On 2020-04-19 12:55:20, user C'est la même wrote:
99.5% specificity in the general population is wildly optimistic compared to serology tests developed by other labs. Claims of "asymtomatic" carriers also reflects symptom reporting biases (surveys/questionnaire answers are not symptoms).
I suggest caution trusting serology based studies like this, unless all positive cases are also confirmed using CT or RTPCR testing.
On 2020-04-17 23:52:12, user Daniel H Vlad, PhD wrote:
Dear Author, <br /> I appreciate your efforts to shed light on this very important topic but I believe the article has a major bias, which is indeed mentioned in the article.<br /> "Other biases, such as ... bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain. "
I think this is a very serious bias and let me explain why. It's very likely that your Facebook ads attracted a fair number of participants that were concerned they may have been infected with Covid-19. People who had experienced Covid-like symptoms in the recent past were more likely to pay attention to your Facebook ad and were also more likely to enroll in your study. Therefore your sample is not random.<br /> Let's make some reasonable assumptions. Let's assume that 10% of your sample, or 333 participants had Covid-19 like symptoms in the past and were seeking antibody confirmation. I believe this percentage is very reasonable. <br /> According to California statistics, approximately 25% of people tested for Covid-19 test positive. It could be very reasonable to assume that 15% of these 333 participants seeking antibody confirmation had been indeed infected. So that will equal to 50 positive participants, or the entire number of antibody test positive participants in your sample.<br /> The example above proves that this bias is a valid concern. Your entire list of 50 antibody positive participants could have been participants seeking antibody confirmation.
There are several ways to remove the bias:<br /> a. You mention in the article that you asked survey participants if they had prior clinical symptoms. You should try to exclude all participants that had symptoms (fever, chest pressure) similar to Covid-19 this year. This will indeed exclude all people that were ill, not only those who replied to the add to seek antibody confirmation. However, it will give you insight into the percentage of silent Covid-19 carriers, which is a question as important as the question you are trying to answer, and in a way equivalent. And the results will be unbiased. <br /> b. Attempt to adjust for this bias. Calculate the percentage of participants in your sample who experienced Covid-19 symptoms and compare this with reasonable epidemiological data. Calculate a weight and apply it to your sample in addition to your zip-sex-race weights.
Regards, Daniel H. Vlad, PhD.
On 2020-04-17 21:38:11, user Michael Stein wrote:
There could be a very large upward bias due to the participants in the study being people who responded to the Facebook ad. It stands to reason that people who suspected they might have been exposed to the virus would be more likely to respond to such an ad. The fact that randomization was used to select who got the ads and that corrections for demographics were made does not address this potentially serious source of bias. There is little doubt that many more people have been infected than the official numbers, but I find the factor of 50-85 rather hard to believe in a place like Santa Clara County that has not been overrun by cases.
On 2020-04-24 17:38:59, user COBiker wrote:
I believe that this same team is doing that NYC study.
On 2021-12-25 09:26:15, user ReviewNinja wrote:
Interesting samples.<br /> One important flaw: you cannot compare Ct values from PCRs performed with different laboratory workflows as is the case here. The Abbot RealTime test for example tests 2 targets in the same channel, which might give you an earlier Ct. Also pre-PCR worklfow matters.
On 2021-12-16 16:22:57, user itellu3times wrote:
While I understand the place for these calculations given the social and government actions around the world, even the necessity for someone to do it, and it comes out on the correct side of things, I must point out that it is also socially deranged and mathematically extremely vague. "The unvaccinated" do not even exist as such in US, EU, UK, and other places where the pandemic has run now for two years, almost all will now have contracted and recovered from COVID, have natural immunity, and be no more at risk of getting or giving COVID than any of "the vaccinated". I would like to see studies of just where these "unvaccinated" patients today are even coming from - are they all immunocompromised, or vitamin D compromised, or are the figures cooked by authorities who use biased procedures "for reasons of public health", to bias the results? We know "the vaccinated" transmit the virus too, is that supposed to comprise the "base rate" here? How about reversing this, what if the vaccinated are excluded, what's the NNT then? But, whatever the calculation here, forward or reverse, the odds calculated are for one event, and when there are N such events even small odds are enough to propagate. Now, perhaps this factor is already comprised in this "base rate", I guess it is, but then the real odds for a single event may be much lower. You are then talking about socially exclusionary processes that even the article states are really for reasons of coercion, for event numbers that are truly tiny. Is this rational? Oh, and the SAR numbers must be considered plus or minus 80%, instances of any type event may be vastly better, or worse. Very hard to use such things to base any serious decisions on at all. Though, in science, I guess someone has to give it a look, as seems to have been done reasonably here.
On 2021-12-01 22:20:28, user Kevin J. Black, M.D. wrote:
One more you may want to look at:<br /> Vitale C, Pellecchia MT, Grossi D, et al. Unawareness of dyskinesias in Parkinson's and Huntington's diseases. Neurol Sci. 2001;22(1):105-106.
On 2020-05-23 02:15:34, user Dick Meehan wrote:
Suppose at the end of it all, when apparent excess deaths are seen in some degree to be accelerated deaths of the moribund, not always unwelcome especially from the perspective of the elders, the greater damage is determined to be lasting health effects and shortened life of the younger recovered? Seems to me that death counts even of the best quality may be a poor measure of the disease outcome, just as ventilator availability was once imagined to be the critical factor.
On 2021-07-18 17:48:18, user Olavo Amaral wrote:
In their discussion, the authors attribute the imbalance between study arms within hospital sites in Table S1 to the fact that randomization was not stratified by study site and to batch delivery of drugs to remote sites. However, if the trial was truly randomized as described in the Supplementary Appendix, it is hard to understand how the extreme imbalance in the distribution of arms in each site could have happened by chance alone.<br /> When one considers the distribution of arms in the 3 centers located in Manaus, where patients are described to have been randomized independently, there is a total of 97 patients in the active group vs. 298 in the placebo group. Using the chisq.test function in R, the p value for obtaining this distribution if patients had an equal chance of being randomized to either group is 4.8e-24 (amounting to a chance of roughly 1 in 200 sextillion).<br /> For the remote sites, one cannot estimate this chance exactly, as the authors report that randomization was performed for batches of 5-50 patients. Nevertheless, the distribution is even more unbalanced (220 patients in the active group vs. 30 patients in the placebo group), and also seems unlikely to have occurred by chance (and in the opposite direction as that of Manaus, thus balancing the distribution in the whole trial almost exactly).<br /> Although this imbalance would not explain the difference in the main outcomes between groups by itself, as the stratified analyses in Tables S3 and S4 show effects favoring the active treatment at all sites, it does call into question whether randomization was really carried out as described. Can the authors please clarify on what happened?
On 2020-08-08 00:52:19, user Cyraxote wrote:
The covidestim site shows 12 rows of 4 states. That's 48 states. Maryland is one of the missing ones, but I don't know the other.
On 2024-07-31 12:40:06, user David Curtis wrote:
The paper presents these findings as if they were novel but in fact the main result, an association of ITSN1 ptvs with Parkinson's, was published on the AstraZeneca PheWAS portal years ago: https://azphewas.com/geneView/ba08a93f-501e-44e6-a332-98ce2f852279/ITSN1/glr/binary The current paper does cite the PheWAS publication but without making it clear that the central results have previously been reported. What the current paper seems to do is to confirm the association in a new sample and an animal model but most readers would be unaware that the main evidence for association represents one finding from the previously reported PheWAS. Failing to mention that the results were obtained as part of the PheWAS is misleading because there were over 18,000 phenotypes tested. Without knowing this, the association results appear to be more strongly statistically significant than they actually are. In fact, correcting for the number of phenotypes tested as well as the number of genes and models tested would mean that the primary results at least would not be regarded as statistically significant. All these issues should be properly discussed.
On 2025-08-10 10:46:04, user Abdullah Jinah Ali wrote:
Why was 3.0% NSH and 75% MFC chosen?
On 2020-07-02 20:46:36, user C'est la même wrote:
The claim of 99.3% specificity seems very high compared to other antibody tests when tested with large population samples.
But that aside, some readers seem to be inappropriately concluding that undersampling in specific regions during that period can be generalised to conclude that the true cumulative incidence is ten times the total number of confirmed cases.
This is unfounded for two reasons. The first is that regions with very high case numbers (Such as NYC) were temporarily overwhelmed in terms of testing capacity and correspondingly very high test-positivity rates. However over time, the testing caught up with demand and with expanded testing, the test-positivity rates dropped by the expected order of magnitude and likely "caught up" for at least some of the participants who were missed.<br /> The second reason is the sample in the study is not a true random population based sample, but a convenience sample which is also biased towards higher test-positivity rates.
Thus while I don't disagree with the conclusion of the authors, I urge strong caution among readers who are tempted to conclude that true case numbers are a magnitude of order higher than officially reported.
On 2021-08-06 06:07:38, user Katie wrote:
You cannot extrapolate your “absolute risk to become a case” by this study—your risk of becoming a case is based on the amount of community spread where you live and your level of exposure.
On 2021-08-18 01:17:09, user Ken wrote:
Everything is wrong with your calculations. The correct test is Fishers exact which gives a p-value of 0.375. This is the sum of the probability of the observed or more extreme data under the null. So no difference. The 26 rows are important, as under the null hypothesis the distribution of the p-value is uniform on 0,1. If I randomly take a sample of 26 uniform(0,1) there is a high probability that one of them will be less than 0.05, so we use a correction for multiple comparisons. No need as the p-value is 0.375.
On 2021-09-10 15:59:18, user skeptonomist wrote:
No, it doesn't mean that the vaccine is not effective, it just means that the number of deaths is too small to draw a conclusion. This is expected from overall death/infection ratio and the size of the study. The actual conclusion of the paper is that the vaccine reduces infection, which is absolutely unequivocal.
On 2021-07-31 09:03:45, user Eli Baied wrote:
Per abstract, vaccine efficacy at 6-months 97% for severe disease. It seems logical to me to vaccinate the unvaccinated globally in hope to better control this pandemic.
On 2021-07-29 13:25:58, user DogDays wrote:
Are there any likewise studies in relation to Nocebo and vaccine efficacy?
On 2021-07-30 13:24:50, user ALEX wrote:
No mention to side effects??
On 2020-06-08 20:18:31, user itdoesntaddup wrote:
I did my own empirical research along these lines for local authorities in England, finding a power law relationship between cases reported by Public Health England and population density, summarised in this chart, made before there was a change in the testing regime:
https://datawrapper.dwcdn.n...
I was inspired to put it together through being a long term observer of the output of the Santa Fé Institute (including some of the papers written by Luís Bettencourt under their aegis). I found that Geoffrey West had published a short note there on the same topic a few days later:
On 2025-07-02 03:29:32, user David wrote:
There appears to be a considerably greater gain in both body weight and total body fat from months 2-6 in the feijoa group vs control group.This seems important given the participants are no longer on controlled diets . Is it possible to include p values for these differences to see if they are significant, or bordering on significant . Both parameters are tending in the same direction & given these were the primary outcome , would it be worth including in the discussion possible reasons why these increased compared to the placebo .Perhaps the feijoa contained more calories than the placebo ?
On 2020-08-22 06:12:18, user Manal Said wrote:
It has been published in PLOS ONE:<br /> https://doi.org/10.1371/jou...
On 2021-01-08 21:27:37, user Marek J wrote:
What type of honey was used? Was the cumin milled before use?
On 2020-12-03 18:13:15, user Nicholas Lewis wrote:
In general the reasoning and modelling in the original (July<br /> 29, 2020) paper seemed sound to me., in fact I thought it was an excellent<br /> paper. The revised (October 29, 2020) version of this paper makes the argument about<br /> varying heterogeneity rather more clearly than did the original version,<br /> although I found the explanations rather too sketchy in some places.
However, it appears to me that – if I understand it<br /> correctly – the revised version introduces some unsupported and unreasonable changes<br /> in assumption, which should be reversed.
In particular, the argument that short term overdispersion has<br /> an effect on the overall epidemic dynamics is insufficiently explained and not substantiated.<br /> It is far from obvious why that should be the case, although superspreading<br /> events may affect its very early stages.
Persistent heterogeneity is quantified by reference to the<br /> characteristics of contact networks, which "are remarkably robust"<br /> and set the value of nu at approximately<br /> 1, implying lambda = 3 (page 6 of the October 29 version). It is accordingly illogical to work on the basis that an individual's number of contacts changes significantly over time, which is what your Eq.[20] and related assumptions appear to imply. In the<br /> absence of such changes, the assumed original susceptibility gamma distribution<br /> will remain gamma distributed with unchanged CV (but lower mean) as the<br /> epidemic progresses [Montalban and Gomes arXiv:2008.00098v1]. No evidence is<br /> given that, by the time that there is sufficient data to model the evolution of<br /> the epidemic, any initial heterogeneity overdispersion will affect the inferred<br /> epidemiological parameters.
One way the supposed 'short term overdispersion' effect could<br /> arise is if a person who is highly connected and, as a result, becomes infected<br /> early in the epidemic thereafter tends thereafter to have fewer contacts, so<br /> that his inability (after recovering) to infect others has less effect on<br /> slowing the future epidemic, while other (uninfected) people on average have<br /> more contacts than previously. However, such an assumption would seem<br /> unjustifiable, save perhaps to a small extent, given the robustness of contact<br /> networks.
I accept that such an effect could perhaps arise from (say) week-to-week<br /> fluctuations in the number of contacts someone has, with their being more<br /> likely to be infected during a week with an unusually high number of contacts,<br /> and possibly being more likely to have more contacts in their following<br /> infectious period. I think that may be what the paper is arguing, although if<br /> so it is not very clearly explained. But, if so, surely that would apply<br /> throughout the epidemic wave rather than being time varying? In connection with<br /> this, you state (page 6) that delta-lambda(t') decreases with time, but it is<br /> not clear to me from Eq.[19] why it should do so, given that there is (and<br /> should be) no assumption that the delta-alpha_i values decrease over time.
Further, the change to assuming zero, rather than modest,<br /> biological heterogeneity in susceptibility is unjustifiable and should be<br /> reversed. Given that individuals vary as to their immune system memory and general<br /> effectiveness, due to differences in age, genetic factors, health status and<br /> life history, they are bound to vary in their ability to resist infection by<br /> SARS-CoV-2, as stated in the July 29 version. The assumed level of biological heterogeneity in susceptibility in the July 29 version, of lamba_b = 1.3, was – as stated<br /> there – a conservative level. It should be reverted to.
On 2020-11-12 15:29:00, user Barbora wrote:
Figure 2 and the description on page 7 for teh late phase do not seem to match:
Description:<br /> "The sensitivities of the assays ranged from 40 to 86% for the early phase samples, 67 to 100% for the middle phase samples, and 78 to 89% for the late phase (Figure 2)."
Fig2: late phase goes up to 100%
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 2023-09-11 12:43:46, user youni wrote:
Can chronotype be discerned from activity tracking data or self-report?
Might chronotype account for a portion of variance in the association with mortality?
Would interpretation vary if the sleep-wake schedule (reported as circadian rhythmicity) were dictated by 'lifestyle" choices versus genetic chronotype?
On 2021-07-30 02:26:52, user tobydelamo wrote:
Unfortunately, publishing this is going to increase vaccine hesitancy among those with prior covid illness. Not sure this is something that should have been studied...
On 2020-04-07 23:26:41, user Sam Raredon wrote:
Here is a video briefly showing and describing the technique:
PReVentS Circuit Demonstration - COVID-19 / Yale / Niklason Lab<br /> https://www.youtube.com/wat...
On 2021-01-20 09:37:27, user Jon Lundberg wrote:
The European countries in EU Momo report weekly all-cause mortality. Indeed there is a lag in the system and as you indicate, likely a longer lag during holidays.<br /> Our study includes data up until week 45 in 2020, so we can essentially only discuss the "first wave". How these data will pan out eventually when all this is (hopefully) over, is still to early to tell.<br /> Nevertheless, the fact remains, all-cause mortality during the spring of 2020 could have been largely predicted simply by looking at variations in mortality 2015-19.
On 2021-05-27 00:37:03, user Daniel A wrote:
From your first link (1) - point (4) is misinformation and discredits the entire article and thus the site.
The technical report in point is https://www.ecdc.europa.eu/..., which states:
"The evidence regarding the effectiveness of medical face masks for the prevention of COVID-19 in the community is compatible with a small to moderate protective effect, but there are still significant uncertainties about the size of this effect.", and
"Although the evidence for the use of medical face masks in the community to prevent COVID-19 is limited, face masks should be considered as a non-pharmaceutical intervention in combination with other measures as part of efforts to control the COVID-19 pandemic."
I'm not even going to bother going over the other cherry-picked/misrepresented studies/reports.
On 2021-05-27 02:28:39, user Stel-1776 wrote:
It did not look at the effectiveness of masks, but the effectiveness of mask MANDATES. It should read "Mask MANDATES did not slow the spread". Why? Too many people who think they know better than professionals who dedicate their lives to studying this field. Too many people not wearing them, wearing them incorrectly, wearing the wrong type, not cleaning them, etc.
N95 masks are better, but there is solid evidence that regular surgical masks also reduce chance of spreading in the community.
This is supported by a systematic review (a review and critique of published studies to date) published in one of the most highly respected medical journals in the world.
"The authors identified 172 observational coronavirus studies across 16 countries; 38 of these studies specifically studied face masks and the risk of COVID-19 illness. The authors found that the use of either an N95 respirator or face mask (e.g., disposable surgical masks or similar reusable 12–16-layer cotton masks) by those exposed to infected individuals was associated with a large reduction in risk of infection (up to an 85% reduced risk). The use of face masks was protective for both health-care workers and people in the community exposed to infection."<br /> [Chu et al. COVID-19 Systematic Urgent Review Group Effort (SURGE) study authors. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020]
On 2021-05-28 07:23:17, user Roger Morrison wrote:
If masks were effective against respiratory illnesses like the flu and Covid-19 the CDC would have recommended it years ago.
From the CDC "surgical masks won't stop the wearer from inhaling small particles which can cause infection. The CDC recommends a surgical mask ONLY for people who already show symptoms of coronavirus and must go outside since wearing a mask can ONLY help prevent spreading the virus by protecting others." [1.]
A study in November 2020 out of Denmark showed that masks do not stop the spread of Covid-19. "The researchers found no statistically significant difference between mask wearers and bare-faced participants." and "The recommendation to wear surgical masks to supplement other public health measures did not reduce the SARS-CoV-2 infection rate among wearers." [2.]
On March 5, 2021 the CDC put out a report which recommends wearing masks, however, the report also says "Daily case and death growth rates before implementation of mask mandates were not statistically different from the reference period." Then why the recommendation? [3.]
Center For Disease Control and Prevention - Time magazine 2020
Annals of Internal Medicine - Effectiveness of Adding a Mask Recommendation to Other Public Health Measures to Prevent SARS-CoV-2 Infection in Danish Mask Wearers<br /> https://www.acpjournals.org...
Center For Disease Control and Prevention - Association of State-Issued Mask Mandates and Allowing On-Premises Restaurant Dining with County-Level COVID-19 Case and Death Growth Rates — United States, March 1–December 31, 2020 | MMWR<br /> https://www.cdc.gov/mmwr/vo...
On 2021-06-15 03:07:06, user Marc Covitz wrote:
None of the doctors you mentioned performed randomized trials. None of the randomized trials of HCQ and AZM showed any benefit.
On 2021-10-21 02:21:14, user JWrenn wrote:
A few odd things with this study.<br /> 1. why wasn't a control group of infection rates /hospitalizations of unvaccinated and never had covid included? <br /> 2. why wasn't a control made for behavior difference?
Seems like the numbers rates they put forward are not a great number to base this all on. Instead we should be looking at difference between totally unimmunized and immunized via vaccination and unimmunized and immunized via infection. Otherwise the numbers come out so tiny that it gets very weird...ie 1 vs 8 out 32k really is almost so small that it becomes random.
However if say 1000 would have been sick with no intervention then you get better numbers. like 1000-1=999 less vs 1000-8=992 less and you can see that both are very effective, but one is more so.
Also, the 2nd point really kind of breaks the whole thing. In my experience people who had Covid (and were not asymptomatic) are far more careful than people who have not gone through that hell.
The info is good just seems incomplete, and that behavior aspect I think is fare more important that we are wanting to admit as well as hard to account for in database studies.
On 2021-08-31 03:10:10, user Victor Lin wrote:
This study does not factor in data for the severity and symptoms of disease in the cohort that was naturally infected. Surely the varying severities of disease and symptoms would affect the level of natural immunity confered on these people.
Vaccination is a uniform dose. Natural infection is not.
On 2021-08-30 07:37:30, user 4qmmt wrote:
This paper has one major flaw that is not discussed: We know for certain who was vaccinated and of those, 199 had symptoms. Though 8 of the matched recovered had symptoms, they were assumed to have been infected because of a + PCR test in the past, which as we all know produces tons of false positives (Israel's Ct is 35-40). In fact, the paper's own data shows that of the 238 PCR+ in the vaccinated cohort, only199 were actually positive, i.e., symptomatic = ill, and in the recovered cohort, of 19 PCR+, only 8 were symptomatic, suggesting at least 60% false positives. Thus, while the number of vaccinated is certain, the true number of previously infected is at most 8, but could in fact be 0, as the Cleveland Clinic study found.
In fact, last weeks' MoH data in Israel shows that 73% of PCR + tests are on people with no symptoms, i.e., not infected. So this paper is actually saying that the risk of infection for vaccinated vs. recovered is between 27X to Infinite X.
On 2022-01-05 08:51:23, user One bird one cup wrote:
I really appreciate the discussion, but I'm a normal non-doctor/academic/researcher. And I just spent 15 minutes trying to find out what OR means.. can you help? Not trying to be snarky... I look up acronyms probably about ten times a day on any and all topics. Sometimes can't find them.. I get a little cranky
On 2021-08-02 12:01:22, user ingokeck wrote:
Dear authors, thanks for putting this interesting data up for discussion. May I propose to change the analysis from Ct values and give median tissue culture infectious dose (TCID50)/mL instead? This would be much more helpful to interpret the data, as it is obvious that for Ct values higher than 25-28 one would need an unlikely big amount of the sample fluid to infect another person. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454307/ gives the example of Ct 33 corresponding to 0.007 (TCID50)/mL, i.e. 142 ml of the patient sample would be needed to infect 50% of cell culture samples.
It should also be noted that from cell culture experiments it is known that high RNA counts after a few days in unvaccinated patients do not correlate with infectious virus any more and thus cannot be reliably been used to assess the infectiousness. See https://www.nature.com/articles/s41467-020-20568-4
On 2021-02-20 03:35:46, user kdrl nakle wrote:
Community participants? 1540 volunteers and then only 340 completing your study? That sounds to me like a failure of your survey. It is not randomized to start with since you only have volunteers and then most of them did not come back for follow-up so I would assume those that did would be in good health and willing. I think your study is just not anything worth relying on.
On 2020-07-02 13:57:50, user Dr. Amy wrote:
It would be useful to see how obesity and A+ blood type change the HLH genetic expression. This paper has extremely useful clues toward targets to reduce severity.
On 2025-02-26 18:14:55, user Benjamin Isaac wrote:
Reference 12, referring to the article here https://pmc.ncbi.nlm.nih.gov/articles/PMC8784688/ doesn't list the journal or date. The APA citation would be: Patterson, B. K., Francisco, E. B., Yogendra, R., Long, E., Pise, A., Rodrigues, H., Hall, E., Herrera, M., Parikh, P., Guevara-Coto, J., Triche, T. J., Scott, P., Hekmati, S., Maglinte, D., Chang, X., Mora-Rodríguez, R. A., & Mora, J. (2022). Persistence of SARS CoV-2 S1 Protein in CD16+ Monocytes in Post-Acute Sequelae of COVID-19 (PASC) up to 15 Months Post-Infection. Frontiers in immunology, 12, 746021. https://doi.org/10.3389/fimmu.2021.746021
On 2020-05-25 18:52:12, user Bruise Wonheal wrote:
Dear Wittkowski, I'm from Brazil and found your videos very comforting in the middle of all this hysteria. Have you looked into the data of how the disease is doing here in Brazil?
On 2020-04-02 12:47:23, user Anders Milton wrote:
The 70-plus Italians would have had BCG vaccinations when they were young, I believe. Still they die due to the covid-19 infection. How to explain that?
On 2020-04-25 05:20:18, user Alma Lopez wrote:
Mexico also gives BCG vaccine and there is no evidence it helps. Early treatment with HCQ would help but they won't give it to you unless you are very ill. Second phase of disease anticoagulants and cortisone and antibiotics are working . But only some hospitals are doing it like INER
On 2020-04-17 16:56:25, user Francisco Ojeda wrote:
Hi, A good case is Chile to compare with USA, Today 04/17/2020, they have 5,434 PCR tests per million, 9,252 detected cases, and 116 total deaths, including post-mortem PCR covid19 tests for every death, they have just a 1.2% deaths over detected cases, and no total lockdown. Almost 100% of Chileans have the BCG vaccine. I think that the BCG vaccine protection is a good hypothesis
On 2020-04-03 00:18:23, user Alessandro Crimi wrote:
Interesting, but you should also correlate with geographic accessibility to airports connecting main epidemic epicenters. I suspect that that's the main factor, and the BCG policies are confounding factors. Also you should discuss in the limitations about the fact that elderly in Italy and Spain have the BCG vaccination
On 2020-04-16 07:46:42, user Hamdi Torun wrote:
https://uploads.disquscdn.c... The dataset in the papers is now outdated. If the authors use the new dataset in terms of the metrics they chose to use, the correlations will be poor. This graph shows an updated version of their Fig. 1 as of April 16th. More importantly, the reliability of the released data by the governments should be questioned. Even if the datasets are reliable, correlation does not imply causality.
On 2020-10-05 16:41:55, user Michael Sibelius wrote:
Nice work! Now that epidemics have progressed quite a bit further in many of the countries mentioned in the paper, is there some evidence that this heard immunity due to variation in susceptibility, as discussed in this paper? I think I have seen it mentioned with regards to some individual geographic pockets in Italy and the US, but would be great to hear from the authors of this work.
On 2021-02-03 00:57:08, user kdrl nakle wrote:
Internationa Univerlsity?<br /> C'mon, nobody proofreading the paper?
On 2024-04-27 19:50:44, user Eve Benson wrote:
I am starting my 8th year of TSW. The first five years were torture, the last few have been manageable. I can identify with every symptom listed in this study. I can also identify with the stress of seeing several "well-meaning" doctors whose only choice of treatment was putting me back on steroids, which I refused thanks to the education I received from online communities of thousands of people who were suffering like myself. More studies like this one are needed. TSW is real. Sufferers of TSW deserve appropriate medical care and care from practitioners who understand the disease and thus provide appropriate treatment. It is time for medical institutions to step up and address TSW.
On 2020-05-12 16:06:57, user Boskley wrote:
First, I'd have to change the question a bit since this study is looking at very late stage treatment. Nobody that I'm aware of is claiming that HC has a dramatic effect for late stage treatment so the VA study is a non-starter along with much of the HC research. The hypothesis is that HC+AZ+Zinc (HCZZ) given as early as possible reduces death dramatically . . . at least by a factor of 10.
If we look at early stage treatment (pre-hospital or possibly upon admission) then we have about 5335 patients treated across 24 sources. The case fatality rate for all of these cases is 0.5% vs 7.1% worldwide case fatality.
Link to sources is here https://docs.google.com/spr...
If we include whole countries and states, it gets a bit messy because many countries started with a different protocol and switched to HC. Also, in the HC friendly countries the protocols are often different. Nonetheless, it's pretty easy to see the pattern for HC friendly locations with a 2.93% overall case fatality rate vs. 7.1% worldwide (higher for countries that don't allow HC treatment).
Next, if you look at locations that have switched from SOC treatment to HC early treatment (ideally HCZZ) you see dramatic drops in fatality. Evidence from Brazil hospitals switching to the Zelenko protocol (HCZZ early), suggest a 95% drop in mortality.
I won't get into the logical evidence for why it should work (e.g., in-vitro studies and causal mechanisms) or the prophylactic evidence shown indirectly through malaria countries not being hit by COVID 19 nearly as hard or Lupus patients not getting it at equal rates (non-lupus patient is about 50x more likely to get COVID 19 but we don't know how much of that is due to HC vs lifestyle choices).
As the mantra goes, we need more research. Nonetheless, anybody who says "no evidence" or "anecdotal" regarding HC treatment is either uninformed or agenda driven. Hope that helps.
On 2021-05-10 06:59:28, user Maria Ban wrote:
The 42% protection after first dose is not a very good protection. How about protection from severe Covid after first dose, is it higher? The reason for my question is that I am not allowed to have a second dose (due to severe allergic reaction) and I fear that I will have to avoid other people for ever.
On 2020-05-30 07:55:21, user Irene Petersen wrote:
You seem to conflate the risk of getting exposed (and thereby infected) and the risk of dying with covid19. However, these risks may vary substantially and therefore we would need a two-step approach to obtain meaningful predictions. For example, age and ethnicity are strong predictors for exposure while diabetes and obesity are strong predictors of mortality once you are infected.
On 2021-07-16 09:27:55, user ??? wrote:
There's recently published article presenting the application of brain temperature rhythm in outcome prediction after traumatic brain injury. This paper should be cited.<br /> "Kuo, L.-T.; Lu, H.-Y.; Huang, A.P.-H. Prognostic Value of Circadian Rhythm of Brain Temperature in Traumatic Brain Injury. J. Pers. Med. 2021, 11, 620. https://doi.org/10.3390/jpm..."
On 2020-05-07 19:08:36, user Cranmount wrote:
The author correctly notes that his model gives "surprising" conclusions, and it is not difficult to conjecture where the model goes wrong, with the result that it makes an incorrect extrapolation to future mortality (including the unwarranted prediction that imposing social distancing (after more than a minimal delay) increases cumulative mortality). Put crudely, the model incorrectly fits the logistic curve by conflating the effects of all control steps into herd immunity, giving the illusion that the epidemic is much farther along than it really is, and thus giving wildly inaccurate estimates of logistic parameters even though a close curve fitting. Unfortunately, the data that would be necessary to build a reasonable (though much more complicated) model is not available.
On 2020-07-21 15:39:16, user OxImmuno Literature Initiative wrote:
On 2020-08-25 19:43:48, user Allan H. SMITH wrote:
Some readers may be misled by the title and abstract of this paper into thinking the Covid-19 epidemic is under control.
Dr. Bhatia acknowledged that I was a reviewer, and I have been reviewing drafts of the paper and providing comments along the way. It is good to see work on epidemiological data concerning Covid-19. More is needed if we are to learn from the tragic mistakes which have been made in responding to the outbreak.
My reason for writing this comment is that I fear it may appear to some that this paper indicates that the epidemic has been under control since early March. The confusion may arise from misunderstanding the term “epidemic growth rate”. If there were a sequence of 1,2,4,8,16,27 one could state that the epidemic growth rates has declined in the last period because the last number is 27, and not 32 which would be expected with a continuing doubling of rates. In fact, the greatest increase in this sequence is in last period, going from 16 to 27, an increase of 11 compared to the previous largest increase of 8. So, stating that the epidemic growth rate is declining does not mean the epidemic is under control.
In fact, the epidemic in the United States charges on and is out of control. You can see evidence for this in Figure 1a in Dr. Bhatia’s paper. Hospital admission rates are mostly much higher than in early March, and many places have evidence of a resurgence in July.<br /> Mortality data give clear evidence of the catastrophe being experienced in some countries, especially the United States, Brazil, Peru, and India. The Johns Hopkins Coronavirus website gives excellent tracking graphs. https://coronavirus.jhu.edu/data/cumulative-cases You can get the death numbers by clicking in the dropdown box on the left. The top line in the graph is for the United Sates. You can see the curve seemed to start to flatten, but it has taken off again. I think this can reasonably be termed a public health catastrophe. Mortality rates like these have not occurred in any other developed nation, and they could have largely been avoided.
To conclude, I thank Dr. Bhatia for his paper and extensive work analyzing the data, and for sharing it with me.
On 2020-04-22 21:16:23, user cinnamon50 wrote:
wow, so imp to reduce bottlenecks at collection and improve safety of people collecting
can we get a transport medium like SDS that inactivztes virus, so it is BL1 sample?<br /> think that would be huge considering total effort to get reportable result
On 2020-06-15 02:32:20, user Sinai Immunol Review Project wrote:
Main findings<br /> Sex-based differences in the immune response have been reported for various types of infections. There is a growing body of epidemiological evidence that supports the finding that men experience more severe COVID-19 disease than women do, but the immune mechanisms underscoring such a difference remain unknown.
Here, Takahashi et al. analyze PBMCs, plasma, and nasopharyngeal swabs or saliva from 93 mild-to-moderate COVID-19 patients (n=93), comprised of 48 women (n=48) and 45 men (n=45), to characterize potential sex-based differences in the immune response to SARS-CoV-2 infection. It is important to note that patients on hydroxychloroquine and Remdesivir were not excluded from a sub-cohort of patients (n=39) evaluated as baseline measures for untampered immune responses to SARS-CoV-2 (these patients were not treated prior to first sample collection). In a second sub-cohort, 54 patients were assessed longitudinally for an undisclosed amount of time. Samples from uninfected healthcare workers were used as controls.
Viral Load (nasopharyngeal or saliva samples)<br /> No significant differences were identified between male and female patients. Still, median viral RNA was higher in male patients at first sample collection and generally throughout disease course.
Antibody production (plasma samples)<br /> Anti-SARS-CoV-2 S1 protein-specific IgG and IgM antibodies were measured in the plasma of male and female patients. Though anti-S1-IgG antibodies were higher in female patients, compared to male patients, no significant differences could be identified either in the baseline cohort or in longitudinal patients.
Cytokine analysis (plasma samples)<br /> Among baseline patients, who had not received immunomodulatory therapy prior to sample collection (except hydroxychloroquine), type I/II/III IFN levels were not significantly different between male and female patients. However, IL-8 was significantly higher in male than in female patients. Of note, among longitudinally evaluated patients, CCL5 levels were significantly higher in male than in female patients. CXCL10 levels show a similar trend, though this was not significant.
Immune cell landscape (PBMCs)<br /> Both male and female patients exhibited a reduction among T cells and an increase in B cells. No significant differences in T cell subtypes (naïve, central/effector memory, follicular, regulatory) were observed between male and female patients. Of note, however, female patients showed (1) a significantly greater proportion of CD38+HLA-DR+ activated CD8+ T cells and (2) a concomitant enrichment of PD-1+TIM-3+ terminally differentiated T cells, compared to male patients. Otherwise, no other significant differences were identified between male and female patients.
The authors subsequently interrogated the peripheral myeloid compartment. Female patients showed a greater increase in CD14+CD16+ intermediate monocytes than male patients, while both patients exhibited a marked increase in total monocytes, compared to the controls. However, male patients showed higher levels of CD14loCD16+ non-classical monocytes than female patients and their uninfected, healthy counterparts. The authors noted that this enrichment of non-classical monocytes was correlated with CCL5 levels only in male patients.
Clinical comparison<br /> Clinical outcomes were tracked for both male and female patients. Clinical scoring was used to separate each group into two sub-groups: patients that had remained stable throughout hospital stay (stabilized) and patients that had worsened since the first sample collection (deteriorated). Deteriorated male patients were significantly older than stabilized male patients; there was no significant difference in age between stabilized and deteriorated female patients. In terms of BMI, both deteriorated male and female patients tended to be higher in BMI than their respective stabilized counterparts. Interestingly, anti-S1-IgG antibodies were higher in stabilized female patients than their deteriorated counterparts, though this trend was not seen with male patients. Otherwise, no other significant differences in clinical parameters were observed.
Additional comparisons between deteriorated and stabilized patients of each sex revealed that certain innate cytokine mediators (TNFSF10 and IL-15) associated with worse outcome in female patients but not in male patients. In contrast, the proportion of CD38+HLA-DR+ activated CD8+ T cells was significantly reduced in deteriorated male patients compared to their stable counterparts, but this was not true for female patients. Indeed, poor CD8+ T cell activation and IFN? production were both negatively correlated with age in male patients, but not in female patients.
Limitations<br /> • A significant number of patients were diagnosed with underlying chronic conditions that have been previously described to associate with poorer COVID-19 outcomes or with a compromised immune system. <br /> • Approximately two-thirds of each group (men and women) were treated with tocilizumab, and nearly a sixth of each group were treated with corticosteroids. While these patients were excluded from the baseline cohort, it is unclear whether or not these patients contributed to the second cohort that was longitudinally examined.<br /> • The mean age for patients is notably higher than the mean age for the HCW control group.<br /> • Duration of hospital stay was not considered, so it is unclear how quickly certain subsets of male and female patients deteriorated. This may be a confounding variable, or at the very least, the kinetics of disease course in male and female patients is a parameter that warrants investigation.
Significance<br /> In summary, Takahashi et al. provide the first report-to-date that delineates immunological differences between male and female patients with mild-to-moderate COVID-19 disease during the initial stages of infection. For example, male patients deteriorate due to less robust T cell-mediated antiviral immunity, compared to their female counterparts. Several of the other findings substantiate previous reports, such as those of significant neutrophil chemotaxis in the lung of COVID-19 patients (and its association with poorer prognosis). This study, therefore, provides an important platform for additional inquiries into key signaling pathways and transcriptional programs that are differentially regulated between male and female COVID-19 patients by specific cell types (i.e. intermediate and non-classical monocytes, CD38+HLA-DR+ CD8+ T cells) identified in this report. These studies, alongside others, are warranted to better tailor therapies for male and female COVID-19 patients.
This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-12-09 07:14:02, user JANAKI RAMANATHAN wrote:
this is interesting and is reinforcing the fact that the pandemic lockdown have created spaces for intervention of yoga therapy on digital spaces too. this is inevitable and may still continue even post covid i guess in different forms. pain is a corolllary in different spatial and socio economic and medical contexts.hope something substantial emerges
On 2021-02-13 01:49:20, user Julia Alice wrote:
Now published in Analytical Methods!! <br /> https://pubs.rsc.org/en/con...
On 2024-02-20 02:54:02, user disqus_y4jraNO9xU wrote:
This paper was published here https://www.nature.com/arti...
On 2020-07-18 11:59:14, user Kevin wrote:
This paper appears to be a good quantitative assessment of the relative risk between full flights and flights with empty middle seats (~44% reduction with empty middle seats).
I believe the author’s attempt to quantify the overall probability of contracting COVID while flying to be highly flawed.
First, the Lancet study states its mask values are from (at worst) 12-ply cotton masks, and this paper assumes 100% mask compliance. Twitter is full of photos of non-compliant airline passengers, and with no federal regulations defining and requiring mask compliance, the author’s assumption skews the assessment.
Second, the author states he makes no attempt to account for duration of exposure. I assume the author believes this is acceptable, since as stated in the paper “the air in the aircraft cabin is constantly refreshed, so the cabin does not constitute a closed indoor space.” Unfortunately, FAA Regulation 25.831 (a) specifies a minimum fresh air volume requirement, but allows that to be mixed with filtered recirculated air. So while better than a room with no fresh air being circulated, it is not the same as standing in an open field. Again, not taking exposure time into account likely skews the outcome to make air travel appear safer than it is.
Third, and this may be the most important, the author does nothing to account for arguably the highest risk activities while aboard an aircraft: boarding and de-planing. As soon as the seatbelt light goes off, passengers jump out of their seats and crowd the aisle, huffing and puffing as they pull their suitcases out of the overhead bin, all while pressed up against fellow travelers. Boarding is a similar mess, but can at least be controlled through passenger metering at the gate and entry door. If the paper doesn’t consider the highest risk activities associated with flying, it can’t attempt to assign an overall probability to air travel.
In summary, this is not a comprehensive analysis of the probability of contracting COVID while flying, and the overall probabilities should not be presented and discussed since the underlying assumptions are both incomplete and likely flawed. This paper does appear to do good work comparing the transmission probability due to passenger proximity (QL), and should concentrate the findings in that area.
On 2021-04-24 20:59:22, user Mike wrote:
The study looked only at infected people, but many writing articles about this study fail to emphasise this properly. As a result, this study is misinterpreted by many as saying: "Instead of protecting you, being vaccinated increases the chance of being infected with the South African variant by a factor of eight." I think the study needs to be updated to warn against this possible misinterpretation, which is very common. Even I have fallen for it initially, and only reading the news article twice clarified for me that's not actually the case.
I'm getting tired of correcting people, and explaining that 100% of the people from the study were infected, so automatically if the vaccine protects better against some virus variants, the percentage of other virus variants in the sample will increase.
This quote from one of the authors doesn't help: "We found a disproportionately higher rate of the South African variant among people vaccinated with a second dose, compared to the unvaccinated group,” Tel Aviv University's Adi Stern said. “This means that the South African variant is able, to some extent, to break through the vaccine's protection.”
Even Dr. Fauci agrees with me: "Speaking directly to the Israeli study, Fauci said it was misleading and makes it sound like people who get two doses of the Pfizer vaccine have a higher chance of COVID infection than unvaccinated people."
On 2021-06-08 17:43:19, user Jan Zo wrote:
The article was published recently in Mol Cell Ped https://doi.org/10.1186/s40...
On 2020-04-11 00:34:41, user Tomas Hull wrote:
How do the deaths of almost 80 doctors age 57 to mid 60 in the hotbeds of COVID-19 in Italy fit into the computation with the probabilities of ...the COVID-19 death risk in people <65 years old during the period of fatalities from the epidemic was equivalent to the death risk from driving between 9 miles per day (Germany) and 415 miles per day (New York City)"?<br /> Have 80 physicians in Italy run the red light, statistically speaking?
On 2022-02-17 10:51:48, user Sandeep Sharma wrote:
where do I find the latest on COVAXIN approval in Germany?
On 2020-05-20 02:15:46, user ed whitney wrote:
I am unable to read any of the figures at the end of the pdf. Nothing past page 13 is legible. This is a problem for anyone trying to examine the forest plots. Anyone else having better luck?
On 2020-05-23 22:06:14, user CKComments wrote:
The authors dismiss the finding regarding the improvement in lung health in their summary. It's messed up lungs that kill patients, so it seems worth emphasizing.
On 2020-07-22 09:15:41, user Ben Berman wrote:
Is there an association with BRCA1/2 germline status? About 17% of TCGA cases are BRCA1/2 germline carriers.
On 2025-06-07 15:03:33, user Nicolai Krekiehn wrote:
The git link does not work for me. I would like to test an use your model. Who is the contact?
On 2020-03-21 18:05:24, user Joanna Haas wrote:
1) There were 26 patients treated with hydroxychloroquine. Of these 6 are excluded because they did not continue the full 10 day course of treatment; 3 were transferred to ICU, one died, one discontinued due to adverse reaction (nausea) and one left hospital early.
Thus among the 26 patients started on with hydroxychloroquine, 15% (4/26) went to ICU or died vs. 0/15 of the controls, a difference that may be due to chance. However the baseline characteristics of the 6 patients who deteriorated rapidly are not presented in Supplementary Table 1. The analysis of outcomes of the treated group are based on the remaining 20 treated patients.
Among the 36 patients presented in S Table I the treated group is significantly older and more have lower respiratory tract disease at baseline (30% vs 12.5%).
What is the impact of treatment on clinical course? Among the 20 patients who received hydroxychloroquine, six were given azithromycin "to prevent clinical infection". Since 5 of 6 had negative swabs at day 3, it is possible that clinical and viral status diverge.
While it is to be hoped that hydroxychoroquine with or without azythromycin will be of use in treating patients infected with SARS CoV2, the data presented in this manuscript, which is focused on viral shedding, are limited. Clinical outcome and safety data are needed. An intention to treat analysis needs to be included for the clinical outcomes.
On 2022-05-11 01:52:14, user bioRxiv wrote:
This preprint is participating in the Comment-a-thon pilot initiative by bioRxiv/medRxiv at the Biology of Genomes CSHL meeting. You can enter the competition if you are registered for this conference by signing up using the link provided at the meeting. Remember to add #BoG22 to your comments.
On 2021-06-11 13:41:25, user Christy Blanchford wrote:
We don't develop long lasting immunity to the other 4 common covid viruses so why would we have long term immunity to covid 19? This was only 42 days out, we get reinfected with the covid common cold after 1-2 years. Manus, Brazil showed us that despite 80% covid infection rate that should have conferred herd immunity , 6 months later they were digging mass graves again. This paper is doing a disservice....
On 2021-06-09 17:03:22, user Squid Tractor wrote:
Were all participants exposed to the live virus to ensure infection would have happened?
On 2021-06-18 15:27:12, user Matt Jolley wrote:
The study reports no reinfections. This recent UK PHE data shows 15,983 <br /> "possible reinfections". https://www.gov.uk/governme...
On 2021-09-19 07:32:59, user Nomoglobalization wrote:
Because we’re not going to have long term outcomes at this point.
The mechanisms of the vaccine and natural immune response are identical.
Even if antibodies aren’t circulating 6 months post the T cells will still kick in quickly.
At some point you’ll lose those memory cells, but that’s going to occur years later, not within months.
Unless this virus is unique relative to anything we’ve vaccinated for before, there is no reason to believe we’ll lose those T cells at such an early date.
Because it’s not really at all dependent on the virus at all, it’s dependent on T cells which behave consistently regardless of the pathogen.
That said, this could be a magical virus that wipes out the memory T cells.
I should be quiet, before I give anyone new ideas.
On 2020-05-26 12:28:41, user aa wrote:
Yes, we will be publishing updated results soon
On 2021-10-14 23:01:32, user doseofcommonsense wrote:
Public Health has estimated Delta's CFR to be 0.096 %.
On 2020-05-15 22:43:37, user Milesy Mathis wrote:
Seems like the takeaway is include zinc and start the HCQ/AZM early, not after they become acute and are getting put on ventilators (itself questionable since the standard ARDS vent protocols don't seem well matched to the peculiar oxygen deprivation symptoms without pneumonia that COVID seems to present with). That Univ. of Albany study that CNN was touting has NO MENTION of zinc, another retrospective in the same State - hard to believe that level of ignorance of past research into coronavirus or RNA virus therapy exists among some of these providers.
On 2020-08-09 21:39:07, user Detrick Snyder wrote:
This study is almost completely undermined by unmeasured confounders, a gross ecological fallacy, lack of adjustment for multiple comparisons, and other fallacies ecological studies like this are prone to. Please continue to eat your fermented foods, but this isn't a study that shows much of anything except that it's a subject needing further investigation.
On 2021-03-02 18:20:29, user Martin Hepp wrote:
Ok, this is only a preprint. However, a wording like "provides a precise estimate of the true underlying SARS-CoV-2 transmission risk in schools and day-care centres." in the introduction sets all alarm bells of any scientist ringing. "precise" and "true" are bold words, rarely used in serious academic publications (where typically a prominent "threats to validity" section would highlight and discuss the limitations of the findings) - in particular, if the underlying method is relatively weak. Some limitations are discussed on pp.12 and 13, but in a rather superficial way.
Just a few major questions that challenge the overall contribution:
During the major part of the period of the analysis, the incidence was very low, in particular among young people. See https://corona-data.eu/medi... for a heatmap. Of the total duration of the study of ca. 17 weeks, only the last 5 - 6 weeks and thus less a mere 30 % had a significant incidence in the age-groups 0-4, 5-9, and 10-14, and it was lower than in the general population.
As children are less likely to be symptomatic and the testing regime has a strong bias towards symptomatic patients, it is a valid assumption that the share of undetected infections is higher among students and children than in the general population. As the authors' entire analysis and model for transmission is based on test-confirmed public health cases, the authors should have tested this hypothesis, e.g. by random PCR tests in areas and during periods with a sufficient community incidence. If you miss asymptomatic cases, you are not only invalidating your aggregate statistics, but of course also the entire graph of infections becomes incomplete and questionable.
On pp. 6 an 7, the authors cite the official definitions for cases and procedures; however, there is no information whether the theoretical guidelines for contact tracing, testing, non-pharmaceutical interventions like social distancing, masks, ventilation etc. were actually followed, and if the compliance remained stable over the course of the analysis and representative for the different groups. For instance, one could hypothesize that the effect of wearing mask in classrooms after November 20 is partially obscured by a reduction in ventilation due to cool weather and in general more time spent indoors. Taking the textbook definition of a characteristic of an observation and then assuming it to match the data is a significant threat to validity.
The same holds for the approach of instructing the DPHAs on how to use the questionnaire but not testing the quality of the results statistically or by cross-validation. How do you know that the DPHAs understood and applied your instructions properly? And even if they did, how do you know that the data they were using was correct? it is not a lot of effort to rule out or estimate the margin of error of a potential weakness.
The entire statistical analysis method is only a bit over half a page of largely spaced text (p. 8).
The claim that children are less likely to produce a sufficient viral load to infect others is highly disputed in the literature, see e.g. https://zoonosen.charite.de... these findings are not uniformly agreed (see e.g. https://www.sciencemediacen... "https://www.sciencemediacentre.org/expert-reaction-to-a-preprint-looking-at-the-amount-of-virus-from-those-with-covid-19-in-different-age-groups/)"), but it is not commonly accepted that children are unlikely to infect others. This challenges the assumption that asymptomatic individuals are unlikely to infect others even if they are themselves infected.
The authors state on p.12 that the rate of asymptomatic infections was relatively low with ca. 17%. Unfortunately, this population aggregate used by the authors obscures the influence of age on the likelihood of asymptomatic infections and hence on the number of undetected infections in school settings. A recent meta-study https://www.frontiersin.org... suggests that the rate is higher in children (p=0.5, CI 0.21 - 0.79) than in adults (p=0.3, CI 0.13 - 0.56). There is a lot of variance observed in the underlying studies, but the order of magnitude could explain a major share of the reported higher likelihood of infections originating from teachers than from students alone.
The focus on "hygiene practices" (p.13) as a recommendation conflicts with the widely accepted view that SARS-CoV-2 transmission is largely airborne and that sustained social contact in indoor environments is a high-risk setting, even with masks.
If the risk of students in school infecting teachers is so low, one should immediately stop the priority vaccination of teachers. I think the priority vaccination is justified.
For lay people: If children are less likely to show symptoms than adults, and testing and hence becoming an index case is more likely for symptomatic individuals, it will be no surprise that teachers, who are adults, are more often identified as index cases than children. If the data graph of humans interacting in the pandemic is incomplete, and there is a systematic bias that leads to more missing index patients being children, your findings can easily be a simple artifact resulting from the chosen approach.
Now, all science is tentative; we all know our papers could be improved, the evidence or data be more convincing, additional aspects be considered. The problem arises when this is combined with politics. The introduction (p. 5, 2nd paragraph) is heavily focussed on a positive view on re-opening school. The arguments raised are not wrong per se, but they are also not balanced - in a pandemic with a novel virus against which the majority of the human population seems to be immunologically naïve, other societal risks should be given the same space. If you motivate your research with the wish to reopen schools, readers have reason to assume that you are not neutral as to the outcome.
This is all common in the daily struggle of anybody in research and academia.
But when you combine such very preliminary work with substantial threats to validity with a bold claim in the intro and a conclusion in which you report with certainty that only 1 in 100 infected students will infect another person in school, knowing that there is a lot of heated debate in the society, then your "Ethical Statement" should be amended by "We knowingly accept that populist media like BILD, interest groups, and decision makers will use our fragile findings and our wording as solid evidence for a risk-prone opening strategy. Since we are so confident in our research, we take full responsibility for the societal consequences."
Doing preliminary research is unavoidable. Distributing it in a form that is the perfect bait for media and decision makers is unethical.
This
https://www.bild.de/ratgebe...
is the direct effect of your work.
More than ca. 3 million daily visitors on bild.de (likely largely from the German population) have seen their variant of your message.
On 2021-04-29 18:57:59, user Kirsty Short wrote:
Yes it has been accepted at CID https://pubmed.ncbi.nlm.nih...
On 2023-04-14 09:15:23, user Alexander Kastaniotis wrote:
Very nice work! A comment on lipoic acid: <br /> lipoic acid does enter mitochondria, and it is used in standard mitochondrial disorder treatment cocktails, where it works as a potent antioxidant. However, in contrast to some prokaryotic lipoylation systems, mitochondria lack the machinery to activate free lipoic acid for attachment to pyruvate dehydrogenase, alpha-ketoglutarate dehydrogenase E2 subunits etc. When mitochondria are equipped with a lipoic acid activating enzyme, externally supplied lipic acid can be used for attachment. Please have a look at our work: <br /> Pietikäinen et al 2021: Genetic dissection of the mitochondrial lipoylation pathway in yeast. doi: 10.1186/s12915-021-00951-3<br /> It may also be worth noting that the complete KO of Mecr in mice causes embryonic lethality (Nair RR et al 2017; doi: 10.1093/hmg/ddx105)
On 2020-08-14 16:20:29, user Paul Gordon wrote:
Very interesting, thanks for posting. The paper described 649 genomes, but only 253 appear to be in GISAID. Do you know if the remaining genomes will be released? Thanks!
On 2022-10-23 23:35:14, user Aditya Awasare wrote:
I really enjoyed reading the paper and it is amazing to see what the future of diagnosis and disease modelling could one day be. I was really curious about the criteria used for the definition and classification of signs and symptoms but could not find the attached supplemental tables. Also, since one of the factors that makes the diagnosis of neurological diseases so hard is the presence of comorbidities, can this model be extended to detect their presence? What would the training data look like for this or how would the signs and symptoms classification be modified to accommodate this?
On 2022-01-12 08:03:35, user Olivier Archambault Bouffard wrote:
Was there any crp follow up after those 14 days?
On 2021-04-25 13:30:44, user Robert Saunders wrote:
Clery and colleagues state that “evidenced based treatments are available” for chronic fatigue syndrome. These are listed as Cognitive Behavioural Therapy-for-fatigue (CBT-f), Activity Management (AM) and Graded Exercise Therapy (GET).
In 2017 the US Centers for Disease Control and Prevention concluded that there are no effective treatments for CFS, after it re-examined the scientific evidence and removed CBT and GET as recommended treatments [1].
Similarly, the 2020 draft NICE guideline for ME/CFS specifically warns against the prescription of CBT and GET as treatments due to the evidence that they are ineffective and potentially harmful [2]. 89% of outcomes in studies of non-pharmacological interventions for ME/CFS have been graded as “very low quality” with a high or very high risk of bias by NICE’s independent experts. And no outcomes in any studies of CBT or GET are graded as better than “low quality” [3].
Clery and colleagues cite Nijhof et al (FITNET) [4] for their claim that “at least 15% of children with CFS/ME [sic] remain symptomatic after one year of treatment”. It should be noted that Nijhof et al used the 1994 CDC Fukada diagnostic criteria [5], which is less specific than other criteria as it does not require post-exertion malaise (PEM) as a symptom.
Evidence suggests that most people with fatigue and other persistent symptoms following viral infection will recover within 2 years with no treatment, but a minority with ME/CFS will not recover [6,7]. There is no reliable evidence to suggest that long term outcomes are any better for those who have been prescribed CBT or GET and there is good evidence to suggest that these interventions are harmful [8].
There is undoubtedly a need for children and adults with post-viral fatigue syndromes and ME/CFS to be given appropriate advice and support to manage and cope with the effects of their illnesses. However, acknowledgement of the very low quality of past studies and the evidence that CBT and GET are neither safe nor effective treatments for ME/CFS should be considered a prerequisite for any research pertaining to the provision of such services.
References:
On 2019-07-18 18:38:41, user Guyguy wrote:
EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI
Show solidarity with the Congolese people in the 10th Ebola outbreak declared a health emergency of international concern: understand a qualitative study of variables of hospital activities on infection control practices in Kinshasa city
Wednesday, July 17, 2019
Statement Ebola outbreak in DRC as a health emergency of international concern
Following the recommendations of the international expert committee, WHO declared on Wednesday, July 17, 2019, that the Ebola epidemic in the DRC was a health emergency of international concern.<br /> The Ministry of Health accepts the evaluation of the expert committee. The ministry hopes that this decision is not the result of the many pressures from different stakeholder groups who wanted to use this statement as an opportunity to raise funds for humanitarian actors despite the potentially harmful and unforeseen consequences for the affected communities that depend on them. greatly from cross-border trade for their survival.<br /> While the Government continues to openly share with partners and donors the way in which it uses the funds received, we hope that there will be greater transparency and accountability of humanitarian actors in their use of funds to respond. to this Ebola outbreak.<br /> The Ebola epidemic is above all a public health crisis that requires a response by actors with real technical expertise. However, the main difficulty is that this epidemic occurs in an environment characterized by problems of development and shortcomings of the health system.<br /> Furthermore, we regret that after spending almost a year in this epidemic, certain groups of people in the community continue to adopt irresponsible behavior that causes the geographical spread of the virus. It is important to remember that in the cases of Goma and Uganda, the patients knew that they were at risk but refused to respect the health recommendations and deliberately traveled to another area. The Government will consider what steps need to be taken to prevent these high-risk groups from continuing to spread the epidemic in the region.
Follow-up of the situation of the pastor's contacts who traveled to Goma<br /> Vaccination around the confirmed Goma case continues at the Afia Himbi Health Center in the Goma Health Zone. All contacts in the city were found in less than 72 hours, including the motorcycle taxi driver that the pastor had used to get to the health center. The response teams from Beni and Butembo continue the investigations to trace the pastor's journey and identify his contacts in these two cities.
The epidemiological situation of the Ebola Virus Disease dated 16 July 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,522, 2,428 confirmed and 94 probable. In total, there were 1,698 deaths (1,604 confirmed and 94 probable) and 717 people cured.<br /> 374 suspected cases under investigation;<br /> 10 new confirmed cases, including 6 in Beni, 2 in Mabalako, 1 in Katwa and 1 in Mangurujipa;<br /> 10 new confirmed cases deaths:<br /> 5 community deaths, including 3 in Beni, 1 in Mabalako and 1 in Mangurujipa;<br /> 5 deaths at Ebola Treatment Center (ETC) including 4 in Beni and 1 in Katwa;<br /> 7 people cured out of Mabalako Ebola Treatment Center.
No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 136 (5% of all confirmed / probable cases), including 41 deaths.
Deaths and cures data recorded in ETCs for the period 9-11 July 2019 are now available and have been added to the summary table.<br /> In total, 12 deaths were recorded in ETC during this period:<br /> 7 deaths at the ETC de Beni<br /> 3 deaths at Butembo ETC<br /> 2 deaths at Katwa ETC<br /> In total, 7 cures were discharged from ETC during this period:<br /> 5 cured at Butembo ETC<br /> 1 cured at the ETC of Beni<br /> 1 cured at Katwa ETC
75,697,081 Controlled people<br /> 80 entry points (PoE) and operational health checkpoints (PoC).
Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of the Congo
On 2020-06-30 11:18:06, user Kevin McKernan wrote:
Interesting work. Great to see the qPCR replicated at another lab and spike in controls.<br /> It would be very helpful to sequence the Amplicons to see if any variation exists that can augment the phylogenetkcs of the disease.
On 2021-03-15 10:49:33, user Mav Rick wrote:
If NHS staff were not being tested when community prevalence was high, or only being tested once a week for a virus that van be infectious in 3 days the floodgates were open for staff both in hospitals and care homes to transmit the virus through asymptomatic/presymptomatic transmission.
The move to testing more staff 3 times a week was far too late, and not reliably implemented. A lesson not learned from first wave.The virus effectively went through an open door.
This testing policy failure was far more responsible for thousands of infections and deaths in care home and hospital settings than the unsafe discharges from hospital, but almost never reported on, or researched.
On 2020-04-23 17:44:51, user Alexey Karetnikov wrote:
In that case, I suggest you to change the title of your manuscript to "Patient-derived mutations impact pathogenicity of SARS-CoV-2 in cell culture". Otherwise, it's very misleading and creates false impression about the clinical relevance of your findings. Exactly from this point of view, your manuscript has already been picked up by popular press, with numerous articles misrepresenting your findings as if they had direct clinical significance.
On 2021-01-06 12:33:57, user C'est la même wrote:
The authors state that there were 25 cases of GBS in London during the sampling period, which would lead to an estimated occurrence rate of 0.82 GBS cases per 1000 COVID-19<br /> infections.<br /> Yet they discount this by citing a claim that 17.5% of individuals London had been infected by that time. We now know that estimate was wildly inaccurate.<br /> Serological survey data collected by the ONS found that prevalence in London was just under 0.4% around that date (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.07.06.20147348v1)")
Which works out to around 36,000 people in comparison to the 26,784 PCR confirmed cases. <br /> This would lead to an estimated occurrence rate of ~0.6 GBS cases per 1000 COVID-19 infections which certainly seems suggestive of an association.
The authors also performed genomic analyses to rule out molecular mimicry due to epitope similarities.
I'd like to draw attention to the fact that many of the known viral triggers of GBS also do not have evidence of molecular mimic epitopes, instead suggesting other mechanisms of generating autoimmunity including the co-capture hypothesis, (http://www.pnas.org/content... "http://www.pnas.org/content/114/4/734)"), given that spike protein interactions with gangliosides have already been characterised in a substantial number of publications to date.
As such, while the lower population incidence during the observed period is compelling, that data alone is not enough to rule out the association of GBS with SARS-CoV-2, given the impact of lockdown measures on other infectious causes that happen to have lower infectivity (basic reproduction number) than SARS-CoV-2.
On 2022-01-14 00:43:08, user disqus_mV149tuM7g wrote:
I am not a medical professional, but a common sense confounding variable immediately popped up in my mind, for which this (and most other studies) did not control for (though I understand it may not have been possible to control for it in this study given the data collection method, but more so I am baffled that from what I see 0 scientists and humans on earth apparently have thought of this common sense confounding variable and 0 studies that I know for attempted to control for it):
A) Do we not know that omicron is more similar to the common cold compare to delta? B) Do we not know that there is at least some common T cell protection across different coronaviruses, such that even T cells produced from a common cold give at least some protection against covid?
So then, without any further medical knowledge, the immediate common sense confounding variable that pops up in my mind using basic inferential logic is that if A and B are true, could it be that given the timing of omicron (came in early winter) compared to delta (came in summer), much more people had a common cold before omicron as opposed to delta? Also, less people abided by restrictions in Fall 2021 compared to Spring 2021. So couldn't this partially be the reason for why "omicron" is more mild than delta? Of course, that would mean that "omicron in those who had a common cold recently" is more mild than delta, NOT that "omicron" is more mild than delta. Do you see how dangerous it is (for people who did not have a common cold in a long time, especially if unvaccinated) to claim that "omicron" is more mild than delta? Again, I don't know if all of this is true or not, but I certainly think it warrants a more closer look.
Another confounding variable I can think of (though this one I am less certain of, but I don't think it hurts to put it out there): I remember early studies in 2020 showed viral load was associated with illness severity, and that those who wore masks tended to have less severe illness. Assuming those studies were correct, could it be that because omicron is more transmissible, more people are getting infected with omicron with low viral load compared to delta? For example, maybe more people are getting delta through droplet spread resulting in higher viral load, and more people who wear surgical masks but get omicron due to being in a small store with enough aerosols going through the mask and giving them omicron get omicron, resulting in less viral loads overall for omicron infections. Has this been controlled for? I have yet to see any studies that controlled for it.
On 2020-07-21 19:08:09, user Jeremy Rolls wrote:
Fascinating paper. Looking at the antibody data (such as there is any published here in the UK) about 18% of people in London have antibodies compared to about 8% nationally. On that basis alone 82% of Londoners may still get infected compared to 92% nationally - i.e. you would expect the mortality rate in London still to be pretty close to the national rate. Yet the hospital death stats for covid-19 in recent weeks shows London's rate consistently to be less than 40% of the national rate. Something else must, therefore, be going on - a) London is locking down better (unlikely), b) antibody immunity does not give the complete picture (possible given the data coming out of Sweden showing that for every person having antibodies two others have T-cell immunity) or c) there is a % of the population who have pre-existing resistance (from exposure to other corona-viruses) or are biologically incapable of getting infected. Ruling out a), a quick bit of maths shows about 75% of the population must fall into b) or c). So, on that basis, in London well over 90% have either been exposed to the virus or have pre-existing immunity and maybe 80-85% nationally. I suggest herd immunity has probably been achieved in London and is close in many other parts of the UK.
On 2023-01-21 12:41:08, user Joel Pessa wrote:
There appear to be 3 paths for CSF to drain (recirculate) from the brain:
direct drainage through CSF channels in the dura (hence the name canaliculi) directly along the falx to the thoracic duct
thru arachnoid granulations into the sagittal sinus veins
thru extra-dural lymphatic communications to the head and neck (scalp, nasal sinus)
If the first path is blocked, CSF backs up in the optic nerve (pia and epidural CSF channels), leading to optic disc edema.
This is a proposed etiology for the spaceflight-associated neuro-ocular syndrome.
On 2021-05-30 06:25:34, user Allan Saul wrote:
I was struck by the difference in the efficacy estimates in this paper and the estimates from Israel for efficacy against the B.1.117 variant e.g., https://www.medrxiv.org/con...<br /> It would be useful if the authors could make some comment on the apparent differences in results.<br /> Also, I am a bit perplexed at the time frame for estimating efficacy following first vaccination with BNT162b2 vaccine. Paper says that the efficacy was measured "21 days or more after the first dose up to the day before the second dose" . Recommended time for the second dose IS on day 21 so how come there are ANY cases? Presumably, second doses were delayed. In view of earlier data that suggests that the BNT162b2 is substantially more effective in the 4th week following a single dose (in absence of a second dose) than in the third week, it would be useful if the authors can be more explicit about the observation windows.
On 2021-04-10 18:48:39, user Daniel Haake wrote:
Regarding version 6 of your study, I have pointed out with my comment which statistical problems are present due to your study design, which leads to an overestimation of the calculated IFR (cf. https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v6?versioned=true#disqus_thread)"). Thank you very much for your reply to my statement. I think that an exchange is important, because this is the only way to get reasonable results. Therefore, please do not regard my comments as criticism, but as suggestions for improvement on how to achieve correct values. Since my statement is still valid with version 7, I answer to your answer, in which I comment here in version 7.
Here you seem to have misunderstood me. I meant that with your example wave of infections and starting the study shortly after the peak of the wave, there is the problem that antibodies have not yet been formed by many people by the time the study starts. By choosing the time of death then, you caught 95% of the deaths, but only a much smaller proportion of those infected. This leads to an underestimated numerator and thus an overestimated IFR.
Just because it was also done that way in the Geneva seropaevelence study does not automatically mean it is correct. So there are also very much studies where the study date was chosen for the number of deaths. For example:
https://www.who.int/bulleti...<br /> https://www.medrxiv.org/con... <br /> https://www.medrxiv.org/con...
?However, I agree with you that the Santa Clara County study should be taken with a grain of salt, as here the subjects were called via a Facebook ad and thus bias may have occurred.? As I said, I understand the idea of taking a later date for the number of deaths. However, the associated problems regarding the underestimation of the infected, which I wrote about in the previous answer, still remain.
It is still incomprehensible that you calculate a difference of 22-24 days, but then take a value 28 days after the study midpoint. This puts them 4-6 days behind your own calculation and thus automatically increases the IFR. Why do you elaborately calculate the difference of 22-24 days to determine the correct time, but then don't use that value??? Let me open up another example. Let's say we are testing at the peak of an infection wave. But now we count all the dead who showed up after a certain time, but we don't take into account that a large number of people still got infected after that. Some of the counted dead will also have become infected after the study. Then we have recorded all the dead, but not all the infected. Or do you want to say that all the dead are from the first half of the infection wave and none from the second part of the infection wave (especially since that would lead to an IFR of 0% for the second part of the infection wave). As you can see, it is problematic if you assume the number of deaths in the much later course, because you then choose the denominator of the quotient too small and arrive at an IFR that is too high.
In general, only deceased persons who are clear to have been infected before the latest time at which study participants may have become infected may then be included. This is not the time of the study, since the antibody tests can only be positive after some time following an infection.
Is it really "PCR testing per confirmed case", not "PCR testing per capita" that is the important parameter? Let us assume two example scenarios for this purpose. Let's assume that we test every resident and at that time 1% of the population is in the status where the PCR test is positive. Then we currently know from everyone what their status is. But then we would only get 1 positive tested person out of 100 tests performed. This test would then not be taken because of the too low ratio of tests per positive case. And this, although we would have tested even everyone. Now let's assume the opposite case. We test in a country where we don't know exactly where how many people are infected. Now we test in one region and assume that this result is transferable for the whole country. But actually this region is not as affected as other regions, we just don't know. Now we do 10,000 tests and find 20 infected people there. Then we come up with a ratio of 1 positive test per 500 tests performed. That test would then be included in your selection, even though the ratio of infected is actually higher. Therefore, it is just not the "per confirmed case" that is the important parameter. Because if there is a high number of cases in the country, you could now double and triple test everyone and know very well and still this investigation would be excluded. At the same time, however, studies can be included with few tests and thus a high statistical uncertainty for the reasons mentioned earlier.??
The comparison with South Korea is also problematic. 0 or 1 seropositive results are far too few to have any statistical significance. The statistical uncertainty here is simply too high. And, as already mentioned, the results of these investigations cannot be transferred across the board to the other investigations. ??
Including reported case numbers from countries that have a tracking system that works well for you leads to an overestimation of IFR.
That you screen out studies, based on recruitment I can understand. I think that is statistically correct. I also see the danger with recruitment that you can't get representative results. Therefore, it is also understandable that you want to see which studies are useful and which are not.<br /> Nevertheless, you just sort out the studies that have a low calculation of IFR and leave studies with high values in your study. This leads to a shift toward the high values. Furthermore, studies that are straight up deviant are more problematic because a larger shift is possible in that direction. Let's say there is a hypothetical virus with an IFR of actually 0.5%. Then we have a study with a value of 0.3% and a study with 1.5%. The high value in particular is further away from the actual value and thus shifts the calculated value upward. If you have an actual IFR of 0.5%, you can misestimate by a maximum of 0.5 percentage points on the downside and by 99.5 percentage points on the upside in theory. This is also not surprising because such distributions are right skewed. If I remove both, the study with the too low value and the study with the too high value, the actual value does not change. If I remove both, the calculated value shifts upwards, because a stronger shift is possible in this direction. This leads to an overestimation of the IFR.
You write in your reply that this is not relevant because reported deaths were used and not excess mortality. In Appendix Q you write: <br /> "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“. You may not have applied this to other studies. However, you are using a study that did. Accordingly, this is crucial and has an impact on your result.
You nevertheless calculate an age-specific IFR for COVID-19 and calculate the IFR as it would look if there were an equal distribution across age groups, which in fact there is not. At the same time, you say what the IFR is for influenza, which, as shown, you understate. After all, the comparability of numbers due to changing life circumstances do not change in a short period of time. Therefore it is no problem to use the IFR for influenza of several years. Thus you suggest a comparability of the numbers. It is not possible to compare an IFR that assumes an equal distribution of age groups with an IFR that does not assume an equal distribution. However, this is exactly what is being suggested. By the way, it is not only the media, it was also taken up by Dr. Drosten. For another reason the comparability is difficult. Namely, an IFR is compared of influenza, where we could already protect the vulneable groups to some extent by vaccination and also an infection could have been gone through in the past, which helps to fight the disease and can therefore lead to fewer problems. However, to be honest, one can of course argue here that this is just the way the situation is. Therefore it is also understandable for me if one nevertheless makes such a comparison. Then, however, by assuming an equal distribution over the age structure for both viruses, or the actual distribution for both. By the way, there is another problem. There is a comparison of an estimated IFR with a measured one.
With the studies to date, it is very difficult to estimate how high the IFR actually is. This is because there are problems with all methods. If you take antibody studies, there is the problem that antibodies are not detectable in all infected people. If you take the reported numbers of cases, there is the problem of the dark field. How could one calculate a clean IFR? By actually testing a certain proportion of the population as a representative group on a regular basis. For example, you can test 1 per thousand of the population every week and see if they are positive for COVID-19. Then look at how many people have died over time from the group of positives. Those deceased could then be autopsied by default to determine whether they died from or with COVID-19. In doing so, one must then determine what period of time after infection is still valid to count as a COVID-19 dead person. After all, is a person who died 10 months after infection still a COVID-19 dead person? After all, it is the elderly who are dying. But it is not atypical that they would have died over time even without infection. Now imagine that a 94-year-old dies 10 months after an infection. Can one then still say whether it was due to COVID-19? In this case, one would probably have to look at the medical history before and after COVID-19 and also see what symptoms the deceased had after the infection. Only with such a procedure it is possible to calculate a clean IFR. For a correct comparability with influenza, this procedure would also have to be used for the calculation of the IFR of influenza. If you are really interested in a scientific comparability of the IFR, you should proceed in this way.
On 2021-02-20 09:50:26, user Darren Brown; HIV Physiotherap wrote:
Thank you for this comprehensive study. In your methods you reference inclusion of 2 functioning/disability measurement tools; (a) World Health Organization Disability Assessment Schedule (WHODAS) 2.0; (b) Washington Group (WG) on Disability Statistics. Which WHODAS and WG questionnaires did you use as there are different versions? You have not reported any results from these measures of functioning/disability. Are you able to provide these results either in the main article or supplemental material, as they are very important data.
On 2020-05-12 13:44:41, user Abdul Mannan Baig wrote:
What a brilliant contribution to ongoing COVID-19 research.<br /> The olfactory bulb findings took the science way forward to understand the loss of smell and taste.<br /> Wow<br /> Abdul Mannan Baig
On 2020-04-14 09:12:21, user Lisa Kane wrote:
'Hoax' seems a rather strong comment, and to dismiss the whole paper is not helpful. The authors all appear to be legitimate scholars. While causality is not indicated, possible associations are useful to identify at this stage of exploration of the pandemic and can be further tested by other scholars.
On 2021-12-06 16:42:17, user Kristi Leach wrote:
Sociology student, here, currently writing a paper on issues with the online vax schedulers and the whole idea of using them. I would like to respectfully suggest that you consider focusing on other mechanisms in addition to the city's vax distribution strategy. On March 28, we were only 6 days into phase 1B+ and 50 days into Protect Chicago Plus. Meaning shots had been available to people in Plus neighborhoods much longer than it had been available to most other residents, unless we're suggesting it would have been appropriate to divert from nursing homes, jails, and healthcare workers. That's outside of my expertise, but as a lay person, it's unconvincing. I'm piggybacking on my advisor's findings that we are neglecting the social safety net as COVID mitigation https://www.newsweek.com/wh... For example Not to mention other efforts such as contact tracing and masking. Dr. Parker mentions the lack of hospitals in Black and Brown neighborhoods.
On 2020-04-18 15:24:11, user Robert Clark wrote:
This is potentially a bombshell report, of especial importance for health care workers, showing 100% protectiveness against COVID-19 using interferon. A flaw in the report though is that while it gives a total number of health care workers who didn't take the drug contracting COVID-19, it doesn't compare that to the total of all health care workers. So we can't make the comparison in percentage terms of how many on interferon who contracted the disease (0% according to this report) compared to those not on interferon who contracted it.
Robert Clark
On 2021-09-12 16:56:24, user Quid Veritas wrote:
Myocarditis is a condition that occurs after viral infections.
On 2021-09-10 21:38:17, user anime profile picture wrote:
This study completely misses the point of young kids getting vaccinated. COVID is infectious. Meaning when someone gets the virus, it can be passed on. Whether or not they are at high risk relative to the adverse side effects, they should be vaccinated to reduce the probability of older, more at-risk people from getting it. In short, young boys should get vaccinated to protect them, their parents, their teachers, and their grandparents. Consult with your doctor of course. I am no medical professional, but I understand that a vaccine does more than protect the person being vaccinated.
On 2021-03-24 00:09:50, user Elle wrote:
Also, how is it that patients were not broken out into smokers/non-smokers? All of these symptoms I think would be exacerbated by a smoking habit.
On 2023-06-01 13:18:49, user Ning Zhang wrote:
Please link this to the journal version. This manuscript has been accepted by the Global Health Journal (https://doi.org/10.1016/j.g... "https://doi.org/10.1016/j.glohj.2023.05.001)"). https://www.sciencedirect.c...
On 2023-11-23 22:56:05, user Elizabeth Korevaar wrote:
This paper has now been peer reviewed and published at Research Synthesis Methods:
Korevaar, E, Turner, SL, Forbes, AB, Karahalios, A, Taljaard, M, McKenzie, JE. Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study. Res Syn Meth. 2023; 14(6): 882-902. doi:10.1002/jrsm.1669
On 2020-05-13 19:33:46, user Emmanuel Martínez wrote:
Is Table S1 available? I apologize in advance if I am missing something.
On 2020-04-18 12:30:31, user Jack Prior wrote:
Is it possible that people are seeking treatment for flu-like symptoms at a higher rate than normal due to anxiety over consequences of severe Covid-19 infections?
On 2021-01-16 15:36:22, user Robert Flisiak wrote:
Article is already published in PAIM and is available at the <br /> https://www.mp.pl/paim/issu...
On 2020-07-18 12:58:04, user Anand Srinivasan wrote:
I understand that the risk calculations are done with the assumption of 1E8 RNA copies per mL of viral load in the saliva. I would like to know whether the risk estimates are directly proportional to the viral load (whether linear or non-linear dependency). Also, if the viral concentration in the saliva is lower by two orders of magnitude (1E6 RNA copies per mL), then what will be risk for the same conditions described in this pre-print? <br /> Thanks and Regards.
On 2022-12-04 10:47:01, user Andronikos Koutroumpelis wrote:
The correlation between COVID and RSV history in patient-level data is interesting, but confounded by the very different characteristics of the subgroups (eg SES, age, race). Could the authors report a multivariate analysis to check if the two histories are correlated independently?
On 2020-03-31 23:07:45, user skwique wrote:
Hi. I'm a non-scientist who has arrived here via a link in a tweet. This yet was in a Twitter thread involving an excited discussion about the extent to which the UK government was lying in its announcement, and repeated assertion today, that there is a delay in the supply and distribution of covid19 tests due to the lack of reaction agents available in the supply chain for the test. Whether or not this is the case, or is part of a deliberate govt policy to allow the virus to spread and to reach 'herd immunity' is a matter of heated debate, but not necessarily of concern to you. However, should your simple pre-heating method be proven effective and reliable, it would clearly be a game-changer. So, my question to you, as a layperson, is this: how much peer review is required to establish this process as proven safe and reliable, in scientific and legal protocol terms, and how quickly do you expect this to be achievable? Thank you.
On 2020-10-16 14:28:33, user Gijsbert P. van Nierop, PhD wrote:
This could indeed be a potential explanation. However, from influenza it was shown that the boost of antibodies to prior infections correlates with antigenic seniority, meaning higher boost of most senior antigens. Translated to our findings on HCoV, this would suggest that OC43 is the most senior antigen, meaning the initial coronavirus infection of these severe patients. An alternative explanation of the lower CFR is that social distancing and masks may reduce the infectious dose that people are exposed to, which subsequently may ameliorate the clinical outcome.
On 2021-09-05 11:07:51, user Jessie Abbate wrote:
Also, should use ISO country codes.
On 2025-10-12 13:03:25, user Ceejay wrote:
There are many other plausible mechanisms than antigenic imprinting for the "counter-intuitive" result, some vaccine-related, but others such as nutritional state and prior flu or indeed C19 exposure. May be too late for this paper, but my belief is that all such investigations should include measured Vitamin D status. The effect of Vit D on respiratory tract infection resilience is well known, and particularly over the winter months covered in this study, vitamin D titre will naturally fall due to reduced sun exposure. In similar vein, those who decline flu vaccination may adopt a significantly different health regime to those accepting vaccination, obviously not terribly easy to capture. But one I think you could capture is the C19 and C19 shots status, since I would imagine many of those tested might have taken part in your earlier studies. Those declining a flu shot could easily coincide with those declining a C19 shot. It all certainly shows vaccination science is complex.
On 2021-08-13 05:55:13, user Joseph Akins wrote:
The PhD finding is not surprising. Given how most PhDs live their lives the difference in getting the vaccine vs not getting it may be viewed as negligible. That pro vaccine "arguments" broadly fall into one of three logical fallacies there is reasonable skepticism as to the motivation behind them. The three basic fallacies used are: appeals to authority, appeals to sentiment and ad hominem attacks.
Additionally, those with PhDs are hopefully trained to not be fooled by logical fallacies and those that have no university "education" have not been brain washed to mindlessly find them compelling (at least that's what 10 years enlisted in the military showed me).
Those with PhDs in the sciences are more likely to understand that science is a process and cannot "say" anything and is only "followed" by fools. This is similar to David Hume's idea that "you cannot derive an ought from an is". How the world operates tells us nothing about what we ought to do in any situation including whether any particular individual "ought" to get vaccinated, or wear a mask.
Those with PhDs in non STEM related fields are in my opinion, as one with a PhD in the physical sciences, much more likely to see science as a social instrument and probably support vaccine promotion and even coercion. Unfortunately, too many people, in general, ignorantly see science as an instrument with which they can bludgeon their political adversaries. This is willful ignorance that results in people being treated as means towards an end and not as thinking people with their own ends in mind.
I not only have a PhD, but I left Academia and have worked as a Registered Nurse for over 15 years. As to whether I personally believe in vaccines is of no importance because the disagreement is actually not over vaccines, or masks, but over the millenia old tension between individual autonomy and the collective "good". I fall squarely on the side of individual autonomy and against arguing by logical fallacies regardless of any view on whether I ought to get a vaccine. That I, on occasion, actually take care of COVID positive ICU patients is not a factor most people need to consider.
On 2020-03-26 15:11:11, user Sinai Immunol Review Project wrote:
Study description: Plasma cytokine analysis (48 cytokines) was performed on COVID-19 patient plasma samples, who were sub-stratified as severe (N=34), moderate (N=19), and compared to healthy controls (N=8). Patients were monitored for up to 24 days after illness onset: viral load (qRT-PCR), cytokine (multiplex on subset of patients), lab tests, and epidemiological/clinical characteristics of patients were reported.
Key Findings:<br /> • Many elevated cytokines with COVID-19 onset compared to healthy controls <br /> (IFNy, IL-1Ra, IL-2Ra, IL-6, IL-10, IL-18, HGF, MCP-3, MIG, M-CSF, G-CSF, MIG-1a, and IP-10).<br /> • IP-10, IL-1Ra, and MCP-3 (esp. together) were associated with disease severity and fatal outcome. <br /> • IP-10 was correlated to patient viral load (r=0.3006, p=0.0075).<br /> • IP-10, IL-1Ra, and MCP-3 were correlated to loss of lung function (PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury) with MCP-3 being the most correlated (r=0.4104 p<0.0001 and r=0.5107 p<0.0001 respectively).<br /> • Viral load (Lower Ct Value from qRT-PCR) was associated with upregulated IP-10 only (not IL-1Ra or MCP-3) and was mildly correlated with decreased lung function: PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury).<br /> • Lymphopenia (decreased CD4 and CD8 T cells) and increased neutrophil correlated w/ severe patients.<br /> • Complications were associated with COVID severity (ARDS, hepatic insufficiency, renal insufficiency).
Importance: Outline of pathological time course (implicating innate immunity esp.) and identification key cytokines associated with disease severity and prognosis (+ comorbidities). Anti-IP-10 as a possible therapeutic intervention (ex: Eldelumab).
Critical Analysis: Collection time of clinical data and lab results not reported directly (likely 4 days (2,6) after illness onset), making it very difficult to determine if cytokines were predictive of patient outcome or reflective of patient compensatory immune response (likely the latter). Small N for cytokine analysis (N=2 fatal and N=5 severe/critical, and N=7 moderate or discharged). Viral treatment strategy not clearly outlined.
On 2025-06-11 02:25:36, user Stephen Jones wrote:
Please be advised that this preprint is now accepted as of June 10, 2025 for publication in Imaging Neuroscience as article number IMAG-25-0074R1 and has been “put into production for copyediting and ‘ahead-of-publication’ posting” and will be appearing online in 2 or 3 weeks. <br /> BR, Stephen C. Jones
On 2023-10-17 03:28:40, user CDSL JHSPH wrote:
Dear Dr. Bi et. al., <br /> I would like to express my appreciation for your preprint. This preprint provides valuable insight into the phenomenon of declining effectiveness of repeated flu vaccinations. n this influenza pandemic season, it is important to have in-depth research on the issue of the effectiveness of the influenza vaccine. Your study provides timely insights. You used real-world data covering multiple seasons, which demonstrates a comprehensive understanding of vaccination and infection.
However, I have some comments and questions that I hope will help improve the paper and deepen my understanding of the study. The discussion of potential causes of reduced vaccine effectiveness was insightful. However, it may be useful to discuss the practical implications of these findings for vaccination policies and recommendations. How might this research guide public health decision-making? Would age be one aspect that might influence the reduced effectiveness of repeat vaccination? As age often plays a role in immune responses. <br /> Furthermore, I encourage you to include a section on future directions, highlighting potential areas of research or specific questions that have emerged from this study. This could inspire further investigations in the field of influenza vaccine effectiveness. To enhance the clarity of your work, I also suggest incorporating a clear statement in the abstract or introduction section that succinctly outlines the problem your research aims to address. This would assist readers in swiftly understanding the primary focus of your study.<br /> Overall, your preprint is valuable and thought-provoking, and I look forward to seeing how it progresses in terms of publication and further research.
On 2021-01-07 03:20:52, user Robin Whittle wrote:
The Mark Alipio article (43) is fake and was withdrawn in August. Please see https://researchveracity.in... for information on this article and two more by "Raharusun" et al. and "Glicio" et al.
On 2020-08-30 11:33:10, user Martijn Weterings wrote:
One problem with those S(E)IR compartmental models is that they always assume/pretend that a virus is spread homogeneously among a well mixed population. According to such models, the chance that someone in a small village in the South infects somebody else, is the same chance for anyone. The same for somebody in the North as somebody in their immediate family or other people in close neighborhood.
Such compartments are obviously not realistic for modeling an entire country. More suitable are networked S(E)IR' or spatial S(E)IR models. In such models, the virus spreads more like an ink blot.
Due to the local saturation, growth rates are already decreasing early on. Models that do not incorporate local saturation will 'compensate' (in order to get the same early deflection) by either reducing R0, or the (effective) population, or the reporting factor (upscaling the number of infected). If you try to fit a simple compartment SIR model to real data, then you will get unrealistic epidemiological parameters.
What they are doing in this article, dividing the population into layers with different rates of infection, is effectively shrinking the population that is 'reached' by the virus.
So this effectively makes the population smaller, but the question is whether it is the right way to shrink the population? Instead of a parameter in a mechanistic model, it might better be regarded as a parameter in an empirical model. It is an extra variable to ensure that the unsuitable simple SEIR model corresponds somewhat better with the measurements.
In reality, there are several effects that cause the observed epidemiological curves to deviate from the simple models (Besides heterogeneity, the use of local distribution in spatial or networked S(E)IR models, instead of global homogeneous compartments, is another important one).
By only including only a single effect in fitting, you get that all other effects are absorbed by that one effect. The result is an unrealistic estimate of the epidemiological parameters, which will not be suitable for extrapolation (for example calculating the 'herd immunity' percentage).
It is to be expected that this model, with only the heterogeneity incorporated, will likely underestimate the percentage to reach herd immunity. This is because it is overestimating the effect to compensate for the lack of other non-incorporated effects (and spatial models will be able to model the same deflection of the curves, but with less reduction of the herd immunity).
The above is a severe systematical problem, which will result in a bias towards smaller herd immunity percentages.
In addition: The fit with the curve is strongly determined by an interaction of the population size and the factor between the reported infections and actual infections (in a simple S(E)IR model, the two have the same effect). Such correlation between the two parameters will cause great inaccuracy.
And these are considerations that do not yet mention the problems with measurements of the epidemiological curve. For instance, the inaccuracies in reporting are not easily solved with a single (constant) reporting fraction. In order to estimate epidemiological parameters we need more direct experimental data (e.g. detailed information about contact tracing). From those we can deduce more directly the variations in infection rates and estimate the potential impact on herd immunity. Just fitting a model to the curve is a bad idea.
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 2021-05-02 07:00:13, user Peter McIntyre wrote:
This is an interesting and detailed analysis. One metric not provided is whether any identified infections arose from persons who had no travel history outside Australia. Such persons from NZ have not been required to quarantine on arrival in Australia so comparison of this metric would be helpful for policy assessment. A long and extensive list of potential interventions and/or policy changes to reduce or eliminate infections in MIQ is provided but their relative cost/ time to implement or difficulty not discussed. Vaccination is listed as only of value if transmission is eliminated - while this is the case if the target is no instances of infection in the context of a population unprotected by Immunization, once vulnerable populations are protected and risk of adverse outcomes from infection greatly reduced, this will have a major impact on the cost-effectiveness of a continued zero infection target and therefore on cost-effectiveness of listed interventions. <br /> Although this retrospective review is valuable, forward thinking is now needed to estimate future cost effectiveness
On 2020-04-16 14:51:37, user agoraks wrote:
Quote: "The assays are sensitive and specific, allowing for screening and identification of COVID19 "
Question: what is the actual sensitivity and specificity of the assay for detecting active disease by IgM or convalescence IgG in Sars-CoV-2 infected patients ?
On 2020-03-23 09:26:33, user Eric Henderson wrote:
Edit line 143 with correct sample age range if known.
On 2021-09-12 05:10:38, user kdrl nakle wrote:
This is really a weak study. It jams together 4 vastly different vaccines and then reports without differentiation. So what do we know out of that? It could be one has 100% protection and another 50% and we would claim it is 75% overall?
On 2020-03-29 21:18:16, user Randy k wrote:
This study is guessing at how many people were infected. The whole formula is based on a guess.
On 2024-12-04 12:43:08, user Hanna Dellago wrote:
This article has been published in a peer-reviewed journal: https://www.dovepress.com/decongestant-effect-of-coldamaris-akut-a-carrageenan--and-sorbitol-con-peer-reviewed-fulltext-article-IJGM <br /> The article has been indexed on Pubmed: https://pubmed.ncbi.nlm.nih.gov/39534593/
On 2020-06-26 16:13:44, user Veli VU wrote:
the authors do not detect SARS-CoV-2 in samples from 2019 March. Rather, they do detect IP2/IP4 resembling SARS-CoV-2. Whatever virus it is it does not have the E and N1/N2 of SARS-CoV-2. Fluctuations in qRT-PCRs even in 2020 samples -different sewers- are way too high to trust the reliability of the RT-PCRs. However, their approach is amazing. I hope they use a metagenomic approach to sequence to sewers rather than doing an RT-PCR assay, which doesn't look very rigorous.
On 2021-10-23 19:26:47, user kdrl nakle wrote:
Not going into formulas used but the premise is good and last paragraph of conclusion in regard to boosters is also very good argument.
On 2024-11-30 22:32:43, user xPeer wrote:
Summary<br /> The preprint investigates the remodeling effects of icosapent ethyl (IPE) supplementation on plasma lipoproteins and its subsequent impact on cardiovascular disease (CVD) risk markers in normolipidemic individuals. The study finds that IPE supplementation significantly enhances eicosapentaenoic acid (EPA) levels in the plasma, reducing major CVD risk markers such as triglycerides, remnant cholesterol, and apoB levels. There are consistent alterations across all lipoprotein classes, influencing their lipidomes, reducing proteoglycan binding properties, and potentially decreasing the atherosclerotic risk. However, the study's small sample size and short duration limit the generalizability of findings.
Example: Expand the cohort size and extend the duration to assess long-term impacts and variability of EPA incorporation among different CVD risk groups (Discussion, Page 14).
Detailed Mechanistic Insights:<br /> The precise mechanisms by which IPE alters lipoprotein characteristics and its direct influence on cardiovascular outcomes remain unclear.
Example: Detailed mechanistic studies on how IPE-induced lipid species changes relate to atherosclerosis progression are needed (Results, Page 11).
Individual Variability Analysis:<br /> The study underscores substantial interindividual variability in response to IPE supplementation, calling for personalized treatment approaches.
Example: Investigate genomic or lifestyle factors contributing to variability in response to IPE (Results, Page 13).
Proteoglycan Binding and Aggregation:<br /> The study notes reduction in proteoglycan binding and different responses in LDL aggregation among participants but lacks detailed analysis.
Specific errors include inconsistent capitalization in headings and figure labels requiring standardization (Introduction, Page 2; Results, Page 8).
AI Content Analysis:
Overall, the preprint presents insightful preliminary findings on the cardioprotective impacts of IPE supplementation, recommending essential improvements and comprehensive validations for future extensive studies.
On 2020-07-19 14:36:08, user Ulrich Müller-Sedgwick wrote:
Great to see this paper published with interesting results. I was the lead clinician for the CPFT Adult ADHD Clinic until March 2017 (when I moved to London). How should we screen for hoarding symptoms? Is there a screening version of longer questionnaires or 1-2 questions that we can ask in our clinical interview, especially in patients with procrastination as a main symptom?
On 2021-04-14 08:04:37, user Muhammad Yousuf wrote:
Implications of SIREN Study regarding immunity and reinfection after documented SARS-CoV-2 infection
According to this study (1) done in the UK in Health Care Workers (HCWs), the cohort having evidence of previous documented SARS-CoV-2 infection had the following observations:<br /> 1. The immunity was noted up to 7 months after the incident COVID-19 infection<br /> 2. 155 infections were detected in the baseline positive cohort of 8278 participants (1.87%).<br /> 3. The cohort with past COVID-19 after reinfection were mainly asymptomatic or had milder symptoms with no mortality.<br /> This augurs well regarding immunity in most people who have recovered from COVID-19. Immunity may last for over 7 months (more follow up of this cohort will be more informative to assess the long-term immunity. However, most of such HCWs were female and younger. The immunity duration after SARS-CoV-2 infection will need more such studies in people >65 years particularly in males who are mainly being affected by COVID-19 pandemic.
On 2020-11-11 19:43:04, user Dr. Amy wrote:
"1081 patients with a diagnosis of COVID-19 were admitted between May 5 and July 31, 2020 in our hospital. 793 patients had mild disease. 545 patients received steroids, and 125 patients received TCZ along with steroids for treatment. We did not have any control group as TCZ was available in our hospital and was a part of the treatment protocol since we started treating COVID-19 patients." I'm a bit confused as to why you can't use some of the 956 patients who didn't get TCZ as controls? Since patients on room air did receive TCZ, surely there are patients at all levels of severity who could serve as a control group to demonstrate that early course TCZ matters?
On 2021-09-27 19:49:44, user Awadhesh Kumar Singh wrote:
This is for your kind information that this paper is now published. Please find the link to access this paper, if you are interested. https://www.sciencedirect.c...<br /> or<br /> https://doi.org/10.1016/j.v...
On 2025-08-14 03:59:47, user Khoa Pham wrote:
This work has been accepted to MICCAI 2025 (Main Conference)
On 2020-06-29 17:14:09, user Aiman Tulaimat wrote:
The study reports a mortality ~40% in patients on vent on the control arm. This is much lower than what is reported by the critical care audit from the UK, which reports mortality > 60% in such patients. The study reports ~25% mortality and ~60% discharge alive. Are we missing 15% of patients? If the analysis is a Cox hazard, why is the report using relative risk? How did 13% of patients with no oxygen therapy die? This is very high? Did their covid deteriorate or did they die from other reason? why were these patients hospitalized if they were not hypoxic? did they decline life support when it became needed? where they not on oxygen because they were in hospice like setting? how did the other patients die? Were patients on the ventilator made DNR early? Was prone position used? Was it used more in the dexa arm? Was there imbalance in the ramdomization by center?
On 2021-02-11 16:06:09, user David McAllister wrote:
Congratulations on this excellent work. The potential for ICS therapy to improve outcomes for intermediate risk individuals not yet vaccinated is tantalising.
No doubt the paper is currently under peer-review, but if the authors have time it would be great to know the following:-<br /> 1. How many of the primary endpoint events included hospitalisation.<br /> 2. How was such a high proportion of positive tests for SARS-CoV-2 obtained? Was this based on subjective clinical judgement, or was there some other factor driving the high pre-test probability ?<br /> 3. How difficult was it to teach adequate inhaler technique?<br /> 4. Did any of the participants have wheeze or other signs of reversible airflow obstruction?<br /> 5. Were any steps taken to exclude participants who might have had a lobar pneumonia (eg by excluding individuals with purulent sputum)?<br /> 6. In the Guardian interview it was mentioned that at least 5 other trials were investigating this use of ICS. Is it possible to say when these are due to report?
On 2021-02-10 18:47:50, user moshkreit wrote:
This study does not show anything until the authors release the details of the age distribution for the two groups. W/o that, UC groups could have 10 people above 75, mitigated by 10 younger people to keep the mean in check. Naturally, a group with people over 75 would have more subjects at risk at day 26 than a group where the oldest subject is only 71.
On 2020-04-22 17:28:38, user Gooney wrote:
You can’t expect prospective randomized placebo controlled conclusions from a retrospective study. You can conclude that the manner in which the drugs were use led in a VA setting showed no benefit and demonstrated harm. Further detailed studies with significant power would be needed for more elaborate conclusions. Why are we using the drug? Based off a study that had 30 patients and reported outcomes that were not likely to occur given the number of patients included in the study. You need 100 patients for one death. Over 70 patients in the same 100 will recover without incident. Yet people are prescribing a drug that potentially could do harm.
On 2021-11-21 15:58:57, user Eric William Smith wrote:
“1st May 2020 - 1st Sep 2020” and what happened Oct 2020 - Jan 2021? The winter surge destroys this analysis. So do the current western pacific surges
On 2022-02-09 11:30:32, user Felix Schlichter wrote:
The authors explain that the data was gathered from community testing. They further note that mass testing has been available to "Dutch citizens experiencing COVID-19 like symptoms or who have been in contact with someone testing positive for SARS-CoV-2".
If one assumes that the inmune status affects the intensity and probability of exhibiting symptoms, wouldn't the sample be biased? Even if the real odds of being positive for individuals with primary vaccionation and booster were equal, the ones with booster would be underrepresented as they would not test as often if they tend to exhibit less symptoms. Is this not a limitation of the study?
Could the authors not show the results separated by the reason for testing (contact vs symptomatic) to account for this limitation? if the reason for testing was having been a contact, this limitation would not be there.
On 2020-12-29 00:37:11, user Olga Matveeva wrote:
Several recent preprints support some of this manuscript findings.<br /> 1. Authors from Sweden and China in a study entitled “Pulmonary stromal expansion and intra-alveolar coagulation are primary causes of Covid-19 death” demonstrated that “The virus was replicating in the pneumocytes and macrophages but not in bronchial epithelium, endothelial, pericytes or stromal cells. doi: https://doi.org/10.1101/202...<br /> 2. Researchers in Brasil investigated SARS-CoV-2 infection of PBMCs and found that in vitro infection of whole PBMCs from healthy donors was productive of virus progeny. They also found that “SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from COVID-19 patients, and less frequently in CD4+T lymphocytes” The preprint is entitled “Infection of human lymphomononuclear cells by SARS-CoV-2”. <br /> doi: https://doi.org/10.1101/202...<br /> 3. SARS-CoV-2 infection of macrophages and some other immune cells in deceased patients was suggested in other autopsy related preprint entitled “Broad SARS-CoV-2 cell tropism and immunopathology in lung tissues from fatal COVID-19” doi: https://doi.org/10.1101/202... The study was done by US researchers from Pittsburgh. <br /> 4. Researchers in France demonstrated “that SARS-CoV-2 efficiently infects monocytes and macrophages without any cytopathic effect.” Their findings are reported in the preprint entitled “Monocytes and macrophages, targets of SARS-CoV-2: the clue for Covid-19 immunoparalysis” doi: https://doi.org/10.1101/202...
On 2021-02-24 13:41:54, user Nicolas Gambardella wrote:
Figure 1 shows a clear bi-modal distribution of post-infection both at baseline and after one shot. About a third of the patients do not get immunity. It would be interesting to look at the age distribution and time since infection for the two populations.
On 2021-11-20 09:36:30, user Amador Goodridge wrote:
Great ongoing work of Amanda et al bringing to the light of scientific evidence the dramatic situation of migrants. While looking forward findings and results of this study, hope this warning help Panama together with other agencies continue to reinforce POC,<br /> & clinical diagnosis as well as on-site treatment strategy in order to assure the public health. Congrats!
On 2020-10-27 16:47:39, user Kamran Kadkhoda wrote:
Low prevalence....low PPV....meaning serology is not reliable.
On 2022-09-14 16:05:23, user Roy Miller wrote:
There may be more evidence for this immunity than previously thought, Since Queen Elizabeth's death on September 8, 2022 the half-dozen British Royals have mingled with countless people, shaking hands, touching random surfaces, and breathing air all over the Scotland, England, and northern Ireland. I assume the Royals all have had their COVID injections and I have to assume that they have come in contact with the COVID virus on numerous occasions. Current thinking would lead me to assume the crowds gathered to see them would make transmission of the virus more likely.
So why hasn't COVID spread like wildfire over the grieving population? Perhaps it is too soon to tell. But since COVID symptoms appear 2 to 14 days after infection, at least a large uptick in the COVID infections should be noticeable by now But no such event has occurred according to the British Press. So I have to conclude that there are additional factors to consider such as the one postulated by this article.
PS: I have no medical training although my job involves helping the medical community take advantage of high technology.
On 2020-10-22 21:07:13, user Lee Jimmy wrote:
How does this relate to the nursing home outbreak in the La Crosse area ?<br /> James T. Lee, MD PhD FACS FIDSA FSHEA
On 2020-09-16 02:09:39, user Peter Lange wrote:
Thanks, interesting paper. To my knowledge reporting appears complete but may I suggest statement that the paper is consistent with the relevant EQUATOR guideline and completion of the check-list - I think it would be STARD?
On 2020-04-16 23:52:26, user Geoff wrote:
Why were they bronching these patients?? Sounds incredibly dangerous.
On 2021-08-30 22:37:50, user Dave Kavanagh wrote:
Will C.1.2 be the next pandemic wave of Covid to sweep the globe and will this potential vaccine resistant variant pose a greater problem to the WHO when considering the sharing of information to the general masses?
On 2020-04-11 17:14:42, user Sissy Lona Moxley Skaggs wrote:
I was concerned with the volcano activity having some hand in its derivation --Question: Is the virus containing some form of volcanic ash inits formation? Or Is the ash in the air form the recent activity a part of the illness? I am just a MS in psychology with credits in Human Services for a Phd. I am just a grandma right now.
On 2022-12-22 00:47:40, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint.
I apologize if I am overlooking something, but I was trying to learn more about Supplemental Table S1. Is this missing a reference in the main text?
Also, separate from Table S1 in the PDF, there is an uploaded Excel file that says "vip_gene_count" in the sheet name. Is this meant to be referenced in the first paragraph of the results for the 'VIP' database? If so, should there be a main text reference and an identifying number such that the total tables would then become Table S1-S5 (instead of the current Table S1-S4 in the PDF)?
Thank you very much!
Sincerely,<br /> Charles
On 2020-05-15 20:46:52, user Davide Valeriani wrote:
There is a typo in the title (CVOID-19 should be COVID-19)
On 2021-03-23 14:35:54, user Malcolm Semple wrote:
Hi Folk, Your search strategy missed "Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020; 369 doi: https://doi.org/10.1136/bmj... (Published 22 May 2020). This paper describes predictors of mortality, and describes length of stay. Other papers missed include all global ISARIC reports in MedRxiv. These alone would give you an additional samples size of 300,000 cases.
On 2020-03-24 13:29:19, user Sharon Tracy wrote:
Can anyone explain why the endpoint on copper is so much higher than that of the other materials (cardboard, plastic)? Does it matter that there is a higher concentration of virus on copper over a longer time than the other materials where the end concentration is lower?
On 2021-12-16 12:09:06, user Ichizo Nishino wrote:
Now the paper has been published online in Neurology.<br /> https://pubmed.ncbi.nlm.nih.gov/34873015/
On 2025-06-04 21:03:24, user Meg McSorley wrote:
The unadjusted risk estimates are exactly the same as the adjusted, down to the confidence intervals and p-values?
Where is table 1, comparing baseline characteristics of the comparison groups (vaccinated and unvaccinated)? This would inform which covariates should be included in the model. This is actually the most important table because the vaccinated likely do have different characteristics than the unvaccinated that would affect the risk estimates.
How was influenza ascertained? Self-report? Employee Health testing? Why aren’t raw numbers reported?
Is there a reference for the Vaccine Efficacy calculation? Is there a statistical rationale for this calculation?
On 2020-12-07 10:44:02, user David Ian Walker wrote:
This is an interesting article. What was your sample volume?
On 2022-09-20 10:24:38, user Okechukwu Onianwa wrote:
Excellent work tracking levels of MPXV environmental contamination during the course of infection. Do you have any thoughts on the impact of daily cleaning on the study data?
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 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 2020-07-04 10:35:54, user Vincent Fokker wrote:
I was reading your publication and I had some questions: (1) how does individual predicted probability work? is this based on certainty that it is this type of cell (as with resnet ?) and what thresholds are used? are different thresholds used for different cells it detects? and how did you define your final metrics?
Definitely seems like an awesome solution to bridging the gap of knowledge and interpretation of medical personnel in assisting them to practice their job more informed and efficient. Keep up the good work!
On 2021-08-07 15:14:47, user A Call for Honesty wrote:
What a number of critics ignore in their comments is that there are really poor countries that have a very, very limited supply of expensive medicines. If they have a good supply of Ivermectin, like one I know well, a caring doctor would not hesitate to try this medicine. I have had family that were quite ill with covid and responded very well to ivermectin. If their GP was in the US, UK or some other EU country, he would quite likely have his license withdrawn at the behest of politicians and academic experts who are not working with patients at the coal face. Less doctors in these situations would mean even more suffering and deaths.
On 2021-07-29 23:48:53, user Nicholas Morrish wrote:
What if the IgG you were reading was from something else, like SV40? Anecdotal evidence of pregnant women tested in the same regions showed that roughly ~10% of the populace had a SV40 infection.
On 2022-01-30 16:41:48, user Mary Beth Baker wrote:
Am a non-medical, non-math lay person, but in the comparison with the influenza pandemic of 1918/1919, it says that 1/4 of the US population was infected whereas 1/5 has been infected with Covid-19. Doesn't that make influenza worse, so far anyway? 6 in every 1,000 died of the spanish flu in US. How many per 1,000 of Covid so far?
On 2021-01-15 14:58:17, user Lane Dedrick wrote:
What was the standard care treatment regimen? Did those in the experiment arms receive steroids? Did those in the standard care control arm receive steroids?<br /> What were the outcomes of the ivermectin only arm?
On 2025-04-30 18:19:45, user James Pirruccello wrote:
This was published in 2024 in the EHJ at https://academic.oup.com/eurheartj/article-abstract/45/40/4318/7731683
On 2020-04-14 19:47:35, user Sinai Immunol Review Project wrote:
Main findings:
The aim of this study was to assess an association between reduced blood lymphocyte counts at hospital admission and prognosis of COVID-19 patients (n=192). The authors found:<br /> - Patients with lymphopenia are more likely to progress to severe disease or succumb to COVID-19 (32.1% of COVID-19 patients with lymphocyte reduction died). <br /> - Reduction of lymphocytes mainly affects the elderly (> 70 years old). <br /> - Lymphocyte reduction is more prevalent in COVID-19 patients with cardiac disease and pulmonary disease, patients with increase in the chest CT score (key marker of lung injury) and a decrease in several respiratory function markers (PaCO2, SpO2, oxygenation index).
Limitations of the study:
Reduced blood lymphocyte counts with aging have been known (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.08.20031229v2)") https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/epdf/10.1111/sji.12413)"). Therefore, it is not unexpected that a larger fraction of COV ID-19 patients above 70 years old have lower lymphocytes counts. Since age has been reported to be a major factor that determines outcome for COVID-19, lymphocyte counts and prognosis should have been adjusted by age. Multivariate analysis to identify independent risk factors is lacking.
Relevance:
Previous studies demonstrated that SARS-CoV-2 infection leads to a decrease of the T cell count. This study confirms these results and shows that lymphocyte reduction mainly affects the elderly. Lymphopenia was associated with disease severity as well as worse prognosis. Future studies need to address if lymphopenia is a negative predictive factor independent from age.
Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2022-04-15 17:26:22, user Young Juhn wrote:
A version of this article has been accepted for publication in the Journal of the American Medical Informatics Association (JAMIA) published by Oxford University Press. A link will be forthcoming.
On 2022-08-14 18:37:59, user Jason wrote:
Interesting manuscript, can see how it can easily be used in practice looking at the predictive variables chosen
On 2021-07-15 07:57:35, user John Lambiase wrote:
What's truly interesting is that no one is talking about Fructose and refined sugar activating long term NLRP3 inflammasome in the Monocyte which reprograms the monocyte on a path of inflammation and upregulation of Reactive oxygen species which lowers nitric oxide in Endothelium. Sars CoV-2 seems to have a knack of exploiting and exacerbating this chronic inflammation in patients leading to an acute medical malody.
What deactivates NLRP3 inflammasome? Olive Oil, Melatonin, Vitamin D Fish Oil, Resveritrol, Quercetin etal. Been taking doses of Oleic Acid for months because of all the research I was finding. Cashews high in Oleic Acid. If this all comes down to Anti Oxidants vs Oxidants then medicine is going to have serious explaining to do. Unfortunately that would never get out.
On 2025-10-09 06:04:12, user Christiane Wesarg-Menzel wrote:
The peer-reviewed version of this article has now been published by Journal of Neural Transmission: https://doi.org/10.1007/s00702-025-03034-7
On 2020-04-28 05:45:23, user Dong-Ha Oh wrote:
Where are Figures 1, 2, and 3?
On 2021-02-22 02:12:28, user Sanjeev Mangrulkar wrote:
Was there a control group in this study where the neutralising antibodies developed after natural infection were tested for their efficacy against the newer mutants of the virus?
On 2020-03-31 22:51:51, user Ruth Etzioni wrote:
When will the actual description of the model be available?
On 2021-03-23 17:35:47, user Nita Goldstein Goldband wrote:
With respect to seniors and cancer patients. Those in long term care received doses according to the manufacturers schedule. We need to closely monitor immunity in those who have just received first doses. My concern is how slowly we make decisions in Canada and how nimble our provinces can be about rescheduling second vaccines if further research supports a more rapid response
On 2021-08-16 00:29:09, user Robert Kaus wrote:
There is no Sputnik vaccine in the US, but good question about the J&J vaccine. No one seems to care about that one so it never gets looked at.
On 2020-04-19 10:43:00, user n1oftheabove wrote:
Would you share the curated data publicly, e.g. via github?
On 2025-07-21 19:05:55, user Anna Holmes wrote:
This article has been published in Clinical Infectious Diseases, published by Oxford University Press: https://doi.org/10.1093/cid/ciaf265
On 2021-08-27 10:14:26, user Guy André Pelouze wrote:
I have a question: is there any further details about the AU used in this recent paper? Are WHO equivalent mentioned anywhere? <br /> Thank you.
On 2020-08-22 20:39:16, user Abhishek Singh wrote:
Reminds me of one of the paper we had written before COVID - https://arxiv.org/abs/1912....<br /> This looked at the physician notes instead of X-ray. A combined multi-task learning model could be more powerful.
On 2021-01-19 14:48:37, user Laura Green wrote:
The authors' inability to control for potentially important cofounders renders this paper's conclusions unreliable.
On 2020-03-05 09:13:52, user Jørgen K. Kanters wrote:
Important paper but needs to be improved to be a High flier. First around 50 % had a hypertensive history. In an American population that would mean hypertension is protective. You need an age gender matched control population from the same area to compare with. Furthermore you miss a very important point. Which medicine prescriptions had the patients before admission? ACE Inhibitors and A2 antagonists as the most interesting. Again compared to a control population
On 2020-10-07 12:44:20, user Iratxe Puebla wrote:
Review completed as part of ASAPbio’s #PreprintReviewChallenge
The study examines the incidence of heart disease deaths in the early pandemic period in the US (30 March to April 26) in areas without large COVID-19 outbreaks. The authors sought to study whether a decline in acute myocardial infarction (AMI) admissions was linked to either a higher mortality rate (which would suggest avoidance of care seeking), or lower mortality (which may suggest less triggers for AMI). The authors use data from the CDC’s s National Center for Health Statistics and apply inclusion criteria requiring >97% completeness for the data.
The study includes data from a reliable source and includes controls involving a comparison to incidence of heart disease deaths in the same period in 2019 and 4 weeks earlier in 2020. While the study is observational and can only point to trends and not explain the reported decrease in incidence of heart disease death in several states during the study period, it helps surface this trend and opens lines for further research to evaluate whether the trend will sustain over a longer period and if so, look into the potential factors behind the trend. If the trend were to sustain over time and was found not to be associated with misclassification of death cause, it may provide avenues to identify factors that can reduce triggers for AMI.
Minor comments<br /> - The authors indicate ‘The primary analysis captured 747,375,188 person-weeks for the early pandemic period and 101,620,248 person-weeks for the 2019 control period’ the number of person weeks for the control period is considerably lower, can the authors provide some context for this, and whether this may have any influence on the analysis?<br /> - The abstract indicates ‘The mean incidence rate (per 100,000 person-weeks) for heart disease in states without excess deaths during the early pandemic period was 3.95 (95% CI 3.83 to 4.06) versus 4.19 (95% CI 4.14 to 4.23) during the corresponding period in 2019’, the Results section reads ‘The mean incidence rate (per 100,000 person-weeks) for heart disease in states without excess deaths during the early pandemic period was 3.95 (95% CI 3.83 to 4.06) versus 4.35 (95% CI 4.23 to 4.48)’ it appears they need to be updated to match?
Questions for the authors<br /> - Now that we have data from four additional months into the pandemic, are the authors planning an extension to the analysis?<br /> - For the states where an increase in the incidence of heart disease deaths was observed, the authors mention the possibility of harm due to avoidance of care, misclassification during a period of excess deaths and COVID-19 itself increasing cardiovascular deaths. Do the authors think that capacity at hospitals may have been a factor behind any increase in heart disease deaths? E.g. related to prioritization of COVID-19 admissions vs others.
On 2025-05-28 14:47:48, user Soroosh Solhjoo wrote:
Published in Heart Rhythm: https://doi.org/10.1016/j.hrthm.2024.08.054
On 2022-12-15 10:49:12, user Author wrote:
We would like to reply to a comment entitled “Japan preprint on myocarditis used inadequate methods to suggest COVID-19 vaccines cause more myocarditis deaths”: a review by Health Feedback (Editor: Ms. Flora Teoh). <br /> https://healthfeedback.org/...
We thank them for commenting on our paper. We understand their main points of criticism were three summarised as followings:
Their 2nd point is based on the fundamental misunderstanding on the methods of our study. They erroneously stated "The authors' association of change in the risk of myocarditis death associated with COVID-19 vaccines was based on comparing pre-pandemic and post-pandemic rates of myocarditis death". <br /> We compared myocarditis mortality in the SARS-CoV-2 VACCINATED population with that of the 2017-2019 (pre-pandemic period: reference) population; we did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods.<br /> Because of the misunderstanding the fundamental methods of our study, the following criticism have no sense:<br /> “But this assumes that the only thing that changed between the two periods is the availability of the COVID-19 vaccines. It excludes, without justification, the possibility that COVID-19 itself could produce an increase in myocarditis deaths. No reason was given by the authors for excluding COVID-19 as a potential explanation, despite the fact that COVID-19 is a more likely explanation than COVID-19 vaccines for an increase.” “This is because we know—based on previous published studies—that COVID-19 is more likely to lead to cardiac complications than the vaccines [1,2]. Therefore, the alleged causal association rests on the assumption that only COVID-19 vaccines can explain the change in myocarditis mortality, which isn’t true.”<br /> However, we would like to comments on “COVID-19 is more likely to lead to cardiac complications than the vaccines” referring reports by Block et al [1], and Patone et al [2,3].<br /> It is important to consider following three points; vaccines are not given to dying persons and to persons with fever or other acute diseases. Hence vaccinated people are relatively healthier than the non-vaccinated (healthy vaccinee effect) [4]. Conversely, vulnerable persons (frail, suppressed immunity due to stress or sleep debt etc) are more likely to be infected with SARS-CoV-2 (vulnerability confounding bias: VCB) [5].
Patone et al. [1] stated in the discussion section as follows: “Of note, the estimated IRRs were consistently <1 in the pre-exposure period before vaccination. ---- This was expected because events are unlikely to happen shortly before vaccination (relatively healthy people are receiving the vaccine).” This is exactly the same as the healthy vaccinee effect [4] and it is the lowest at day 0 of vaccination [2]: for example, IRR of arrhythmia at day 0 of BNT162b2 vaccination was 0.33 (0.29 to 0.37) compared with 0.72(0.70 to 0.73) during -28 to -1 days before vaccination [2]. <br /> Paton et al [1] also discussed that the estimated IRRs were consistently >1 in the pre-risk period before a SARS-CoV-2–positive test. They thought that events are more likely to happen before a SARS-CoV-2–positive test (as a standard procedure, patients admitted to the hospital are tested for SARS-CoV-2). But they missed to discuss that IRRs on day 0 of vaccination are the most prominent (with 10 times more than that in the pre-risk period, because standard testing of SARS-CoV-2 is mostly done on the day of admission). Hence, constant IRR >1 during -28 to -1 days before vaccination may be another cause. It may be explained by the vulnerability confounding bias [5].<br /> We estimated the effect of vulnerable person’s susceptibility to infection (vulnerability confounding bias: VCB) from the pre-risk period (-28 to -1 days) of the SARS-CoV-2 test-positive group: 2.84 (1.89 to 4.28) for myocarditis and 4.82 (4.68 to 4.97) for arrhythmia. When applied these data for the index of VCB, VCB-adjusted IRRs are 3.44 (2.11 to 5.59) and 1.11 (1.07 to 1.16) which are similar to or less than the healthy vaccinee effect adjusted IRRs of myocarditis (3.97: 3.05 to 5.16) and arrythmia (2.70: 2.38 to 3.05) respectively [4].<br /> It is not possible to estimate the healthy-vaccinee effect and VCB directly from the report of Block et al [3], however, post-SARS-CoV-2 infection/post-vaccination myocarditis risk ratios may be less than 1.00 in almost half of those listed when above adjustments were applied.
This point is also derived from the fundamental misunderstanding on the methods of our study. We did NOT compare myocarditis mortality between POST-PANDEMIC and pre-pandemic periods BUT compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. <br /> Therefore, as a rule, deaths following SARS-CoV-2 infection were not included in this study. In fact, none had COVID-19 listed in the death cause column of cases included in this analysis.<br /> Moreover, in the MHWL list we referred; most deaths included brief medical history as well as the cause of death. We clearly stated that “these were myocarditis death cases reported by physicians as serious adverse reactions to the vaccine” in the Methods section.<br /> Furthermore, as we stated in the discussion section, myocarditis deaths in the 2017-2019 (reference) population were also based on a doctor's diagnosis, with no other medical history known. Mevorach et al [6] also analysed using the same methodology and already published as a peer reviewed paper.
This point is also derived from the fundamental misunderstanding on the methods of our study. We compared SARS-CoV-2 VACCINATED population for 28 DAYS after vaccination with pre-pandemic periods. Hence this sample size was enough to demonstrate increased myocarditis mortality rate ratio after vaccination.<br /> As we stated in the end of the discussion section and in supplemental Table S6, all of the Modified US Surgeon General criteria for causal were satisfied.
Sincerely,<br /> Watanabe and Hama.
References<br /> [1] Block JP, Boehmer TK, Forrest CB, et al. Cardiac Complications After SARS-CoV-2 Infection and mRNA COVID-19 Vaccination - PCORnet, United States, January 2021-January 2022. MMWR Morb Mortal Wkly Rep 2022; 71:517-23. DOI: http://dx.doi.org/10.15585/...<br /> [2] Patone M, Mei XW, Handunnetthi L, et al. Risk of Myocarditis After Sequential Doses of COVID-19 Vaccine and SARS-CoV-2 Infection by Age and Sex. Circulation. 2022; 146(10):743-54. doi:10.1161/CIRCULATIONAHA.122.059970<br /> [3] Patone M, Mei XW, Handunnetthi L, et al. Risks of myocarditis, pericarditis, and cardiac arrhythmias associated with COVID-19 vaccination or SARS-CoV-2 infection. Nat Med. 2022; 28(2):410-22. doi:10.1038/s41591-021-01630-0<br /> [4] Hama R and Watanabe S. The risk of vaccination may be higher by considering “healthy vaccinee effect” Response to Husby et al: https://doi.org/10.1136/bmj... (Published 16 December 2021)<br /> Available at: https://www.bmj.com/content...<br /> (Accessed 30 November 2022)<br /> [5] Hama R and Watanabe S. Vulnerability confounding bias should be taken into account in assessing risk of post SARS-CoV-2 infection: an opposite concept of healthy-vaccinee effect (Under submission)<br /> [6] Mevorach D, Anis E, Cedar N, et al. Myocarditis after BNT162b2 mRNA Vaccine against Covid-19 in Israel. N Engl J Med. 2021; 385(23):2140-49. doi:10.1056/NEJMoa2109730
On 2020-03-25 17:03:41, user Sinai Immunol Review Project wrote:
Summary: Most common chronic conditions among 25 patients that died from COVID-19 related respiratory failure were hypertension (64%) and diabetes (40%). Disease progression was marked by progressive organ failure, starting first with lung dysfunction, then heart (e.g. increased cTnI and pro-BNP), followed by kidney (e.g. increased BUN, Cr), and liver (e.g. ALT, AST). 72% of patients had neutrophilia and 88% also had lymphopenia. General markers of inflammation were also increased (e.g. PCT, D-Dimer, CRP, LDH, and SAA).
Limitations: The limitations of this study include small sample size and lack of measurements for some tests for several patients. This study would also have been stronger with comparison of the same measurements to patients suffering from less severe disease to further validate and correlate proposed biomarkers with disease severity.
Importance: This study identifies chronic conditions (i.e. hypertension and diabetes) that strongly correlates with disease severity. In addition to general markers of inflammation, the authors also identify concomitant neutrophilia and lymphopenia among their cohort of patients. This is a potentially interesting immunological finding because we would typically expect increased lymphocytes during a viral infection. Neutrophilia may also be contributing to cytokine storm. In addition, PCT was elevated in 90.5% of patients, suggesting a role for sepsis or secondary bacterial infection in COVID-19 related respiratory failure.
On 2025-06-10 14:15:34, user Mark Drakesmith wrote:
Article now published in Proceedings of the Tenth Workshop on Noisy and User-generated Text
On 2020-07-22 03:42:29, user Steven Hall wrote:
I would love to know was there any determination as to the best wearable. <br /> I run a Circulation Clinic and We have all of our Clients use wearable to help us get the best results. You feed back would be very helpful. Blessings Steven Hall Director of the Fountain of Youth Circulation Clinics 425-770-9466 https://happyheartclinic.com/
On 2022-04-14 07:45:08, user Ross wrote:
How was first time infection defined? If it is purely based on a reported infection from previous testing this would appear to be a major confounding factor. Assuming that an unidentified asymptomatic or mild case of Delta provides moderate or better protection against subsequent Delta infection and symptoms, this may increase the chances that this is a first time delta infection and hence may bias it to being more severe than if it was a reinfection. By contrast if the same infection provides only limited protection against omicron infection but reduces symptom severity - as the vaccines designed for earlier variants are reported to do with omicron - this could be a 2nd covid infection, with the first being delta and now this one omicron. Hence it appears to be biased to being less likely to be a first covid infection for the omicron group than the delta group despite the Delta2 group added in this study.
On 2025-02-21 05:12:28, user Evan Stanbury wrote:
Re "PVS participants also had lower anti-spike antibody titers, primarily due to fewer vaccine doses", ie the people with more vaccine doses had less PVS. This contradicts the hypothesis that vaccines cause Post-Vaccine Syndrome, since the dose-response relationship contradicts the hypothesis.
On 2021-10-27 15:17:33, user Edward Jones wrote:
I find this study very biased considering they use the 16.7% with such a small sample size, usually you'd discount that number. Also no consideration was given to the type of virus being investigated, the paper is regarding SARS COV and yet you quote 16.7% inaccuracy in Ebola virus. Furthermore, the statement saying that uninfected individuals will be in risk of exposure is nonsense. A false positive would mean they may have to isolate, having the opposite effect.
On 2020-05-20 14:45:40, user OxImmuno Literature Initiative wrote:
On 2020-05-07 13:34:42, user Heather Lipkind wrote:
Hoping this sparks more research. We have localized it within the placenta to the syncytiotrophoblast. Much to learn about SARS-CoV-2.
On 2021-03-14 10:34:35, user KalleMP wrote:
There are a number of data errors in this report. Having looked at 5 of the original 25 papers listed here I find that errors that are significant have been made in at least 4 of them.
The Turkey values are 75.5% deficient and 16.61nmol/l median instead of the listed 70nmol/l.
Bosnia reads 24.4% and should be 60.6% (their mean is 48.25nmol/l)
Italy reads 33.3% but a weighted average is closer to 30.7%<br /> Italy has used values from the highest performing Vitamin-D region and compared them to the national CoViD-19 figures which are accepted to be low.
Finland has used the native Finnish values and compared them to the national CoViD-19 figures which include immigrants who are more deficient yet represent a larger portion of the CoViD-19 figures.
On 2021-01-27 10:19:32, user Fred wrote:
I am not convinced of the data. Eg for Germany it is presumed that only about 1 of 10 infections is detected. The data I know from Germany say this number ist only 2-4 . So the IFR for Germany would not be O.2% but at least o.4 or even near to 1 %