma infusion from a prior infected person who has existing antibodies. <br /> Any of these three paths, can provide immunity to a pathogen.
The false narrative being pumped by the media is that the only way to overcome a pathogen is through inoculation (vaccination).... that quite simply is not true, neither in medical sense nor in scientific sense. As a mater of medical fact, some inoculations (specifically mRNA) have been proven to enhance a pathogens capability to infect. This condition is known as Antibody Dependent Enhancement or (ADE).
On 2021-08-27 17:26:33, user Mohammad Ali wrote:
Please find the published article at BMJ Open here: https://bmjopen.bmj.com/con...
On 2021-08-03 22:35:32, user Gemma Hollie wrote:
Can you advise how many pregnant women / live births / still births there was during this period. Also, for moderate to severe disease, you report 45% for the delta variant. Can you advise, is this of the total 3371 women or the 1137 admitted due to symptoms of covid? <br /> Vaccine safety for pregnant women has not been well filtered down to health care professionals, therefore the uptake of women getting the vaccine has not high. Now more women are having the vaccine, i wonder if future results of women getting moderate to severe disease will continue to support recommending the vaccine. Will you be doing another study in the near futute to update the findings? Thank you
On 2021-08-04 16:08:46, user Sam Chico wrote:
They compare raw Ct values which are meaningless in qPCR testing and must be correlated with something, e.g. a dilutions series of positive control COVID RNA at the very least. Should really be related back to viral counts using plates and plaques.
This is very lazy research that only shows that vaccinated people can harbor detectable amounts of viral RNA, its quite a reach to say that implications of this data is to say that vaccinated people are infectious. The only way to do that without clearly and reproducibly demonstrating it is looking at the population level data.
Its irresponsible and borderline unethical to publish data like this, the MIQE guidelines made clear what publishable qPCR data should look like. These sorts of publications undermine the impact of properly performed studies with truly actionable data. The authors should know better.
On 2021-08-05 17:53:16, user Ultrafiltered wrote:
Not even peered reviewed and yet the authors and local news stations in Dane County are promoting and reporting on this paper. That sends a dangerous message to the public that anyone can write anything and its believable/credible. Seeing the other comments here on the fallibility of the PCR test, as CDC has called out and published authoritatively along with other FDA withdrawals of rapid assay test procedures, indicates mine and their comments are just as good as this paper. The authors should remember that information is like a match, it can burn a whole lot of people if left unchecked/ unqualified / uncontrolled.
On 2021-08-09 18:06:43, user MJ wrote:
".....assuming that baseline mitigation measures of simple ventilation and handwashing reduce the second+ary attack rate by 40%".
Congress gave billions of dollars to upgrade schools, to properly fit them with the necessary equipment for proper ventilation/air purification. Schools have had a year to do it, yet it hasn't been done. Schools are going to be reopened in the next few weeks without proper mitigation strategies for airflow.. Schools will be a breeding ground for this virus, even scarier now that the transmission rate is triple what it was a year ago. Speaking as someone who was infected via classroom exposure, and still suffering effects from the virus, more needs to be done to protect and reduce the risk to the children and staff
On 2021-12-08 00:42:55, user Sam Smith wrote:
Summary:<br /> These data demonstrate that both heterologous Ad26.COV2.S and homologous BNT162b2 increased antibody responses in individuals who were vaccinated at least 6 months previously with BNT162b2. <br /> Ad26.COV2.S and BNT162b2 led to Similar antibody titers by week 4 following the boost immunization but exhibited different immune kinetics5. <br /> Ad26.COV2.S led to greater increases in CD8+ T cell responses than BNT162b2!!<br /> However, the durability of these immune responses remain to be determined.<br /> These data suggest different immune phenotypes following heterologous (“mix-and-match”) compared with homologous boost strategies for COVID-19.
On 2021-12-08 18:41:55, user Brian Mowrey wrote:
~18k rapid antigen tests from before Nov 8 were added to the NICD data on November 23, and are included in the results in this update per the text.
How many positives in the Nov 23 dump were missing sample dates? Sample dates are implied not to be "complete within the data set" per the text. Hence why receipt dates were used - but "problems have been identified with accuracy of specimen receipt dates for tests associated with substantially delayed reporting from some laboratories," again per the text.
How many with missing sample dates were scored as "reinfections" in November? 1? 100? 1000? Delayed, non-sample-dated rapid antigen tests for summer Delta infections dumped on Nov 23 could account for the entirety of the "Omicron reinfections" presented in the update, or certainly a significant portion of them.
On 2021-12-12 01:32:39, user Wild Bill, Jr. ????:????:?? wrote:
When I first read this paper, a year ago, I was skeptical about it. The alarmists presented what seemed to be some strong arguments against it
However, with the passing of time, infection-fatality rate statistics and deaths from all causes statistics support the Stanford team's hypothesis: The dreaded virus has turned out to be a lot less lethal than the alarmists claimed it was.
On 2020-04-23 15:59:44, user gmshedd wrote:
If we take the observed fatalities (by residence) in the Bronx (2258 as of 4-22) and Queens (3432), and apply the suggested infection fatality rates of 0.12% to 0.20%, we can infer that between 80% and 133% of Bronx residents have already been infected, and that between 76% and 127% of Queens residents have also been infected. Therefore, Bronx and Queens residents have achieved herd immunity, so they can re-open everything immediately. This is such great news! Oh, but you say, these populations aren't similar. OK, so I'll use Nassau County (Long Island)--median income $111k vs $116k in Santa Clara County. 1431 Nassau County residents have died, from which we would infer that between 53% and 88% of the 1,356,924 county residents have been infected. My point is that the suggested infection fatality rates don't pass the eye test, and, since they are derived from the infection rates that are at the center of the controversy, it would seem that the publication's Santa Clara County infection rates are higher than seems reasonable for the NYC area--unless California COVID-19 has a significantly lower infection fatality rate than New York COVID-19.
On 2020-04-25 07:19:42, user John Smith wrote:
On 2020-04-25 21:08:15, user outdoorgirl0814 wrote:
My primary question on this study is why the IgM and IgG specific results were not presented, but rather pooled together. This seems like important information. From what I can tell, the test identifies them separately.
On 2020-05-01 18:30:42, user Dean Karlen wrote:
The findings reported in the first version suffered from serious mistakes in statistical treatment. Now two weeks later, the authors have slightly adjusted their stated confidence intervals reported in the abstract and elsewhere in the paper. Ignore the abstract and skip to the final page.
There, the authors finally admit that their 95% CL intervals would contain 0% if the analysis is done correctly:
There is one important caveat to this formula: it only holds as long as (one minus) the specificity of the test is higher than the sample prevalence. If it is lower, all the observed positives in the sample could be due to false-positive test results, and we cannot exclude zero prevalence as a possibility.
So in order to report intervals that exclude 0%, they have to assume that the prevalence is high enough to use an approximate approach that will yield intervals that exclude 0% prevalence. This is nonsense. The abstract should clearly state that the study cannot exclude 0% prevalence at 95% CL.
On 2020-05-03 01:41:44, user Danny C. wrote:
Can we get the rigor of statistical analysis for the rt-PCR tests being used currently please? So many experts here weighing in.. But what about the current tests providing the current numbers? Thanks!
On 2020-05-06 14:10:54, user David wrote:
Whitman et al. evaluated Premier Biotech Biotest test used in this study using 108 pre-COVID blood samples (collected July 2018). They found 3 false positive, giving a specificity of 97.22% (92.10-99.42% 95% C.I.). I note that the authors updated their paper with tests run on many more pre-COVID samples, so this might just be bad luck.
Whitman, J.D., Hiatt, J., Mowery, C.T., Shy, B.R., Yu, R., Yamamoto, T.N., Rathore, U., Goldgof, G.M., Whitty, C., Woo, J.M. and Gallman, A.E., 2020. Test performance evaluation of SARS-CoV-2 serological assays. medRxiv.
On 2021-12-13 18:57:26, user joetanic wrote:
breakthrough infections in previously infected
Is this some assumption of the equality of natural immunity to vaccinations? It seems there are more antibodies generated in natural immunity, some that are external to the spike, and furthermore natural immunity seems far longer lasting than does vaccination.
So from whence does this belief come?
On 2021-12-17 16:06:10, user PasserBy wrote:
This article makes several claims of significant differences, yet does not report the statistical tests used. Given the very small sample size, it would be beneficial to know the tests used, the actual data, and the probability levels associated with the statistical test values.
On 2021-12-21 11:23:13, user Shallee Page wrote:
These descriptive data provide really useful data for thinking about LC and the visualizations are good.
It seems like there is a lot of room for more caveats for this self-selected groups.
The speculation about why so many tested negative for COVid seems shoddy. Primer sets are not the main problem here. There is a subset of respondents where there is little evidence that their symptoms are caused by SARS-CoV2. The number of negatives is higher than the false negative rates reported by the molecular biologists. Consulting some would be wise because I think timing of virus load is much more likely to be the problem than some primer pair problems that tests suffered from in early 2020.
On 2021-12-22 18:19:23, user Kurt the Turk wrote:
I hope the post boost samples can be made available to the Emory lab to show neutralization of live viruses. That would really complete the picture.
On 2021-12-29 20:08:54, user Greatwiz wrote:
Did they change anything about the booster shot. Any update sequencing?
On 2021-12-23 12:09:50, user Matthew Nelson wrote:
This is a superb paper, especially the careful approach to CNV calling and the Bayesian methods used throughout. Searching through the supplementary material, there is one important piece of the story that appears to be missing. Where are the counts of de novo missense, PTV, and CNV mutations per gene? These were nicely captured in the Satterstrom and Kaplanis NDD publications. These are really important for understanding potential population sizes for prioritizing therapeutic discovery and development. Have I missed them?
On 2021-12-29 00:23:23, user madmathemagician wrote:
How do they deal with a zero in the data set, there are no zeros in a Benfords' distribution.
On 2021-12-30 08:12:32, user J Sato wrote:
Could you breakdown the risk of hospitalization/death by vaccination status/time?
On 2021-12-30 16:39:32, user Igor Chudov wrote:
Very interesting article!
Could you please clarify something for me.
You say "Moreover, the magnitude of Omicron cross-reactive T cells was similar to that of the Beta and Delta variants, despite Omicron harbouring considerably more mutations. Additionally, in Omicron-infected hospitalized patients (n = 19), there were comparable T cell responses to ancestral spike, nucleocapsid and membrane proteins to those found in patients hospitalized in previous waves dominated by the ancestral, Beta or Delta variants (n = 49).".
This is great, but no Covid vaccine generates or in any way is related to "nucleocapsids" or "membrane proteins". If so, is the mentioned T-Cell reaction to nucleocapsids somehow related to vaccination? Covid-naive vaccinees never come in contact with "nucleocapsids".
Were the nucleocapsid T cell reactions only in the covid-recovered?
A clarification would be greatly appreciated. Thank you very much.
On 2021-12-31 07:47:11, user eriugena wrote:
The current stats in countries like Ireland show 1/3 to 1/2 people testing positive every day. Ok, the PCR test - putting it mildly - is imperfect. However, we can take it using very simple math that 100% of the population is infected within a week. End of "pandemic".
On 2022-01-10 20:48:26, user Siguna Mueller, PhD, PhD wrote:
Dear authors,
Thank you for providing these important data. I am afraid I cannot see how you actually arrive at your conclusion. Can you please help me understand:
How did you actually know people got infected - with the respective variant? You state that cases were identified by “by whole-genome sequencing or a novel variant specific PCR test targeting the 452L mutation.” How many were verified by the former? As for the PCR test, can you please comment how your modifications overcome the limitations as recently announced by the CDC when withdrawing its EUA (https://www.cdc.gov/csels/d...? "https://www.cdc.gov/csels/dls/locs/2021/07-21-2021-lab-alert-Changes_CDC_RT-PCR_SARS-CoV-2_Testing_1.html)?")
Can you say more how VE relates to individual people rather than the numbers that your results and conclusions are based on? You seem to draw your conclusion on statistical means alone. Yet, the average person just does not exist. Moreover, your analysis involves huge standard deviation values.
You say that in the first month already, for some, VE is only 23.5% or even -69%, for vaccination with the Moderna or Pfizer vaccine respectively. How is this supporting your conclusion, please? Are you suggesting more repeated boosters than every month? What would that do to the already minimal or even negative protection? How could a repeated and ongoing “negative protection” – meaning that the vaccine causes an increased risk of infection - as experienced by some (Moderna), suddenly become positive and even truly protective?
You also say that VE is re-established upon revaccination. I am afraid I do not see the results. All I see is numbers – which are less than encouraging: the for the BNT162b2 vaccine: 54.6%, 95% CI: 30.4 to 70.4%. Even if for many the VE may be 54%, this does not mean that VE is re-established. But perhaps the data supporting the VE are still missing? I cannot find details supporting your assertion. The legend to the Table states that there was “[i]insufficient data to estimate mRNA-1273 booster VE against Omicron.” Yet, I am having difficulty finding the data that would support your conclusion that VE is reestablished for revaccination with the Pfizer vaccine. Actually, both the Figure and the Table show the same details for both vaccines under consideration. I am afraid I don’t see anything in your manuscript as to why there are more data for Pfizer, and why these would support your assertion.
Your methods section describes that unvaccinated individuals were followed up from November 20th. Unfortunately I do not see any specific outcomes for the unvaccinated. Your data in the Table and Figure list vaccinated persons only.
Can you please further explain how VE is calculated? Is zero efficacy relative to those who were unvaccinated? Yet, in the Results section you say that, relative to the booster you used “those with only primary vaccination as comparison.”
Further, when assessing the effect of boosters, you state that your “analysis [is] restricted to 60+ year-olds.” Yet, as stated in the beginning of the Results section, the median age of those infected with Omicron by December was 28 years. Why are you suddenly shifting to a different population group when analyzing boosters?
You suggest that 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.” While behavioral issues may indeed impact the outcome of your analysis, your data are not merely negative in the final period. Moreover, for Moderna, there were obviously always some individuals for whom the VE was indeed negative. By contrast, for Pfizer, during the first month at least, VE was in the non-negative range (larger than 23.5, that is). So, it cannot be behavior alone. Else, how could there be such a drastic difference between Pfizer and Moderna (23.5 versus -69.9)?
Cases of reinfections are exploding for both vaccines, for Omicron but also Delta, in an exponential way (as seen in the Table). Moreover, the negative entries in most of your points of analysis, makes me wonder how VE relates to individual patients? Other than in the 1-30 day group for Pfizer and Omicron, each of the other points contain individuals that obviously are more susceptible to infection once they have been vaccinated. Can you say more about those, please? You say that “VE was calculated as 1-HR with HR (hazard ratio) estimated in a Cox regression model adjusted for age, sex and geographical region.” From your data it is apparent that there are many in your study population who experienced negative VE. They are obviously a large proportion, and according to your VE calculation, apparently not isolated cases only. It instead seems as if those with negative VE comprise entire groups of individuals, stratified according to your analysis. Who are they? Are these the elderly, the immune-compromised, or who else?
On 2022-01-02 11:26:15, user Lu Ko wrote:
the difference between midturbinate and nasophyryngeal swab is around 15-20%. https://pubmed.ncbi.nlm.nih...<br /> https://pubmed.ncbi.nlm.nih...<br /> The saliva swab is more error prone in real world setting I guess . Does it make sense to switch from NP to throat swab with antigen testing?
On 2022-01-03 14:25:00, user sad wrote:
The disrespect of these French researchers for international efforts of genomic surveillance is astounding. GISAID explicitly requests that all providers of sequences analyzed be credited, and a collaboration is encouraged. None of this happened here. <br /> And they dare name a variant based on a hospital acronym despite the Pango designation. Just mediocre and sad… Luckily other countries are more respectful.<br /> A proper analysis would have also estimated the emergence date with confidence intervals (not as done here) and the growth dynamics of the lineage.
On 2022-01-04 18:02:40, user Barbara A. Zambrano wrote:
What do the 21 “uncomplicated pregnancies” mean with respect to the babies, and how does this differ from the 29 who delivered healthy babies? Were the 21 babies born from uncomplicated pregnancies also healthy???
On 2022-01-05 17:26:08, user Terry Baines wrote:
I greatly appreciate this effort of trying to analyze the unique challenges which Tribes and Tribal members in managing COVID-19 when confounding social, medical, and community issues are involved. With the complex challenges Tribes face, unique and robust solutions need to be devised. Thank you for those efforts; I would love to see more like it.
On 2022-01-06 12:47:12, user Anonymous wrote:
As far as I know that delta variant doesnt have the Deletion at 69 and 70. Yet the Taqpath kit gave sgtf s gene target failure for 8 delta variant samples. This shows that the kit is not a foolproof kit and rather has many issues which is why it was taken out from the market when it started giving false negative results during the alpha variant breakout. Also the authors have conveniently kept mum and skipped over the fact of these questionable 8 delta variant positive cases.
On 2022-01-07 19:58:49, user Clive wrote:
Hi, please correct me if I'm wrong, but reading this study I'm not seeing any comparison to the baseline population that has not contracted COVID. If, as I'm sure you're aware of, a broader worsening of health conditions has increased across the population during the time frame of this study, could the data be misrepresenting a change to the mean in both the vaccinated and unvaccinated groups. For a specific example, I've heard anxiety rates have tripled since the start of the pandemic across the population, and if that pattern was ongoing at the time of this study could the study be overestimating the increase in anxiety among both vaccinated and unvaccinated groups, as everyone, not just those who contracted COVID, experienced an increase in anxiety?
On 2022-01-08 19:16:50, user madza wrote:
Excuse my ignorance but when SAR % is lower in unvaxxed than in vaxxed, how come the Conclusion is that vax reduces the transmissibility?
On 2022-01-09 17:21:03, user Kevin McKernan wrote:
You should test if the primers light up on SARs-CoV-2.<br /> With the high break through rate, People want to differentiate vax from C19. Would also be interested to know if SARs-CoV-2 also ends up in the lipid fraction. I suspect not. The heavy methylation of these RNAs changes their lipophilicity.
Longer testing window would help answer some questions.
Thank you for putting the primers public.
On 2022-01-11 20:23:50, user Sam Smith wrote:
What is the optimal RH = relative humidity for health? Too dry air increases covid risk because it is not healthy for your airways.
On 2022-01-12 17:54:37, user Martha Metevelis wrote:
I am blown away with the similarity to PCS and toxic B6 symptoms I have endured for over three years. I and many others are often dismissed as just suffering anxiety and told thatour symptoms are not real. Many of us rely on a face book site called exploring b6 toxicity for support and share our experiences and what helps. Being ignored and unfairly labeled is the norm. Suicide has occurred with those unable to cope with what becomes a debilitating issue. We experience insomnia, intense pain, sensory and mobility issues, tinnitus that is unbearable, facial pain, anxiety off the chart,vision changes,peripheral neuropathy,swallowing issues, palpitations, internal and extremity tremors, brain fog, memory loss, hair loss to name a few. <br /> I am anxious to know if the subjects involved in your research have vitamin n6 levels checked and if they are taking more than rda of b6 in any form.<br /> I pray that we all get answers soon. The vitamin I took contained 75 mg of b6! And was suggested by my physician. I had been 100% healthy! This upended my life. <br /> I know exactly how long haul suffers feel and hope my input leads to a solution. Thanks for listening.
On 2022-01-17 11:18:12, user Free Man in London wrote:
This is another Coke vs. Pepsi paper. There are only 2 conditions in this paper: vaccinated vs. unvaccinated. There are multiple conditions: vaccinated with supplements, unvaccinated with supplements Vitamin D, Quercetin, Melatonin, unvaccinated with supplements quercetin and zinc, etc. The paper prima facie is interesting but devoid of an answer to the real underlying question: are supplements more effective than the vaccines? Moderna and pfizer are more interested in keeping the question framed around simply 2 conditions. Until we force a re-framing to the way this effectiveness question is answered, we are not really solving the problem of stopping the virus. That said, omicron seems to be doing that quite well on its own.
On 2022-01-18 16:19:10, user NoSafeSpaces wrote:
Perfect information on who received a vaccine versus imperfect information on who got COVID. Add in a side of everyone gets the vaccine and not everyone gets COVID with a dash of some of the people getting the vaccine have natural immunity from COVID, and you get a weak justification for putting your children at risk because you don't understand bad assumptions.
On 2022-01-31 18:32:19, user Jared Roach wrote:
These are very minor comments about the wording of the Abstract that I would make if I was reviewing the paper:
"strikingly different"<br /> I would delete the vague adjective "strikingly" as it could be interpreted many different ways. Rather insert the exact number of mutations or some similar quantitative metric.
"rapidly replaced"<br /> maybe OK to leave "rapidly", but also add in a sense of how long (e.g., over a period of 3.5 weeks) - or whatever the number of weeks was.<br /> "rapidly" makes sense only in context of other strain replacements such as Alpha-> Delta or Delta-> Omicron. If it isn't much faster than these, I would use an exact time rather than the comparative and vague adjective "rapidly"
On 2022-02-04 18:46:24, user asciguy wrote:
Thank you for the new vocabulary word. I should have noticed ‘inflammasome’ by now in “Laboratory Medicine” but must have missed it somehow.
On 2022-03-22 03:17:02, user ST wrote:
The scientific article was very intriguing, and the abstract sparked my interest when the link between detecting changes in the gut microbiome and concussion diagnosis was made. The introduction was informative and included different helpful statistics on football players and the number of concussions they may receive in a singular football season. <br /> The PCR target region was the full 16S gene (V1-V9). The reason why the authors sequenced the full gene rather than the more common use of one region (i.e. V3-V4) should be included. A strength of the paper is that the scientists included the number of PCR cycles, however, 35 cycles is quite high. The higher the cycle number, the more errors that can be produced (Sze & Schloss, The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data 2019). This should be listed as a potential study limitation. PCR primers were not specifically listed and should be included in the methods section to ensure replicability. IF part of the Barcoding Kit, that should be stated as well. The scientists did state that there were problems with the ONT primer but there should be follow-up discussion of Bifidobacteriaceae taxa found in this study, if any. No quality controls/filtering steps for the sequences (such as removing low quality sequences and chimeras) were included. The scientists do not analyze specifically dominant and rare taxa. Given that microbial communities are highly dominated and uneven, analysis of the dominant taxa alone can be revealing, since metrics using all taxa can be strongly affected by the very numerous but low abundant rare taxa. The rare and dominant taxa of environmental pathogens are mentioned but what does this mean for the microbes found in the oral cavity and the gut? I suggest that bioinformatics should be performed on dominant as well as all taxa. The scientists do include rare taxa abundance in three of their figures to show the abundance of the different microbes found in fecal samples and salivary samples. I thought that the description of the sampling was good but there could be more of a description of what was in the tubes for collection? Were buffers used? What specific tubes were used? Were the samples taken straight to the lab? It would also be helpful to say what kit the tubes were from before stating that a collection tube and funnel were used. The fecal sample and saliva sample collection were described well but I think that scientists should make saliva sample collection have its own paragraph. One flaw of the article is that the scientists never discussed variance in 16S copy number and genome size in bacteria, which affects abundance data of 16S profiles. Were sequences binned or clustered before identification with Kraken? A rarefaction curve would be beneficial to include, and also sequencing coverage measures, such as Good’s coverage and Chao1 compared to observed OTUs. The article should include internal standards for PCR, DNA extraction, and sequencing. These internal standards may include utilizing a negative control, a strain grown in the lab, running a gel, and utilizing a known DNA sequence. I would like to comment that DNA extraction and amplification should be in the same section as I was a tiny bit confused when I read amplification in the sequencing section. The best option for readers would be to list the methods performed in chronological order. The sampling strategy is very well described and figure 1 demonstrates the samples collected as well as a visual representation of the collection strategy. The scientists discuss the number of classified reads for the samples but the number of reads per sample is not stated. Additionally, the study does not include any methods that quantify total bacterial numbers (16S community sequencing is normalized and only contains relative abundance data, not absolute abundance). <br /> Authors should include the specific parameters that were utilized when the samples were vortexed for replicability. Additionally, the stool and the saliva samples were stored at room temperature, and this needs clarification as storing the samples at room temperature for a long period of time may invalidate the experiment. The amount of time that the samples were stored at room temperature needs to be added for clarification. The specific statistical test that is done in figure 2e should be clearly stated. Data that was collected in this experiment could be compared to The Human Microbiome Project.<br /> The optic nerve sheath should be better explained, and it would be beneficial to know what is normal without a concussion and with a concussion. The relationship between the optic nerve and concussions should be better explained so the reader understands its significance.
Overall an interesting study! Thank you!
-Sydney T.<br /> #SHSU5394
On 2023-01-13 11:35:49, user SB wrote:
Hi, thanks for the preprint.<br /> I do not understand the last sentence of the conclusion "Individuals with hybrid immunity had the highest magnitude and durability of protection against all outcomes". That's because the results section pointed out: "Against reinfection at 6 months, there was similar protection from HE with first booster vaccination (effectiveness 46·5% [36·0-57·3%]), HE with primary series (60·4% [49·6-70·3%]), and prior infection alone (51·2% [38·6-63·7%]), with all three types of immunity conferring significantly greater protection than primary series vaccination alone (15·1% [11·3-19·8%]) or first booster vaccination alone (24·8% [18·5-32·5%])"<br /> So the protection against reinfections seems to be for hybrid immunity NOT OVER then for other types of immunity.
On 2024-02-26 17:17:59, user Ciarán McInerney wrote:
The sensitivity analysis is<br /> commendable. If I understand your logic correctly, the 20% inaccuracy in<br /> clinical coding informs a random 20% reclassification of cases and controls. To<br /> commit to this logic, all +19-thousand clinical codes also need to be randomly<br /> reclassified because there is no reason to assume that only the clinical coding<br /> for cancer is inaccurate in 20% of your sample.
On 2020-07-04 07:47:05, user Martijn Hoogeveen wrote:
The updated re-submitted pre-print clarifies the methods more in detail, the definitions used, provides a somewhat more expanded review of COVID-19 & meteorological variables, and log10 transforms seemingly logarithmic datasets. Conclusions are the same, though here and there more cautiously formulated.
On 2024-07-31 10:58:02, user Vincent THIBAULT wrote:
With all my respect, it is unfortunate that the authors did not take into account our remarks regarding the use of the quantitative HBcrAg assay https://www.journal-of-hepatology.eu/article/S0168-8278(23)00232-5/fulltext . By not complying to the manufacturer's instructions they promote the publication of inaccurate data. This is not how to improve science quality.
On 2025-06-21 07:53:30, user Laszlo Szabo wrote:
Fig 3 / c<br /> Plot labels and fig caption are different. Check which colour represents the new and original reviews.
On 2022-02-03 21:28:46, user Bruce Futcher wrote:
Here, as with other vaccines, titres against omicron are only about one-tenth as high as against the vaccine strain. There is no indication of this central fact in the title or the abstract. There is no indication here that this vaccine is relatively better against omicron than is any other vaccine.
On 2022-02-05 13:34:23, user GregoryGG wrote:
Hello,<br /> I saw an error after posting my comment, so I post it again because I do not see it, my apologies in advance if it was ok.
Unless I misunderstood :
1)<br /> How can you guarantee that the people's behaviour (over-confidence of vaccinated people) , the transmission prevention mesures and the testing entry rules were the same between <br /> A) delta and omicron<br /> B) vaccinated and unvaccinated people. <br /> => this could impact ratios like the positive rate of a group.
2)<br /> Assuming that immunity against a specific variant diminishes over time, can we still considered single- double -dosed people as still immunised. <br /> Could they be considered as non-vaccinated after a given time?
Thank you
On 2022-02-09 21:44:37, user Xin Wu wrote:
Since this preprint published in 2020, (1) the CDC changed its mask guidance from wearing face coverings to protect others to wearing proper masks to protect you and others on Nov, 2020, and suggested wearing N95 and KN95 in later 2021 and 2022; (2) the White House Coronavirus Task Force reported some covid-19 strategies were compromised in many places on Dec. 2020; (3) More articles were published regarding patient isolation and contact tracing problems; (4) More covid-19 dashboards monitoring health care capacity were created; (5) free N95 masks to public from government in 2022; (6) COVID lockdowns had ‘little to no effect’ on mortality rate, study says (https://sites.krieger.jhu.e... "https://sites.krieger.jhu.edu/iae/files/2022/01/A-Literature-Review-and-Meta-Analysis-of-the-Effects-of-Lockdowns-on-COVID-19-Mortality.pdf)"). This paper discussed all of these issues with Table and figures.
On 2025-10-18 15:00:12, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
The combined biotic/abiotic approach can guide resource allocation, outbreak management, or pollution mitigation efforts specific to each region's exposure landscape.
Abiotic signatures help to identify the cause of biotic signals which could be driven by infectious disease outbreaks or changes in human hygiene, diet, or mobility.
This study supports a much larger-scale spatiotemporal range than most wastewater epidemiology efforts enabling the evaluation of seasonal shifts, regional differences, and post-pandemic changes in microbial and chemical profiles.
Wastewater surveillance has typically been a reactive infection-tracking tool. Continuously mapping exposures, as performed here, can identify risks before they manifest clinically, supporting not only intervention but prevention.
On 2022-02-15 21:25:00, user Cabeça Livre wrote:
Introduction
The spread of a novel infectious agent eliciting protective immunity is typically characterised by three distinct phases: (I) an initial phase of slow accumulation of new infections (often undetectable), (II) a second phase of rapid growth in cases of infection, disease and death, and (III) an eventual slow down of transmission due to the depletion of susceptible individuals, typically leading to the termination of the first epidemic wave. The point of transition between phases I and II is known as the herd immunity threshold (HIT) [...]
Did you mean "between phases II and III"?
On 2022-02-17 20:37:35, user RT1C wrote:
Please consider correcting the following: "To adjust for this, we defined the proximate overt immunologic challenge (POIC) as the most recent exposure to SARS-CoV-2 by infection or vaccination." The vaccine does not give SARS-CoV-2 exposure. Thus, as written this is confusing and incorrect.
On 2020-03-22 16:58:48, user Peterson Biodiversity Lab wrote:
Unsolicited peer review …
Comments on “Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate”<br /> Comments offered by A. Townsend Peterson, University of Kansas
I read the above-referenced manuscript preprint with interest. Araújo and Naimi are leaders in the field of distributional ecology, so I was (and am) interested in their perspective on the distributional ecology of SARS-CoV-2, and the COVID-19 disease that it causes. Obviously, the question is quite current, such that I’m interested in the topic and the ideas, but also in getting answers that will prove robust and predictive. For this reason, I take the rather odd step of offering a peer review, even though no one has asked for it…
My interest in this manuscript was piqued most of all by the statement in the Abstract that “the disease will likely marginally affect the tropics.” If true, this would be an interesting, and quite promising and optimistic result, as many are concerned about what will happen when SARS-CoV-2 begins to spread in Southeast Asia, Oceania, sub-Saharan Africa, Latin America, etc. So I will focus my comments on this result and the assumptions that revolve around it.
PRESENCE DATA<br /> The occurrence data in the analysis were derived from available COVID-19 case occurrence data. “Coronavirus cases by the 10/03/2020 with data compiled and made available to the John Hopkins University Mapping 2019-nCoV portal... Regions with fewer than 5 positive cases were not included in the models. Exclusion of such sites was based on the working assumption that sites with small numbers of positive cases are likely imported from infected regions, thus failing to provide evidence that the SARS-CoV-2 Coronavirus is being transmitted locally within its ecological niche.” And also, in the Discussion of the manuscript… “Firstly, there is little reason to suspect that out-of-China contaminations would have occurred only, or mainly, with trade partners in the northern hemisphere... China is a big world player, having key commercial partnerships with Africa and Latin America. Yet there is not indication that meaningful local infections have taken place in these areas despite the global reporting of Coronavirus cases generally attributed to travelers coming from infected regions.”
This seemingly logical methodological step has rather important implications. It is true that many of the “singleton” occurrences will represent imported cases that were the result of infection taking place in early foci of infections (e.g., central China, Italy) and being taken to those places by travelers. In that sense, this methodological step is reasonable and logical. However, one should consider some important sources of bias that are likely associated with elimination of regions with few records…
Connectivity is nonrandom. That is, consider Wuhan, in central China. Check out the airline network connectivity visualizations shown in http://rocs.hu-berlin.de/co.... Its primary connections are all to northern, mesic, and temperate regions, and not at all directly to tropical and arid regions. Indeed, even given that major desert regions lie not too far to the west of Wuhan, they are not densely populated, such that land-based connections are also mostly to the east, and not to desert or tropical regions.
Testing bias. The relative unavailability of testing for COVID-19 cases has been commented extensively, even in the United States. The lack of testing clearly has resulted in broad (but silent) spread of COVID-19 prior to recognition of the broader extent of case distributions, as has been documented in Washington State, a COVID-19 focus in the western United States. With developing-world public health infrastructures being challenged with other, perhaps more pressing and immediate questions (e.g., dengue, malaria), COVID-19-caused respiratory problems and a few deaths from some pneumonia syndrome of unknown cause can easily go unrecognized. As COVID-19 testing becomes easier and faster, and the possibility for point-of-contact testing becomes a reality, I strongly suspect that many more tropical/humid cities will emerge as additional sites of viral transfer and COVID-19 infection.
ABSENCE OR BACKGROUND DATA<br /> The authors appear not to have provided any information about the region over which they calibrated and evaluated their models (I will not go into problems with ROC AUC that are well-known, and do not need restating). This choice is well-known to make important differences in modeling outcomes [see Anderson, R. P., and A. Raza. 2010. The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography 37:1378-1393.]. Indeed, the calibration region should be a combination of the area that has been accessible to the species over relevant time periods, and the area over which sampling was conducted (Barve, N., V. Barve, A. Jimenez-Valverde, A. Lira-Noriega, S. P. Maher, A. T. Peterson, J. Soberón, and F. Villalobos. 2011. The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecological Modelling 222:1810-1819.).
Not only should the calibration area and the assumptions that led to that set of choices be made explicit, but they should be made in consideration of real-world features of the system at hand. That is, if the authors indeed calibrated their models at global extents, then that is a tacit assumption that the virus had had access to the entire Earth’s surface, but only “liked” (in a niche sense) the places where >5 cases have been manifested. At least in the early stages of this epidemic, this assumption clearly is not robust, and will bias the model results against the suitability of humid tropical areas. A better approach might be to take primary and perhaps secondary linkages of air and land transportation, and to use them to create an accessible area hypothesis that responds better to the actual phenomenon at hand. Such a more conservative estimate of an accessible area might well reveal that hot and humid sites were not available to SARS-CoV-2 given realistic hypotheses of the dispersal potential of the virus, and the resulting conclusions about the distributional potential of the virus would likely be quite different.
CONCLUSIONS<br /> The authors interpreted the importance and significance of their results thus: “… it appears the virus favors cool and dry conditions being largely absent under extremely cold and very hot and wet conditions.” And… “Much of the tropics have low levels of climate suitability for spread of SARS-CoV-2 Coronavirus owing to their high temperatures and precipitation (used here as a surrogate for humidity), followed by polar climates, where conditions of extreme cold temperatures seem to be beyond the virus critical minimum tolerance values. In most of such low climate suitability areas, human populations will likely be spared from outbreaks arising from local transmissions...”
Very simply, methodological decisions were made in this analysis that will lead to different conclusions. Decisions such as eliminating sites with few records, or using a global calibration area, as well as the testing bias discussed above, all align in leading to overfit models that will snug overmuch to the areas and environmental regimes where cases have already occurred. I do not think that the conclusions about low tropical/humid suitability are likely to prove robust, and I do not know enough about the conclusions of seasonality (which I have not discussed or assessed above).
Town Peterson
On 2020-04-01 08:31:20, user Bob O'Hara wrote:
The paper refers to Supplementary Material which gives details about the model fitting. Could this be uploaded too, please?
On 2022-02-19 00:11:02, user Sam Smith wrote:
Breakthrough cases were defined only from day 8 (in each arm as well as in the control group), to exclude early infections due to exposure before vaccine is effective.
Was 8 days too early?
Was control group a good idea, instead randomly been given a vaccine or not be given a vaccine?
On 2022-02-19 17:39:42, user Zywicki wrote:
Are the Supplementary Tables available? Particularly Table S4 with the breakdown of AE's. There is a Figure that summarizes it but not the raw data. I didn't see it linked here--forgive me if I overlooked it. Thanks.
On 2022-02-21 00:22:33, user consalg wrote:
Log0=1, not zero. That distribution is discontinuous. Shouldn’t you recalculate means without counting in the samples that produce no foci?
On 2025-11-30 16:56:07, user Cyril Burke wrote:
RESPONSE TO REVIEWER #1
June 27, 2022<br /> Re: Longitudinal changes in creatinine signal early decline in glomerular filtration rate without consideration of age, sex, ‘race’, and nationality
We greatly appreciate that the reviewers were thorough, fair, and helpful in their comments.
Comments to the Author
Reviewer #1: Burke et al submit a somewhat unusual paper, devoted to a topic of potential major clinical relevance, and as yet understudied.
General comments
1. The thesis of the authors, that using the baseline serum creatinine of a given patient would potentially improve the earlier diagnosis of kidney disease, even in the normal range, is in line with the experience of this reviewer, who always retrieves, whatever the difficulty of reaching that goal, past results of blood tests, and uses them as a way to date the onset of kidney disease, sometimes with important prognostic implications.
Your experience adds support to the literature suggesting that historical sCr levels provide a context for sCr changes. These benefits might encourage investments in digital data exchanges so that electronic health records (EHRs) can ease collection and presentation of sCr results from multiple commercial and hospital laboratories.
2. Yet, the authors do not provide data strongly supporting their thesis. For instance, when looking at case 2 [now Patient 3], should the last point (the most recent one) be omitted, there would be very little evidence supporting progressive early kidney disease.
We advocate prospective monitoring of longitudinal sCr as a proxy for glomerular filtration rate (GFR). The Cases were meant to show that charting the data and simple follow-up over several visits and months can allow general clinicians to differentiate CKD from other explanations for increased sCr. The four case histories represent patients in a non-nephrology medical practice with borderline eGFR that raised the possibility of CKD. In each of these cases, retrospective collection of sCr values suggested varied explanations for the elevated sCr, and we expect many cases will represent sCr influences other than CKD, not necessarily warranting nephrology referral. Armed with this tool, and used prospectively, Physicians, nurse practitioner, and physician assistants (PCPs) might identify and manage the 90% of patients with currently unrecognized CKD.
3. The claim that the statistics fit the data better when all points are used (page 9,11) should not come as a surprise. Using thresholds instead of the full range of values has long been known to be more powerful for statistical analysis. But fitting the data does not equal to a high positive predictive value!
We agree that this is counterintuitive, so we thought this was an important point to discuss. Research methods that get translated into clinical settings rely on assumptions that are not always familiar to healthcare workers. Whatever the merits of thresholding conventions, understanding their mathematical underpinnings can inform a more nuanced interpretation of lab results. The revision includes our initial, intuitive assessment of the data and the interpretation of the residuals – from a mathematics perspective. Lack of awareness about residuals can easily lead to improper interpretation of thresholded lab data. The use of statistics is not intended to document superiority of fit but rather to demonstrate how simplifications with practical clinical value may gloss over clinically relevant information in some cases. The inclusion of additional charts seeks to take it away from abstracted statistics and toward more intuitive clinical concerns. We favor early diagnosis of kidney injury through investigation of nonspecific changes in longitudinal sCr. This method seems usable and may be manageable by PCPs using a time frame of several visits over several months to separate false positives, which may be influenced by chance attributable to the mathematical properties of lab data.
4. A key question is whether in a real-world context, the earlier diagnosis of kidney disease would be possible, without too much background noise from intercurrent illness (functional), drugs (NSAIDS, etc.). In other words, would the specificity (or PPV) of the suspicion of early kidney disease be reasonable enough to catch the attention of clinicians
We think so. We believe longitudinal serum creatinine (sCr) will encourage dialogue between patients and clinicians, raising awareness of the importance of avoiding kidney injuries that often happen out of sight and out of mind until, for far too many, culminating in urgent dialysis. In the same way that patients now ask for their blood pressure, we anticipate patients tracking their own sCr and kidney risks. Decades after introduction of the mercury sphygmomanometer, PCPs learned how to manage blood pressure to improve health. We believe longitudinal sCr can soon be a widely used tool because the concepts are old, there is a broad literature supporting this approach, and the value can be enhanced by more frequent testing of sCr. This is what PCPs do – sort the random cough, costochondritis, or stress response from nascent pneumonia, angina, and hypertension. PCPs already worry about the kidneys. They may welcome a tool to accompany the chest radiograph, electrocardiogram, and sphygmomanometer.
Of interest, the decision analysis by den Hartog et al found markedly more false-positive diagnoses of CKD with eGFR than with serum creatinine alone.
5. Even though there has been improvement in the standardization of measurement of serum creatinine (IDMS), the comparability of results measured by different labs remains suboptimal, at least in the experience of this reviewer, and medical shopping is not uncommon, making the availability of all previous results in the same graph a logistical challenge.
We share this concern, which laboratorians have wrestled with for many years and will not be solved soon. However, we propose utilizing the maximum serum creatinine (sCr-max) to smooth the variability of these inputs (as well as the variability from patient diet and hydration). One laboratory will be the highest, and when patients use multiple laboratories, one laboratory may more often define the sCr-max. As patients learn the rationale for using the same lab, we believe most (not all) will voluntarily use one or perhaps two labs (as they mostly do when we repeating longitudinal MRI imaging studies, for example). The sCr-max reduces the effect of variability between laboratories, allowing clinical insights even without future improvements in sCr assays.
Australia, Canada, and the United Kingdom have stricter sCr analytical performance goals than the United States, which could improve its sCr comparability by matching their standards.
Specific comments
1. The authors should mention that the USPTFS decided a month ago to revisit the question of screening for kidney disease in high-risk groups (page …)
One reference stated that this initiative has not been announced publicly but is “under active consideration” by USPTFS because “…for a screening to help people live longer, healthier lives, clinicians must be able to treat the condition once it is found. The existence of effective treatments is one of many important factors that the Task Force considers.” This perspective is surprising because it ignores the potential of effective prevention by avoiding NSAIDs, hypotension, dehydration, and nephrotoxic medical treatments (e.g., aminoglycosides). We, too, look forward to updated findings from USPTFS.
2. Even though ESRD has a legal meaning in the USA, not very relevant to the topic of this paper about early kidney disease, the authors should stick to the nomenclature proposed by a recent KDIGO consensus conference (see Levey et al. Nature Reviews in Nephrology). In particular, use kidney failure instead of ESRD/ESKD. When the topic is glomerular filtration, use that wording instead of kidney function (page…)
We have adopted this terminology and would welcome any further recommendations.
3. The authors allude to the concepts of prediabetes and prehypertension. But this reviewer points to the fact that the levels used to define those entities are currently “generic”, rather than based on previous values in an individual subject. Please discuss.
We understand that the normal population ranges for serum glucose and blood pressure are narrower, with less interindividual variation, so population reference ranges work well for monitoring diabetes mellitus and hypertension. Unfortunately, this is not true for serum creatinine, though within-individual reference of longitudinal sCr appears to facilitate diagnosis of pre-CKD.
4. The authors repeatedly mention in the discussion section evidence that even small increases in serum creatinine have prognostic significance. This has indeed been known for decades but is a different topic: AKI. Admittedly, there is growing evidence that AKI and CKD are linked. But that the stability of a biological parameter is prognostically best is all except surprising: the same is true for body weight, mood, blood pressure etc.
We agree that AKI and CKD appear to be merging and this may become clearer from more frequent sampling and charting of longitudinal sCr. What has been missing is graphical representation of the data to allow quick assessment for CKD in long-term trends, and this may soon be obtainable from EHRs and IT departments, which should end the practice of deleting historical data of value to longitudinal analysis.
[See next comment for Response to Reviewer #2.]
On 2025-12-01 00:10:43, user Cyril Burke wrote:
[Note: This is the third of several rounds of review of an earlier version of our combined manuscript, aiming to reduce ‘racial’ disparity in kidney disease. The comments were kindly offered by nephrologists, through a medical journal, and we remain grateful to them for the time and care they gave to improve our manuscript.
We removed identifying features and included our response, at the end. The changing title and line numbers refer to earlier versions.]
October 10, 2022
Dear Dr. Burke III,
REDACTED.
Editor: The re-revised manuscript is further improved. However, it remains the main issue of the extremely length of the manuscript, as highlighted by both Reviewers, that precludes me to accept this paper in the present format. I could offer you the possibility to shorten the manuscript just focusing on what you define “Part One” plus “section A of Part Two”. You can briefly address the “race” issue in the discussion. The full content of “Part Two, section B”, “Part Three” and “Part Four” could be for another manuscript.
REDACTED.
Reviewer #1: The authors have improved the readability of their paper, which remains however very lengthy! As part one is important and should trigger further studies, after reading the comments of reviewer 2 , I am ready to recommend acceptance, leaving of course the final decision to the Editor in charge of the paper
Reviewer #2: Thank-you for the opportunity to review this manuscript- which raises some important issues.
As in round 1 of reviews it is still this reviewer’s opinion that the manuscript is too lengthy and covers such a large range of topics that the scientifically meaningful points are lost in the commentary/ perspective style of the manuscript and lack of robust evidence. This concern was shared with the editor in round 2. To quote the editor in round 2:
“… the authors need to drastically shorten the manuscript focusing on the main key message. Please consider that the race part is irrelevant for the key point (race does not change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be deleted.”
Instead of drastically shortening the manuscript the authors have added to the length thereof. The manuscript (without figures) is now at 77 pages! In round 1 and 2 the manuscript was 27 and 67 pages respectively. This reviewer has chosen not to provide further comment on the new additions to the manuscript.
524 – 527. Once again, this reviewer in no way questions the often-overlooked inaccuracies in mGFR methods. However, the authors cannot quote a well conducted review which shed light on the methodological bias and imprecision which exists between mGFR methods and claim that this methodological bias is “physiologic variability”. The authors should review: Rowe, Ceri, et al. "Biological variation of measured and estimated glomerular filtration rate in patients with chronic kidney disease." Kidney international 96.2 (2019): 429-435. Intra-individual variation (CVI) for serum creatinine ranges from around 2.8 – 8.5% while cystatin C ranges from around 3.9 – 8.6%, inter-individual variation (CVG) of serum creatinine: 7.0 – 17.4% and cystatin C: 12 – 15.1%. Biological variation (CVI and CV¬G) are not the same as analytical variation, which also exists for serum creatinine and cystatin C. The author’s statement is not backed up by scientific evidence.
It is this reviewer’s opinion that this manuscript is unpublishable as a research article, due to the authors unwillingness to shorten and focus their work. This is a shame as the main point of the article, although difficult to decipher, is highly relevant.
RESPONSE TO EDITOR AND REVIEWERS
December 1, 2022
Early detection of kidney injury by longitudinal creatinine to end racial disparity in chronic kidney disease: The impact of race corrections for individuals, clinical care, medical research, and social justice
Editor: The re-revised manuscript is further improved. However, it remains the main issue of the extremely length of the manuscript, as highlighted by both Reviewers, that precludes me to accept this paper in the present format. I could offer you the possibility to shorten the manuscript just focusing on what you define “Part One” plus “section A of Part Two”. You can briefly address the “race” issue in the discussion. The full content of “Part Two, section B”, “Part Three” and “Part Four” could be for another manuscript.<br /> Comments to the Author
Reviewer #1: The authors have improved the readability of their paper, which remains however very lengthy! As part one is important and should trigger further studies, after reading the comments of reviewer 2 , I am ready to recommend acceptance, leaving of course the final decision to the Editor in charge of the paper
Reviewer #2: Thank-you for the opportunity to review this manuscript- which raises some important issues.
As in round 1 of reviews it is still this reviewer’s opinion that the manuscript is too lengthy and covers such a large range of topics that the scientifically meaningful points are lost in the commentary/ perspective style of the manuscript and lack of robust evidence. This concern was shared with the editor in round 2. To quote the editor in round 2:
“… the authors need to drastically shorten the manuscript focusing on the main key message. Please consider that the race part is irrelevant for the key point (race does not change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be deleted.”
Instead of drastically shortening the manuscript the authors have added to the length thereof. The manuscript (without figures) is now at 77 pages! In round 1 and 2 the manuscript was 27 and 67 pages respectively. This reviewer has chosen not to provide further comment on the new additions to the manuscript.
524 – 527. Once again, this reviewer in no way questions the often-overlooked inaccuracies in mGFR methods. However, the authors cannot quote a well conducted review which shed light on the methodological bias and imprecision which exists between mGFR methods and claim that this methodological bias is “physiologic variability”. The authors should review: Rowe, Ceri, et al. "Biological variation of measured and estimated glomerular filtration rate in patients with chronic kidney disease." Kidney international 96.2 (2019): 429-435. Intra-individual variation (CVI) for serum creatinine ranges from around 2.8 – 8.5% while cystatin C ranges from around 3.9 – 8.6%, inter-individual variation (CVG) of serum creatinine: 7.0 – 17.4% and cystatin C: 12 – 15.1%. Biological variation (CVI and CV¬G) are not the same as analytical variation, which also exists for serum creatinine and cystatin C. The author’s statement is not backed up by scientific evidence.
It is this reviewer’s opinion that this manuscript is unpublishable as a research article, due to the authors unwillingness to shorten and focus their work. This is a shame as the main point of the article, although difficult to decipher, is highly relevant.
We totally understand that our manuscript may no longer be a good fit for [the journal].
Although we feel the Parts work best together, in a single manuscript, we tried to make it more readable even though we could not significantly shorten it. All the suggested methods for trimming length put more work on the reader (e.g., to search original sources for the quotations we found illuminating) than simply inviting the reader to skip over paragraphs, sections, or Parts of less interest, as readers often do.
We cut a fair amount, but then added (e.g., to the creatinine Part 2, an important section on “gold standard” references prompted by the excellent reference kindly offered by Reviewer #2). In the second and third rounds, major then less-than-major revisions lengthened the manuscript. We have now reorganized in hope of improving readability; updated the title and abstract, hoping to convey that ‘race’ is a main topic; and again, tried to respond to your kind criticisms, which we believe greatly strengthened the nephrological portion of the manuscript.
We sincerely appreciate the Reviewers’ and Editors’ comments and criticisms, which helped to improve the manuscript. We submit this revision mostly as a final opportunity to thank the Reviewers and Editors for giving so much of their time. We thank you.
On 2020-05-16 02:45:51, user Sinai Immunol Review Project wrote:
Functional alteration of innate T cells in critically ill Covid-19 patients
Jouan Y et al., medRxic 2020.05.03.20089300; doi: https://doi.org/10.1101/202...
Keywords<br /> • Innate T cells (MAIT, iNT, ?? T cells)
• Critically ill COVID-19 patients
• SARS-CoV-2
Main findings<br /> Innate T cells are a heterogeneous group of immune cells, including mucosal-associated invariant T cells (MAIT), NKT cells (NKT) and ?? T cells, which differ from conventional T cells based on their unique features considered characteristic of both the innate and adaptive immune system. In this preprint, Jouan et al. aimed to address the potential contribution of innate T cells to COVID-19 lung immunopathology. SARS-CoV-2 PCR-positive ICU patients, diagnosed with severe COVID-19 according to clinical symptoms, were enrolled in a prospective study on average within 10 days of disease onset, and were immunologically followed up for two weeks. Of 30 patients, 24 presented with ARDS with the need for invasive mechanical ventilation either at the time of ICU admission (n=20) or later on (n=4); to maintain sufficient systemic oxygenation, one patient further required extracorporeal membrane oxygenation (ECMO), whereas another patient succumbed to disease two days post study inclusion. By the end of the observation period, 15 individuals could be discharged from ICU, while 9/14 ICU patients remained on invasive mechanical ventilation. Similar to several recent reports, the authors observed COVID-19-associated mild to severe lymphopenia, which correlated with disease severity, as well as concomitantly higher neutrophil-to-lymphocyte ratios when compared to age- and sex-matched healthy controls (n=20). Likewise, albeit relatively low overall, plasma levels of the proinflammatory cytokines IL-1?, IL-6, IL-1R? and IFN-?2, a marker of the antiviral type I IFN response, were found to be higher in COVID-19 patients vs. healthy individuals (n=10). Of particular note, these cytokines were further increased in endotracheal aspirate (ETA) supernatants relative to whole blood samples obtained from the same patients (n=20), suggesting locally enhanced airway inflammation at the time of ICU admission. Moreover, analyzing relative frequencies of circulating innate T cells in COVID-19 ICU patients (n=30) vs. age-/sex-matched healthy controls (n=20), the authors report a profound decrease in MAIT (ca. 6-fold; identified by 5-OP-RU loaded MR1 tetramer vs. unloaded control tetramer) as well as in iNKT cells (ca. 7 fold; identified by PBS-57 glycolipid-loaded CD1d tetramer vs. unloaded control tetramer), while ?? T cell frequencies remained largely stable (with the exception of a slight decrease in the V?2 subset as well as a likely increase in the V?3 subset). Interestingly, both MAIT and ?? T cell were detected in early ETA samples of 12/21 ICU patients on invasive mechanical ventilation, whereas iNKT cells were seemingly absent. Of importance, relative MAIT but not ?? T numbers were increased in ETA specimens when compared to whole blood samples obtained from the same donors, suggesting enhanced airway recruitment in contrast to potential extravasation processes as a result of inflammation-induced capillary leakage. Accordingly, the presence of innate T cells was restricted to ETA samples containing high levels of chemo-attractive CXCL10 and CXCL12. Moreover, very basic phenotypic analysis of blood-circulating innate T cells by flow cytometry revealed increased activation based on enhanced expression of CD69 and PD-1 in COVID-19 vs. healthy controls, as well as a potential trend towards exhaustion based on continuous PD-1 expression observed after repeated sampling. Enhanced levels of CD69 and PD-1 were further associated with high levels of plasma Il-18, which has been previously linked to innate T cell activation in viral infections (https://www.jimmunol.org/co... "https://www.jimmunol.org/content/194/8/3890)"). In line with the above observations, innate T cell-expressed activation markers were found to be higher at the presumable site of inflammation as compared to peripheral circulation. Additionally, functional analysis of circulating MAIT, iNKT, and ?? T cell in COVID-19 vs. healthy controls showed substantially reduced IFN? along with slightly increased production of IL-17a following stimulation with PMA/Ionomycin, corroborating a potential tendency of peripheral cellular exhaustion. Conversely, both IFN? and IL-17a were increased in ETA over plasma samples in COVID-19 ICU patients and levels of both cytokines were equally higher in those ETA specimens containing innate T cells suggesting that these cells contribute to local airway proinflammatory cytokine production. Most importantly, while repeated sampling revealed a further decrease of CD69+ activated circulatory MAIT, iNKT, and ?? T in COVID-19 over time, the authors observed that higher frequencies of activated circulatory MAIT and iNKT cells on day 7 correlated moderately with increased PaO2/FiO2 ratios, indicative of preserved lung oxygenation, thus suggesting that activation of innate T cells in early COVID-19 may be used as a predictor of disease severity.
Limitations<br /> Technical/biological<br /> In addition to the relatively small study size, it remains somewhat unclear how many ICU patients were included in some experiments pertaining to ETA sample collection. Furthermore, a more stringent clinical characterization of enrolled patients (in addition to basic information presented in table 1) would add further impact to the observations made here regarding the potential role of innate T cells in critical COVID-19 as well as regarding any further association with ARDS severity, death or clinical course in general. To adequately address some of these questions, larger studies including patients across all clinical stages as well as early-onset and longitudinal sampling will be needed. Similarly, it would be of great interest to further examine kinetics of both potential innate T cell migration to inflamed airways and general function by means of repeated sampling. Additionally, extended flow-cytometric phenotyping of both circulatory and airway innate T cells will be needed to further characterize these populations beyond their basic activation status. In this context, general gating strategies should be included in the supplemental.
General<br /> Throughout the manuscript, some references pertaining to specific figures or their content are incorrect. For example, supplementary figure 1A shows a COVID-19 lung CT scan, but no healthy control scan as suggested by the complementary figure legend. Data on plasma and ETA IL-1R? levels are displayed in figure 1 E & F, not in supplementary figure 1B as stated in the manuscript. Supplementary figure 1B does not exist. Similarly, some of the literature references seem to have been mixed up (cf. reference 16 on the role of MAIT cells in diabetes and obesity vs. on the role of type I IFN responses as cited in manuscript; reference 23 is missing).
Significance<br /> Innate T cells have been previously shown to mediate potent antiviral immune mechanisms in both mouse and human studies (reviewed in https://link.springer.com/c... "https://link.springer.com/content/pdf/10.1007/s11684-017-0606-8.pdf)"). Observations made here generally corroborate limited data reported in two recent preprints (https://www.biorxiv.org/con... https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.05.20046433v1.full.pdf)") on activation status and frequencies of ?? T in COVID-19 patients. Notably, this preprint by Jouan et al. is first to assess basic functional changes of circulatory innate T cells (including MAIT, iNKT and ?? T subsets) as well as frequencies and limited phenotypic analysis of airway innate T cells in critical COVID-19. Further studies including larger cohorts of patients of variable disease are now needed to conclusively determine the role of innate T cells in COVID-19 immunopathology.
This review was undertaken by Verena van der Heide as part of a project by students, postdocs and faculty at the Precision Immunology Institute of the Icahn School of Medicine at Mount Sinai.
On 2022-03-01 06:21:04, user Nun Daled Yud wrote:
It would be useful to measure the protection of good ventilation in the form of windows and doors that can be open especially in primary care consulting suites and waiting rooms . Many have no windows and the carbon dioxide concentration is likely high . Carbon Dioxide meter readings would be interesting . Meanwhile all new clinics and long term care facilities ought be designed to improve ventilation -safety and security is an issue as is energy cost but there may be benefit as respiratory virus are effective through the air and creativity ought be better .
On 2022-03-01 12:10:22, user Eric Fauman wrote:
There's a reason we use 5e-8 for detection of significant GWAS hits and that's because below that you're swamped with associations that are likely not real. You shouldn't do pathway enrichment on genes identified from SNPs at 1e-6; the results are likely meaningless.
On 2022-03-01 17:11:24, user Rogerblack wrote:
An interesting paper, there are some unclarities in the paper that I'd like to see addressed.
' Full-time sick leave was reported by 9.4% of test-positives and 6.5% of test-negatives (RD=3.20, 95% CI 2.88-3.47%),' This whole paragraph is unclear, and the interpretation depends critically on the question asked.
Does 'Full time sick leave' here mean leave from full time work, or does it mean having to abstain from work, be it part-time or full time. Similarly, does 'part-time sick leave' mean reduction in hours worked due to illness or having to abstain from part-time work.
I find unfortunate the seeming lack of a question around severity. I have had a post-viral disease for the past 40 years. For most of that time, I would have ticked a 'fatigued' box.
This simple binary conceals that during that time, this has meant fatigue that means I can't do normal activities after 8 hours work, to bedbound.
A plea, to this team and others.<br /> Please ask about impact on normal role.<br /> 'Have you had to at this time give up caring/work/educational responsibilities due to illness' (for example)<br /> I have had most of the symptoms mentioned in figure 1, at varying severities for the past decades, with prevalance and severity varying.<br /> I DO NOT CARE ABOUT ANY OF THEM.
I care about if I can
A) Perform normal activies as before illness onset.<br /> B) Have to pick between normal activies and drop some I am no longer able to do.<br /> C) Have to give up many normal activies and only able to do some.<br /> D) Extremely limited ability to do normal activities.<br /> E) Self-care only.<br /> F) Unable to perform self-care to a reasonable quality.<br /> (For the last weeks, E)
On 2022-03-01 21:32:30, user Maurizio Rainisio wrote:
Computing NNT would have highlighted that to spare one hospitalization over the 4 months duration of the vaccination protection would require approximately 15,000 full treatments for the 5-11 kids and 7,000 for the 12-17. Adding an NNT column to table 1 would be of great help.
On 2022-03-02 17:00:41, user Carol Taccetta, MD, FCAP wrote:
Re the severe disease (hospitalizations), I posed a question to the corresponding author: does Table 11 from NY's Pediatric COVID-19 update (link below) reflect admitted FOR covid or all comers (admitted FOR + WITH covid)? There appears to be a similar table (Table 1) in this pre-print.
Table 11 is described within the link below as "Examining new hospital admissions with laboratory-confirmed COVID-19, per Table 11:", yet it does not specify a differentiation between admitted for and admitted with covid-19:
On 2022-03-09 21:24:14, user Les Funk wrote:
Figure 1 caption contains the false statement, "Disposition is presented for all enrolled participants..." There are 1841 participants not included in the figure after dose 2: 1258 from the BNT162b2 group and 583 from the placebo group. What happened to them?
On 2022-03-13 07:22:25, user Hanjun Lee wrote:
Dear Dr. Colson,
Thanks a lot for sharing this preprint and for your work regarding the virology of SARS-CoV-2. I would like to ask regarding the possibility of Amplicon-related artifacts in the Nanopore sequencing data. One of your key results that surprised me the most was the identification of a recombination event at p.156–179 of the SARS-CoV-2 Spike protein. However, looking into the Artic amplicon panel (ARTIC nCoV-2019 Amplicon Panel v4.1) that has been utilized during the amplification step, I have a fear that the recombination event at p.156–179 may be the consequence of primer set #73's preference toward a specific variant. The Artic amplicon panel that has been utilized has 99 different primer sets. While many regions are covered by more than two primer sets, some regions are covered by a single primer set. If a region is covered by a single primer set, this makes it harder to distinguish true hybrid "deltamicron" or "deltacron" genomes from a co-infection or co-incorporation of delta and omicron genomes. Spike protein's p.125–178, which has a very great overlap with the p.156–179 region that has been identified by the authors, is one such region; it is covered by ARTIC's 73rd primer set, but is just outside of the regions covered by the 72nd and 74th primer sets. Do the authors think it is possible that the 3' end of the recombination event (p.178 or 179) might be the starting site of the forward primer of the 74th primer set, not a bona fide recombination locus? Or do the authors think there is enough evidence that can outlaw such a possibility?
Again, thank you so much for your work, and stay safe.
On 2022-04-19 13:42:56, user Matthew MacLeod wrote:
This document was peer reviewed and published by Defence Research and Development Canada, which does not provide DOIs, so cannot be linked to by medRxiv. Permanent link to the citation record is here:
https://pubs.drdc-rddc.gc.c...
And PDF of the final version is here:
On 2022-05-18 16:21:05, user Carly Boye wrote:
Great presentation at #BoG2022! The scale of this study was very impressive. I was interested in your idea that BCRs might affect DD risk through noncoding mechanisms by disrupting lincRNA genes. I typically study smaller variants (such as SNPs) and it made me wonder if SNPs could potentially disrupt an interaction between lincRNA and DNA, and if this would affect transcription (and then if it could lead to DDs). Also, will your model be available once this work is published?
On 2022-06-14 12:08:52, user Robert Clark wrote:
In Fig. 3, it is notable that for the severe cases, the ivermectin group had a factor of 1.79 advantage on the scale of faster time to recovery. The question arises in this subgroup analysis of whether this is a real effect?
A good way to check is to also look at the hospitalizations, emergency room visits, urgent care, etc. numbers specifically for the severe cases. Note that its expected that a large proportion of these should come from the severe cases. Then does IVM have such a clear advantage for the severe cases here as well?
Robert Clark
On 2022-06-17 09:43:38, user Will wrote:
A persistent link to the supplementary materials can be found here: https://doi.org/10.5281/zen...
On 2022-06-17 14:00:48, user Todd Lee wrote:
The authors are to be commended on a very important comparative effectiveness trial. Thank you! From a reporting standpoint:
Additional details worthy of reporting in Table 1 are the presence of "do not resuscitate orders" which would prevent intubation and the administration of co-interventions like remdesivir which may impact both the need for ventilation and mortality (CATCO, CMAJ 2022). Could the authors provide this information?
Additionally, the composite outcome is non-inferior. Yet, the composite contains two clinical outcomes which (a) differ in importance in that death is worse than ventilation and (b) may be related along a causal pathway in that patients who are ventilated are more likely to die. Could the authors revise the manuscript text or tables to include each component of the composite outcome separately by Day 28 or report death in the "adverse events"?
Additionally, could post-hoc subgroup analyses be performed by age, gender, vaccine status, and CRP greater than and less than 75 (recovery criteria).
I appreciate these were not pre-specified outcomes on clinicaltrials.gov, but they are essential to peer review and to contextualizing these results with the existing literature.
On 2022-06-23 08:44:59, user Andrew Fischer Lees wrote:
This is interesting work. Your 4th limitation is the biggest issue from a clinical side (I say this as a clinician). 99% of the time I order a hemoglobin it is as part of a CBC. Sometimes I order the hemoglobin because I want the Hb, but sometimes I order it because I want the WBC, or the platelets, or all of these things. There is not a great reason to order just WBC if that is all I care about, because the same reagents are used in the lab to process the sample, so the extra tests are "free information", that is, the marginal cost is zero.<br /> Your stability model should really be executed at the level of the CBC bundle. Asking clinicians to not order a Hb when the marginal cost is zero doesn't really save any money for the system or lab draws for the patient. But If the whole CBC is predictably stable, in that situation it would make sense to surface that information to the clinician in the form of Clinical Decision Support.
On 2022-07-08 18:08:52, user Angel perez wrote:
This paper has been published in Applications and Applied Mathematics.<br /> The published version can be found at https://digitalcommons.pvam...
On 2022-07-29 04:26:04, user Miguel García Grimshaw wrote:
Hello, can you please include de link for the published version?
DOI: 10.1111/ene.15504
On 2022-08-05 06:55:12, user Robert Clark wrote:
The quite key question however remains unaddressed: how does vaccination status effect rebound?<br /> Because of the evidence overtime the vaccine reduces immune response, the correlation to number of shots and time since vaccination should also be made in regards to rebound.
Robert Clark
On 2020-04-02 00:27:40, user Sinai Immunol Review Project wrote:
Summary:<br /> The authors of this study provide a comprehensive phenotypic analysis of the adaptive immune cell pool in 38 COVID-19 patients with mild or no symptoms in comparison to 18 healthy donors. Using flow cytometry of circulating PBMC, the authors found that the total lymphocyte count in COVID-19 patients was slightly reduced. This is in striking contrast to severe patients in which severe lymphopenia is common and correlates with severity of disease. The relative CD19 cell count including particularly Germinal Center B cell (GCB) counts were significantly increased in COVID-19 patients. With respect to T cells, the authors describe no substantial differences in CD4 and CD8 T cell counts. However, when analyzing T cell subsets, the authors discovered significantly increased expression of CD25 and PD-1 as markers of late activation and exhaustion in CD8+ T cells, respectively. Moreover, the amount of naïve CD4 T cells as well as of T follicular helper (Tfh) cells were significantly increased in COVID-19 patients.
In an attempt to find correlations between patient characteristics and the status of the adaptive immune system, the authors applied Person’s correlation coefficient and found no major impact of age on CD8+ T cell activation as well as on Tfh and GCB-like T cell differentiation.
Summing up, the authors performed a thorough phenotypic analysis of B and T cell subsets in COVID-19 patients giving a promising insight into the status of the adaptive immune system in their patient cohort with mild symptoms.
Critical analysis:<br /> The strength of this study is the in-depth multiparameter analysis of adaptive immune cells in a reasonably sized cohort of 38 patients and 18 healthy controls. The assumption that COVID-19 patients with mild disease show an appropriate antigen-specific response due to an increase of Tfh and GCB cells is justified by the data.
Implications of the findings in the context of current epidemics:<br /> The study clearly shows that patients with mild-disease have increased Tfh and GCB and understanding the specificity of this response and how they correlate with disease course will be interesting to explore. Defining patient groups at risk for severe courses is crucial in order to be able to intervene early using experimental therapeutic strategies.
On 2022-08-13 18:37:14, user Christian Fiala, MD, PhD wrote:
Is there any information as to the requirements and/or use of face masks by pregnant women during the pandemic in the region?
On 2022-08-15 10:05:00, user james hurley wrote:
I congratulate the authors on their protocol for a ‘Systematic Review and Meta-Analysis of Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients’ [1]. That there are already over 40 published articles with “Systematic Review”, “Meta-Analysis” and “Selective Digestive Decontamination” in the title indicate that this is a vexed topic and the definitive publication is yet to appear. <br /> A simple reading of recent Cochrane reviews appears to indicate that SDD lowers both infection incidence and mortality in this patient group, whereas four other interventions do not [2-7]. However, what are the substantial areas of doubt and how can these be best addressed [8]?<br /> May I make some suggestions that might increase the chance that their proposed Systematic Review might be definitive?<br /> Firstly, is the mechanism of action of how Selective Decontamination of the Digestive Tract decrease infection and mortality in invasively ventilated patients understood? Are the animal studies undertaken in mice in the early 1980’s, from which the term ‘Selective Decontamination’ originated, still regarded as valid? Is the term “Selective Digestive Decontamination” a triple misnomer? Several have proposed that the term ‘Control of Gut overgrowth’ as a more accurate term to describe the presumed mechanism [9, 10].<br /> Second, is it true to state that the “Uncertainty about the effectiveness of SDD is due to concerns about the generalisability of RCTs with limited internal and external validity.”? Why did the use of SDD fall out of favour among neutropenic patients in the 1990’s? Is there a potential for rebound infections? Will this proposed systematic review address the question of rebound? Is there a possibility that SDD is ineffective among ICU patients? Is there a possibility that SDD and the rebound effect on its withdrawal is harmful? <br /> Thirdly, the authors will need to confront data inconsistencies between various versions of the published SDD trials that appear in the two Cochrane reviews of this topic [2, 3]. The earlier review obtained ‘Intention to treat’ data from several of the authors of the primary SDD studies which differs from the ‘on treatment’ data as published. The latter often excluded patients who died before completing the four days regarded as necessary to achieve ‘Selective Decontamination’. As a consequence, there is both survivorship bias and an underestimation of infection and mortality incidences in the ‘on treatment’ data. In addition, will the authors use the original data for the study groups as randomly allocated or will they use the adjusted data as published?<br /> Fourth, the authors propose a subgroup analysis comparing the results for “Individual patient vs unit level randomisation (i.e. cluster and cluster/cluster-cross-over).” However, their hypothesis is that the effect is unidirectional, i.e. they expect a benefit to be “,…greater in individual patient randomised trials compared to unit level randomised trials.” This expectation is a restatement of the ‘Stoutenbeek’ postulate, stated in the first SDD study undertaken in the ICU setting, that there would be a contextual effect of using SDD in the ICU context and that this effect would be beneficial to any concurrent control groups patients and, as a consequence, bias downwards the estimates of the SDD intervention within individual patient randomised trials [11, 12]. Stated otherwise, this postulate implies a herd effect similar to that of herd protection from vaccination within a population. <br /> This postulate creates several difficulties for this proposed systematic review. By raising this postulate, does this invalidate the Stable Unit Treatment Value Assumption (SUTVA) that is fundamental to valid estimates of effect size from concurrent controlled trials? If the SUTVA is questioned here, will this invalidate the estimates from the proposed systematic review? Moreover, given this postulate and proposed subgroup test, will the test be one-sided, with the expectation that the effect is uni-directional [only beneficial effect possible], or two sided?<br /> There is evidence that the results of individual [i.e. concurrent control] patient randomised trials of SDD differ to those of unit level [or historical control; i.e. non-concurrent controls] randomised trials and that the SUTVA is questionable for these studies. This has only been addressed in first and second meta-analyses on this topic both published 25 years ago [13, 14]. These indicate that the effect is greater in the former, i.e. contrary to the direction postulated by Stoutenbeek. There is further and more recent evidence for this discrepancy. On the one hand, the three largest subsequently published studies of SDD versus either standard care or SOD, which were all undertaken using unit level randomization [i.e. and non-concurrent controls], showed absolute differences in bacteremia and mortality [before any statistical adjustments] of less than 5 percentage points [15-17]. On the other hand, the most recent Cochrane review of the studies of SDD in this population, which included mostly trials using individual patient randomization [i.e. and concurrent controls], showed absolute differences in pneumonia and mortality of five percentage points or greater [3]. <br /> Will the proposed protocol use the unadjusted data or the adjusted data from these trials? Does the data adjustment account for the Stoutenbeek effect?<br /> Finally, to provide a definitive review, the authors will need to explain why event rates [pneumonia, bacteremia, candidemia and mortality] are generally higher among control groups within trials using individual patient randomization [i.e. with concurrent controls] versus control groups within trials using unit level randomization [i.e. with non-concurrent controls], versus control groups from studies of interventions other that SDD, and versus groups of studies without an intervention. Moreover, why is it that the event rates in the SDD intervention groups are similar to intervention groups from studies of interventions other that SDD in this patient group? The higher event rates are apparent in closer scrutiny of the summary results of the five Cochrane reviews [3-7]. On the one hand, the median control group event rates for pneumonia and mortality [18] are highest within the control groups of studies of SDD versus control groups of studies of other interventions and yet, on the other hand, the event rates for the intervention groups are paradoxically similar to intervention groups of studies of other interventions.<br /> I wish the authors well and hope that they succeed in providing the definitive systematic review of this topic over the arc of time [19].<br /> References<br /> 1. Hammond NE, Myburgh J, Di Tanna GL, Garside T, Vlok R, Mahendran S, Adigbli D, Finfer S, Goodman F, Guyatt G, Venkatesh B. Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients in an Intensive Care Unit: A protocol for a Systematic Review and Meta-Analysis. medRxiv. 2022 Jan 1.<br /> 2. Liberati A, D'Amico R, Pifferi, et al: Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. Cochrane Database Syst Rev 2009; 4: CD000022.<br /> 3. Minozzi S, Pieri S, Brazzi L, Pecoraro V, Montrucchio G, D'Amico R. Topical antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving mechanical ventilation. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: CD000022.<br /> 4. Wang L, Li X, Yang Z, Tang X, Yuan Q, Deng L, Sun X. Semi-recumbent position versus supine position for the prevention of ventilator-associated pneumonia in adults requiring mechanical ventilation. Cochrane Database Syst Rev 2016(1). DOI: 10.1002/14651858.CD009946.pub2.<br /> 5. Gillies D, Todd DA, Foster JP, Batuwitage BT. Heat and moisture exchangers versus heated humidifiers for mechanically ventilated adults and children. Cochrane Database Syst Rev. 2017(9). DOI: 10.1002/14651858.CD004711.pub3.<br /> 6. Bo L, Li J, Tao T, Bai Y, Ye X, Hotchkiss RS, Kollef MH, Crooks NH, Deng X. Probiotics for preventing ventilator-associated pneumonia. Cochrane Database Syst Rev. 2014(10). DOI: 10.1002/14651858.CD009066.pub2.<br /> 7. Zhao T, Wu X, Zhang Q, Li C, Worthington HV, Hua F. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2020(12).<br /> 8. Hurley JC Selective digestive decontamination, a seemingly effective regimen with individual benefit or a flawed concept with population harm? Crit Care. 2021;25(1).<br /> 9. Silvestri L, Miguel A, van Saene HK. Selective decontamination of the digestive tract: the mechanism of action is control of gut overgrowth. Intensive Care Med. 2012;38(11):1738-50.<br /> 10. Hurley JC (2020) Structural equation modeling the “control of gut overgrowth” in the prevention of ICU-acquired Gram-negative infection. Crit Care 24(1):1-2.<br /> 11. Stoutenbeek CP, Van Saene HK, Miranda DR, et al: The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med 1984; 10(4):185-192.<br /> 12. Hurley JC. Incidences of Pseudomonas aeruginosa-associated ventilator-associated pneumonia within studies of respiratory tract applications of polymyxin: testing the Stoutenbeek concurrency postulates. Antimicrob Agents Chemother. 2018;62(8):e00291-18.<br /> 13. Vandenbroucke-Grauls CM, Vandenbroucke JP. Effect of selective decontamination of the digestive tract on respiratory tract infections and mortality in the intensive care unit. The Lancet. 1991;338:859-62.<br /> 14. Hurley JC. Prophylaxis with enteral antibiotics in ventilated patients: selective decontamination or selective cross-infection?. Antimicrobial agents and chemotherapy. 1995;39(4):941-7.<br /> 15. de Smet AMGA, Kluytmans JAJW, Cooper BS, et al: Decontamination of the digestive tract and oropharynx in ICU patients. N Engl J Med 2009, 360:20–31.<br /> 16. Oostdijk EA, Kesecioglu J, Schultz MJ, Visser CE, De Jonge E, van Essen EH, Bernards AT, Purmer I, Brimicombe R, Bergmans D, van Tiel F. Notice of retraction and replacement: Oostdijk et al. effects of decontamination of the oropharynx and intestinal tract on antibiotic resistance in ICUs: a randomized clinical trial. JAMA 2014; 312 (14): 1429-1437. JAMA 2017; 317(15):1583-4.<br /> 17. Wittekamp BH, Plantinga NL, Cooper BS, et al: Decontamination strategies and bloodstream infections with antibiotic-resistant microorganisms in ventilated patients: a randomized clinical trial. JAMA 2018;320(20):2087-2098. <br /> 18. Hurley JC Discrepancies in Control Group Mortality Rates Within Studies Assessing Topical Antibiotic Strategies to Prevent Ventilator-Associated Pneumonia: An Umbrella Review. Critical care explorations. 2020;2(1).<br /> 19. Pizzo PA. Management of patients with fever and neutropenia through the arc of time: a narrative review. Ann Intern Med. 2019;170(6):389–97.
On 2022-08-24 17:17:22, user Mario Malicki wrote:
The references are not linked to any statements in the manuscript. Could you fix this?
On 2022-09-29 18:22:07, user Nikhil Padhye wrote:
Published version can be found here:<br /> Padhye, N. (2022). Bayesian Measurement of Diagnostic Accuracy of the RT-PCR Test for COVID-19. Metrology, 2(4), 414–426. https://doi.org/10.3390/met...
On 2020-04-25 04:04:36, user Deevish N D wrote:
The radiometer used in this study - UV513 AB detects a peak wavelength of 365 nm as per its manual. But the actual germicidal wavelength is around 254 nm. I believe the dose needed for UV disinfection has been under-reported in this article. Authors please correct me if am wrong.
On 2020-04-25 18:43:20, user Retelska wrote:
Excuse, me, I don't know if I understand correctly. Do the 2 Elisa essays yield 5% false positives? Were these tests used to establish that 5% of general population has now been infected? You expect 5% false positives, right? How do you correct for this effect? Only the 3rd test with 0% false positives seems specific enough.
On 2020-04-25 21:34:02, user Christopher Rentsch wrote:
We believe that Magagnoli et al failed to correctly identify intubation occurring in hospitalized patients testing positive for COVID-19. They used CPT codes 31500, 94002, 94003, and E0463 and ICD-10 procedure codes indicative of assistance with respiratory ventilation, or extracorporeal membrane oxygenation (ECMO). We identified 5,906 COVID-19 patients treated in the Veterans Health Administration between March 1 and April 21, 2020. In addition to the above CPT codes, we identified intubation according to ICD-10 procedure codes for insertion of endotracheal airway, and respiratory ventilation, which were usually concordant. We cross-validated with medications typically used during intubation, such as neuromuscular blocking agents (e.g., succinylcholine, rocuronium) and short acting sedatives (e.g., propofol, midazolam). We also found these intubation codes most frequently in the context of intensive care. We did not find similar evidence of face validity for ventilation assistance codes. No instances of ECMO were found as this procedure is unlikely to be used in the Veterans Health Administration.
We classified 307/5,906 = 5.2% patients as intubated. Using the Magagnoli algorithm, only 96/5,906 = 1.6% patients were said to be intubated. Of these, 37 were classified based on ventilation assistance codes, not indicative of intubation.
List of ICD-10 Procedure codes used to identify intubation
Codes in both Magagnoli and Tate lists<br /> - Respiratory Ventilation (5A1935Z 5A1945Z 5A1955Z)
Codes in Magagnoli list, but not Tate list<br /> - Assistance With Respiratory Ventilation (5A09357 5A09358 5A09359 5A0935B 5A0935Z 5A09457 5A09458 5A09459 5A0945B 5A0945Z 5A09557 5A09558 5A09559 5A0955B 5A0955Z)<br /> - Extracorporeal Oxygenation, Membrane (5A1522F 5A1522G 5A1522H)
Codes in Tate list, but not Magagnoli list<br /> - Insertion of Endotracheal Airway Into Trachea (0BH13EZ 0BH17EZ 0BH18EZ)
Janet P. Tate (Janet.Tate2@va.gov)<br /> Christopher T. Rentsch (@DarthCTR)<br /> Joseph T. King Jr.<br /> Amy C. Justice
VA Connecticut Healthcare System<br /> West Haven, CT
On 2020-04-26 13:05:40, user Bin_Pei wrote:
Thanks for kind reminder of the reviewers, there is an unintentional editing error that we accidentally mixed the name of two cities in affliation in the original manuscript. We have submitted a revision already, there might be few days delay and we will be more careful in the future work.
On 2020-04-26 15:20:46, user Robert Clark wrote:
I was interested to read of your report on over 4,000 COVID-19 cases in New York. Collecting health histories for a large data set of patients of COVID-19 may provide a rapid means of determining which medicines could be effective in combating it:
Big data to fight COVID-19 and other diseases.<br /> https://medium.com/@rgregor...
The idea is to find if certain medications are *missing* from the patients prior health histories, suggesting those medications may be protective against the disease.
Robert Clark
On 2020-04-27 11:07:45, user Pilar Domingo Calap wrote:
We have detected a small factual error in the text. The sentence containing the error is the following:
"The first confirmed case in the Iberian Peninsula was communicated on February 24, 2020 in Burriana, a small town nearby the city of Valencia, followed by another case the following day in Valencia."
This sentence should be instead be:
"The first three confirmed cases in the Iberian Peninsula were communicated on February 25, 2020 in Madrid, Barcelona, and Villareal, a small town nearby the city of Valencia."
Pilar Domingo-Calap (co-author of the preprint)
On 2020-04-28 16:33:17, user Katri Jalava wrote:
Interesting paper, and fascinating model. I was a bit curious of your contact percentages. How do you come up with the numbers? E.g. for CS adult-adult would be reduced only by 20 % by closing the public events. I could argue that it is at least 60 %, especially if you have a look on SF1 in 10.1371/journal.pcbi.1005697. Also, if you have both CS and HO in place, you get 80 % + 20 % =100 % reduction for child-child contact(?).
Getting any data on impact of the closure measures from publications is hard. I think they have tried this in the UK from the case load data. Do you think you could do a telephone survey among Germans? Or if an app company would make a data collection tool where everyone could register their daily contacts during the outbreak, that would be cool. Good luck and thank you.
On 2020-04-29 21:17:20, user Rick56 wrote:
The authors are addressing an important question. But I believe they have underestimated the length of time between exposure and testing positive.
This matters because if you look at the raw data currently available for Wisconsin at the Johns Hospkins github, you see what appears to be a flattening of the number of new cases starting April 6 -- followed by a substantial spike starting April 22.
Given this, it is especially important how one models the time between exposure (election; April 7) and testing positive. Because if that time could be 15-19 days, then there is a very plausible spike resulting from election exposures.
The time from exposure to positive test = <br /> exposure to symptoms (incubation period) plus <br /> symptoms to testing (let's call it "testing delay").
But the testing delay is also influenced by how readily testing is available.
So, two problems:
But the Li paper notes that the 95%ile for this distribution is 12.5 days.
When I use R to generate gamma distributions with a mean of 5.2 and 95%ile at 12.5, the SD is substantially greater than 2.3. Also -- that gamma gives about 18% of the incubation periods <2 days.
Based on this, it seems likely that the author's distribution has a much thinner right tail than is consistent with the Li data. And perhaps 18% of their distribution could be < 2 days. So we need the specifics of the distribution the authors created.
So, was the testing delay in Wisconsin as short as that in Beijing? Did the average person in Wisconsin get tested 4.3 days after symptoms start? Seems unlikely. Since the US has had such a terrible problem getting people tested, we need evidence that their testing delay is reasonable for Wisconsin.
Unless the authors can address these points, I think it very inadvisable to claim that the spike in positive cases starting April 22 is completely unrelated to the April 7 election.
[you'll have to look up the Wisconsin data on your own. I attempted to attach a plot multiple times without success].
~~~~~ here are the methods details from the authors' supplementary
"We assume the incubation period distribution is gamma with mean and SD of 5.2 and 2.3 days [3]. We assume that the distribution of the time between symptom onset and confirmation is gamma with mean and standard deviation (SD) of 4.3 and 3.2 days, based on 186 cases reported in Jan-Feb 2020 in Beijing [4]."
from their References<br /> 3. Li Q, Guan X, Wu P et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199-1207.<br /> 4. Leung K, Wu JT, Leung GM. First-wave COVID-19 transmissibility and severity in China outside Hubei after control measures, and second-wave scenario planning: a modelling impact assessment. Lancet 2020;395:published online April 8.
On 2020-04-30 08:59:22, user Skerdi wrote:
It would be great if you could compute the RAASi/nonRAASi difference adjusted for the relevant comorbidities you cite (age, gender, ICU entry on day of admission, serum ferritin, insulin-dependent diabetes mellitus and cardiac arrhythmia history).
This would help give a sense of whether RAASi are likely to work only if taken chronically before encountering Covid-19 (in which case their effect adjusted on clinical baseline would shrink or disappear) or if they could also work when started after disease onset (if adjustment for clinical baseline does not erase their effect).
On 2020-04-30 13:35:27, user John Lambiase wrote:
This has greater implications than just covud 19. It could effect most "enveloped" respiratory pathogens. The antimicrobial processes signalled by vitamin D are absolutely fascinating. They trigger multiple facets of immunity.
On 2020-04-30 20:54:07, user Frank Conijn wrote:
I don't have any objections against retrospective cohort studies, because they are sometimes all one can do, and they can give valuable insight. But the compared groups must have equal baseline disease severity. And am I overlooking something, or is that information missing?
Furthermore, the Brief Summary on page 2 says (emphasis by me): "The use of antiviral drugs (chloroquine, oseltamivir, arbidol, and lopinavir/ritonavir) did not shorten viral RNA clearance, especially in non-serious cases." But the text and figures show that that still concerned patients with pneumonia or worse. I don't find that non-serious cases. Those are moderate cases on a scale from light - mild - moderate - severe - critical.
On 2020-05-01 05:08:22, user Adapt Research wrote:
Hi, far too early to be speculating on this. The high GHSI countries are also the high GDP ones and the high air traffic ones. The number of tests is mostly correlated with the number of cases, not the GHSI. It may yet be the case that high GHSI countries end up with less deaths per capita than low GHSI ones. We are nowhere near the end yet, and don't know what will happen in Africa where the largest concentration of low GHSI countries is. The correlations are interesting, but we're not able to draw conclusions yet.
On 2020-05-02 06:26:29, user Jasmin Zessner wrote:
How come the authors only looked into countries most affected by SARS -COV-2 while ignoring the ones where lockdown was effective (Austria, Germany) and extrapolate that “lockdown is not effective in western Europe”
On 2020-05-03 09:08:28, user Daniel Corcos wrote:
These calculations rely on wrong estimates.<br /> 1) The delay between infection (does it include incubation time?) and death is based on a preprint from data on the Diamond Princess epidemic. There were 7 deaths at that time but the current number is 14 (1). The case fatality rate in South Korea was 1.6%, but now it is 2.32% (2). Delayed deaths should be taken into account.<br /> 2) A zero generation time is unrealistic, as the virus must multiply before spreading, and estimates of the generation time have been calculated to be between 4 and 5 days (3,4) .<br /> Changing these parameters should alter the conclusions.
1) https://en.wikipedia.org/wi...<br /> 2) https://en.wikipedia.org/wi...<br /> 3) https://www.sciencedirect.c...<br /> 4) https://www.medrxiv.org/con...
On 2020-05-04 06:30:08, user japhetk wrote:
This study has serious flaws and I will reject if I were a reviewer.
First, this study doesn't have a control data such as the blood sample of a few years ago. Although, the kit maker advocates the specificity of 100%, various test kits including the innovita's one which championed 100% specificity were already shown to show the inferior data compared with the maker's advocates.
Second, as pointed out,
Tests were done for randomly selected preserved serum from patients who visited outpatient clinics of the hospital and received blood testing for any reason. Patients who visited the emergency department or the designated fever consultation service were excluded to avoid the overestimation of SARS-Cov-2 infection.
SARS-COVID-19 is already known to cause atypical symptoms even in the "asymptomatic" (in terms of typical symptoms of infection) such as stroke, and various other thrombotic symptoms. So, this exclusion criteria is not enough apparently to avoid biased sampling and overestimation.
In Japan, this apparently seriously flawed study without review is reported widely and people even some doctors now say the real fatality rate of SARS-COVID-19 is 0.05%! based on this study (they seemed to have forgotten Japanese patients in the diamond princess ship showed the higher mortality rate compared with age-matched patients of westerners in the same ship). This is a nightmare for the public health of Japan.
On 2020-05-05 20:54:15, user japhetk wrote:
This study has serious flaws and I will reject if I were a reviewer.
First,<br /> this study doesn't have a control data such as the blood sample of a <br /> few years ago. Although, the kit maker advocates the specificity of <br /> 100%, various test kits including the innovita's one which championed <br /> 100% specificity were already shown to show the inferior data compared <br /> with the maker's advocates.
Second, as pointed out,
Tests<br /> were done for randomly selected preserved serum from patients who <br /> visited outpatient clinics of the hospital and received blood testing <br /> for any reason. Patients who visited the emergency department or the <br /> designated fever consultation service were excluded to avoid the <br /> overestimation of SARS-Cov-2 infection.
SARS-COVID-19 is already <br /> known to cause atypical symptoms even in the "asymptomatic" (in terms of<br /> typical symptoms of infection) such as stroke, and various other <br /> thrombotic symptoms. So, this exclusion criteria is not enough <br /> apparently to avoid biased sampling and overestimation.
In Japan, this apparently seriously flawed study without review is reported widely<br /> and people even some doctors now say the real fatality rate of <br /> SARS-COVID-19 is 0.05%! based on this study (they seemed to have <br /> forgotten Japanese patients in the diamond princess ship showed the <br /> higher mortality rate compared with age-matched patients of westerners <br /> in the same ship). This is a nightmare for the public health of Japan.
On 2020-05-05 14:23:59, user John Huppenthal wrote:
From January 1, 2020 to April 11th, the study period, over 40,000 fewer people died in 2020 than in the same period in 2019.
That's an amazing number.
You would expect an additional 13,000 people would die in 2020 just from the increase and aging of the population.
Adjusted for that effect, 53,000 more people died in 2019 than in 2020.
By the logic of the study, Covid-19 had 53,000 excess deaths in 2019.
A lot more than the 15,000 it had in 2020.
Every year, they do a vaccine effectiveness study. The results of that study need to be coughed up a whole lot sooner this year to unravel the true numbers.
This study did not produce the true numbers, not even close.
On 2020-05-05 20:58:20, user japhetk wrote:
A brief comment. <br /> This study's conclusion that the proportion of asymptomatic patients among the infected is 99.99% is not consistent with the fact that 9 Japanese out of 300 infected Japanese passengers among 1341 total passengers in the diamond princess ship (where all passengers went through PCR testing) have died (half the infected (which was confirmed by PCR) showed the symptoms by the way). And their fatality rate was higher than the age-matched westerners. Although, they were mostly old, so are the 30 percent of Japanese.
On 2020-05-05 21:56:48, user Un Kwon-Casado wrote:
Hi Anne- Great and exciting work! Do you know if its primarily shedded viral particles versus infected cells in the saliva samples?
On 2020-05-07 02:38:21, user Variant wrote:
In most cases, peak deaths and infections preceded the point at which any SAHO orders could have had impact. In fact, virus "curves" are nearly identical between states where there have been significant movement restrictions and those that haven't.
On 2020-05-07 20:33:43, user Walter Langel wrote:
The fit and extrapolation of the total infection number by a logistic function has been continued till first week of May and extended to several countries. Now the excel file containing all data and fits is available at researchgate:<br /> https://www.researchgate.ne...
On 2020-05-08 18:55:00, user hans wrote:
Please consider to cite:
Bartels, Christian (2020): Predicting the impact of a new infectious disease based on the basic reproduction number is challenging. figshare. Presentation. https://doi.org/10.6084/m9....
On 2020-05-10 14:19:22, user Minta Péter wrote:
Hi, something has happened to the pdf, can't download and doesn't show the size.
On 2020-05-11 09:18:27, user David Sbabo wrote:
Russia and Ukraine in the HCQ group?
Their third death occured in March in both countries. HCQ was autorised in mid April in both countries Unless they can go back in time, HCQ cannot have any influence here.
So the main result of this study is null and void.
On 2020-05-11 20:53:16, user Erik Hansson wrote:
Thank you for your work, it is valuable to consider that that people have different social activity levels, but I am concerned that your approach miss two important aspects which will underestimate the herd immunity threshold/make it less valid as an indicator of the risk of new severe epidemic flares:
Lower social classes may be less able to achieve a low level of social activity due to household crowding, dependence on public transportation and inability to work from home due to having manual work. This may lead to higher disease transmission in lower than higher social classes. Add to this the situation in elderly care in which the absence of PPE has probably led to quite intense transmission both from and to workers, who are strongly concentrated to lower social classes in Stockholm.
Outcome data is scarce but there seems to be empirical evidence of such a social gradient in covid-19 transmission both in hospitalized cases and very limited seroprevalence studies (contact Björn Olsen in Uppsala for more details or read media reports from last week - their study found 0% seroprevalence at Östermalm (~Knigthsbridge) in the end of April, n=?). Information from other major cities tell a similar story of a social gradient.
Under "normal" circumstances persons from lower social classes may not necessarily have higher levels of social activity than persons from the more affluent classes. I am concerned it may rather be the opposite as people from higher social classes may more likely engage in several activities less accessible to persons from lower classes, activities that do not happen in a semi-quarantine setting, such as culture and sports events, parties, eating out, bars, office work and meetings, conferences, university education, etc, but that are expected to be possible to do in a society having reached herd immunity.
Furthermore, due to prevailing segregation by class and ethnicity such post-social-distancing activities will likely primarily be done together with other persons likewise having been able to limit their activities during the first phase of the epidemic. There are thus conditions that allow rapid disease transmission within more affluent social strata if these go back to business as usual. It may even be argued that estimating a herd immunity threshold as an average percentage within a strongly segregated city is not especially meaningful. If there are large enough pools of connected susceptible individuals there is still a possibility of epidemics that overwhelm the healthcare system.
Another concern, which is partly related to the present manuscript is the use of quite uncertain and potentially inflated modeled estimates to make predictions of when Stockholm will reach the disease-induced herd immunity threshold, in June 2020, less than 3 weeks from now. This model estimated 26% had been infected by May 1. A critique of this model estimated 5-10% (https://twitter.com/AdamJKu... "https://twitter.com/AdamJKucharski/status/1254084771535376391)"), and the only (to my knowledge) somewhat representative seroprevalence study found 7.5% (Björn Olsen) at that time. Two separate methods that concur so well seems more credible than one modeled estimate.
The combination of estimating an artificially low herd immunity threshold and using potentially exaggerated cumulative infected proportion risk declaring “all-clear” in Stockholm much prematurely.
Erik Hansson, <br /> MD, MSc Epidemiology
On 2020-05-24 21:33:11, user Tim Tarr wrote:
DOXY was suggested as replacement for azithromycin for those with heart issues. Seems azithromycin may compound HCQ risk to the heart. Now put Zinc in the mixture.<br /> DOXY+HCQ+Zinc sulfate <br /> The lab work should be run for vitamins D&C deficiency and Zinc. Lab work on kidney & liver status is pretty standard for admission.Also if heart function not known that should be checked, also usually a basic admission process.
On 2020-04-03 14:51:49, user Jack Debrueil wrote:
What is the biological plausibility of this association. The ABO-type is related to red blood cells. IT is important to know associaitons with leukocytes and HLA-types. Before concluding anything these reulst must be stratified by HLA-types.
On 2020-03-23 16:59:18, user Rintaro N. wrote:
?“The distribution of ABO blood groups in blood donors... (ref.7)” might not be a good control. It can be different from “true” distribution.?
?cf.?<br /> ?Blood Type and Blood Donation Behaviors: An Empirical Test of Pure Altruism Theory<br /> https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171957
On 2020-08-04 12:21:27, user Mike Dammann wrote:
Here are some of the important parts from the revised version of the NYP study highlighting the important of the Rh negative blood group: https://www.rhesusnegative....
On 2020-04-04 01:25:42, user GLB wrote:
The data from Wuhan are used to characterize the influence of social distancing. From the paper "To be specific, the generalizable information from Wuhan was the impact that social distancing had on maximum death rate and time to reach the inflection point.". Many sources have raised doubts about the veracity of the Wuhan data. Does this render the characterization of the efficacy of social distancing methods in the model suspect? Can the model be tested by using a different location (say, Italy) as the training data set to see how the analysis changes?
On 2020-04-02 04:55:23, user Sola Grantham wrote:
I would like to see an explanation of why states with lower current rates of growth are projected to have later peaks. This makes sense to me only in the case of herd immunity being the cause of the peak. Then the area under the graph would remain the same. Thus, to reach the critical percentage of population with immunity, a slower rate of infection would lead to a later peak. But if the cause of the peak is the assumed perfect adherence to social distancing, then wouldn't the date of the peak be more related to the date of practical enactment of the social distancing measures?
On 2020-04-02 16:55:14, user VWFeature wrote:
What happens if instead of "assuming full social distancing through May 2020" we see what's actually happening? (Deaths go way up.)<br /> What's the assumption of death rates when hospitals and ICUs exceed capacity?
When no beds are available, a reasonable assumption would be that 80% of people needing hospital, and 100% of those needing ICU would die.
This study keeps getting cited as "best possible outcome." It's intellectually dishonest to present a "best possible" without a "most likely" and "worst case" projection.
This study is already inducing a false sense of security. This is the BEST POSSIBLE outcome. The most likely is far worse.
On 2020-04-02 22:52:25, user Qi Ying wrote:
The error function used in the study can be derived from the assumption that the daily death follows a normal distribution. Our experience in China shows that it is not the case. The tail in the daily death rate distribution is much longer. The predicted deaths are likely underestimated. Also, the error function fitting leads to significant under-predictions when the inflection point in the death rate has not arrived, which is likely the case for many US states. Thus, I believe these estimations presented in the paper as well as on their website are going to be significantly biased low. The actual situation could be much much worse.
On 2020-04-04 14:57:41, user Alexandros Heraclides wrote:
Maybe better to refer to "differing Relative Risks for dying", rather than "differing mortality impacts"? The latter points to absolute risk difference, while you are referring to relative risks. Great paper though!
On 2020-04-06 16:47:11, user smallbusinessrocks wrote:
A MORE REASONABLE DEATH RATE FOR THE C-19 FLU 4/6/2020
Food for thought.
As a young actuaries, many years ago a group of us tried to identify causes of death from older people dying with several SUD (serious underlying disease). We gave up, cannot clearly identify cause. Most doctors certifying cause of death do not know what caused it, if from SUD. Most people age 65 and over have two or more SUD. Seven thousand people with SUD die each day in the United States.
People have touted various rate of death from the C-19 flu in America, starting with 4.5% and reducing quickly to current 1.29%. There will be many more deaths from the current infected.
These death rates are grossly overstated – every pandemic, it is the same thing – death rates are wildly overstated at the beginning. A calculation, using a better basis, is 0.73% -<br /> more than the ordinary flu, but not 1.3% or 4.5%
Truer denominator: in all but Iceland and Pacific Princess, we need to multiply the total cases by four. Why? Because we are only testing a segment of symptomatic cases (coughing, etc), but the asymptomatic cases are 80% of the total. Except for Iceland - they tested a large group drawn from the general population, not just the ones showing<br /> symptoms, found 75% asymptomatic (multiply denominator by factor of four). The<br /> Pacific Princess tested all 2500 on boat – the Pacific Princess 1% death rate<br /> is highly affected by median age of cruise passengers, in general, of 60 to 69<br /> years. Diamond Princess has asymptomatic 83% - multiply by five
Truer numerator, is much less than the reported deaths, we would estimate about 0.2 of the 81% of deaths who have "SUD" - serious underlying disease; and 1.0 for all others. We estimate a weighted multiplier as (.2 deaths x 1.0 + .8 deaths x 0.2 = .36 of deaths<br /> reported). Why? Because many die from pneumonia in the USA each year, typically as the final stage of some other SUD (per NCHS). Doctors cannot prove a death from someone having the C-19 flu is CAUSED BY the C-19 flu, rather than the person with C-19 flu died WITH the C-19 flu. Needs research, but impossible to split causes. Reported deaths of<br /> person WITH C-19 flu now are 100% ascribed to C-19 flu currently.
In USA, a truer estimate of the<br /> actual death rate is therefore, at April 5, 0.73%:
Numerator: 8173 deaths x .36 = 2942<br /> deaths FROM C-19 flu – multiply this times 3 for future deaths from this<br /> cohort equals 8826 - divided by - Denominator:<br /> 301147 cases x 4 = 1204558 to include asymptomatic – yep, the current number of<br /> cases is four times the reported numbers. This is very good news, because it<br /> reduces the mortality rate.
Twelve months from now, we can look<br /> at the total deaths in the USA, and compare that with the 2.8 million deaths<br /> for 2018. 2.6 million of 2018 deaths were from about seven serious<br /> underlying diseases, many people having three or more suds.
Equals 0.73% truer death rate...more<br /> than 0.12% from ordinary flu, but well below 1.29%
The C-19 flu is just a flu. <br /> The C-19 flu is just a flu <br /> The C-19 flu is just a flu
Pete A
On 2020-04-07 14:19:14, user Sudin Bhattacharya wrote:
When will the data be available in GEO?
On 2020-04-08 13:20:06, user Devi Dayal wrote:
Through this publication, we just added some more data to the recently published articles on a protective role of BCG vaccination against COVID-19, reassuring for countries with limited resources to fight the pandemic on their own.
On 2020-04-09 03:16:28, user Knut M. Wittkowski wrote:
You state that "the central government of the People's Republic of China imposed a lockdown and social distancing measures in this city and surrounding areas starting on January 23 2020", without reference. On that date, travel restrictions were imposed, preventing citizens of Wuhan to leave by train (starting in the morning) or car (starting in the afternoon). Do you have primary references indicating when which social distancing measures were imposed?
On 2020-04-09 10:11:57, user Andrea Zille wrote:
Thank you for your excellent work. I have a suggestion to improve the protocol. In my opinion the 4 day "rest" of the PPE especially the masks should be implemented after the disinfection step. Leave used mask for 4 days could improve the proliferation of bacteria. Especially for the low temperature (80ºC) treatment, this could lead to a substancial bacterial load that a this temperature could improve the selection of more resistant and nasty bacteria. Fort this, I will also suggest to not use low temperature alone but eventually as a further step after UV treatment that affecting directly the DNA/RNA is much more effective in degrading virus and bacteria.
Andrea Zille, PhD<br /> 2C2T - Centre for Textile Science and Technology, University of Minho<br /> Campus de Azurém<br /> 4800-058 Guimarães, Portugal<br /> Tel: +351-253510285 <br /> Fax: +351-253510293<br /> e-mail: azille@2c2t.uminho.pt
On 2020-04-14 01:27:03, user Sinai Immunol Review Project wrote:
Title: Association of BCG vaccination policy with prevalence and mortality of<br /> COVID-19
Immunology Keywords<br /> Bacillus Calmette–Guérin (BCG) Immunization, COVID-19 prevalence, COVID-19 deaths
Main findings<br /> Previously reported immunization programs using BCG vaccines have demonstrated heterologous protection against other unrelated pathogens that associated with lower mortality and morbidity risks [1]. Therefore this study investigated the possible correlation between COVID-19 death cases or prevalence with BCG vaccination. The authors used publicly available COVID-19 data from 136 countries as well as vaccination demographics from the BCG World Atlas to perform a linear regression modeling.
After correcting for life expectancy and the onset of the spread of the virus (n=40), the analyses revealed a positive effect of current BCG vaccination programs and controlling the number of COVID-19 cases and deaths.
The amount of variance explained by BCG vaccination was 20% for number of cases and significant for both groups of countries, the ones that used to have a BCG immunization program in the past (b = 0.6122, p = .0024) and the ones that never have it (b = 0.6511, p = .0326).
Only the group of countries that never vaccinated against BCG showed significance in deaths/cases ratio but explains only 3.39% of the observed variance.
The authors concluded that BCG immunization may provide protection against COVID-19 probably due to the infection spread reduction. BCG immunization doesn’t have a significant impact in the mortality induced by COVID-19.
Limitations:<br /> As acknowledged by the authors of this study, there are large number of unexplained potential confounding variables such as BCG immunization coverage, and onset of virus spread in different countries. <br /> The authors cite that BCG immunization coverage could be variable among countries, but they didn’t explore it. Further, vaccination coverage changes at different rates over time across countries for different reasons [2]. Additionally, the authors did not consider the variable immunization coverage within countries, where unequal access to healthcare is frequently observed [3, 4]. <br /> The authors do not adequately control for time of spread in infection for each country [5].
The authors discuss the importance of validating experimentally the results observed and claim that BCG vaccination could provide non-specific protection against COVID-19. A stronger discussion of the use of BCG vaccine would have included known considerations on efficacy considering route of administration (intravenous, intradermal), vaccine strains which are known to differ in the number of viable bacteria and duration of protection.
Relevance: <br /> This study presented preliminary data on possible non-specific protection by BCG immunization on COVID-19 infection.
References
Review by Alessandra Soares Schanoski as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-03-15 09:12:21, user fuyutao wrote:
Wow, this paper may be a historical one when the findings are verified. I would encourage the authors to refine grammar and stick with accepted virology terms. For example "<br /> HKU-1 and OC43 (the source of FCS sequence-PRRA) caused influenza" is an easy target. But, the content of the paper does fill in several important pieces of the SARS-CoV-2 puzzle. It took so long for this boot to drop, I am surprised social media hasn't jumped on this yet :)
On 2020-03-18 08:25:22, user Alberto wrote:
Althought It could survive for some period of time, its title (concentration) maybe is constantaly descending as a negative exponential function. That means that in a shorter period of time the efective probability of transmisión is lower. I have studied bacteriophages, but I suppose that dynamics of inhabilitation shows the same dinamics.
On 2020-03-21 19:57:31, user KnowItAll wrote:
I am struggling to understand the labeling of the individual sequences in the tree. For France there are sequences such as hCoV-19/France/IDF0372/2020 and hCoV-19/France/IDF0372-isl/2020. IDF refers to Isle De France and I assume 0372 refers to a patient or sample number, so what does the -isl refer to, are these two sequences from the same sample? Same with hCoV-19/France/IDF0386-islP1/2020 and hCoV-19/France/IDF0386-islP3/2020
On 2020-03-22 16:23:48, user Sinai Immunol Review Project wrote:
Main findings: Colonic enterocytes primarily express ACE2. Cellular pathways associated with ACE2 expression include innate immune signaling, HLA up regulation, energy metabolism and apoptotic signaling.
Analysis: This is a study of colonic biopsies taken from 17 children with and without IBD and analyzed using scRNAseq to look at ACE2 expression and identify gene families correlated with ACE2 expression. The authors find ACE2 expression to be primarily in colonocytes. It is not clear why both healthy and IBD patients were combined for the analysis. Biopsies were all of children so extrapolation to adults is limited. The majority of genes found to be negatively correlated with ACE2 expression include immunoglobulin genes (IGs). IG expression will almost certainly be low in colonocytes irrespective of ACE2 expression.
Importance: This study performs a retrospective analysis of ACE2 expression using an RNAseq dataset from intestinal biopsies of children with and without IBD. The implications for the CoV-19 epidemic are modest, but do provide support that ACE2 expression is specific to colonocytes in the intestines. The ontological pathway analysis provides some limited insights into gene expression associated with ACE2.
On 2020-03-25 18:42:15, user Sinai Immunol Review Project wrote:
This study describes the occurrence of a cytokine release syndrome-like (CRSL) toxicity in ICU patients with COVID-19 pneumonia. The median time from first symptom to acute respiratory distress syndrome (ARDS) was 10 days. All patients had decreased CD3, CD4 and CD8 cells, and a significant increase of serum IL-6. Furthermore, 91% had decreased NK cells. The changes in IL-6 levels preceded those in CD4 and CD8 cell counts. All of these parameters correlated with the area of pulmonary inflammation in CT scan images. Mechanical ventilation increased the numbers of CD4 and CD8 cells, while decreasing the levels of IL-6, and improving the immunological parameters.
The number of patients included in this retrospective single center study is small (n=11), and the follow-up period very short (25 days). Eight of the eleven patients were described as having CRSL, and were treated by intubation (7) or ECMO (2). Nine patients were still in the intensive care unit at the time of publication of this article, so their disease outcome is unknown.
The authors define a cytokine release syndrome-like toxicity in patients with COVID-19 with clinical radiological and immunological criteria: 1) decrease of circulating CD4, CD8 and NK cells; 2) substantial increase of IL-6 in peripheral blood; 3) continuous fever; 4) organ and tissue damage. This event seems to occur very often in critically ill patients with COVID-19 pneumonia. Interestingly, the increase of IL-6 in the peripheral blood preceded other laboratory alterations, thus, IL-6 might be an early biomarker for the severity of COVID-19 pneumonia. The manuscript will require considerable editing for organization and clarity.
This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2020-03-25 20:57:32, user Sinai Immunol Review Project wrote:
Summary of Findings: <br /> - Study used online datasets (scRNAseq GSE131685, scRNAseq GSE107585, Human Protein Atlas, GTEx portal, CCLE) to analyze ACE2 expression in different human organs. <br /> - Study re-analyzed three clinical datasets (n=6, n=99, and n=41) to show 3~10% of 2019-nCoV patients present with abnormal renal function. <br /> - Results indicate ACE2 highly expressed in renal tubular cells, Leydig cells and seminiferous ductal cells of testis.
Limitations: <br /> - Very preliminary transcript/protein dataset analysis in healthy cohorts; does not necessarily translate to actual viral tropism and permissiveness. <br /> - Clinically, would be important to determine with larger longitudinal dataset if SARS-CoV-2 infection changes sperm quality or testicular inflammation. <br /> - Similarly, would be important to determine if simultaneous HBV or syphilis infection and orchitis impacts SARS-CoV-2 severity. <br /> - Examination and follow-up of renal function and viral orchitis/sperm quality of CoVID-19 patients not done in this preliminary study.
Importance/Relevance: <br /> - Kidney ACE2 result supports other concurrent sequencing studies (https://doi.org/10.1101/202... ) and clinical reports of abnormal renal function or even kidney damage in patients infected with 2019-nCoV (https://doi.org/10.1101/202... ). <br /> - High ACE2 expression in testis suggests potential tropism of the virus to testicular tissues and indicates potential risks for male fertility. Viral orchitis reported for SARS-CoV previously [1], but no clear evidence so far of infertility in SARS, MERS or CoVID-19 patients.
References:
Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-03-26 19:48:29, user Dan Grey wrote:
There were references for the choices of values for "proportion of population at risk of severe disease".
On 2020-03-29 19:06:26, user MingXia Gao wrote:
Fever also can cause damage to the sperm, leading to a high LH. Most of your objects had a fever, so I don't think the increase of LH is because of COV-19. You should test the tissue or seminal fluid to make sure whether there are COV-19 exist in male reproductive organs.
On 2020-03-30 17:42:47, user Preci Genome wrote:
Digital PCR appears to be a very powerful tool to diagnose the infection of COVID-19.
On 2020-03-31 18:01:15, user bixiou wrote:
There is a syntax mistake in the abstract I guess: "Notably, all 4 patients progressed to severe illness that occurred in the control group." should be "Notably, all 4 patients who progressed to severe illness ~~that~~ ~~occurred~~were in the control group."
On 2020-04-02 00:26:45, user Rick wrote:
This must have been translated from Chinese, because some sentences make no sense, and probably have placed the wrong words in places of importance, ie. Besides, a larger proportion of patients with improved pneumonia in the <br /> HCQ treatment group (80.6%, 25 of 32) compared with the control group <br /> (54.8%, 17 of 32). Notably, all 4 patients progressed to severe illness <br /> that occurred in the control group." Flip the words, improved and pneumonia, and the whole meaning changes. Did patients "improved with pneumonia", or what?
Also, what the hell does absorption of pneumonia mean? Did they get better or worse? It's very hard to tell from this translation.
On 2020-04-01 13:02:24, user Carlos Affonso wrote:
Congratulation for the amazing article ! <br /> Are the data image available ?
On 2020-04-01 17:13:32, user Hikmat Ghosson wrote:
I do not understand why Lebanon is considered as a country with high rate of COVID19-related deaths. Actual data (01/04/2020) do not demonstrate this assumption:
14 deaths (2 deaths in 1 M of population), vs. 43 recoveries (0.33 of deaths-to-recoveries ratio).
Meanwhile for Italy:<br /> 13155 deaths (218 deaths in 1 M of population), vs. 16847 recoveries (0.78 of deaths-to-recoveries ratio).
For The Netherlands:<br /> 1173 deaths (68 deaths in 1 M of population), vs. 250 recoveries (4.69 of deaths-to-recoveries ratio).
For Belgium:<br /> 828 deaths (71 deaths in 1 M of population), vs. 2132 recoveries (0.39 of deaths-to-recoveries ratio).
For The U.S.:<br /> 4394 deaths (13 deaths in 1 M of population), vs. 8698 recoveries (0.50 of deaths-to-recoveries ratio).
Otherwise, how other determinant factors potentially influencing infection and death rates (e.g. age medians, healthcare systems, population concentrations, social traditions, screening test numbers, crisis management policies) can be assessed and then excluded from the correlation models?
Thanks in advance.
(data source: https://www.worldometers.in..., 01/04/2020 - 4:45 PM GMT update)
On 2020-04-03 00:55:22, user ???? wrote:
What I felt strange was, in Japan, though the number of the infected persons have been increasing, the fatality rate is apparently low in comparison with the corresponding numbers in the U.S. and in the Europe (except Portugal, in which the BCG vaccination is mandatory, while the fatality rate in Spain, where the vaccination is NOT mandatory, has become around 60 times more than in Portugal).
I think the number of the infected persons in Japan must be much higher than the one reported so far (i.e., there must be a lot of actually infected people not diagnosed with the new coronavirus); however, it cannot explain the low fatality rate in Japan.
In addition, it's notable that those who passed away due to the virus in Japan (except the foreigners, who account for as much as around 30% of the infected persons in Japan) are almost limited to elderly persons, while the BCG vaccination became mandatory in 1940s and 70-year-old or older Japanese tend not to have taken the vaccination.
On 2020-04-02 10:15:46, user Francois Alexandre wrote:
This study is interesting, but the reasoning is incomplete. Indeed, it takes about 1 month to die from the time when people get infect (about 10 days of incubation + 20 days between symptoms onset and decease). Therefore, the real number of patients infected is between 670 000 and 3.3 millions 1 month before the time where the decease number was collected, i.e. near the end of February. For an estimation of the number of cases at the end of March, we should wait for the number of deceased patients at the end of April.
On 2020-04-03 02:08:32, user Shawn wrote:
There seems to be no discussion in this paper of the fact that the exponential spread could be accounted for by close in-person contact. One could reason that a virus can spread quickly in a susceptible population regardless of weather if there is a short distance between an infected and susceptible individual. A viral particle won't need to spend much time in the environment in this particular scenario and likely can avoid any negative impacts due to ambient temperature/humidity.
The authors should have refrained from making such a definitive conclusion about SARS-CoV-2 in any respect.
On 2020-12-05 20:10:39, user Ian Tomm wrote:
Thank you for this important work to help rationalize indoor exposure. I have used your model for a number of small, confined spaces (cabin of an A-Star B2 helicopter, etc) and its been very useful. There is a bug on the online calculator that crashes the site repeatedly, happy to provide further info if interested, please DM me.
On 2020-12-06 16:52:10, user Jammi N Rao wrote:
This paper has two major flaws: <br /> 1. is the sample size. There is no mention of whether there was any prior sample size estimation and if there was, which of the 6 primary outcome measures determined it. If mortality at 30 days was the main outcome measure then there is no statement as to the minimum reduction that was considered clinically relevant and was therefore the delta that informed the sample size calculatiom for adequate power.<br /> 2. Perhaps the bigger problem is that it was NOT an intention to treat analysis. Two patients were randomised to the Itolizumab arm but withdrawn due to a reaction. One of these patients sadly died at 9 days. If these 2 patients are added into the analysis then the result of the mortality outcome becomes statistically non-significant.
I wrote this up in an op-ed piece here:
On 2020-08-04 19:58:40, user Marm Kilpatrick wrote:
Thank you for this study. Could you please report your raw results by age categories? Specifically, please indicate the sample size for each age range and the number that were seropositive. <br /> Could you also post the deaths by age of people in the study area?<br /> Thank you!<br /> marm
On 2020-08-08 18:25:58, user DFreddy wrote:
Overall, valuable study that gains some more insight into Belgian's seroprevalence. From the charts, I see clear waning of seroprevalence for those 80 and older.
I have problems with one statement in the conclusion: "... The latter (i.e. the response to future waves) is still a challenge as the low reported seroprevalences (2.9-6.9%) are far from required herd immunity levels. "
What are required herd imm. levels to avoid deaths? This is a debatable number, since most covid-19 deaths come from seriously unhealthy people who likely die not so much from covid, but from their frail health and /or old age. As far as I know, dying is a reality of living a life.
On 2021-01-17 10:18:21, user T.R. Raju wrote:
I am unable to figure out the time points- when the study was initiated and when it was closed.
On 2020-08-13 12:21:38, user ArthurVandelay wrote:
This is a fascinating study ! To the authors: any preliminary speculation on the mechanism(s) of why there would be a protective effect of using ARBs ?
On 2020-08-14 09:04:15, user Alexandre Júlio wrote:
The first female medical Doctor from "La Sapienza" lived in Barcelona during the "Spanish Flu". She knew the importance of sunlight and open air to continue the work at "Casei dei Bambini". Working with less than 60 children up to 7 years old. Not thousand(s) of passionate teenagers & young adults of most high-schools & universities.<br /> What will you do in crowded spaces to drink or eat?
On 2020-08-14 23:07:40, user Luis Carlos Gutiérrez-Negrín wrote:
But even taking into account the probable under-count of Covid 19 active cases and deaths in Kenya in April 30-Jun16, the prevalence of the IgG antibodies in 5% of the population (1 in 20) is astonishly high. These results are, however, compatible with the find of IgG antibodies in seric tests made in other country on samples taken months before the first outbreak of the virus in Wuhan. One sounding alternative explanation could be that one or more older corona viruses, sharing almost the same protein forming their respective peaks, have previously infected Kenya (or certain counties like Nairobi, Mombasa and Kisumu), as well as the country where old IgG antibodies were found.
On 2020-08-17 01:51:08, user Jon Twiss wrote:
Herd immunity occurs only when enough of the population acquires immunity to suppress the spread of a virus, yet there is no clear evidence how long immunity for SARS Cov-2 will even last. To suggest Sweden has herd immunity is not at all credible. Estimates for SARS Cov-2 herd immunity range between 60%- 70% if it exists at all, and even with unreported case estimates, Sweden is a long, long way from getting there.
On 2020-08-17 05:22:58, user Liz Halcomb wrote:
Please note that there are three errors in the preprint version of this paper. These are as follows;<br /> 1. Section 3.2.1 - The first three lines should read "Over three quarters of participants (77.2%) identified the need for access to an adequate supply of personal protective equipment to enable the provision of quality routine care during the pandemic. This accounted for some 29.3% of the overall statements."
Section 3.2.2 - The first three lines should read "Just over half of the participants (55.4%) referred to the need for high level communication supports in order to continue to provide quality nursing care."
Table 1 Key category Funding of services should read;<br /> Total 134 (11.0%)<br /> Nurse telehealth item 62<br /> Nurse billing 36<br /> Fund services 18<br /> Job security 15<br /> Nurse practitioner 3
On 2020-08-17 13:17:29, user jrzsy7 wrote:
In this study, we provided the first mapping of immune responses in paired blood and lung samples using the scRNA/scTCR-seq. In severe COVID-19 patients, there are increased functional paralyzed myeloid suppressor cells (MDSCs) in peripheral blood. In contrast, monocyte-macrophages in the lung are producing high levels of cytokines and chemokines, but no IFNs. For the lymphoid compartment, we found depletion of innate T cells and CD8+ T cells. In contrast, CD4+ T cell responses and clonal expansion dominate. Peripheral T cells (most likely non-specific bystander cells) are massively recruited to the lung.
On 2020-08-18 12:07:32, user Hagai Perets wrote:
A possible explanation for these results could be related to the following study:<br /> https://www.preprints.org/m...<br /> discussing possible immunity by prior virus infection.
Note the following quote from the paper:
"It is possible, and even likely that in a fraction of the cases the preceding low-virulence strain (LVS) only provides partial immunity, allowing for the SARS-CoV-2 high virulence strain (HVS) infection, but leading to a less virulent form of COVID-19. In such a case we might expect a higher fraction of newly identified COVID-19 patients to be, on average, more symptomatic during the early phases of the pandemic when it still spreads exponentially, before the LVS achieves her-immunity. At these early times the LVS has not yet infected the majority of the population and newly HVS-infected people are not likely to have been previously infected by the LVS, and be partially immunized. After the LVS infected a large fraction of the population, new HVS-infections are far more likely to be of previously LVS-infected people, who already acquired partial immunity.We might therefore expect a lower fraction of asymptomatic cases, and a higher morbidity rate during the early exponential growth of the HVS, in comparison with later times after sub-exponential-growth and later decay in the number of cases is observed."
On 2020-08-19 17:08:34, user diymasks wrote:
Graph 3.1 shows the impact the change in HSE mask policy had from April22d. Strong data collecting, would like to see more.
On 2020-08-19 19:37:03, user Michelle Furtado wrote:
Im curious, isnt it possible that the three fishermen who had antibodies prior to departure were in fact the ones who spread it to the rest of the crew? They had neutralizing antibodies prior to leaving, which indicates a possible mild infection that in fact made them asymptomatic spreaders and subsequently infected the rest of the crew. No one is mentioning how these people who have antibodies could be spreading the infection. Those three should have never been allowed out of port on that ship if they knew they had been infected.
On 2020-08-24 14:30:47, user Javeria wrote:
This will definitely help everyone out until we find out an ultimate treatment. Brilliant work done by the authors!!
On 2020-08-24 15:23:42, user Ivan Berlin wrote:
Very nice study, maybe the first one that includes a control group.<br /> The authors do not discuss the intriguing discrepancy in the results that is that hospitalization for COVID-19 is less likely among NRT users compared to non-users but mortality is substantially higher. Is it conceivable that NRT users (proxy of smokers) are less likely to be hospitalized than nonsmokers? A selection bias potentially originating from the fact that smokers have more respiratory (and other) symptoms than nonsmokers leading to reduced hospitalization rate of smokers by health care providers. If this is likely, then hospitalization cannot be a proxy of COVID-19 severity.
On 2020-08-27 23:02:34, user drklausner wrote:
This is an important and innovative report demonstrating the value of hospital based surveillance and how that informs our understanding of the Covid-19 epidemic. It demonstrates that there was an early and rapid introduction of cases resulting in hospitalizations in February and March. <br /> The continued monitoring of hospilazation data showed the severity of the "second wave" in the end of June and July was less severe than some thought.<br /> The report also describes the heterogeneity of the epidemic in the United States and both the time-dependent and geographical variation. Understanding that heterogeneity is critical such that the United States as a whole is not considered monolithically or with a one-size-fits-all approach. <br /> In terms of the measure of epidemic growith, the rate of change in incidence over time, that is similar to acceleration or deceleration in velocity and a useful parameter for epidemic monitoring. Epidemics may decelerate prior to declines in incidence.<br /> Congratulations to Dr. Bhatia
On 2020-08-28 09:33:01, user Dr Aniruddha Malpani, MD wrote:
Shouldn't the Conclusion be exactly the opposite ?
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines thelow risk of acquiring COVID-19 from surface and air contamination in managing COVID-19.
Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.
On 2020-08-29 19:38:23, user Davers wrote:
So much missing from this study. Why weren't blood levels of en vivo vitamin D, magnesium, etc taken and reported per patient before, during, and after DMB? This is helpful to know when and where this protocol will be most beneficial. Significant shorter hospital stay was identified as an outcome, but no numbers are given, nor was given the criteria by which patients were released. Why does the table appear to be incomplete with regard to those needing ICU care (see LB comment). Studies like this could be transformative but little mistakes like these (even if they are just reporting errors) make them too easily dismissed as being low quality.
On 2020-08-30 04:40:35, user puzzled_one wrote:
Hi Joao - Thank you for the analysis - good effort. Question: your paper states just over 40% of patients over 60 were vaccinated against influenza in the past two years. But other papers state over 70% of Brazilians over 60 are vaccinated. Since less than half your cohort were vaccinated, does this imply vaccination has further protective effects (unhospitalised infections)?
Second question: on page 11, people over 60 show a *positive* association (1.12) between past influenza vaccination and Covid-19 mortality. Is that correct? Does this group mean people vaccinated in the last two years (prior to the current campaign)?
On 2020-09-01 21:00:35, user Brian Gardner wrote:
What is catching my eye here is the second morphological alteration: spherocytes.
Assuming that controls were in place for spherocytosis, this seems to indicate (along with other findings showing decreased hemoglobin), that there may be an elevated risk for severity for those with spherocytosis.
On 2020-09-01 22:49:40, user AlvaroFdez wrote:
I think this is a very in-depth, useful paper, and tool. Among other things, it proves the main shortcoming of the 6-foot social distancing rule regarding indoors given the chance of rapidly accumulating infectous quanta over time in this kind of environment.
It would very interesting to expand on the model described (well-mixed room ventilation, "ventilation outflow rate (Q), as distinct from air recirculation rate," as cited by the paper, as in many buildings there is (and will continue to be) a very strong influence of recirculation air vs outdoor air intake, and commonly used MERV or F filters in HVAC would not be enough to properly filter micrometer or less-sized aerosols, specially when generated continously). For many buildings, seems common a 80 (recirculated air)/20 (outdoor fresh air intake) ratio. There are some works considering this latter aspect (ie, FATIMA by NIST, https://doi.org/10.6028/NIS..., giving a calculation for recirculation airflow rate) that it would be nice to incorporate in this work.
On 2020-09-06 01:19:56, user Jack Winters wrote:
I've done a lot of modeling of physiological systems (e.g., writing a textbook on physiological modeling and control, Emeritus Professor of Biomedical Engineering), and implementation of the core SEIR model is straightforward (e.g., set up Matlab and JavaScript versions, as classic forward dynamic simulations). I'm aiming to use (and perhaps improve) this model. But I've dug around, and despite manifold data and supplementary info, I cannot find any examples of representative parameters (e.g., sigma, alphas, gamma1&2, and so on - it's not a large list). I know they're fit (e.g., by state), but there should be "typical values" available. Maybe for Oregon (where I live now)? I'm less worried about Beta, essentially the input signal (surprised AI methods (vs stat fitting) aren't being used for it, but that's a separate matter). Did I miss something? Can someone help?
On 2020-09-11 05:09:05, user Ramesh Sharma wrote:
Biomed now uses the Multiplexing QM system instead of QS which is a vast improvement to test quality and testing speed.
On 2020-09-17 07:41:34, user Dhriti Gupta wrote:
What are the 3 phenotypes? The article has not defined the 3 types of clinical phenotypes anywhere.
On 2020-09-17 17:32:47, user kpfleger wrote:
This is a great analysis. It is a shame it hasn't gotten more widespread attention. Causal inference is an important technique that not enough researchers have close familiarity with. The analysis here is not hard to follow, even for non-experts or non-scientists. While there were other important pieces of evidence linking vitamin D causally to health outcomes in other infectious disease and lung injury prior to 2020, this was the first paper to provide solid evidence of a causal relationship between vitamin D and COVID-19 specifically.
On 2020-09-18 14:18:31, user mark.goldberg@mcgill.ca wrote:
Similar to the ecological studies on mortality fro covid and air pollution this study is biased. You may want to read the paper by Paul Villeneuve and myself in Environmental Health Perspectives: "Methodological Considerations for Epidemiological Studies of Air Pollution and the SARS and COVID-19 Coronavirus Outbreaks", https://doi.org/10.1289/EHP...