On 2021-05-26 03:59:44, user Steve Kirsch wrote:
Why hasn't this paper been retracted yet?
They reversed the numbers for the Niaee study which was pivotal to their conclusion. See this tweet from CovidAnalysis for details on the switch. There is also a video from Niaee himself attesting to the fact ivermectin works.
When you use the correct data, it shows ivermectin works. No surprise.
On 2021-08-01 21:38:38, user Dan Jones wrote:
Should we be concerned that deaths were 14 among the vaccinated and 15 among the unvaccinated placebo group?
On 2022-01-03 01:52:37, user Mike Austin wrote:
Looking for Table S4 referenced in this document? The link to Supplementary material is hidden on the first four tabs of this page. You can find it here: https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.supplementary-material
On 2021-10-18 22:45:59, user Sir Henry wrote:
Table S3 of the Supplementary Materials shows 262 "severe" adverse events for the vaccine, compared to 150 for the placebo. The difference of 112 is too large to be a statistical fluke (p < 0.001) and is a multiple of the number of "severe COVID-19" cases (30) for the placebo (Table S6). In terms of "severe" outcomes (COVID-19 or adverse events), the vaccine appears significantly more dangerous than the placebo over the four month observation period.
On 2020-08-14 20:29:53, user Liisa Laine wrote:
Thank you for your feedback. We will be updating the manuscript shortly.
On 2020-04-08 18:01:50, user Terry McInnis wrote:
What are LabCorp and Quest using? Are they using the same 9 probes on their PCR tests?
On 2021-08-27 17:40:44, user David Wells wrote:
Table 1a shows that your 'vaccinated individuals' group exhibited higher rates of comorbidities. Comorbidities are therefore possibly correlated with vaccination. The model results show insignificant comorbidity effects, suggesting the possibility that your 'vaccination' effect is really (or partially) a case of stolen significance. Did you try removing the vaccination variable to find out if comorbidities then become significant? Or what if you matched on comorbidity rates, not just demographics?
On 2021-08-29 11:56:44, user Hilary wrote:
This is retrospective observational research, so causality isn’t the conclusion here. As mentioned before we don’t know health status of those observed. The journal does mention that the vaccine protects further for those previously infected. The greatest benefit was noticed for those infected at the beginning of the pandemic (Jan- Feb 2020).
On 2021-09-01 18:15:52, user Matthew Bartlett wrote:
How do you distinguish "previously infected" and "previously tested positive" (for being infected) as different?
On 2021-10-30 18:29:19, user Ben Veal wrote:
The CDC study compares people who went to hospital as a result of Covid-19 like symptoms, whereas the Israeli study compares people who were tested for Covid-19, and may or may not have gone to hospital. <br /> The data in the CDC study shows ~6x more vaccinated people went to hospital with Covid symptoms than previously infected people, but a lower percentage of the vaccinated ones actually had Covid-19. So perhaps the previously infected people are less likely to go to hospital in the first place when they don't actually have Covid-19, or to put it another way; vaccinated people are more likely to go to hospital with Covid-19 symptoms when they don't actually have Covid-19. <br /> If people who are more vigilant/paranoid are more likely to get vaccinated and also more likely to report into hospital, I think that could explain the difference in the results, but it's just a hypothesis.
On 2021-08-30 13:38:34, user Brunhilde wrote:
"many different reports" citation needed. A *peer-reviewed* CDC study from Kentucky (MMWR Aug 2021) reached the opposite conclusion. Note that this one is only a preprint.
On 2021-09-09 17:13:36, user Ozz in Texas wrote:
Logistic regression model was used, not linear...
On 2021-09-02 16:39:02, user Jennifer LeBlanc wrote:
Do self-proclaimed scientists and scholars looking for trust from the public use terms like lunatics when reviewing scientific journal studies? I think not.
On 2024-05-10 00:42:15, user Jess Keller wrote:
Thank you for conducting this important research. I have been going through TSW for 2.5 years and the lack of understanding or consensus within the medical community has made it extremely difficult. We desperately need more research like this in order to start to understand who gets TSW, why and what the treatment options there are.
On 2024-04-27 16:09:01, user Alicia F. wrote:
As someone who is 19 months into TSW, this research is SO important. It helps us get one step closer to finding a treatment and understanding how our bodies are affected by topical steroids.
On 2020-08-09 20:47:59, user med sci wrote:
Perhaps this topic "Seroprevalence of COVID-19 in Niger State" may be modified to reflect that the study was conducted only on small population of Niger state.
On 2021-01-23 12:27:23, user Martin wrote:
Testing was forced and not voluntary. No ethical guidelines were followed. Slovak citizens were forced to go on testing sites to get tested otherwise they would get fine and can not go to job. Even prime minister Igor Matovic (who made people go to testing by force) said few weeks after testing in national TV, that testing was not voluntary.
There is a lot of sources online, even on Youtube (in Slovak language).
In these days, another forced testing is in progress with more severe penalties for people who not attend. For example - who did not attend this testing, from 27th January cant even go out to the nature alone (with no people around). Violation = 1000 eur fine (averege monthly salary in our country).
This goverment totally ignores rule of law and basic principles of law state.
On 2021-01-21 18:33:05, user Calogero wrote:
Slovak people were forced to participate to the testing under threat of losing their jobs. One months after testing we were and now still are among the countries with higher deaths rate pro capite in the world. People had to wait per hours outside in severe november weather to be tested and after that wait inside for the results risking to be infected. During the weeks after testing the number of daily pcr tests was significantly lowered, that is reason why there were less new covid cases after testing. And despite whole scientific and medical community is contrary to the wide-testing, it is to be repeated next week, same conditions, not tested not allowed to go to the work, risking unexcused absence standing on the words of minister of labour, without any financial compensation. Unbelievable but true. (sorry for my english)
On 2021-01-23 12:29:18, user Martin wrote:
And one more thing. No one in Slovakia knew that we are testing subjects. It was forced without any formal (or informal) consent of testing subjects.
On 2020-09-16 21:36:10, user Qunfeng Dong wrote:
An updated version of this manuscript is now accepted for publication at JAMIA (Journal of the American Medical Informatics Association)
On 2021-09-17 17:37:04, user kdrl nakle wrote:
Separate two different vaccines you have and then rewrite paper with separate conclusions, not just giving tables. You cannot jam them altogether, tables are not enough.
On 2021-06-23 21:55:50, user David Wiseman PhD wrote:
Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.
Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..
As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.
You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).
To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.
Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.
So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).
Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.
Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.
We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).
Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).
Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.
Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.
_________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.
With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”
We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.
On 2020-08-13 00:49:59, user Jesse Baker wrote:
Regarding a passage in this MedRxiv post (July 21, paragraph 3 with citation to reference #15), “Additionally, recent clusters of COVID-19 cases linked to a…restaurant in Wuhan are suggestive of airborne transmission.”
Although the index case having lunch on Jan. 24 was from Wuhan, the restaurant was in Guangzhou. Indeed, its location far from Wuhan so early in the spread of Covid increased Guangzhou CDC’s confidence that the other patrons were infected by the index case and no one else.
On 2020-08-24 06:26:34, user Stan Himes wrote:
For COVID-19 you should include co-morbidity data, without this key information (which may be contained in full article) the data presented is worthless.
On 2020-06-24 18:16:30, user Gerard Cangelosi wrote:
Nice study, and a very valuable addition. I collaborated on one of the previous studies you cited (Tu YP et al, 2020). May I suggest an alternative explanation for the difference between your findings and ours? You used all-purpose flock swabs, and we used foam swabs. These differences aren't trivial (e.g. see https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.28.20083055v1)"). I would urge you to note this possibility in your manuscript. Thank you!<br /> Jerry
On 2020-04-30 13:54:19, user Charles R. Twardy wrote:
Nice. As A. Kumar notes in Twitter, this is productive engagement rather than just critique.
Note: The result in the abstract & discussion seems to combine two estimates from the results section: the [0.27%, 1.72%] unweighted estimate and the [0.49%, 3.21%] using the original authors' re-weighting. Not sure if this is copy/paste error or deliberately using widest range.
On 2020-06-12 04:50:15, user Paul_Vaucher wrote:
Dear authors,
Thank you for this interesting article of major interest. I find the process and research question to be most relevant. I however have a few questions that remain open to understand how the study could come to the conclusion that aerosols and surfaces were not important vectors of covid-19.
This study investigates an important question. I am however not convinced the method used truly answers the question as the public seems to understand it. Their is indeed room for misinterpretation and for the public to consider contact and air contamination not to occur at any time.
To avoid any overinterpretation, it seems important to clarify that this study only tests risks of air and surface contacts days after people have been placed in quarantine when we don’t suspect them to be very infectious anymore.
On 2020-10-23 05:06:05, user Robert Clark wrote:
This is another paper where positive effects of HCQ are left out of the conclusions the paper reports. In the Table 2, the line for mortality at 28 days shows a cut by a factor of 0.54 on HCQ. The difference is not at the standard 0.05 significance level, with a p-value of 0.22. However this does not mean the result is false. It could just as well be the sample size is not large enough for the significance to reach the 0.05 level.
Too often this is overlooked in medical studies. For instance a significance level of 0.05 means there is 5% chance that the difference is just by chance. Or said another way there is a 95% chance that the difference is not by chance alone, meaning the difference is a real effect.
But by the same token a statistical significance of 0.22, i.e., the p-value being 0.22, means there is a 78% chance that it is a real effect. In other words in probability terms it’s more likely than not to be a real effect.<br /> {There are several online calculators of, for example, the Fishers Exact test of statistical significance, such as here: https://www.graphpad.com/qu...}
Yet, often when a result does not reach the 0.05 significance level, it is common, and mistakenly, reported as the result being proven wrong.
In this regard it must be remembered that these calculated levels of statistical significance are dependent on the sample size. For instance with the mortality rates for the HCQ and non-HCQ cases the very same as in this study but at a large enough sample size the statistical significance could be at the 0.05 level. This is especially important in a study such as this one where The originally planned on number of subjects had to be greatly reduced because of a reduced number of cases of the illness.
Another aspect of this Table 2 becomes apparent from unwrapping the data. The study uses what is called a “composite endpoint”, or “composite outcome”. This means two subcases are combined into one. In this study, the cases of “invasively mechanically ventilated”, i.e., intubated, and “deaths” are combined, called the “Primary outcome” in the Table 2.
But the number of deaths specifically on invasive mechanical ventilation is an important number to find out. This is because the mortality rates for that category have been so high. So, the RECOVERY trial for example counted it as a breakthrough when dexamethasone cut deaths in that category by 30%.
In this study, the “Primary outcome” is the union of the two sets, “invasively mechanically ventilated” and “deaths”. What we want though is the number of those ventilated patients who died, the intersection of the two sets.
Use the formula |A ? B| = |A| + |B| – |A ? B|, which simply means the number in the union is found by adding the numbers in the two sets minus the number in the overlap.
We want the number in the intersection though so we’ll turn it around to get:
|A ? B| = |A| + |B| – |A ? B|
For HCQ:<br /> |ventilated?deaths| = |ventilated| + |deaths| – |ventilated?deaths| = 3 + 6 – 9 = 0. So 0 deaths out of 3 patients on invasive ventilation on HCQ.
But for non-HCQ:<br /> |ventilated?deaths| = 4 + 11 – 12 = 3, so the number of deaths on invasive ventilation not taking HCQ was 3 out of 4.
The numbers are too small to draw firm conclusions though. It is unfortunate that the study could not be completed with the originally planned number of cases.
One last fact left out of the conclusions of the paper that supports benefits of HCQ:
Figure 2. Analysis of outcomes in predefined subgroups.<br /> For analysis of the primary outcome in the subgroup of patients receiving azithromycin at randomization, the relative risk could not be calculated because the primary endpoint occurred in 0 of 10 patients who received both azithromycin and hydroxychloroquine compared to 3 of<br /> 11 patients who received azithromycin and the placebo.
???????
Robert Clark
On 2024-05-02 18:05:18, user Keith Robison wrote:
Using mutagenesis of DNA to break up repeats in sequencing has an intellectual history that is not captured in this version and really should be - e.g. <br /> Unlocking hidden genomic sequence from NAR in 2004 and Facilitated sequence counting and assembly by template mutagenesis in PNAS in 2014 represent two different research groups; there may be others
On 2020-04-11 05:44:42, user Serge wrote:
Please be aware and advised that the situation is still developing in many jurisdictions and <br /> the information in the paper represents only a snapshot in time. Only <br /> following the developments over certain period can provide more <br /> confident background for further statistical studies.
On 2020-10-16 19:04:34, user rick wrote:
The results contradict those published in NEJM for remdesivir. This trial is slightly larger, but the NEJM study was better described, and more homogeneous in its methods. It's unclear to me how the two patient populations compare, although some comments here suggest that, in this trial, they tended to be fairly sick. I would also like to see more work on this drug given early. By the time a person is hospitalized, their immune response to the virus may be more important than viral load, and antivirals can't do anything about that.
On 2020-05-26 10:38:48, user OxImmuno Literature Initiative wrote:
On 2020-07-24 04:38:59, user Dr Prachi Sinkar wrote:
How are you sure it was not against COVID? if you are negative when you are tested for COVID but exposed to it before or after - still Ab against COVID possible?
On 2022-01-12 20:21:08, user Mike B wrote:
Do we know it's VOC? Dogs detect Parkinson's using same technique, perhaps from minor expression of misfolded protein. Discrimination and scent memory by dogs is much more complex than we know. <br /> Implies we could build a molecular filter to mimic dog's nose. That however, is elusive.<br /> Fantastic study. Much ??? to working dogs, especially Belgian Malinois!
On 2021-08-10 22:41:16, user Ashley Derrick wrote:
I had covid in May of 2020. I had many long covid issues for months after - but the biggest issue was chest (heart and lung) pain. The symptoms included shortness of breath, burning sensation in the chest, difficulty getting a deep breath and feeling as if I was having a heart attack when more active (exercising). I went to both a heart specialist and a pulmonary specialist and nothing ever was found to be "wrong". The heart doctor was convinced it was inflammation in and around my lungs and heart that caused this. After 9 months, it went away (shortly before my first vaccine). I felt "normal" for about 4.5 months, but two weeks ago, the chest and heart issues came back. Same feelings. I am a fit 49 year old woman. Wondering if there are any studies that see symptoms going away and then months later coming back. Thanks -
On 2021-08-11 10:34:14, user Apriyano Oscar wrote:
I am sorry, I am just a layman. I want to ask about the 1.8% tested positive (608 people). Does it mean that the effectiveness of the Pfizer vaccine in this study is 98.2% ? And is this also the same as what is called as 'efficacy' ?
On 2020-07-18 11:46:21, user Benjamin Kirkup wrote:
Could you address the observations [ie. https://www.medrxiv.org/con...] of viral loads up to 10^11 viruses per ml saliva?
On 2020-05-26 06:06:22, user Stanley wrote:
Modeling is far from accurate. Modeling predicted 2.2 million deaths in this country.
On 2020-05-18 13:48:34, user OxImmuno Literature Initiative wrote:
On 2021-08-19 17:26:48, user Pasco Fearon wrote:
Great you have the CDI data! And yes, the modelling could be a bit complicated with potential interactions I agree, but maybe not impossible. And you might still see a main effect in a regression discontinuity analysis - I think that would be worth trying, although you'd need to think carefully about how to specify it. With an administration effect I guess you'd expect a step-down pattern and no linear trend after pandemic measures came in, whereas in a 'developmental impact' model you'd expect to see 'softer' discontinuity, with a likely linear trend setting in after the pandemic measures came in. Don't know if there's enough data there to tease that all apart of course!
On 2020-06-27 15:36:00, user Dr SK Gupta wrote:
Researchers have rightly pointed out that plasma works in Non intubated patients and not so in Intubated patients meaning thereby.. plasma therapy if given late in course of disease may not be effective. Intubated patients obviously meant more sick and those with advanced disease who failed to respond to nasal oxygen inhalation, High flow nasal oxygen and CPAP... <br /> (Authors may please clarify the criteria of intubation if different.)<br /> Findings are in line with the observation that once the cytokine storm has done the damage, Neutralizing antibodies in plasma may not be able to reverse it. Cytosorb or ECMO is the only modality left for such patients apart from usual care.
Good interesting finding was effectively of use of plasma with titres 1:320 while most of the studies are done with titres 1:640.<br /> Study has the limitation of using retrospective cohort as control arm. Prospective selection for inclusion in Plasma arm and retrospective selection in control arm both can increase observer bias.
Nevertheless it is important be because it may not be practically possible to take consent of sick patient to be included into placebo group depriving him of so much advertised benefits of plasma therapy.
On 2021-06-04 11:45:26, user Stephen Smith wrote:
Thanks, Robert. These data shocked me and I was one of the few ID docs using the higher doses of HCQ and AZM. The data suggest that AZM is needed to improve survival. However, when we looked at all the pts who received >3g HCQ, those who also received >1g AZM, on average, received more HCQ and than those who didn't. BTW, I have no idea why Dr. Raoult added AZM to the HCQ regimen. But at the time, those were the only clinical data available. So, we used that regimen. We then realized that the weights of pts varied enormously. So, we started aiming for 80 mg/kg cumulative dose of HCQ (6,000 mg HCQ divided by 75 kg or average adult weight). But overall, the data strongly suggest that AZM co-administration is needed to achieve this success in increased survival.
On 2020-05-05 22:07:15, user Katri Jalava wrote:
I would be quite cautious about shielding. If possible to isolate to an island or similar, then ok, but within the community, I think that is a major challenge. I think it may only create highly vulnerable clusters within the population which then in turn increase the force of infection substantially. One of the main thing to be done in EU is to break these chains of transmission within care homes by e.g. excluding exchange of staff between units, removal of symptomatic from the care homes to isolation units and preventing staff from working even if a suspect case within their household. It may be that this outbreak is much driven by care home clusters.
On 2021-06-17 11:47:05, user Jay wrote:
What are the doses or the duration of the treatment with glucocorticoids<br /> of this study? I'm in a middle of a prednisona treatment I will get the<br /> Pfizer vaccine in a few days. I have a cycle of 50mg x3days, 30mg <br /> x3days, 15mgx3 days and 5mg x2months and I want to know if it's better <br /> now (15mg) or next week with 5mg but more time with the treatment.
On 2021-01-26 07:48:38, user Oliver Kumpf wrote:
This is an interesting study. Regarding some analyses I would be interested in the distribution of organ dysfunction. Were vasopressor-free days and pulmonary-support-free days equally distributed in the therapy vs control cohorts? What were the age groups who profited most? Younger patients were much more likely to survive as is represented in the suplementary material. The Kaplan-Meier curves were without statiscal analysis. Was there a statistically significant difference between pooled IL-6Ra treated patients and controls? What is the number needed to treat. This therapy is expensive and especially in countries with restricted ressources it would almost be impossible to use such treatment.
On 2021-01-21 15:04:47, user CB Bass wrote:
Been saying this for 9 months but Ignored by all MSM outlets. Our published study found that the culprit in the cytokine storm and Covid severity is IL-6. Guess what else? Your gut bacteria- specifically Bifidobacterium regulate IL-6. This is why we are not seeing severe cases of covid in children. They have much higher concentrations of Bifidobacterium in their guts than adults do and it down regulates IL-6 which is pro inflammatory, while up regulates interferon and IL-10 which are anti inflammatory.
Also a study coming out of Hong Kong university last week not only confirmed what our Initial study and discovery showed, it found that patients with Covid severity had deficiencies in Bifidobacterium.
Here is a summary of our study if you’d like to read more on how IL-6 plays a major role in Covid severity in high risk individuals.
On 2021-09-03 13:01:15, user David B wrote:
Wouldn't Figure 4 and Suppl. Table 2 suggest that for Biontech/Pfizer 19-29 days between doses should be preferred over 30-44 days for people aged 50-64 years, if longer periods between doses aren't possible? Seems to me that there is something in the data, some confounding variable that leads to lower VE in that group. If this is not due to some uncontrolled underlying factor, then 3 weeks between doses should be the preferred option over 6 weeks, if you want to stay within the range suggested by the manufacturer.
On 2021-02-16 20:52:40, user Chris Cappa wrote:
Very interesting study. Interesting to see that exercise doesn't appear to increase the smaller particles but does the larger particles. In any case, two factors you might consider in revision. First is the differential dilution that will occur between different activities. Breathing and talking expiratory airflow rates differ substantially from coughing, from the various ventilatory therapies, and importantly from the OPC. Thus, there will be different levels of dilution associated with each activity that you might factor in to facilitate comparison between activities. It doesn't appear this was done (although I could be wrong). Or, at least note that this likely had an influence. The second issue relates to the comparability between the different activities. For example, talking was continuous whereas coughing was just 6 times in a minute. If a person had (for example) been asked to cough twice as often the number of particles measured would have doubled. Or, if there were more breaks in speech the number of particles would have differed. You might consider normalizing to per second of activity to allow for greater comparability.
On 2020-10-29 16:33:54, user Najmul Haider wrote:
Dear Readers-- the original article of this Preprint is Published as a Peer review article at "BMJ Global Health". You can download the published article from here: https://gh.bmj.com/content/...
On 2022-01-04 18:56:26, user Thomas Barlow wrote:
Did you account for pre-existing T-Cell immunity (pre-2020) which was known in 30 to 50% of the population in year 2020?<br /> Did you account for the fact that most people have had covid (most without even noticing) and will have developed T-Cell immunity naturally? How did you control for that? It's not a static measure and is more expressed and less expressed at different times of month, year.
Studies on INFECTION FATALITY RATE (IFR) - peer-reviewed studies [Studies conducted long before any vaccine] :
(notice the one confirmed for publication by the W.H.O. in September 2020 [published Oct. 2020] - A 0.23% IFR...about the same as flu).
MARCH 2021<br /> “the available evidence suggests average global IFR of ~0.15%”<br /> https://onlinelibrary.wiley...
FEB. 2021<br /> “The infection fatality rate for both the Bureau of Prisons and U.S. was 0.7%. Among institutions that tested >=85% of inmates, the combined infection fatality rate was 0.8%”<br /> https://www.ncbi.nlm.nih.go...
JAN 2021<br /> “The overall non-institutionalized IFR was 0.26%.”<br /> - https://www.acpjournals.org...
DEC 2020<br /> “This rate varied from place to place, with a lower range of 0.17% and a highest estimate of 1.7%.”<br /> https://www.sciencedirect.c...
DEC. 2020<br /> “Results show a fatality ratio of about 0.9%, which is lower than previous findings.”<br /> https://www.mdpi.com/1660-4...
NOV. 2020<br /> “The overall infection fatality risk was 0.8%”<br /> https://www.bmj.com/content...
NOV 2020<br /> “In the United States, COVID-19 now kills about 0.6% of people infected with the virus, compared with around 0.9% early in the pandemic, IHME Director Dr. Christopher Murray told Reuters.”<br /> https://www.reuters.com/art...
NOV. 2020<br /> “The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community.”<br /> https://www.nature.com/arti...
OCT 2020<br /> “We know that antibody tests are not perfect, and there may be a considerable number of people who do not mount a detectable antibody response to SARS-CoV-2. However, even when this uncertainty is taken into account, we still find that COVID-19 has a high fatality rate - on the order of 1% for a typical high-income country.”<br /> https://www.imperial.ac.uk/...
SEPT 2020<br /> The W.H.O. posted a heavily peer-reviewed & critiqued study from May 2020, showing the deaths per cases are 0.23% overall, and going up to 0.5% in the worst hit cities. 0.05% for under 70s - The W.H.O. reviewed it again, then published it in September:<br /> - https://www.who.int/bulleti...<br /> - https://apps.who.int/iris/h...
AUG 2020<br /> The medical journal 'Nature' had an analysis and stated that:<br /> "This result was used to calculate an overall IFR for England of 0.9%”<br /> https://www.nature.com/arti...<br /> ________________
On 2021-08-22 14:03:14, user Herbert wrote:
There is strong incentive to upgrade your level of education, especially to the highest option available and perhaps to a lesser extent an MD because of the subject matter. There is much less incentive to downgrade your level of education. So I see no reason to put as much scrutiny on those values to begin with.
Additionally, I'd wager there would be less suspicion if the PhD values wouldn't stick out like a sore thumb. 1% would seem suspicious as well, although I agree with your point that less people would complain in that case. 5% to 15% is what I'd expect to see going by the other values. It is a form of confirmation bias, but partly informed by the data.
Also, the PhD holders make up a relatively small part of the respondents, so even if we'd assume a set percent of dishonest people that pretend to have a completely random level of education, it would matter more in that group.
On 2021-08-13 17:27:45, user Chuck Crane wrote:
If you look at the questionnaire (the "supplementary materials" link) you find that the MD's and DVM's are "professional degree," and there is no "PhD" classification at all. It says "Doctorate," which includes Jill Biden's Ed.D. and so on. So the chart is deceptive.
D8 What is the highest degree or level of school you have completed?<br /> 1. Less than high school<br /> 2. High school graduate or equivalent (GED)<br /> 3. Some college<br /> 4. 2 year degree<br /> 5. 4 year degree<br /> 6. Master’s degree<br /> 7. Professional degree (e.g. MD, JD, DVM)<br /> 8. Doctorate
The paper is not in sync with the questionnaire, saying, e.g., "Those with professional degrees (e.g., JD, MBA) and PhDs were the only education groups without a decrease in hesitancy, and by May, those with PhDs had the highest hesitancy." I can't see how an MBA could look at the question and check "Professional degree" instead of "Master's Degree."
Think the paper needs a good proofreading.
Participation bias is a big issue. They asked a lot of people to participate, but only a small percentage did. The rather inane attempt to correct for this is to assume that if a particular class of respondents is under-represented, just assign responses from that class more weight, according to their proportion of the population ("post stratification adjustment").
On 2020-10-20 14:30:05, user Martin Dugas wrote:
In our paper serum was collected at the first patient contact (see material and methods). We used a validated commercial test kit to measure OC43 and HKU1 antibodies. We assessed 60 patients in three groups, age and gender matched. Yanqun Wang analyzed 12 severely ill and 11 mildly ill patients.
On 2020-10-14 16:22:00, user Yevhen Pylypenko wrote:
And then — https://www.medrxiv.org/con... :-|
On 2020-04-30 13:35:27, user John Lambiase wrote:
This has greater implications than just covud 19. It could effect most "enveloped" respiratory pathogens. The antimicrobial processes signalled by vitamin D are absolutely fascinating. They trigger multiple facets of immunity.
On 2021-06-30 18:55:18, user Medini A wrote:
Hello! Thank you for flagging this typo; yes, this is meant to be March 2021. The text will be updated shortly in the next version.
-Medini
On 2025-05-16 17:44:24, user Adil Muneer wrote:
Published version of this article titled "Development of a brain-penetrant G9a methylase inhibitor to target Alzheimer’s disease-associated proteopathology" is now available: https://www.nature.com/articles/s41467-025-59128-z
On 2020-08-13 03:24:12, user sjh007 wrote:
As a health care professional I have read this interesting article and find this approach to be extremely beneficial. Time is of the essence, as we all know.. I hope that the proper time and effort are given to review this so that there won't be any delays in getting a "good thing" into the public stream.
On 2025-07-30 13:21:44, user Abebe Almu Fola wrote:
the preprint has been published PMID: 40681807 PMCID: PMC12274420 DOI: 10.1038/s43856-025-01008-0. Can you help me to do connection between the preprint and the published version
On 2020-08-05 20:03:26, user Shi ZHAO wrote:
The peer-reviewed version is published in BMJ Open (DOI: 10.1136/bmjopen-2019-035308), access via: https://bmjopen.bmj.com/con...
On 2021-05-16 08:22:03, user Kohsuke Imai wrote:
Yang Y, Shen C, Li J, Yuan J, Wei J, Huang F, Wang F, Li G, Li Y, Xing L, Peng L, Yang M, Cao M, Zheng H, Wu W, Zou R, Li D, Xu Z, Wang H, Zhang M, Zhang Z, Gao GF, Jiang C, Liu L, Liu Y. Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J Allergy Clin Immunol. 2020 Jul;146(1):119-127.e4. doi: 10.1016/j.jaci.2020.04.027. Epub 2020 Apr 29. PMID: 32360286; PMCID: PMC7189843.
On 2021-06-15 18:59:56, user Quixander wrote:
Do you happen to know of a good primer for laypeople warning them of the pitfalls of diving into the world of scientific studies? I have learned a good number of caveats along the way (the most important being that this is not a layperson’s game), but not before being misled and manipulated a few times by agendas that misrepresented or mischaracterized the science. If there isn’t a one-pager on this, there should be, because (mis)interpreting scientific studies seems to have become a national pastime.
On 2021-08-13 16:42:49, user Dr. Jon wrote:
Isn't it pretty normal to assume those who have recovered from a disease are unlikely to get the same disease again?<br /> Why is this a controversy?
On 2021-02-28 12:37:41, user micro dentist wrote:
Many thanks for your effort. Very useful data, yet requires cautious interpretation.<br /> It is important not to aggrandise conclusions when the sample population is skewed due to disproportionate under-representation.
Such an aggrandisement potentially occurs here:<br /> “The observation that the seroprevalence amongst dental practice receptionists, who have no direct patient contact, was comparable to the general population, supports the hypothesis that occupational risk arose from close exposure to patients.’
Whilst in comparison to 16% of clinical staff 6% of receptionists were seropositive, it is important to also acknowledge that 21.6% of practice managers (also non-clinical) were seropositive.
Where significant conclusions may be derived through occupational comparisons, the effect of disproportionality should also be independently validated through careful examination of the internal validity of any inferred conclusions.
Here this would show lack of consistency with the derived conclusion. Should there still be a requirement a desire for an assumption, it may be worth considering combining any smaller similar samples (such as receptionists and practice managers in this case). In this study such combined group would show a seropositivity of 12.2% (n=131).
Through erroneously overlooking disproportionate occupational representation, there is the real potential of developing ludicrous conclusions: the most obvious being that seroprevalence is related to the amount of occupational administrative paperwork completed by each member of the team: practice managers>dentists>receptionists.
Clearly such a conclusion is neither desirable or valid.
On 2021-06-05 13:34:28, user Gowtham Kumar wrote:
We were trying to access the scans available in Oasis-1 dataset but we were unable to open the scans because the extensions of the files are .nifti.img , if you have any idea to open this please help us.
On 2022-01-05 19:42:22, user Christopher Hickie wrote:
Sorry, your paper is not valid. Omicron not dominant in US until week of Christmas so your 2-week sampling ending on Christmas week is way too premature. Please retract your preprint and wait several months to do this analysis.
On 2020-07-02 23:29:56, user Sergey wrote:
This pre-print does not have a Methods section- how did the medRxiv QC miss this?
On 2021-08-03 11:51:04, user cinnamon50 wrote:
Don't we have to know that testing occurs the same for vaxxed and unvaxxed people ?<br /> am I missing something there in how the selection for testing occurs ?<br /> what if unvaxxed people are selected for testing differently ?
thanks
On 2021-08-13 17:47:57, user Leper wrote:
The problem with studies in "extremely high" vaccination rate locations, is that they include any "herd immunity" effect in both the vaccinated and unvaccinated samples. This makes the comparison between vaccinated and non-vaccinated trickier
On 2021-07-14 13:52:21, user djconnel wrote:
Cases in San Francisco have gone from a 7-day average of 10, to over 70 in just 20 days, with positivity rates from 0.5% up to 3.2%, which for a serial interval of 5 days, corresponds to an R of exp(5*ln(6.4)/20) = 1.6 (using positivity). Your algorithm needs to be tuned if it's yielding < 1.1. Plot of rolling 7-day case totals.
On 2020-04-22 02:06:38, user David B Joyce wrote:
19 patient shifted fromNo HC to HC(7) or HC+AZ(12) after ventilation. Ventilation is obviously a sign of increasing severity and greater risk of death. If all of these ventilations resulted in death, then the pre ventilation treatment fatality statistics might look like 22%(HC), 13% (HC+Az) and 21% No HC. Need the data on individual outcomes. Also not impressed with the cohorts.
SPo2 >95: 63%(HC) 57.5%(HC+Az) 73.4%(No HC)<br /> BP> 159: 19.6% 9.7% 9.5%<br /> creatinine>5 17.5% 11.5% 7.6%
On 2020-04-21 20:47:16, user Savio wrote:
Cytokine bath or flooding is causing co-morbidity. HCQ can apparently reduce the inflammation in response to the virus but not counter the virus itself.
Here is a review paper describing the “Mechanisms of action of hydroxychloroquine and chloroquine: implications for rheumatology”.
https://www.nature.com/arti...
“Hydroxychloroquine and chloroquine are weak bases and have a characteristic ‘deep’ volume of distribution and a half-life of around 50 days. These drugs interfere with lysosomal activity and autophagy, interact with membrane stability and alter signalling pathways and transcriptional activity, which can result in inhibition of cytokine production and modulation of certain co-stimulatory molecules.”
On 2020-04-21 21:56:14, user Nom DePlume wrote:
While is true we don't know how the patients were chosen for who received the meds and who did not, it seems reasonable that the sicker, more desperate were given the meds.
While that would bias the results of higher ventilation and/or death, this study DOES show that the drugs ARE NOT MIRACLE CURES OR GAME CHANGERS.
On 2021-02-10 19:35:03, user philippeboucher wrote:
Any link or conflict of interest?
On 2021-07-30 15:16:55, user Resia Pretorius wrote:
This paper does not look at the presence/detection of spike protein in circulation before or after vaccination. The paper only describes the action of spike protein when it is added to blood in the lab, as is the case e.g.during acute COVID-19 infection.
On 2020-12-21 00:47:06, user Nikunj Bhagat wrote:
This paper has now been published by NeuroImage:Clinical and readers can access it at https://doi.org/10.1016/j.n...
On 2020-04-23 19:04:31, user CP wrote:
Gov Cuomo of NY announced two hours ago (4/23) results of antibody study of 3,000 in 19 NY counties. Result showed average infection rate of 13.9% - higher in NYC, lower in rural areas.
On 2020-04-18 23:20:34, user dixon pinfold wrote:
You mean not overrun by serious illness, surely. The number of cases is what we're trying to find out.
On 2020-04-18 16:27:29, user spacecat56 wrote:
Very interesting, thank you. I see I am a bit "late to the party".
But, I'd like to suggest that the self-selection of participants would not be specific to prior COVID-19 infection (which is unknown) but rather on recent prior experience of "flu-like symptoms". Some subset of those, perhaps a majority, actually had influenza.
I'm not sure, though, how you might incorporate this into the analysis...
On 2020-04-23 17:13:56, user gmshedd wrote:
Infection fatality rate of 0.12% to 0.2% means ~100% of Queens and Bronx residents have been infected.<br /> https://uploads.disquscdn.c...
On 2020-04-21 20:29:57, user rerutled wrote:
The Santa Clara results calibrated the test using known gold-standard "negative for covid19" blood. They had 401 "pre-covid19" samples; of those, 2 resulted in "positives". Thus, the test produces a "false positive" rate of 2 out of 401.
They gave the test to 3330 people, and 50 of those tests returned positive. How many of them can be produced by the observed "false positives"?
On average, for a sample of size 3300, one expects approximately 3330*(2/401) = 16.6 false positive results. Is that statistically significantly different from the 50 detected? It depends on what the uncertainty in that "16.6" is. Playing fast and dirty, take the Gaussian uncertainty in that false positive rate to be fractionally, 1/sqrt(2) = 0.70. So the false positive rate is 16.6+/-11.7. Which means, the 50 detected is only (50-16.6)/11.7 = 2.9 sigma above the false positive rate.
Nobody in science should claim a detection which is only 2.9 sigma different from the false positive number. This result is not significantly different from NO DETECTION of positive anti-bodies.
It's even worse than that, though, because I used the Gaussian uncertainty; and as you know - since you have taken an undergraduate class in statistics and understand the binomial, poisson, and Gaussian distributions - the Gaussian approximation isn't accurate for this situation, and one should use the Poisson distribution, or better yet the binomial distribution; but, again, because of that undergraduate class, you remember that both the Poisson and binomial distributions have a broad wingy excess in the higher direction above the mode; which means, the significance of even the detection of ANY positive covid19 antibodies in the Santa Clara County results is even LESS significant than 2.9 sigma.
The authors of this result should answer this criticism, stating what they believe to the the probability of detecting 50 positive results among 3330 tested, given their measured false positive rate.
On 2020-04-17 22:25:59, user LCMB wrote:
So they test a bunch of very wealthy white women from Facebook and Palo Alto/Mt. View, and think that represents a real finding? Why was the socioeconomic characteristic not noted in this report? It goes without saying that this particular demographic likely was self-distancing and using precautionary measures, able to shelter at home, and had the financial means to stay home. If this many people tested positive for anti-bodies, imagine if a real cross-section had been tested. The numbers here are way lower than the actual prevalence of antibodies found in our community. Regardless if the researchers proclaim they were able to adjust/weight the under-represented demographics or not.
On 2020-04-19 14:46:45, user IJ wrote:
A critical piece of information that is missing is the precise ad (graphics and all) that was used on Facebook, and the exact script that was followed when people responded to that ad. Since response bias is an important concern for this study, and since it is well known that even minor changes in how a question is asked can have a large effect on the results of a survey, this needs to be included as supplemental material.
Of particular concern is how the ad shaped participants' expectations about whether they would be told the results of their own test. Since people who have had symptoms or who have had contact with someone who has tested positive are more likely to have been infected, and since those people might be more curious about whether they had been infected, the desire to find out their own results could significantly bias the results of the study. Ideally, to eliminate this effect, the ad would say up front that participants would not be told the results of their own test. In any case, the way the ad shaped expectations about this is important information that is necessary to interpret the results of the study.
On 2021-06-01 22:05:39, user st_publichealth wrote:
The article is interesting. A few questions to the authors. Did you pre-specify the definition of negative PCR?<br /> Did you compare changes in viral loads from the baseline? Since the median viral load was higher in the placebo arm, this might have affected the likelihood of viral clearance at day 6 and it should be accounted for.
On 2020-07-05 18:52:28, user Kamran Kadkhoda wrote:
Neigher Abbott nor the authors used well pedigreed serum samples from patients recently infected with common coronaviruses . this means the specificity arm of the study is very well biased and essentially hundred percent is neither mathematically nor statistically possible.
On 2020-04-19 16:51:41, user Sinai Immunol Review Project wrote:
Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications
Fan Wu et al.; medRxiv 2020.03.30.20047365; doi:https://doi.org/10.1101/202...
Keywords
• Neutralizing antibodies<br /> • SARS-CoV-2<br /> • pseudotype neutralization assay
Main findings
In this study, plasma obtained from 175 convalescent patients with laboratory-confirmed mild COVID-19 was screened for SARS-CoV-2-specific neutralizing antibodies (nABs) by pseudotype-lentiviral-vector-based neutralization assay as well as for binding antibodies (Abs) against SARS-CoV-2 RBD, S1 and S2 proteins by ELISA. Kinetics of neutralizing and binding Ab titers were assessed during the acute and convalescent phase in the context of patient age as well as in relation to clinical markers of inflammation (CRP and lymphocyte count at the time of hospitalization). Across all age groups, SARS-CoV-2-specific nAbs titers were low within the first 10 days of symptom onset, peaked between days 10-15, and persisted for at least two weeks post discharge. In contrast to spike protein binding Abs, nAbs were not cross-reactive to SARS-CoV-1. Moreover, nAb titers moderately correlated with the amount of spike protein binding antibodies. Both neutralizing and binding Ab titers varied across patient subsets of all ages, but were significantly higher in middle-aged (40-59 yrs) and elderly (60-85) vs. younger patients (15-39 yrs). However, plasma nAb titers were found to be below detection level in 5.7% (10/175) of patients, i.e. a small number of patients recovered without developing a robust nAb response. Conversely, 1.14% (2/175) of patients had substantially higher titers than the rest. Notably, in addition to patient age, nAb titers correlated moderately with serum CRP levels but were inversely related to lymphocyte count on admission. In summary, the authors show that patients with clinically mild COVID-19 disease mount a strong humoral response against the SARS-CoV-2 spike protein. Compared to younger patients, middle-aged and elderly patients had both higher neutralizing and binding Ab titers, accompanied by increased CRP levels and lower lymphocyte counts. These patients are usually considered at higher risk of severe disease. Therefore, robust neutralizing and binding Ab responses may be particularly important for recovery in this patient subset. Conversely, patients who failed to produce high nAb/binding Ab titers against spike protein did not progress to severe disease, indicating that binding Abs against other viral epitopes as well as cellular immune responses are equally important.
Limitations
This study provides valuable information on the kinetics of spike protein-specific nAb as well as binding Ab titers in a cohort of convalescent mild COVID-19 patients of all ages. However, similar studies enrolling larger patient numbers, including those diagnosed with moderate and severe disease as well as survivors and non-survivors, especially in the elderly group (to rule out potential bias for more favorable outcome), are warranted for reliable assumptions on the potentially protective role of Abs and nAbs in COVID-19. Moreover, longitudinal observation beyond the acute and convalescent phase in addition to stringent clinical and immunological characterization is urgently needed. <br /> In their study, Wu et al. did not measure binding Abs against non-S viral proteins, which are also induced in COVID-19 and therefore could have added valuable diagnostic information with regard to patients who seemingly failed to mount both binding and neutralizing Ab responses against the SARS-CoV-2 spike protein. Likewise, while this study excluded cross-reactivity of nAbs against SARS-CoV-1, no other coronaviruses were tested. Of additional note, neutralizing activity of plasma Abs was only assessed by pseudotype neutralization assay, not against live SARS-CoV-2. Generally, while these are widely used and reproducible assays, in vitro neutralization of pseudotyped viruses does not necessarily translate to effective protection against the respective live virus in vivo (cf. review by Burton, D. Antibodies, viruses and vaccines. Nat Rev Immunol 2, 706–713 (2002)). Further studies are therefore needed to assess the specificity and neutralizing characteristics of these Abs to test whether they could be candidates for prophylactic and therapeutic interventions. In this context, setting arbitrary cut-off values (ID50<500 vs. a detection limit of ID50 < 40) and thus classifying up to 30% of patients in this study as “weak” responders does not take into account our currently limited knowledge regarding protective capacity of these nAbs and should therefore have been avoided by the authors.
Significance
This preprint is arguably the first report on neutralizing and binding Ab titers in a larger cohort of mild COVID-19 patients. Assessing Ab titers in these patients is not only important in order to confirm whether mild COVID-19 elicits robust nAb responses, but also adds further information regarding the use of plasma from mild disease patients for convalescent plasma therapy as well as vaccine design in general. Future studies will need to address now whether the nAb responses generated in mild disease will be protective or (functionally) different from nAbs generated in moderate and severe disease. The findings in this study are therefore of great relevance and should be further explored in ongoing research on potential coronavirus therapies and prevention strategies.
This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-11-19 15:54:00, user Lorenzu Borsche wrote:
Hello, this sentence:
Subsequently, we calculated sample size assuming a 50% between-group difference in hospital length of stay (considering 7 days as a median time of stay, with an expected variability of 9 days).
to me is not quite clear: do you mean, that you preset a desired length of stay to 7 days and the grouped the data so that both groups fit these 7 days? Thus did you mean a 50:50 distribution wrt the 7 days? If so, this cannot be done without distorting the data. If not, please explain, TIA Lorenz Borsche
On 2021-08-03 00:44:35, user practiCalfMRI wrote:
Any chance you could assess hematocrit levels in the COVID group before/after? While I wouldn't expect any significant changes in Hct for the controls, the WBC count for the post-infection group could be significantly increased, with RBC count decreased accordingly. (Pretty standard post-viral infection effect, esp. w/ serious disease.) If Hct is indeed lower, I would then wonder if there might have been a compensatory boost in local CBV which, by virtue of the different blood and tissue T1s, happens to manifest as patchy changes in apparent GM volume.
On 2021-09-12 06:33:29, user kdrl nakle wrote:
Very interesting, so mixing is the way to go. I want to see now J&J with AZ.
On 2021-12-08 20:43:25, user Peka Bali wrote:
page 15 of the full text report reads: "Symptom probability time courses for participants with confirmed COVID-19 (n=1020, RT-PCR, antigen, or antibody tests) overlapped significantly with probability estimates from the whole population (Figure 7), except for “changes in sense of smell/taste."
How does this coincide with the conclusion of the report on the first page?!
"Conclusions. Patients with Long COVID report prolonged multisystem involvement and significant disability. Most had not returned to previous levels of work by 6 months. Many patients are not recovered by 7 months, and continue to experience significant symptom burden. "
I am simply flabbergasted by this pseudo-scientific conclusion, not to mention giving it a collective name!<br /> If anything, the only rational conclusion that can be drawn is that other than the altered sense of taste/smell there is NO correlation or causation whatsoever between Covid-19 and the other 65 symptoms as described!<br /> The insinuation as posed above in the publication, stating negative PCR and antibody tests as "suspected cases" is just absurd. What do you base this assumption on?!
This report makes no sense: when you have a control group and baptizing your control group to "suspected cases" to justify the conclusion, which is that these symptoms are Covid related when they are clearly not.
On 2021-04-26 04:10:49, user ????' ???? ??? wrote:
Tables S4-S7 show coefficients for both male and female. How come? One should be the reference.
On 2020-06-06 13:59:32, user Nayo57 wrote:
While the result of this interesting and meaningful analysis may be statistically correct: a reduction of R of 0.04 with 10% mobility reduction does not explain the vast reductions from R = 3-4 at outbreak to below 1. A rough analysis of WHO reported case data and Google mobility data gave a similar result e.g. for time to reach R<1 or cumulative cases per population, measures one would expect to be impacted by effective social distancing. The best conclusion may be that mobility index as provided is not a suitable measure to assess or guide policies to contain COVID-19: Fig1 (Germany: increase in mobiilty index while R stays <1), Fig. 2(USA: decrease in mobility by further increase in R) and the scatter in Fig 3 support this view.
The interpretation would rather be that BEHAVIOUR during mobility activities matter much more than the QUANTITY of mobility. Alternatively, more focussed indexes (restaurants/bars; cinemas/theaters..) may be worth while to examine if they could be useful.
On 2023-10-06 12:33:05, user Ashok Palaniappan wrote:
An advanced version post peer-review is now available [open-access]:<br /> Muthamilselvan S, Palaniappan A. CESCProg: a compact prognostic model and nomogram for cervical cancer based on miRNA biomarkers. PeerJ. 2023 Sep 27;11:e15912. doi: 10.7717/peerj.15912. PMID: 37786580; PMCID: PMC10541812.
On 2021-08-26 05:47:36, user MarcoBonechi wrote:
You assume 2-3 students being infected at the beginning. Out of 500 students. 2.5/500=0.005 i.e. 500 cases per 100k.<br /> That's 10x actual Aug-2021 US rate at 46 (https://www.nytimes.com/int... "https://www.nytimes.com/interactive/2021/world/covid-cases.html)").<br /> 18x the CA rate of 26.
Your study has <10% chance of happening?
Please explain.
You should redo the study using several scenarios using randomized chances of a student being positive from outside.
Then also randomize symptoms, as symptomatic cases will stop spreading or be caught altogether before reaching school.
Then also randomize mask failure rate, badly worn masks, ineffective masks etc..
Finally add testing with weekly or twice-weekly universal antigen with their success rate.
You got to put more work!
On 2022-01-29 14:26:08, user Alberto wrote:
Thank you for this study. It's important to have this kind of study in a country like Greece where mortality has been very high in 2021 (compared to other European and worldwide countries and compared to itself in 2020) because we can appreciate the difference between the reality observed and the projected modeling based on the data that is available about vaccination status. The resulting model, which is incompatible with the reality observed worldwide, is a good measurement of the quality of the data available. I hope this can be looked at in more <br /> detail by more people thanks to this study.
On 2024-08-14 07:26:35, user Christina Dahm wrote:
Hi there!<br /> Since posting here, our paper has been published. Could you add this information to the preprint listing? Thanks!<br /> 10.1007/s00394-023-03090-3<br /> bw Christina
On 2020-06-24 20:42:23, user Ece Demirbas wrote:
TzanckNet will be a useful method in clinical applications by not only providing high accuracy, sensitivity and specificity but also lowering the cost of diagnosis for erosive-vesiculobullous diseases. Congratulations to the authors for such promising research. Ece Demirbas ,MD
On 2021-02-25 15:12:43, user jubel wrote:
"It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms." – do these 80% refer only to infected people who were hospitalized, or are mild cases (no hospitalization) included? That would be very important to know.
On 2021-08-21 04:36:50, user Fergal Daly wrote:
This applies linear regression to cumulative cases against NPI scores. It does not specify any model that justifies this. Simple models suggest a linear relationship between NPI scores and estimated R_t or log(case-growth). No model would suggest a linear relationship between these two. In the simplest example, if NPIs bring R_t below 0.9 it leads to very few cumulative deaths, with no much difference between very strict and less-strict, as long as R_t is < 0.9. Conversely, all NPI that leave R_t above 1.1 , lead to explosive growth and very similar large numbers of cumulative deaths. The relationship is highly non-linear and applying linear regression has no justification. The statistically significant outcome must be either chance or systematic result of the mis-specification.
On 2021-08-27 04:39:32, user William Brooks wrote:
The authors use cumulative deaths from June 2020 but don't explain why they omit deaths before June 2020 (i.e., the <br /> whole first wave). Since many of the deaths during the omitted period <br /> occurred in states with strict NPIs such as Maryland (Fig.1a), this probably biases the results in favor of stricter states since they would have had smaller susceptible populations after the first wave than other states. Another study got around this problem by excluding northeastern states from the main analysis of the summer wave and including them in the analysis of the autumn/winter wave [1]. Because different NPIs were introduced/lifted at different times in different states, it would be interesting to see how consistent the correlation between NPI strictness and cases/deaths is during different waves.
Also, Fig. 3a shows that case trajectories are clearly effected by geography, so rather than directly compare two states with different NPI strictness from different regions (Maryland and Tennessee), it might be more informative to compare two states with different NPI strictness from the same region (e.g., Louisiana and Florida).
On 2025-06-26 07:45:58, user Stephen Jones wrote:
Now fully available as of June 26, 2025 at<br /> https://doi.org/10.1162/IMAG.a.76
On 2021-03-17 20:32:20, user Alessia Visconti wrote:
Now published in the British Journal of Dermatology (https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/full/10.1111/bjd.19807)")
On 2021-08-03 21:39:28, user Fruit Gal 522 wrote:
Judging from how long ago this was published...June 1, and it's now Aug. 3, it's taking excessively long for this to be peer reviewed. Sources from a google search say it should take 3-4 weeks to review.
On 2021-08-17 21:21:24, user Harold Saive wrote:
There is no evidence that masks provide mitigation
On 2021-01-09 09:39:25, user Dr. Sebastian Boegel wrote:
This is a wonderful study. Congratulations. I am very honoured that you used my tool, seq2HLA. As seq2HLA also output HLA gene (and allele) expression (normalized to RPKM and the coounts), i am wondering why you additionally used AltHapAlignR for obtaining read counts for HLA genes. Did you experience any issues with seq2HLA? If yes, i am happy to help. <br /> All the best for you and keep up the great work,
Sebastian
On 2020-04-11 15:25:20, user Sinai Immunol Review Project wrote:
Title: Level of IL-6 predicts respiratory failure in hospitalized symptomatic COVID-19 patients
Summary:<br /> As hospitals around the world are being overwhelmed due the COVID-19 pandemic, it is of utmost urgency to identify biomarkers which would accurately predict patient outcome at an early stage. Severe COVID-19 complications often include acute respiratory distress syndrome.<br /> The authors analyse clinical and laboratory findings in 40 COVID-19 patients: 13 required mechanical ventilation, and 27 did not. Age, comorbidities, radiological findings, respiratory rate or qSofa score (mortality prediction score upon sepsis) did not discriminate between<br /> both groups. However, all patients requiring intubation were male, and the pulse as well as several laboratory parameters correlated with the risk of respiratory failure. The strongest correlation was found to be with serological IL-6. Elevated IL-6 was found to be associated with the risk of respiratory failure by a factor of 22.
Critical analysis:<br /> The value of 80pg/mL of IL-6 was identified as decisive, as 92% of patients who reached this cut-off required intubation 0-4 days afterwards. The study must be extended to a larger cohort, however, to perhaps identify a more precise cut-off.<br /> Moreover, the causality between IL-6 and respiratory distress remains unclear. Understanding the mechanisms behind these correlations could pinpoint new therapies to prevent respiratory failure.
Relevance to current epidemic:<br /> Foreseeing respiratory distress at an early stage in admitted COVID-19 patients could allow hospitals to better manage their resources, both human and material.
By Maria Kuksin
On 2020-07-14 03:14:19, user Robert Kernodle wrote:
I hope a statistical expert looks at this, because I suspect significant flaws in methodology that do not justify the conclusions. Based on physics, fluid dynamics -- the extension of these basic principles to the structure of woven cloth masks, in relation to infectious aerosols -- this supposed statistical study does not seem to hold up to reality.
On 2020-05-10 04:11:31, user JM V wrote:
Not taking shot noise of the 7 deaths into account. If the expected deaths would have been 14.6, the two sigma plausibilty range would have been 7 to 22. The error bounds in this paper do not reflect this.
On 2021-03-03 01:17:29, user Dawn Christine Khan wrote:
I am a covid survivor, and said the same. 95 symptoms was incomplete. <br /> I had 150. This is the most comprehensive Long Haul research I have seen. I recommended it for CDC/NIH publication. Community NEEDS this!! May I receive a text or spreadsheet list of symptoms and categorization used? for more information http://www.linkedin.com/in/...
On 2022-01-24 21:23:22, user KBNJ wrote:
Am I wrong in thinking it's entirely possible that the negative effectiveness of the vaccine for recovered people is real (biological), and not behavioral? Our bodies don't have unlimited immune resources. If vaccines induce an immune response that is less effective than recovered immunity at fighting an evolved variant (which various studies have shown at least for Delta), I would think this would be expected, and not a surprise.
On 2021-12-25 19:28:38, user Nate Dyer wrote:
Decades of coronavirus research warned us about this.
Why was it ignored?
Why did scientists choose to make vaccines that specifically target the spike protein when so many studies made the warning below?
"We suggested that antibodies against spike proteins of SARS-CoV may cause ADE effect. This data raises reasonable concern regarding the use of SARS-CoV vaccine and shed light on some roles in SARS pathogenesis."
On 2020-04-10 11:48:59, user Srinivasa Kakkilaya wrote:
It's a very interesting analysis which should show the way forward in this crisis. If I'm allowed, I am posting a brief analysis that I did the day before, with data collected from various official sources and publications. It's here below:
Corona Virus Disease (COVID) 2019: Comparison of Cases in India and Abroad
Summary:
The trends of COVID 19 infections, complications and mortality are similar in almost all the countries, including India.
Risk of developing severe disease and death is higher in those aged 60+ years, and particularly in those with modern diseases such as hypertension, diabetes and coronary artery disease.
In India, 8.5% of the population is aged 60+ years, and 4-11% of the population aged less than 40 years is afflicted with hypertension and diabetes, and these are vulnerable to severe COVID 19.
The common factor for increased risk of severe COVID 19 is the presence of the so called metabolic syndrome at any age, old or young. These disorders are related to consumption of sugars and sweets, fruit juices, sweetened beverages, processed and fast foods, fried foods etc., and also alcohol consumption, and smoking. Avoiding these will be helpful in combating COVID 19.
COVID 19 remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are likely to have very mild or no symptoms.
Details:
India has already recorded about 5500 cases and more than 160 deaths due to COVID 19. The following analysis is based on the scientific and media reports published so far from India and elsewhere.
Corona Virus Infections - Age Distribution:
India:
47% of infections in age <40 years<br /> 34% in age 40-60 years<br /> 19% in age >60 years.
Wuhan, China:
27.2% in the age 0-39 years<br /> 41.6% in 40-59 years<br /> 31.2% in >60 years
It's almost identical in India and China and it correlates with the age distribution of population.
China:
<60 years - 82% of the population, 69% of infections
60 years - 18% of the population, 31% of the infections.
India:
<60 years - 91.5% of the population, 83% of the infections
60 years - 8.5% of the population, 19% of the infections
The higher percentage of infections in the elderly is likely due to more prominent symptoms than the younger population and hence presentation to the hospitals in more numbers.
COVID 19 Deaths: Age Distribution and Risk Factors
India
63% of deaths in those 60+ years of age 30% in those aged 40-60 <br /> 7% in those below 40 years
Average age of victims - 60 years
Average Case Fatality Rate -2.7%<br /> 0.4% for those below 40 years<br /> 2.4% for 40-60 years<br /> 8.9% for those above 60 years
86% had pre-existing conditions<br /> 17% had more than three diseases<br /> 40% had two<br /> 35% had one<br /> 56% had diabetes<br /> 47% had hypertension<br /> 20% had lung disease<br /> 16% had heart disease with diabetes and/or hypertension.
This pattern is also comparable with other countries.
China
81% deaths in age 60+ years<br /> 16.4% in 40-60 years<br /> 2.6% in 10-40 years<br /> 0 in <10 years
The average case fatality rate 2.3%;<br /> 0.2% for those below 40<br /> 0.85% for 40-60<br /> 8.8% for those above 60 years<br /> (14.8% in patients above 80 years)
Italy
95% deaths in age 60+ years<br /> 4.7% in 40-60 years<br /> 0.27% in 0-40 years
99.2% had one or more pre-existing diseases (75% had high blood pressure, 35% had diabetes and 33% had coronary heart disease)
United States (of the first 1150 deaths)
89.9% in 55 years and above<br /> 9.4% in 35-54 years<br /> 0.7% in 0-34 years
UK (of 750 deaths)
69% aged above 75+ years<br /> 96% had pre-existing conditions
These details clearly show that in all the countries, the case fatality of COVID 19 has shown direct correlation with age of the patients and with age-related diseases such as hypertension, diabetes and coronary artery disease and that the mortality was higher in men compared to women.
In India, 63% of deaths occurred in those above 60 years of age, and 30% deaths occurred in those aged 40-60. Considering the fact that 86-90% of the deaths occurred in those who had pre-existing diseases, the higher number of deaths in the 40-60 years age group seen in India is attributable to younger onset of these diseases in Indians. In India, the overall prevalence of hypertension is about 30%, and about 11% in the age group of 40 years or lesser. Type 2 Diabetes has an overall prevalence of 16-19%, whereas in the young, it is about 4-8%. These diseases, coupled with consumption of alcohol and tobacco, increase the risk for COVID 19 complications in those aged above 60 and also in those who are younger. Otherwise, COVID remains a mild illness in almost 80-90% of those infected, and many patients lesser than 30 years of age are very likely to have very mild or no symptoms.
If I may add, it appears that the deaths are directly related to metabolic syndrome linked disorders and the 33 cases that apparently had no identifiable cause in NY in your series might have had other problems of metabolic syndrome such as hypertriglyceridemia or premature balding etc., all of which are linked to hyperinflammatory state.<br /> Thank you again for the interesting and path breaking effort!
On 2020-07-21 02:10:17, user Paul Gordon wrote:
Hi, thanks for posting this. I see that this is in press at JCM, congratulations. Might it be worth noting that the described mutation occurs not just in the 8 described genomes in the manuscript, but also these 7 in GISAID?
Belgium/rega-0423297/2020 <br /> Belgium/rega-0423298/2020<br /> Belgium/rega-0423299/2020<br /> Belgium/rega-0423300/2020<br /> Belgium/rega-0423301/2020<br /> Belgium/rega-0423302/2020<br /> Belgium/rega-0423303/2020
Or is this a resampling of some of those same genomes? Thanks for any clarification you can provide.
On 2021-08-27 02:02:02, user Alexis Hill wrote:
Nice search, I wonder will you provide sample collection time, information on transmission pair?
On 2020-02-13 11:26:17, user ClosedCloak wrote:
I couldn't find any information about the data of bed numbers in 99.com.cn. The authors should clarify it.
On 2020-09-24 00:34:03, user Peter Olins wrote:
@Bjorn, <br /> Perhaps I'm missing something, but I don't understand why you assume a 12-second interval between breaths when the resting rate for adults is typically one breath every 3-5 seconds. In addition, I suspect that a high breath rate would be expected for people socializing and eating lunch in a crowded restaurant. <br /> What effect would a 4-fold increase in respiration have on your calculations?
Peter Olins, PhD.
On 2021-07-18 18:29:06, user Paul Buehrens wrote:
Who is modeling the rapid growth of the delta variant in the US?
On 2025-02-27 18:33:49, user Gholamreza Farnoosh wrote:
The authors of this manuscript information and data about the people of Iran and their health and health society without documents and only based on personal opinions and mentalities, which need to be answered.<br /> In this MS, Iranian scientists, researchers, and medical personnel have been accused of becoming populists in the field of health during the Covid-19 epidemic, despite the fact that more than 400 physicians, nurses, etc. died while treating patients.<br /> The publication of more than 16,000 valuable articles in the field of Corona by Iranian researchers (Indexed in Scopus, which ranks Iran in the 15th ranking in the world) cannot be ignored.<br /> Failure to purchase vaccines from foreign countries and lack of vaccination on time were among the issues mentioned in this MS, while at first the purchase of vaccines and vaccinations were on the agenda of the managers of the Iranian Ministry of Health, but the American sanctions were an obstacle in this way. Of course, with great efforts, the purchase of vaccines from China and Russia and the continued research and production of Iranian vaccines were carried out.<br /> Attendance at religious ceremonies and its relationship with the increase in deaths in Iran are among the other issues that have been mentioned, while during the Corona epidemic, Iran's political and spiritual leaders emphasized not to hold mass ceremonies in mosques, etc., and even the shrine of Imam Reza, which is one of the holiest places for Muslims in the world and Iran, was also closed. In addition, the trends of the disease Prevalence peaks in Iran were similar to the peaks created in the world.<br /> Why is the percentage of deaths in Iran compared with countries like Iraq, Pakistan, Afghanistan, Yemen, etc., where vaccination was not important? Why Iran has not been compared with advanced countries such as America, France, Brazil, Russia, India, etc., which were mostly vaccine manufacturers and whose death rate was higher than Iran?<br /> Why have Iranian researchers, who were among the creators of the vaccine, been insulted based on the false data that was published in one of the magazines? Why has the understanding and intelligence of the civilized people of Iran been insulted with the title of being populist? Were the Pfizer, AstraZeneca, etc. vaccines completely effective and safe? And many questions that can be asked to the authors of this paper.<br /> In the end, I would like to thank the administrators of the medrxiv site, who provide the basis for the publication of articles to help solve the problems of in the field of health, and I will soon present you a letter in this regard with documentation. I hope that the explanations presented can have a desired effect on the fair reaction of the medRxiv administrators in the continuation of the publication of the considered MS.
On 2021-08-18 19:00:42, user FABIO LIPIANI wrote:
very interesting, the immune system undergoes a very evident reprogramming of which no long-term effects are known,
On 2025-06-10 22:21:20, user notAloser wrote:
good read
On 2021-07-31 13:57:16, user Richids Coulter wrote:
Data from the UK shows an almost complete decoupling between cases and hospitalizations/deaths - this won’t pass peer review because it’s complete and utter nonsense, par for the course for Fisman and Tuite who have been almost completely wrong with their modelling the entire pandemic.
On 2025-09-09 18:10:14, user José I Cuende wrote:
https://www.medrxiv.org/content/10.1101/2025.05.05.25327022v1#comments
The article has been accepted and published ahead of print:
Cuende JI, Guijarro C. Vascular age calculation from SCORE2 and SCORE2-OP cardiovascular risk tables. Clin Investig Arterioscler. 2025 Aug 1:500841. http://dx.doi.org/ 10.1016/j.arteri.2025.500841.
On 2021-10-08 21:10:26, user Me wrote:
Deeply flawed seems like an overstatement unless the PCR false-positive rate is high. For instance, this UK study says it's up to 5%. Is that high enough to discount this preprint?
On 2021-12-29 00:23:23, user madmathemagician wrote:
How do they deal with a zero in the data set, there are no zeros in a Benfords' distribution.
On 2020-10-03 21:14:41, user Alberto Villena wrote:
Please, read the following Preprint: L. Ferretti et al., The timing of COVID-19 transmission.<br /> https://www.medrxiv.org/con...
On 2021-02-02 03:28:10, user Kenneth Sanders wrote:
Given the prevalence of individuals with previous asymptomatic infection due to SARS-<br /> CoV-2, is there an implication that all individuals (not already confirmed to have had the disease) should be tested for existent antibodies to SARS-CoV-2 prior to first dose of vaccine? Subsequently, only those naive to SARS-CoV-2 before vaccination would receive two doses.
On 2020-09-27 22:00:20, user nan wrote:
It would be be helpful to know the dosage.
On 2025-12-04 07:03:00, user Paul Olale wrote:
Very insightful with very viable best practices worth emulating in future cholera response expeditions as well.
On 2021-10-20 08:13:57, user ClearSkys wrote:
"...the vaccination coverage rate is inversely correlated to the mutation frequency of the SARS-CoV-2 delta variant"
Correlation != causation
Motives are questionable especially when the authors then go on to recommend the public health policy based solely on the correlation.
On 2021-08-11 19:32:53, user SuperbFlab wrote:
You're skating incoherently. The particle size of a virus goes right through the mesh of surgical/medical/cloth masks. And no, the fact is not on your side. We have 100 years of studies that refute your claim. The onus is on YOU to over turn the body of evidence and it has never been done.
On 2021-09-25 10:25:07, user Stephen Dedalus wrote:
I just want to see your face when you read this treasure by the CDC themselves here: <br /> https://wwwnc.cdc.gov/eid/a...
On 2021-04-25 20:26:35, user Steven Wouters wrote:
Since this study group consisted of health care workers, it is likely that natural immunity was acquired in these individuals. That immunity may also have been acquired asymptomatically without ever testing pcr positive.(1)
It is possible to find out whether there is naturally acquired immunity by using biomarkers. There is a difference between the antibodies elicited by natural infection compared to that from the vaccine. Since the vaccine does not have other parts besides the S-protein in contrast to wild virus.
Zhongfang Wang, Xiaoyun Yang, Jiaying Zhong, Yumin Zhou, Zhiqiang Tang, Haibo Zhou, Jun He, Xinyue Mei, Yonghong Tang, Bijia Lin, Zhenjun Chen, James McCluskey, Ji Yang, Alexandra J. Corbett & Pixin Ran https://www.nature.com/arti...
On 2020-07-30 06:51:21, user Marm Kilpatrick wrote:
Thank you for this important study. Could you clarify if there is a typo in both Table 1 & 2 concerning the 2nd age group? It says 25-29 in both cases, but this would mean there are no data for ages 15-24. Should the age group in both cases be 15-29?<br /> thanks,<br /> marm
On 2020-09-25 03:59:30, user Eitan wrote:
The results are interesting and promising. However, the fact that they are statistically significant does not mean that they are statistically "strong" as long as the R^2 of the linear regressions is not presented. The plots are very scattered and it seems that the R^2 value is much smaller than 0.5. If this is the case, the readers should be very cautious when drawing consequences. If for example the R^2 is 0.3, the statistical meaning is that only 30% of the variance is significant and can be explained by the vitamin D values. But 70% of the variance is affected by other factors. Can you present the R^2 values?<br /> Thanks
On 2023-06-22 16:56:53, user Benjamin Shuman wrote:
This is interesting work. The descriptions of methodological variation (task, muscles measured, amplitude normalization method) certainly impact how the studies compare to one another. However, there is no discussion of filter parameters and its impact on the interpretation of synergy metrics. With more aggressive filtering the resultant processed EMG signal is less variable which also has a direct impact on the number of synergies extracted or tVAF1/DMC. The Collimore article is noted for having the least number of synergies identified but also has the most aggressive LP filtering (4hz). There may be additional trends in the article findings when looked at through a filtering lens and I would encourage the authors to consider this. Finally, please note that DMC is a linear transformation of tVAF1 (Steele 2015). As such the trends in DMC are directly comparable to tVAF1.<br /> Thanks,
On 2020-03-25 03:16:39, user Sinai Immunol Review Project wrote:
Main findings: Antibodies specific to SARS-CoV-2 S protein, the S1 subunit and the RBD (receptor-binding domain) were detected in all SARS-CoV-2 patient sera by 13 to 21 days post onset of disease. Antibodies specific to SARS-CoV N protein (90% similarity to SARS-CoV-2) were able to neutralize SARS-CoV-2 by PRNT (plaque reduction neutralizing test). SARS-CoV-2 serum cross-reacted with SARS-CoV S and S1 proteins, and to a lower extent with MERS-CoV S protein, but not with the MERS-CoV S1 protein, consistent with an analysis of genetic similarity. No reactivity to SARS-CoV-2 antigens was observed in serum from patients with ubiquitous human CoV infections (common cold) or to non-CoV viral respiratory infections.
Analysis: Authors describe development of a serological ELISA based assay for the detection of neutralizing antibodies towards regions of the spike and nucleocapsid domains of the SARS-CoV-2 virus. Serum samples were obtained from PCR-confirmed COVID-19 patients. Negative control samples include a cohort of patients with confirmed recent exposure to non-CoV infections (i.e. adenovirus, bocavirus, enterovirus, influenza, RSV, CMV, EBV) as well as a cohort of patients with confirmed infections with ubiquitous human CoV infe<br /> ctions known to cause the common cold. The study also included serum from patients with previous MERS-CoV and SARS-CoV zoonotic infections. This impressive patient cohort allowed the authors to determine the sensitivity and specificity of the development of their in-house ELISA assay. Of note, seroconversion was observed as early as 13 days following COVID-19 onset but the authors were not clear how disease onset was determined.
Importance: Validated serological tests are urgently needed to map the full spread of SARS-CoV-2 in the population and to determine the kinetics of the antibody response to SARS-CoV-2. Furthermore, clinical trials are ongoing using plasma from patients who have recovered from SARS-CoV-2 as a therapeutic option. An assay such as the one described in this study could be used to screen for strong antibody responses in recovered patients. Furthermore, the assay could be used to screen health care workers for antibody responses to SARS-CoV-2 as personal protective equipment continues to dwindle. The challenge going forward will be to standardize and scale-up the various in-house ELISA’s being developed in independent laboratories across the world.
On 2020-09-17 17:32:47, user kpfleger wrote:
This is a great analysis. It is a shame it hasn't gotten more widespread attention. Causal inference is an important technique that not enough researchers have close familiarity with. The analysis here is not hard to follow, even for non-experts or non-scientists. While there were other important pieces of evidence linking vitamin D causally to health outcomes in other infectious disease and lung injury prior to 2020, this was the first paper to provide solid evidence of a causal relationship between vitamin D and COVID-19 specifically.
On 2021-05-12 13:15:26, user John Smith wrote:
Hi, I see the IHME have just published their excess death figures:
http://www.healthdata.org/s...
I was wondering how they came to their figures on Japan and Kazakhstan which differ from yours substantially.<br /> They have Japan as 108,320 excess deaths and Kazakhstan as 81,696. They also differ with many others also. Interesting reading.
On 2022-02-05 13:03:59, user GregoryGG wrote:
Hello,<br /> Unless I misunderstood, <br /> How do you know that the behaviour of people, the transmission prevention measures and the testing entry rules were the same between delta and omicron; and between vaccinated and unvaccinated ?
Also, since we know that immunity against a single variant may lower down over time, can we still consider single- and double-dosed people as being vaccinated? (they could be considered as non vaccinated over time).
Thank you.
On 2020-05-20 06:25:22, user Bob wrote:
How do the authors reconcile a lower bound of 0.02 IFR with the fact that 0.026% of Americans have already died from SARS-CoV-2? Kind of difficult to have an IFR lower than that.
On 2020-03-24 18:03:39, user Sinai Immunol Review Project wrote:
This study is a cross-sectional analysis of 100 patients with COVID-19 pneumonia, divided into mild (n = 34), severe (n = 34), and critical (n = 32) disease status based on clinical definitions. The criteria used to define disease severity are as follows:
Severe – any of the following: respiratory distress or respiratory rate >= 30 respirations/minute; oxygen saturation <= 93% at rest; oxygen partial pressure (PaO2)/oxygen concentration (FiO2) in arterial blood <= 300mmHg, progression of disease on imaging to >50% lung involvement in the short term.
Critical – any of the following: respiratory failure that requires mechanical ventilation; shock; other organ failure that requires treatment in the ICU.
Patients with pneumonia who test positive for COVID-19 who do not have the symptoms delineated above are considered mild.
Peripheral blood inflammatory markers were correlated to disease status. Disease severity was significantly associated with levels of IL-2R, IL-6, IL-8, IL-10, TNF-?, CRP, ferroprotein, and procalcitonin. Total WBC count, lymphocyte count, neutrophil count, and eosinophil count were also significantly correlated with disease status. Since this is a retrospective, cross-sectional study of clinical laboratory values, these data may be extrapolated for clinical decision making, but without studies of underlying cellular causes of these changes this study does not contribute to a deeper understanding of SARS-CoV-2 interactions with the immune system.
It is also notable that the mean age of patients in the mild group was significantly different from the mean ages of patients designated as severe or critical (p < 0.001). The mean patient age was not significantly different between the severe and critical groups. However, IL-6, IL-8, procalcitonin (Table 2), CRP, ferroprotein (Figure 3A, 3B), WBC count, and neutrophil count (Figure 4A, 4B) were all significantly elevated in the critical group compared to severe. These data suggest underlying differences in COVID-19 progression that is unrelated to age.
Given the inflammatory profile outlined in this study, patients who have mild or severe COVID-19 pneumonia, who also have any elevations in the inflammatory biomarkers listed above, should be closely monitored for potential progression to critical status.
On 2020-05-15 09:34:45, user Dude Dujmovic wrote:
The newer NY survey had the data "weighted" down, with lower rates.
On 2020-04-16 09:50:33, user Herf wrote:
On 2021-06-22 03:53:16, user Bob Horvath wrote:
Thank you for this paper - parents of PANS patients are grateful to see this kind of genetics work being done on PANS. May I ask:
1) What was the total number of variants meeting the criteria described (at lines 141-144 of the document for the European samples, and lines 154-158 for the U.S samples)?
2) Presumably, the list of candidate genes (described at lines 161-162) were all the genes that encompassed the variant lists in 1) above, with the possible exception of MTHC2 and BID that are mentioned as additions. What was the total number of candidate genes considered, before the list was narrowed to the 11 listed?
On 2021-12-22 18:19:23, user Kurt the Turk wrote:
I hope the post boost samples can be made available to the Emory lab to show neutralization of live viruses. That would really complete the picture.
On 2020-04-16 19:48:56, user Stef Verlinden wrote:
It is troublesome that the article does not give the materials and the method used to derive/calculate the QTc. Formally a QTc can only be calculated from a signal derived from Lead II of V5 registered with a 12 lead ECG machine. QTc's calculated by a computer algorithm (unless it is specifically validated to do that job) are not to be trusted. This needs to be done by hand by a well-trained person.
Most importantly, the QTc calculation is not linear. A heart rate of 90 gives an overestimation of the QTc of 50 ms (when using the Bazett method). Did the authors correct for this? Based on this paper, it is not possible to establish whether the QTc's are truly prolonged or that these are false positive outcomes.
For reference check; QTc: how long is too long? https://www.ncbi.nlm.nih.go...
On 2021-10-30 12:26:02, user Siguna Mueller, PhD, PhD wrote:
I am curious about the one unvaccinated individual with the mutations. Did they never have any Covid inoculation at all, or were they just not "fully vaccinated?" In other words, what criteria are you using to determine if someone was fully vaccinated as opposed to unvaccinated? Is it possible that this "unvaccinated" person was actually partially vaccinated? Thanks.
On 2021-10-03 05:25:47, user kdrl nakle wrote:
Put the dates in abstract or in title. June 2020 for this serosurvey. Quite irrelevant now, more than a year after, good only for historical reference.
On 2024-10-01 20:13:12, user Vitor wrote:
This work is now published in Cell Genomics: https://doi.org/10.1016/j.xgen.2024.100636
On 2021-08-23 09:13:18, user Isatou Sarr wrote:
Hi,
is there any approved, readily available prophylaxis (ready to use existing drugs or re-purposed) that can be taken particularly by children to add up to the set-out plan for reducing/stopping the transmission cycle of the virus? I just don't know how effectively applicable the non-medical preventative measures will be in resource limited settings where classrooms are not usually structured to accommodate the COVID-19 preventative measures and access to clean water supply is a problem for atleast the hand washing aspect to be adhered to as it should be.
Thank you.
On 2023-04-25 17:12:24, user Héctor Ramos wrote:
The paper has been accepted and published at Georgian Medical News.
Pubmed: https://pubmed.ncbi.nlm.nih...<br /> GMN: https://www.geomednews.com/...
On 2021-04-28 17:27:54, user Orison Woolcott wrote:
This study has been peer-reviewed and published in Scientific Reports: https://www.nature.com/arti...
On 2025-09-25 01:05:32, user Florian Hladik wrote:
The abstract states, "we treated eight recipients with material from a single donor". However, it seems you treated four recipients with VMT and the other four with the placebo. Correct? It's confusing as written. In the Results too. Otherwise, great work! The other paper reporting the L. crispatus RCT is cool as well!
On 2020-05-12 15:25:42, user Francois Alexandre wrote:
I believe that there is another major limitation for this work, that should be at least acknowledge by the authors in the study limitation section: the R0 of 3.1 they used in the study is the R0 calculated at the beginning of the lockdown in France. Yet, the R0 during the middle of March, in the ascending phase of the outbreak, could be very different at that time. The authors state that this R0 is consistent with those observe in China during the ascending phase of the outbreak during January. However, R0 of a given virus is usually not stable across region and time (depending on several factors not restricted to temperature, such as humidity, weather, natural immunity due to vitamin D...). For example, the other human coronavirus have a R0 below 1 during summer in France, but the R0 increases during winter. And they also have different dynamics with some peaking in november, while some peaking in February. Furthermore, in early March, some observations in other countries (Brazil, West Africa) have underlined lower transmission rates compared with that observed in the countries located in the latitude 25-55° (Europe, USA). Therefore, serious doubts exist that the R0 of March will still be the same at May in France after the lockdown, without taking into account the potential unknown natural dynamic of the outbreak that has been masked by the lockdown and other procedures to slow the outbreak.
On 2020-07-15 16:36:55, user Peter Ellis wrote:
Death data for the UK (and its constituent nations) show a very pronounced weekend effect when analysed according to the day of reporting - see for example the graph at the bottom of this page:<br /> https://coronavirus.data.go...
However, in England the "weekend effect" almost entirely disappears when data are analysed according to the actual date of death rather than the date of reporting - see for example the daily data files reported here:<br /> https://www.england.nhs.uk/...
This therefore appears to be an entirely artefactual phenomenon driven by reduced reporting at weekends. The authors dismiss this possibility and assert that "one would expect for the pooled world data to be averaged rather than this almost weekly periodicity". Unfortunately for their hypothesis, Saturdays and Sundays fall on the same date everywhere in the world.
On 2022-01-07 15:49:40, user Franciska Ruessink wrote:
The study heavily relies on vaccinated and unvaccinated people being equally eager to be tested. But unvaccinated test less https://covid19danmark.dk/#... so probably they only test with more severe symptoms. If the secondary case for unvaccinated is 28-29 % for both Omicron and Delta, there may be a lot more untested Omicron cases behind that than untested Delta, as Omicron is milder.
On 2022-01-31 21:33:01, user Anonymous wrote:
What do the "ref" mean in table 2. Trying to see the figures for fully vaccinated individuals.
On 2022-02-14 04:09:55, user RBNZ wrote:
"The estimates are furthermore adjusted for vaccine status of the potential secondary case interacted with the household variant, and the vaccine status of the primary case. "
There is no information included as to how vaccination status adjusts the odds ratio.
On 2021-08-14 17:37:30, user Uwe Schmidt wrote:
The study states a hospitalisation rate of 6% for children.
This rate needs to be strongly questioned as it is internationally significantly higher than any other rate observed. In fact, it is higher by roughly factor 10-12. E.g. in Germany, at the peak of the pandemic in week 51/2020, less than 100 children were hospitalised nationwide, 1/3 of them newborn, who just stayed in hospital a little longer. The number of positive tested children in that week was ~20,000. For July 2021, the number of hospitalised children is less than 10, no ICU.<br /> In England, one out of 200 (0.5%) children are hospitalised.<br /> In Israel, no patient below the age of 30 is in critical condition.
Questions for the authors:<br /> 1. Does the total number of children tested positive really consist of ALL PCR-positive or only a subgroup reported by certain institutions?<br /> 2. Of those 5,213 hospitalised, how many were hospitalised because of COVID-19 and how many because of other conditions?
On 2021-08-13 00:46:51, user Bung Prachya wrote:
The problem lies in the design. With case-control study, you should get only Odd ratio, not %effectiveness.
They said 80% of their population was vaccinated (the numbers seem wrong). If you found that among 1700+ covid patients, only 10 were vaccinated guys, you should think about effectiveness of 95%something.
On 2020-03-08 05:57:17, user James Nokes wrote:
Highly informative paper. Thank you. A few points/questions:
Table 1 indicates it is contact-based surveillance with higher proportion male than female contrary to the results text.
How was temperature measured and what was the definition of fever?
How were nasal samples collected (eg nasopharyngeal swab, per-nasal swab, aspirates). Did the method differ for contact and case-based surveillance?
Assessing severity status - (i) can you clarify if moderate required all three of fever, respiratory symptoms, and radiographic evidence of pneumonia? What is included in 'respiratory symptoms'? (ii) How did you measure oxygen saturation?
Table S1. It would be useful to include the proportions with fever. The proportion of cases from symptom-based surveillance with shortness of breath (4%) or difficulty breathing (3%) is remarkably low.
On 2020-03-12 04:07:45, user A G wrote:
In response to your comment "Therefore, would it be feasible to look for cross-correlations between the RNA and proteic sequences of the NCOV 2019 and the immunogenic epitopes of the vaccines administeded to chinese children? If a correlation does exist, it could be possible to vaccinate the whole sensitive population."
Is this something that your team is going research on? Do you know of any labs that are doing research on this?
On 2022-07-20 16:59:17, user Tania Watts wrote:
The authors may want to note similar findings in our paper, Dayam et al. Accelerated waning of immunity to SARS-CoV-2 mRNA vaccines in patients with immune-mediated inflammatory diseases, JCI Insight, 10.1172/jci.insight.159721 April 2022. We show anti-TNF treated patients have lower Ab responses, no neutralization of Omicron and enhanced waning of T and Ab responses to SARS-CoV-2 mRNA vaccines after 2 doses.
On 2020-12-15 00:40:01, user David McCarthy wrote:
Great work Pengbo, Christine and the rest of the team!. Love the work. We recently also uploaded our pre-print on this very topic from the Australian context. If anyone is interested, please check it out: http://dx.doi.org/10.13140/...
On 2022-12-09 15:39:45, user Christine wrote:
An updated count of US PICU beds from 2018 by the AAP shows n= 5,388. Still a disproportionate amount compared to Canada!
On 2020-08-17 14:30:16, user Ciron Soauv wrote:
This study was cited in a nice powerpoint (link below) made by the Dr.<br /> Wanderson Oliveira from Brazil Ministry of Health, titled "scientific<br /> evidence of pandemic impact". It is being circulated on WhatsUp, together<br /> with videos from Dr. Ioannidis from Stanford, who suggests mortality rates are<br /> around 0.1%. Unfortunately, many people in that country, where more than 100,000<br /> people died of COVID, minimises the pandemic impact justifying it with those<br /> pre-prints. Noted, no fault on the authors – they are the ones that need to get<br /> “immune” to misrepresentation.
On 2020-04-30 14:30:10, user Ewa Kirkor wrote:
One more difficulty is in establishing the start and end of the period of contagion after the infection takes hold. Some individuals still have positive rt-PCR test outcome 6 weeks after the COVID19 symptoms appear. How would such spread of parametrization of the model affect its predictions? Could you let me know at EKirkor@NewHaven.edu
On 2025-10-26 09:31:29, user Hannah Maude wrote:
A wonderful study and very interesting results! A comment on the discussion, noting on page 22 "Brain-expressed genes contribute to ME/CFS risk". Given the involvement of the peripheral nervous system in ME, might it be valid to say "Genes expressed in brain and neural tissue"? Peripheral nervous tissue is not so well represented in GTEx, although the Nerve_Tibial column shows high PPH4 for several genes in fig 4. Also perhaps of interest are sex-specific patterns of gene expression in peripheral nervous tissue https://pmc.ncbi.nlm.nih.gov/articles/PMC6412153/
On 2020-03-26 02:33:57, user Elisabeth Bik wrote:
Figures 2 and 3 would work better if the letters A, B, C, etc, were replaced by the actual serum marker name. Also, panels G and H in Figure 2 appear to be duplicated (same Y axis label and same graphs). Could the authors check, please?
On 2023-11-13 10:04:59, user Theo Peterbroers wrote:
"The duration from the day of index vaccination to the day of the survey completion was a median of 595 days (Interquartile Range<br /> (IQR): 417 to 661 days; range: 40 to 1058 days)."<br /> That is at least one participant vaccinated before the start of the pandemic.<br /> EDIT Make that one person from early in the vaccine trials. How time flies.
On 2021-12-23 20:05:57, user Julien Reszka wrote:
What does negative effectiveness mean ?
On 2021-01-10 08:48:26, user JS wrote:
This preprint was published in Nature in July,<br /> doi.org/10.1038/s41564-020-0761-6
On 2020-10-14 20:06:18, user Patrick Palmieri wrote:
Did a large number of COVID-19 infected people seeking health services at EsSalud know they were not receiving evidence-based interventions? In addition, were EsSalud leaders aware there were five different 'treatment groups' as well as a ‘control’ group? Thanks to the researchers of this manuscript, we can now ask for more details to address previous 'rumors' about poor clinical management of people with COVID-19. This point is especially important as the EsSalud hospitals have been sealed to family and visitors. As such, patients are left in these hospitals without advocates for their safety and wellbeing.
Although the current study presents an ‘observational cohort’ design, we need to ask about the circumstances, structures and processes, that resulted in the original data reported in this manuscript. The researchers clearly stated they ‘compare[d] five treatment groups to the standard of care treatment regimen, as a control group....’ To this point we need to ask how could these six groups (with significant variations from the care standard) be organically organized from the existing patient population at EsSalud? In this regard, I am not claiming the researchers did anything wrong, but we need to understand how EsSalud ignored or facilitated the reported situation. In fact, we should thank the researchers for exposing some potential issues only believed to be unsubstantiated rumors in the previous months. Again, we need to recognize the vulnerability of patients as there were no family members present to be advocates in the EsSalud hospitals.
In explaining the possibility for five treatment groups and one control group at EsSalud, the researchers added ‘...decision to administer one of these treatment groups depended on clinician’s own criteria guided by the Ministry of Health recommendations, which was changing over time according to updated evidence-based reviews...’ Given the fact that EsSalud is the social security system for workers in Peru with separate clinical practice guidelines claimed to be based on evidence, this statement indicates the quality of care and standards of medical practice were not monitored by EsSalud. Again, this is concerning as the patients were vulnerable, without family advocates, and there is no statement in the manuscript about the normal informed consent process for clinical management.
The current observational study indicates there was a single ‘standard treatment’ authorized yet five alternative protocols were implemented as seemingly 'n of 1' studies. How were physicians in EsSalud able to clinically manage patients with unapproved protocols that significantly varied from the evidence? Were EsSalud leaders aware of the deficiencies in clinical management that probably contributed to the deaths of many patients? As a matter of normal clinical management did each patient consent to the treatment they received understanding there were more effective treatments available?
From the information reported in this manuscript, the clinical management of patients with COVID-19 at EsSalud needs to be reviewed by external authorities to determine the possibility of clinical negligence, medical malpractice, unethical conduct, unauthorized research, and/or illegal activities. Again, I want to emphasize these comments are in no way focused on the researchers. I sincerely appreciate the researchers exposing several serious ethical concerns that have only been rumors for months.
On 2020-07-18 09:38:23, user disqus_LHZMcrKY6P wrote:
Thank you for this informative paper, the statistical probabilities of result significance in the event of a negative result are very important in terms of the application of the test. Is there possibility to follow-up negative lamp results in both asymptomatic contacts and symptomatic cases with laboratory based assays RT-pcr assays (with LOD< 1000 cp/ml) and repeat LAMP assay at say 4-7 days later to evidence the statistical probabilities with true outcome? Do the authors recommend that LAMP assay result is routinely followed up with a more sensitive test for negative results?<br /> I cannot see units for analytical sensitivity is this copies/ml, per reaction or per swab? This is important for selecting a confirmatory test that has a more sensitive limit of detection and for comparing results across assay and finally for understanding the % of samples that would not be detected if this was performed as a single test based on current knowledge of viral loads in samples.<br /> The natural history of covid19 disease makes the means that interpretation of significance of low viral loads is reliant on a number of factors specific to the individual, while it is true that a low viral load is less likely to be infective, we cannot be sure at which stage of infectious course the individual is at and therefore low viral loads cannot be ruled as insignificant in terms of infectious potential nor clinical outcome.
On 2020-04-07 19:20:32, user Andrei Drabovich wrote:
There was no experimental proof presented that these variants impact ACE2 structure or function in any way.<br /> It is also not possible that ~3% genotype frequency for women and 1.5% for men (1.5% allele frequency for p.Asn720Asp) will result in additional >6% lethality. Were would additional 3-4.5% come from?
On 2020-04-27 16:54:09, user kamiya wrote:
In Maryland, stats currently show blacks and hispanics have a much higher incident rate, but far lower death rate than whites and asians. I can't help but think there blood type may be playing a role.
On 2020-05-28 14:09:42, user steve mike wrote:
I'd like to clarify a few major misconceptions people seem to have taken away with regards to this study.
All of the people observed in the study were positive for Corvid-19, the disease caused by the novel corona virus, ergo, a lot of people with type O blood can and do contract Corvid-19
The study says it got the patient test results from three hospitals, but it does not say if the patents in question were hospitalized.This is huge, because, for example, lets say all the people who are A blood type in the study had a mild case of the illness, and all the type O patients were on ventilators.In that case, yes, type O is statistically less likely to get sick from corvid-19, but more likely to get life threateningly ill.
On 2020-05-23 13:15:36, user Peter Frost wrote:
Why is Basu (2020) included in this meta-study? That study is not comparable to the others, since IFR is calculated only in relation to symptomatic cases. That kind of methodology leads to unrealistically high IFRs. For example, Basu (2020) calculates an IFR of 3.6% for King County WA. If we include asymptomatic cases, the real figure would be about a third lower.
On 2020-05-09 21:31:00, user Gennadi Glinsky wrote:
In agreement with this study, recent genomics-guided tracing of the SARS-CoV-2 targets in human cells identified Vitamin D as one of the top 3 potential COVID-19 mitigation agents <br /> https://doi.org/10.26434/ch...
On 2020-05-21 13:02:18, user Fred Douthwaite wrote:
A vaccine will not protect us from each successive mutated or novel virus. Correcting the underlying zinc deficiency that is the common denominator in the Covid-19 comorbidities is the answer.
The federal government should be stockpiling supplemental zinc for distribution to vulnerable groups.
Zinc deficiency is estimated to contribute to over 800,000 deaths per year - primarily in third world countries. This time, zinc deficiency has impacted the whole world. Correcting this problem is long overdue.
On 2020-10-05 11:56:16, user OxImmuno Literature Initiative wrote:
On 2020-05-20 17:21:18, user Christopher Leffler wrote:
Update: now 14 people have died on Diamond Princess. Updated mortality from Diamond Princess as well as coronavirus mortality for New York MTA (transit) workers are in this paper:<br /> https://www.medrxiv.org/con...
On 2021-09-19 12:46:53, user daan joubert wrote:
I rhink you are referring to the article entitled "Africa Dailye deaths.100k etc" showing the difference between the high incidence in the upper and lower parts of the continent compared to the equatorial region where Ivermectin is used against tropical parasites and there are few deaths. It seems to have been removed for some guessable reason.
On 2020-05-31 13:42:32, user Pete Jones wrote:
Really nice work.
"the prescribed minimum dose of 42 hours across 6 weeks" -- dear lord, that's an hour of tetris every day! That's bordering on "guantanamo bay" level torture (and that's speaking as someone who used to run experiments on Perceptual Learning)!
In all seriousness, the fact that that over half of children managed to complete more than half of that is amazing, and really quite encouraging IMHO. Participants were also better and more honest at judging adherence than I would have expected. I'd say this on balance this is Good News (and maybe just a 'lower bound' on what could be achieved if the task was something more addictive, like Fortnite etc.)
It would be nice to see session durations as histograms (maybe even for individual children), and maybe medians/IQR for Table 1. It would also be great to see adherence as a function of time graphically. Finally, I probably just missed it, but do you discuss how adherence compares to standard patching in the literature?
On 2022-11-04 00:15:41, user Brian Mowrey wrote:
Study design inadvertently results in the 3rd-dosed experiencing their "primary Omicron" infections mere weeks after injection. Bias for lower viral loads, antibody masking, and higher false positivity rates (because lower overall infection rates), which the 1-to-1 matching cannot fix. Adjusting for the 90-day delay between positive test and "follow up" start:
"Median duration between the third dose and [primary Omicron test] was [34] days (IQR, [13-53] days), and between the second dose and [primary Omicron test] was [244] days (IQR, [196-281] days)"
Oops.
On 2025-04-22 19:17:14, user chalchew wrote:
Over the past ten years, I have worked on arboviral diseases. The Ethiopian Public Health Institute regularly collects samples from sentinel sites and performs a triplex test (DENV, CHIK, & ZIKV). Most PCR results have been negative for CHIK and ZIKV. Therefore, it would be prudent to verify the kit performance before disseminating findings to the larger group, especially since my follow-up in the region indicates no clinical manifestation of these viruses despite a reported prevalence of 38% for ZIKV. Consequently, the government should consider implementing corrective measures, including health education.
On 2020-04-05 15:51:35, user Sinai Immunol Review Project wrote:
Summary: Multiple studies reported the same level of infectiousness between symptomatic and asymptomatic carriers of SARS-CoV-2. Given that asymptomatic and undocumented carriers escape public health surveillance systems, a better mathematical model of transmission is needed to determine a more accurate estimate of the basic reproductive number (R0) of the virus to assess the contagiousness of virus. The authors developed a SEYAR dynamical model for transmission of the new coronavirus that takes into account asymptomatic and undocumented carriers. The model was validated using data reported from thirteen countries during the first three weeks of community transmission. While current studies estimate R0 to be around 3, this model indicates that the value could range between 5.5 to 25.4.
Critical analysis: The SEYAR model realistically depicts transmission of the virus only during the initial stages of the disease. More data is necessary to better fit the model with current trends. In addition, multiple factors (e.g. behavioral patterns, surveillance capabilities, environmental and socioeconomic factors) affect transmission of the virus and so, these factors must be taken into consideration when estimating the R0.
Significance: Public health authorities use the basic reproductive number to determine the severity of disease. An accurate estimate of R0 will inform intervention strategies. This model can be applied to different locations to assess the potential impact of COVID-19.
Review by Tamar Plitt as part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai.
On 2020-03-23 16:03:24, user Ing. Alessandro Zivelonghi wrote:
no ambient temperature is reported in the study... ?????!!!!!
On 2020-06-08 19:23:59, user Animesh Ray wrote:
This is an interesting study, but the conclusions should be considered with caution. The causal modeling used here "suggest" that the chosen data are consistent with the hypothesis that Vitamin D deficiency might be correlated with increased morbidity of COVID-19. There are several caveats, however. (1) In meta-analysis of this sort, it is very difficult to be quantitative unless the observed data of the same data-type are shown to be at least comparable in variance. I did not see an effort to establish that. (2) Even though statistical analysis by multiple regression is precluded because the data were obtained from different sources, at least an effort to center the various data around means and doing a multiple regression to ascertain the magnitude of the variables' effects and their interactions would have been interesting. The problem here is that there are so many explicit variables and so many hidden ones in each experimental datasets, it is rather difficult to pinpoint any one--in this case vitamin D status--as causal. As von Neumann once stated, 'Give me four variables and I will make an elephant out of them; give me five, I will make it wave its trunk'. (3) Finally, the authors' molecular explanation--that Vitamin D inhibits rennin-angiotensin axis--is as easily explained in favor of the model as against the model (e.g., a lowered expression of ACE2 receptor due to inhibition by vitamin D might enable SARS-CoV-2 viruses to saturate these receptors far more easily than if the receptors are normally expressed, thus precipitate the loss of blood pressure control and cardiac output more readily than otherwise. In other words, the effect of normal vitamin D could enhance, not prevent, SARS-CoV-2 virus's clinical impact.) Thus the value of the molecular causality, as claimed by the authors, as a critically falsifiable test is doubtful. Nonetheless, many epidemiological success stories are built upon causal inferences based on precisely this type of analysis: one can cite examples of cholera on shallow wells in London in 18th century, scurvy and vitamin C, and now well established role of vitamin A and general resistance to childhood infections. On that basis, the idea that vitamin D might indeed be protective against COVID-19 complications merits further study.
On 2020-09-29 06:52:41, user Robert Stephens wrote:
Could it be that the infection fatality rate (IFR) within a community may be determined by the dominant mode/s of transmission within that community? In Mumbai for instance, the adjusted IFR in the Dharavi slum community was 0.076% but in the non-slum community the IFR was 0.27% for the same period (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.08.27.20182741v1)"). This discrepancy is unlikely to be on account of genetic factors, prior exposure to other coronaviruses, and perhaps is not due to age differences either.
Poor sanitation in slums may have resulted in cases of spread through contaminated water. Orofaecal transmission may have resulted in "safer" infections (gastrointestinal infections, oral mucosal & upper respiratory tract infections). <br /> In communities without sanitation issues, transmission of virus via airborne routes perhaps occurs more frequently. Airborne exposure to virus is potentially more toxic than non-airborne exposure as the transmission is more directed to lungs.
Robert Stephens MB BS FACD
On 2020-04-01 20:41:26, user Karen wrote:
Ferguson no longer assumes an IFR of 0,9%. Their latest paper (10.1101/2020.03.09.20033357) calculates an IFR of 0,66% for China.
On 2020-07-14 16:13:09, user CruiseLaw wrote:
Why isn't there a reference to a prior unpunlished study (not peer reviewed either) in the same server three months ago which reached an opposite conclusion? https://www.medrxiv.org/con...
On 2023-02-07 14:43:14, user E Edlund wrote:
Interesting read. Have you seen this similar study, focused on stroke? https://pubmed.ncbi.nlm.nih...
On 2020-03-21 23:09:04, user Moevi wrote:
Do we have any information regarding the patient ethnicity?
Indeed, the authors have chosen to use a study published in 2015 looking at ABO distribution among the Han population in Wuhan (unfortunately i was not able to find this study). However, if my understanding of the ABO group system is correct the distribution of ABO antigen may vary a lot depending on the ethnicity.
On 2022-01-06 10:42:35, user maa jdl wrote:
This paper is an elementary statistical exercice.<br /> At least one paper on this topic with a broader scope and a deeper analysis has already been published. ( https://lnkd.in/e9stiJMD )<br /> This paper does not try to understand "what is behind" these observations.<br /> It also does not discuss if the Benford's law should apply to these data for at least some reason and other conditions of applicability like the sample size or the physical process assumed to generate these data.<br /> Most importantly, without a scientific analysis, this paper could easily be easily used for "conspirationist theories", and has already been used so!<br /> "Have the covid data been manipulated? " That's the question behind this exercise, for many.<br /> I would say that OBVIOUSLY the data have been manipulated!<br /> But NOT in the sense assumed by conspirationists!<br /> Actually all governments have taken measures to limit the development of the pandemic's waves. And this is typically a "manipulation of the data", since this is precisely aimed at moditiying the figures! This argument also shows that the pursuit of the Benford's law for covid data is basically not a scientific endeavour! It doesn't answer a good question! Not more than numerology!
See also: https://lnkd.in/e8H7JPXh
On 2021-03-10 08:27:46, user Eduard Baladia wrote:
This is one of the problems with preprints, there is no minor revision. This is an ecological fallacy and a spurious correlation. This is really misleading information!
On 2023-04-16 07:02:33, user Basheer wrote:
The paper is published at https://www.eurekaselect.co...
On 2024-01-25 16:47:31, user tony gordon wrote:
Traits on Supp sheet 3 not fully legible
On 2021-01-28 09:56:34, user Georges Borgès Da Silva wrote:
Despite some methodological reservations, the effect on the reduction in hospitalizations seems possible.<br /> Statistical significance is limited and does not take into account the multiplicity of comparative tests (absence of Bonferroni correction). The composite outcome is questionable.<br /> It will be necessary to take into account an increased risk of pulmonary embolism in the context of a problematic association (difficult to prescribe colchicine + anticoagulants).<br /> Note a slightly favorable level of comorbidity in the treated group compared to the placebo group.<br /> Our doubts might have been dispelled if the trial had not been interrupted before its end.
On 2023-04-17 21:38:28, user Dean F. Sittig wrote:
Did all of the studies you reviewed use the same order-retract-reorder measure to determine potential wrong-patient errors? If so, did any of them consider the implications of using this type of estimate in situations in which the likelihood of a user noticing that they made a wrong patient decreases as the number of charts open at the same time increases? If this hypothesis re: the limitations of the measurement were true, what would that do to the conclusions drawn from the studies that used the measure?