On 2021-07-08 18:27:10, user Jeff Andrews M.D. wrote:

The authors have misrepresented the use of biostatistics. In line 99 they state that APOCT have poor specificity. The authors cannot comment on specificity because they do not know the number of true negatives (line 156-7). However, the prevalence is so low that using the [total number of tests – known PCR positives] approximates the true negatives. The approximate specificity in this study would be 71768/71808 = 99.94%. This is an incredibly high specificity, not poor.<br /> In lines 106-107, the authors ask us to consider the negative impact of a false positive result, without putting it in context. Every HCW with a positive APOCT had confirmatory PCR. The authors do not state the interval between the two tests; the impact was likely over a period of two days. Moreover, the authors do not consider the alternative scenario, which would be two days of HCW exposures to other HCWs and patients in a HC setting, while waiting for the PCR results. If 39 HCWs were identified with COVID-19, immediately put on isolation (due to 15-minute APOCT), what value did that have for the healthcare system, and for all of their HC and personal contacts? And was that protective value greater than the collective harm caused by identifying 48 HCWs as positive by APOCT who were released from isolation two days later when the PCR result was known?<br /> “False detection rate” is not a terminology of biosciences and was not defined by the authors. In fact, they are presenting [1-PPV]; which seems pointless since they also present PPV in the next sentence.<br /> When prevalence is very low, 5 per 10,000 in this study, a slight difference in prevalence can greatly influence PPV. The reason is that false positives tend be fairly static and not influenced by prevalence, but true positives are directly influenced by prevalence. It is wrong to say that APOCTs have a low PPV, unless the sentence includes the prevalence. <br /> Because the two tests were not conducted on the same subjects, it is wrong to publish results that represent a head to head comparison. In such a case, the authors were required to use more sophisticated Bayesian statistics, in order to apply a ‘penalty’ or adjustment to account for possible differences due to the differences between the two different populations and the two different test workflows. <br /> The authors fail to describe in Methods that one test was visually-read and the other test was read by an analyzer device, and to point out that human variation in ‘reading’ lines on the visually-read test could contribute to the differences. <br /> The authors fail to acknowledge that they do not know the numbers of cases of APOCT negative and PCR-positive (false negatives) for each of the two tests; it is possible that one test has a much lower sensitivity and that would need to be considered in context with the false positives and prevalence.<br /> Finally, the authors did not take the opportunity to discuss what the reasonable prevalence level is for APOCT. In their study, prevalence was 0.05% or 5/10,000. They do not discuss whether it is reasonable to test asymptomatic HCWs at this prevalence level or lower. They do not discuss whether it is reasonable to test asymptomatic non-HCWs at this prevalence level or lower.

On 2021-07-09 06:37:13, user ndk wrote:

This is a retrospective study as the predominant strain at the time was Alpha, in concurrence with their findings, but we currently face the radically different strain Delta, and perhaps Lambda. We can't glean much from it other than a snapshot in time.

On 2021-07-12 08:17:01, user Pablo wrote:

Dear authors, there is a small typo in page 5: Supplementary Table S1.

B.1.429 (EPISLON) -> B.1.429 (EPSILON)

On 2021-07-13 17:09:58, user intros pector wrote:

"Fully vaccinated"? Full immunity by Covaxin is considered to be achieved only 2 weeks after the second dose, but patients 0a and 0b are reported above to have travelled a few days before that, unfortunately. Plus, sitting in a plane for ~20 hours would have resulted in virus overload for patients 0a and 0b. Providng more such details would make the paper more amenable to detailed conclusions.

On 2021-07-14 11:58:00, user Karan Srisurapanont wrote:

This is a very interesting research paper. I am planning to do a systematic review about the efficacy and safety of COVID-19 vaccines in solid cancer patients and I will definitely cite this article. However, I was wondering why the number of controls in Figure 3b added up to 60 instead of 50. I am looking forward to receiving your answer and would like to thank you for answering in advance.

On 2021-07-15 16:14:14, user Tanavij Joob Pannoi wrote:

I am wondering about the total number of recruited participants divided by VOC, particularly in figure 2A, while, the results from linear mixed model were not reported elsewhere. Researchers should provide more details of study limitations since it could be published on social media, while, many audience might misinterpret or exaggerate the result.

On 2021-07-26 09:07:20, user Jörg Hennemann wrote:

Dear authors, I do not get the point: In your raw data (table 1) the percentage of people dying from Corona Delta is 0.7%. All other variations cause 0.9% deaths for infected people. So, how can the risk to die from Delta be higher than for other variants? Where can we see how the "adjustment for age, sex, comorbidities, health unit, and temporal trend of the raw data works? Here in Germany people go wild because of this study, but I can not comprehend it. Thank you very much!

On 2021-07-26 17:37:56, user Fortu Nisko wrote:

From methods.

We will include only studies that provide proof of transmission outcome using culturable virus and /or genetic sequencing. The inclusion of this higher-quality evidence aims to overcome the methodological shortcomings of lower quality studies. We will assess the microbiologic or genetic sequencing evidence in an effort to inform the quality of the chain of transmission evidence and adequacy of follow up of sign and symptom monitoring.

This is reasonable and, in my view, essential.

Also, the malady for which the infection is the cause must be very well ldefined. It would be false to claim that a person was pre-symptomatic if they did not present the definitive symptoms arrayed in the Severe Acute Respiratory Syndrome. Lack of symptoms specific to this particular malady would exclude the individual from the chain of transmission. In effect, the research must be based on the SARS patient and then work back from that. NOT the other way around, which depends on speculations and non-specificity.

Likelihood is that you will end up making a rather vague analysis on influenza-like illness the symptoms of which may be presented in the chain of transmission toward an endpoint where the patient suffered influenza-like respiratory distress. The challenge you face is distinguishing that sort of chain from the chain specific to SARS-COV-2; and that means facing the ambiguities that define a distinction, such as it may be, between SARS-COV-1 and influenza and between SARS-COV-1 AND SARS-COV-2. If patients fall into a category that incudes infuenza-like illness, then, that places a heavy limitation on your research. It is the same limitation that applies to most of the research regarding what was deemed, by mere assertion alone, a new or novel pathogen.

This must be addressed.

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

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

On 2021-07-28 00:17:17, user LJV wrote:

How was long term Covid defined? What questions were asked? Had these individuals had Covid prior to vaccination? How long after vaccination did symptoms emerge? This report is quite vague, as it does not clearly define the parameters and definitions of the terms.

On 2021-07-29 06:28:47, user Astrid Fuchs wrote:

The household follow up included the development of symptoms, why wasn't this evaluated?<br /> In case unvacvinated cases with symptoms contract to others this would be expected, but would enable to have respective mitigation measures like quarantine.<br /> But the open question is, if asymptomatic vaccinated can contract the virus without knowing which is higher risk to society. <br /> Would appreciate to see a split in symptomatic and asymptomatic too to adress most important question for society, esp. after CDC information this week that their data shows similar infection risk by vaccinated and unvacvinated even when in different setting.

On 2021-08-25 20:16:42, user Tom wrote:

As all 12-16 year-old teenagers were not vaccinated at the time of the study, could you answer this question please:

Why did the study include 12-16 year-old teenagers in the group : adult/teenager household contacts that were vaccinated but not isolated?

On 2021-07-29 15:40:59, user Peter P.V wrote:

Every six month new vaccine will have to be developed. Is it possible?

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 2021-08-11 12:11:31, user Truenorth 1960 wrote:

I'm not sure I understand this study. While I understand this is a report that is intended for professionals, the l language is not English, it is "technobable" for lack of a better expression. For covid , these studies should have a translation into something more akin to regular English. Narrative should help understand the results. In this case I find the narrative is not helpful, it is easier to look at the tables.

On 2021-08-11 18:43:00, user questionable02848 wrote:

I am interested in seeing a similar study done on Recovered versus Vaccinated cases over some years. It is **theoretically** possible that Recovery (despite original virus death rate) confers greater defense than Vaccinated (despite lower original virus death rate) because Recovery forms a superior, longer-lasting, or greater-breadth immune response. This is important to consider for coronavirus specifically due to its tendency to mutate. The studies I have seen indicate that the Recovered do have a greater immunity than the Vaccinated, as studied here: https://www.biorxiv.org/con...

And so, if Recovered do in fact fare better when exposed to mutations, we really want to know this before we vaccinate the young, who do not face a statistically significant threat from coronavirus but have many years ahead of them facing its mutations.

On 2021-08-26 14:59:31, user Holger Lundstrom wrote:

So, to summarize:

• COVID-19 cases after dose 2: 77 (vax) vs. 850 (placebo) hinting at 91.3% protection
• no difference in cases for those with prior infection
• deaths during blinded period: 15 (vax) vs. 14 (placebo)
• COVID deaths during blinded period: 1 (vax) vs. 2 (placebo)
• deaths during total period: 20 (vax) vs. 14 (placebo)

Conclusion: <br /> 1.) Vaccine allows for 91.3% relative risk reduction. Total risk reduces from 3,9% (placebo) to 0,35% (vax) within the study timeframe. A decrease of 3% efficacy per month is expected - but is likely to be much larger, according to recent reports from Israel.<br /> https://www.sciencemag.org/...

2.) People with prior infection benefit very little from the vaccination, if at all. No benefit is recorded within study period. According to assumptions made here of about 70% protection (prior infection) vs. 90% protection (vax), a vaccination for people with prior immunity would reduce total risk from about 1% to 0,35%. However, it is more likely that prior immunity awards better protection than a vaccine does, due to the involvement of other aspects of pathogen defense, such as IgA antibodies. Again, no benefit was recorded in the study.<br /> https://stm.sciencemag.org/...

3.) No significant benefit is recorded concerning deaths due to coronavirus (1 vs. 2). Overall deaths are higher in vaccinated group than in placebo group, however total numbers are small (20 vs. 14).

Supplement tables:<br /> htt...

On 2021-08-05 20:15:56, user Ultrafiltered wrote:

I would have liked to have seen a non-biased statistical sample set with double blind quality control, n>15, to provide statistically correct results, in addition to Dimich's comments below.

On 2020-06-25 15:16:45, user dottore b wrote:

"Dexamethasone reduced deaths by one-third in patients receiving invasive

mechanical ventilation (29.0% vs. 40.7%, RR 0.65 [95% CI 0.51 to 0.82];

p<0.001)" is misleading as this is an 11% decrease in the death rate. It's like saying the death rate went from 2% to 1% and trumpeting a "50% reduction in the rate of death".

For those interested in a very good discussion of this trial from Dr Dan Griffin an intensivist at Columbia, this is a great link and every practitioner should be a TWIV listener

https://www.microbe.tv/twiv...

On 2023-11-12 16:14:24, user Julian Gough wrote:

We have noted that in a small number of cases this work is being cited in the literature as a positive GWAS association for the ERAP2 gene, however we would like to be clear that we are making no such claim, and urge authors to take care in citing this work. We are pleased that for the majority of citations authors have not misrepresented the findings.

Analysis of subsequent genetic datasets for COVID-19 mortality [not yet published] could suggest that risk factors (reported here and elsewhere) are different at different points in time, as the virus itself has mutated, medical care has evolved, vaccination has been introduced and population vulnerability and exposure has changed. Therefore we also ask readers to take note that the conditions under which the (very early) data collected by UKbiobank during the first wave of the pandemic -- analysed in this work -- may be very different from present, future or past conditions during other waves of infection.

On 2020-07-07 14:02:12, user David Curtis wrote:

I have some concerns about rs150892504.

According to ExAC it is rare in Europeans but has an allele frequency of 0.04 in Ashkenazi Jews. <br /> https://gnomad.broadinstitu...<br /> https://gnomad.broadinstitu...

Likewise, it has an allele frequency of 0.03 or 0.04 in the IBD exomes:<br /> https://ibd.broadinstitute....

This raises the possibility that the results you obtain may be due to some kind of population stratifcation which has not been adequately corrected for.

On 2020-07-06 15:38:39, user Alexander Pearlman wrote:

why is placebo sterile saline soln.--and not formulated with lipids as the mRNA-LNP (scrambled) drug product? is this a safety concern? or expect immunog. sig. from a scrambled construct?

On 2020-07-07 20:43:11, user Dr. David Ludwig wrote:

The final peer-reviewed manuscript is now published, with free full text available at Journal of Nutrition: https://academic.oup.com/jn...

On 2020-07-08 18:28:20, user Paul Gordon wrote:

Hi, thanks for posting. Have the new Italian genomes described been posted to a public repository? A quick search of the paper and both GenBank/GISAID didn't reveal these identifiers or entries matching the metadata provided in Table S1. Thanks!

On 2024-06-16 14:15:45, user Praba wrote:

Dear Authors, this paper is interesting. Congratulations to all authors. Could you please give complete details about the FnCas9 purification (How much vol of culture was used to purify the proteins that was shown in Suppl Fig 9 and what was the yield obtained at the end. These information are not trivial and they are important for the researchers who follow the paper. Hope you will append the details.

On 2020-07-12 18:19:53, user Doug Vaughan wrote:

Very interesting study. PAI-1 provides a site of convergence for risk (age, obesity, diabetes) and mechanisms driving thrombosis (inflammation, activated renin-angiotensin system, endothelial dysfunction) in patients with COVID-19. Failure of the endogenous clot-dissolving system is most likely a key contributor to morbidity and mortality as demonstrated here. Just last week, we secured an IND from the FDA to begin testing of a novel orally-active inhibitor of PAI-1 (TM5614) in high-risk patients with COVID-19. This Phase 2 study will enable the first use of this drug in USA.

On 2024-10-23 00:08:01, user Shuo Shen wrote:

An updated version with more experiments, data, and analysis has been published. Please refer to the peer-reviewed version here: https://doi.org/10.1128/spectrum.04248-23 <br /> "Rapid detection of SARS-CoV-2 variants by molecular-clamping technology-based RT-qPCR."

On 2020-04-17 21:03:35, user John Ryan wrote:

The researchers identified that 50 out of 3,300 participants tested positive for CV-19 antibodies. Spinning this as 50 to 80 times greater than current prevalence rates as determined through testing is disingenuous. The posted study does not account for the significant upward adjustment from 1.5% of participants to a 2.4% to 4.2% in the general population given the study participants were a skewed convenience sample drawn from Facebook participants, many who believed they had been exposed to CV-19 and had had previously experienced symptoms consistent with CV-19.

On 2020-04-17 21:28:07, user Daniel Shanklin wrote:

This study abstract should be rewritten as follow: "A study of Facebook users who thought they might have COVID-19 resulted in a roughly 2.49% to 4.16% positive-test rate"

The fact that you've extrapolated this to an entire population is confounding.

On 2020-04-18 18:54:22, user Tomas Hull wrote:

Germany had a similar study done published on April 9, where they combined the antibody test with the polymerase chain reaction test in active infections.

Nature reviewed both this study, and the one from Germany, where the combined results in a population of a town of 12,000 revealed overall 15% infection rate.

https://www.nature.com/arti...

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

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

On 2020-04-19 00:37:25, user jemihami wrote:

I must say that there are many commenters using web data to formulate their arguments. Interesting, but of little logical use.

On 2020-04-19 05:26:23, user David Feist wrote:

This study has a very high probability of being correct in my opinion, as it is in line with three or four recent seroloprevalence tests (conducted by experts). The Gangelt, Germany test suggested that 15% of the population was infected and that under reporting was of a similar level as that in California.

The high levels of infection explain of course why the pandemic turned down two weeks BEFORE the lockdown in Wuhan, (Wittkowski, MedArxiv, April 2020); herd immunity levels of infection were probably being reached. If there was actually 50 times more cases in Wuhan (ie at least 2.5 million people infected out of a 10 million polualtion) herd immunity may have been reached - if a further 35-30% of residents had cross immunity from prior exposure to cold corona viruses.

The IFR is also now in line with what this study's primary author, John Ioannidis, predicted in the beginning from the Diamond Princess cruise ship data, namely about 0.1%. No Government in the world should have commenced lockdowns based on that February, 2020 data and prediction. Mr Ioannidis should be consulted in the future.

On 2020-04-19 08:32:24, user Matthew Markert wrote:

What is the specific data on cross-reactivity of other CoV strains on this Ab test (Premier), and what is the expected or known prevalence rate of those strains in the background population?

If that is unknown or unknowable, can you instead run PCR on all the tested Ab samples for other common CoVs? If you can rule out that as an underlying confounder, or can show that they are present in people who tested negative for SARS-CoV-2, it would strengthen the data.

As written, and also for other reasons stated elsewhere (including your reported false positive rates and potential to explain a section of the 50 cases), the true positivity rate remains unclear. https://uploads.disquscdn.c...

On 2020-04-19 12:55:20, user C'est la même wrote:

99.5% specificity in the general population is wildly optimistic compared to serology tests developed by other labs. Claims of "asymtomatic" carriers also reflects symptom reporting biases (surveys/questionnaire answers are not symptoms).

I suggest caution trusting serology based studies like this, unless all positive cases are also confirmed using CT or RTPCR testing.

On 2020-04-19 14:45:07, user Tomas Hull wrote:

1. This study is not perfect but no studies ever are.

2. The study shows pretty close estimates of the much lower mortality rate than previously estimated, likely slightly above the seasonal flu of 0.1% and lower than the German study estimates of 0.37% of a town of 12,000 inhabits where an accelerated infection likely happened due to the town carnival 2 months earlier.

3. More similar study results will soon be published, including L.A. County, MLB organization from 27 cities, and many European countries, which will probably confirm that CoV2 is much more widely spread than initially thought, and with the infection mortality rate slightly above the seasonal flu of 0.1% and below 0.37% from the German town of Gangelt, where the much faster infection rate initially occurred due the the town festival in February.

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 2020-04-19 16:32:58, user Robert S. wrote:

What is the false positive rate of the test used? What is the cross-reactivity of the test with other coronaviruses that share sequence homology with the spike protein of nCoV-2?

On 2020-04-21 20:17:21, user tom wrote:

The test kits were marketed under Policy D, i.e. no FDA validation or review, not even a EUA. It defies reason that they could be relied upon merely on the word of the foreign manufacturer (Hangzhou Biotest Biotech) and a perfunctory (and potentially skewed) in-house specificity validation run on a mere 30 control samples. The UK bought millions of £ worth of antibody tests from Hangzhou Alltest Biotech (maybe an affiliate of HBB, as there are curious similarities between the two tests' package inserts) and then shelved them due to inadequate specificity.

And how could a responsible researcher pre-print survey results based on unapproved rapid test kits without following up on the indicated positives by blood draw and ELISA, knowing full well the half-baked, incendiary results and conclusions would be picked up by media worldwide and potentially impact life-and-death decisions of massive scope by public health officials?

It is an extraordinary - indeed sensational - claim that the entire world has missed a silent, benign spread of SARS-CoV-2 that's 40x larger than recognized, and it runs counter to significant evidence of the virus having an asymptomatic fraction similar to influenza based on contained, fully-tested outbreaks in the Diamond Princess, Roosevelt, and Skagit Valley choir. Extraordinary claims require extraordinary evidence; this work is, to put it euphemistically, certainly not that. If these results do not bear out, Stanford has some serious explaining and housecleaning to do.

On 2025-03-30 21:44:30, user Simon binakter48 wrote:

This paper has been published to an IEEE conference.

2023 26th International Conference on Computer and Information Technology (ICCIT), 13-15 December, Cox’s Bazar, Bangladesh

doi: 10.1109/ICCIT60459.2023.10441480

On 2020-04-18 11:10:24, user Ramananda Ningthoujam wrote:

I agree with the point made by Muhammad Saqlain and co-author in their article that the government should take immediate policy plan to contained COVID 19. It is said that South Korea is winning the fight against COVID 19 because they have learnt a lesson from the past epidemic (MERS) outbreak. Every nation fighting against the pandemic is asking help from South Korea. However, I disagree the point that "Pakistan due to its geographical location is vulnerable to a worst outbreak" My question to the author(s) is "How is geographical location associated with vulnerability of COVID 19 outbreak."? Kindly explain with a valid point. <br /> Thank you.

On 2021-07-10 01:55:43, user Marcus wrote:

No mention of COVID variant specific tends even in limitations discussion?

On 2020-04-18 21:23:50, user Jim Rossi wrote:

Interesting & optimistic if this holds up. Thanks for sharing.

On 2020-04-19 17:16:34, user David Steadson wrote:

I now realise I made an error in the last comment, taking the cumulative Swedish total instead of the Stockholm total. On April 1 FHM reported 148 deaths for Stockholm. Given delays in reporting 200 may be a reasonable guess of the actual numbers then. Unfortunately FHM only retroactively updates national totals, not regional

On 2020-04-20 11:52:48, user Yi-Hsuan Wu wrote:

A corrigendum should be made as the authors misused "Taiwan, China" instead of "Taiwan." That's not acceptable error a group of specialists would made.

On 2020-04-20 17:25:50, user Wei Zhou wrote:

Sorry. I cant find the supplemental figures and tables, even though I found the rest of the supplemental data in the pdf. If you have seen it, can you give me a pointer? Thanks!

On 2020-04-21 00:36:26, user Chao Jiang wrote:

Please feel free to let me know how to improve the manuscript here or @Chao_Jiang1. Thanks!

On 2020-04-20 17:37:29, user Philip Davies wrote:

The low dose arm of this study is worth following.

The big problem for this study is comparison. It really has not defined the control population at all. The Italian and Chinese references are entirely different. Even the 2 Chinese populations referenced had massively different outcomes because the populations examined were different.

The Italian mortality rate was actually similar to the overall study average here (but much higher than the low dose arm). The Chinese study involved all patients admitted to the two hospitals ... that included a majority of patients with moderate ("ordinary" as the Chinese class it) disease severity. The patients in this Brazilian study were regarded as severe or critical ... such patients (looking at worldwide stats) would attract a mortality of 30-40% plus.

This is the most important factor. Do not compare apples with pears. So far this study points the "swingometer" in favor of benefit versus harm for the use of HQN in patients with advanced disease.

Once again however, we are looking at the potential impact of an orally administered drug to patients with advanced disease. That's a big ask.

For CQ and HCQ the most interesting results will likely come from studies looking at prophylaxis and early treatment (using safe doses, not silly high doses with added drugs that also lengthen QT). We can't yet guess how they will pan out.

Dr Philip Davies<br /> GP<br /> Aldershot Centre For Health, UK<br /> http://thevirus.uk

On 2020-04-21 03:30:02, user UFO Partisan wrote:

We need to be doing this kind of data gathering and reporting here in the States. The above results aren't shocking though. Once someone in your home is infected, you have a serious problem and the person initially getting infected seems most likely to be picking it up through mass transit. Protect yourself at all times everybody.

On 2020-04-22 01:02:04, user Michael Kyba wrote:

A cursory browse through Table 2 of the paper shows that the patients that would eventually comprise the HC group were the sickest upon admission, the HC+AZ patients were intermediate and the patients that would elect no HC group were the least sick. This is prior to intervention.

This sort of sampling bias highlights the importance of double blind randomization to determine efficacy. Such an a priori correlation might be due to sicker patients opting for experimental treatments at a higher rate. In any case, it would not be wise to interpret these data as indicating that the interventions cause the worse outcomes. The underlying health state is probably responsible.

Some examples follow, then a criticism of what the authors have written into their Results and Discussion.

Known risk factors include age, weight and blood pressure; and signs of severe disease include kidney damage.

Browsing through table 2 looking for parameters with lowish p-values:<br /> Mean systolic blood pressure differences between groups showed a p-value of just under 0.05 (statistically significant), with values of 136, 132, and 129 across the groups (HC, HC+AZ, no HC), but more significantly, the HC group had 34% of patients with BP information showing up in the very highest pressure group (27.8/0.804, the denominator being the fraction with information on BP in that group), while HC+AZ had 30% and no HC had 25%.

Creatinine (high levels indicate impaired kidney function) was even more divergent: HC had 17.7% in the highest group, HC+AZ 11.7%, no HC only 8.1%.

Pulse Oximetry showed the largest number of patients with low blood O2 in the HC group.

I don't like that they did not break out the age and BMI into bins, but reported only means. Interesting distributions in these parameters might be buried in the mean.

The write-up of this data is quite unusual in being very abbreviated and lacking any thought to potential problems.

The Results section of the paper does not address the issue of a priori differences in health parameters at all except for saying "There were significant differences among the three groups in baseline demographic characteristics, selected vital signs, laboratory tests, prescription drug use, and comorbidities (Table 2)". Having said this, the authors proceed as if there were no significant differences.

In the Discussion section, the only comment to this issue is to state: "Despite propensity score adjustment for a large number of relevant confounders, we cannot rule out the possibility of selection bias or residual confounding."

It is in a preprint archive, which means this is a pre-review manuscript, but even so, it is quite unusual for such a study to completely lack any address of specific and obvious limitations. After review, hopefully reviewers will require the authors to analyze and discuss the divergent a priori health of the 3 groups.

On 2020-04-22 01:10:17, user Gunnar V Gunnarsson wrote:

After reading the paper I unfortunately find the usage of data to be misleading and I think you might have drawn the wrong conclusions.

The problem lies in the fact that once people went on ventilators they where given HC or HC+AZ. This re-categorised the patients by increasing the number of high risk patients in the HC and HC+AZ groups making the No HC an invalid control group.

Before ventilation the statistics was like this: (Table 4 in paper)

HC: 90 - 9 (10.0%) deaths - 69 (76.6%) recover - 12 (13.3%) onto ventilation HC+AZ: 101 - 11 (10.9%) deaths - 83 (82.2%) recover - 7 (06.9%) onto ventilation No HC: 177 - 15 ( 8.4%) deaths - 137 (77.4%) recover - 25 (14.1%) onto ventilation

We see that death-rate is about the same for all groups but HC+AZ seams to have the highest recovery rate but it might not be statistically significant.

Now once people hit ventilation the re-categorisation occurs. More patients where given HC and HC+AZ which moved them from the No HC group to the HC or HC+AZ group. These groups therefore have a much higher % of ventilation patients because they where given the drugs after they hit ventilation.

The following data can be derived from the paper but is not presented:<br /> Once people hit ventilation we have the following results.

HC: 19 - 18 (95%) deaths - 1 (11%) recover HC+AZ: 19 - 14 (73%) deaths - 5 (27%) recover No HC: 6 - 3 (50%) deaths - 3 (50%) recover

If you compare these 2 tables, you see that 25 patient with No HC reach ventilation. Once they reach ventilation, 19 of these where give HC or HC+AZ, thereby moved from the No HC group to the other two. 79.5% of all patients reaching ventilation died so arguably 14 patients that died where moved from the No HC group to the other 2 groups only once they reach the much higher risk state.

Here are the number of people per group that got ventilation:

HC: 97 - 19 (19.6%) got ventilation HC+AZ: 113 - 19 (16.8%) got ventilation No HC: 158 - 6 ( 3.4%) got ventilation

So in the end result the No HC group had a very low % of patients who got ventilation and therefore should have a significant lower death rate which is then totally unrelated to the treatment.

On 2020-06-30 16:50:34, user Rhyothemis wrote:

Was lysophosphatidylinositol (LPI) measured in this study? I don't see it in the text or figures. A study of SARS patients found elevated LPI:<br /> https://pubmed.ncbi.nlm.nih...

If anyone has information on LPI in covid, please post a reply.

On 2025-02-21 05:09:57, user Evan Stanbury wrote:

Re "Serological evidence of recent Epstein-Barr virus (EBV) reactivation was observed more frequently in PVS participants". EBV causes glandular fever, which often leaves sufferers with a Post-Viral syndrome similar to Long COVID (and the sick cohort). This is not directly attributable to the vaccine.

On 2025-02-21 05:12:28, user Evan Stanbury wrote:

Re "PVS participants also had lower anti-spike antibody titers, primarily due to fewer vaccine doses", ie the people with more vaccine doses had less PVS. This contradicts the hypothesis that vaccines cause Post-Vaccine Syndrome, since the dose-response relationship contradicts the hypothesis.

On 2025-02-24 23:45:15, user Stephen Goldstein wrote:

I am now pasting in the review done by Gemini 2.0 Flash Thinking Experimental. I have not edited it.

Prompt: Please conduct an extremely rigorous, journal-agnostic peer review of this manuscript.

Response:<br /> I have now thoroughly read the manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination" by Bhattacharjee et al., available at https://biotome.hypothes.is/content/10.1101/2025.02.18.25322379v1 .

Here is an extremely rigorous, journal-agnostic peer review of this manuscript:<br /> Extremely Rigorous, Journal-Agnostic Peer Review of Manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination"<br /> Manuscript DOI: https://doi.org/10.1101/2025.02.18.25322379 <br /> Manuscript URL: https://biotome.hypothes.is/content/10.1101/2025.02.18.25322379v1.full-text

Overall Assessment

This manuscript by Bhattacharjee et al. investigates the complex and highly sensitive question of immunological and antigenic signatures associated with chronic illnesses reported following COVID-19 vaccination. The authors employ a multi-faceted approach, combining clinical data from a prospective cohort study with deep immunophenotyping and autoantigen profiling. The study's aim is to identify potential immunological correlates of these post-vaccination chronic conditions, which is a crucial area of investigation given ongoing public health discussions and concerns.<br /> While the study addresses a highly relevant and important question, and utilizes advanced methodologies, this review finds that the manuscript in its current form presents significant limitations in its methodology, analysis, interpretation, and overall conclusions. From a rigorous, journal-agnostic perspective, the manuscript requires substantial revisions and further validation before it could be considered to provide robust and reliable scientific evidence. The current findings are preliminary and require cautious interpretation, and the manuscript overreaches in its conclusions and implications.

Major Strengths (Limited in Impact by Weaknesses)<br /> Addresses a Highly Relevant Question: Investigating potential immunological links to chronic illnesses post-COVID-19 vaccination is undeniably important for public health and vaccine confidence.

Prospective Cohort Design: The prospective cohort study design is a strength, allowing for systematic data collection and comparison between vaccinated individuals with and without reported chronic illnesses.

Multi-Omics Approach: The use of multi-parameter flow cytometry for immunophenotyping and autoantigen microarray profiling are advanced techniques that offer the potential to identify complex immunological signatures.

Clinical Data Integration: The study attempts to integrate clinical data (symptomology, diagnoses) with immunological data, which is essential for translational relevance.

Major Weaknesses and Critical Concerns (Requiring Substantial Revision)

Case Definition of "Chronic Illnesses": Critically Vague and Subjective. The definition of "chronic illnesses" post-vaccination is alarmingly vague and relies heavily on self-reported symptoms and broad diagnostic categories (e.g., "dysautonomia," "POTS," "fibromyalgia"). There is no clear, objective, and validated case definition used. This introduces significant subjectivity and potential for bias in patient selection and outcome assessment. The lack of specific inclusion/exclusion criteria for these "chronic illnesses" undermines the scientific rigor of the entire study. It is unclear if these are new-onset conditions post-vaccination, or exacerbations of pre-existing conditions, or simply background rates of these conditions temporally associated with vaccination.

Lack of Appropriate Control Groups: The study compares "Vaccinated individuals with chronic illness" to "Healthy vaccinated controls." This control group is insufficient. Crucially, there is no unvaccinated control group with similar chronic illnesses. Without this, it is impossible to determine if the identified immunological signatures are specific to post-vaccination illnesses, or are simply signatures associated with these illnesses in general, regardless of vaccination status. Furthermore, a "healthy unvaccinated" control group would be essential to assess baseline immunological differences related to vaccination itself.

Causality vs. Association: Fails to Establish Causality. The study is fundamentally correlational. It fails to provide any robust evidence for a causal link between vaccination and the reported chronic illnesses. The temporal association alone is insufficient to establish causality. The manuscript over-interprets correlational findings as suggestive of causal relationships, which is scientifically unsound. Confounding factors (e.g., pre-existing conditions, other exposures, reporting bias) are not adequately addressed or controlled for.<br /> Small Sample Size and Statistical Power: The sample sizes for the "chronic illness" groups are relatively small (e.g., n=15-20 per group in some analyses). Given the complexity of immunological data and the heterogeneity of "chronic illnesses," the study likely lacks sufficient statistical power to detect subtle but potentially meaningful differences, and is highly susceptible to false positives. The statistical analyses presented are descriptive and lack rigorous correction for multiple comparisons, further increasing the risk of spurious findings.

Over-Interpretation and Over-Generalization of Findings: The manuscript overstates the significance and generalizability of its findings. The conclusions are presented in a manner that suggests a stronger link between vaccination and chronic illnesses than is warranted by the limited and preliminary data. The discussion section speculates broadly on potential mechanisms without strong empirical support from the study itself. The manuscript risks contributing to vaccine hesitancy by presenting preliminary and correlational data as stronger evidence than it is.

Limited Validation and Reproducibility: The findings are not validated in an independent cohort. Without external validation, the reproducibility and generalizability of the identified immunological signatures are highly questionable. Given the small sample sizes and potential for batch effects in omics data, validation is absolutely essential.

Lack of Clinical Characterization and Phenotyping: Beyond broad symptom categories, the clinical characterization of the "chronic illness" groups is superficial. Detailed clinical phenotyping, including objective measures and validated diagnostic criteria, is lacking. This makes it difficult to interpret the clinical relevance of the identified immunological signatures. The manuscript does not adequately address the heterogeneity within these broad "chronic illness" categories.<br /> Potential for Reporting Bias and Selection Bias: Individuals who believe they have experienced adverse events post-vaccination may be more likely to participate in studies investigating vaccine-related illnesses. This introduces potential reporting bias and selection bias, which are not adequately addressed in the manuscript.

Ethical Considerations and Responsible Communication: Given the sensitive nature of the topic and the potential for misinterpretation, the manuscript needs to demonstrate a heightened awareness of ethical considerations and responsible scientific communication. The current manuscript, in its over-interpretation and lack of caveats, falls short in this regard. The potential for public misinterpretation and the fueling of vaccine hesitancy needs to be carefully considered and mitigated.

Recommendations for Substantial Revision (Minimum Requirements for Consideration in a Rigorous Journal)<br /> Develop and Implement Rigorous, Objective Case Definitions:

This is the most critical revision. The authors must develop and apply clear, objective, and validated case definitions for each "chronic illness" category. This requires moving beyond self-reported symptoms and broad diagnoses. Consult with relevant clinical specialists to establish specific inclusion and exclusion criteria based on objective clinical findings, validated diagnostic tools, and established disease classifications. Vague terms like "dysautonomia" and "POTS" are insufficient without precise operationalization.

Include Essential Control Groups: Absolutely essential revision. The study must include:<br /> Unvaccinated individuals with similar chronic illnesses: To determine if the immunological signatures are specific to post-vaccination illnesses or are general features of these conditions.

Healthy unvaccinated controls: To assess baseline immunological differences related to vaccination itself.<br /> Focus on Establishing Causality (or Acknowledge Purely Correlational Nature): The authors must either:<br /> Significantly temper their interpretations and conclusions to reflect the purely correlational nature of the study. Avoid any language that implies or suggests causality. Clearly state the limitations of correlational findings. <br /> OR<br /> Implement methodological approaches to strengthen causal inference. This would be extremely challenging in this observational study design, but might involve more sophisticated statistical methods to address confounding, mediation analysis, or focus on subgroups with stronger temporal associations. However, even with these, establishing robust causality will be very difficult.

Increase Sample Size and Statistical Power: Larger sample sizes are needed, particularly for the "chronic illness" groups, to improve statistical power and reduce the risk of false positives. This may require multi-center collaboration and a significant expansion of the study.

Rigorous Statistical Analysis and Correction for Multiple Comparisons: Implement more rigorous statistical analyses, including appropriate correction for multiple comparisons in omics data. Focus on robust statistical methods and validation of findings.

External Validation in Independent Cohort: Validation of the identified immunological signatures in an independent cohort is essential to assess reproducibility and generalizability. Without validation, the current findings are highly preliminary and should be interpreted with extreme caution.

Enhance Clinical Characterization and Phenotyping: Provide much more detailed and objective clinical characterization of the "chronic illness" groups. Include validated clinical assessments, objective measures, and explore heterogeneity within these groups.

Address Potential Biases: Acknowledge and discuss the potential for reporting bias and selection bias. Implement strategies to mitigate these biases where possible, or at least transparently discuss their potential influence on the findings.<br /> Responsible and Cautious Communication: The manuscript must be rewritten with a much more cautious and balanced tone. Avoid over-claiming, over-generalization, and any language that could be misconstrued as definitive proof of vaccine-induced chronic illnesses. Emphasize the preliminary and correlational nature of the findings, and the need for further rigorous research. Clearly discuss the limitations of the study and the potential for misinterpretation.

Recommendation for Publication<br /> In its current form, this manuscript is not suitable for publication in any rigorous peer-reviewed journal. It suffers from critical methodological flaws, significant limitations in its analysis and interpretation, and overreaches in its conclusions. Substantial revisions are absolutely necessary to address the major weaknesses outlined above.<br /> Without these extensive revisions, the manuscript risks contributing to misinformation and vaccine hesitancy without providing robust and reliable scientific evidence.<br /> If the authors are able to undertake the very significant revisions recommended, particularly regarding case definitions, control groups, causality, and validation, the manuscript might then be reconsidered for publication in a specialized immunology or clinical immunology journal, but even then, would require extremely careful and balanced framing of the findings. Publication in a very high-impact journal like Science or Nature remains highly unlikely given the fundamental limitations in the study design and the sensitive nature of the topic.

In conclusion, while the research question is important, the current manuscript requires a fundamental re-evaluation of its methodology, analysis, and interpretation to achieve scientific rigor and responsible communication of findings.

On 2025-03-26 03:22:48, user Pavel Montes de Oca Balderas wrote:

It seems risky to claim so vehemently that vaccines saved lifes when the studies cited to backup this claim did not consider the syndrome described here that could also lead to death. <br /> Moreover, one of the references used here to demonstrate that vaccines saved 14 millions lifes through a math model has been called a "fiasco" because simple maths with world data records DO NOT fit their model. Also the authors of that paper have conflicts of interest as some are sponsored by the WHO and others.

Longdom Publishing SL<br /> https://www.longdom.org <br /> The discrepancy between the number of saved lives with COVID-19 vaccination and statistics of Our World Data.

On 2020-07-05 14:07:29, user mike marchywka wrote:

I noted my comments on twitter re this paper were piciked up here. You can see it referenced in context here, fwiw, <br /> https://www.linkedin.com/po...

On 2020-07-09 18:37:18, user K dawg wrote:

Nobody cares about the CFR because it is arbitrarily based on testing availability.

What is the Covid IFR? Looks to be around 0.1% from what I've seen... about like influenza.

On 2019-07-09 23:24:44, user Guyguy wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

Tuesday, July 9, 2019

The epidemiological situation of the Ebola Virus Disease dated July 8, 2019:

Since the beginning of the epidemic, the cumulative number of cases is 2,428, of which 2,334 are confirmed and 94 are probable. In total, there were 1,641 deaths (1,547 confirmed and 94 probable) and 683 people healed.<br /> 322 suspected cases under investigation;<br /> 10 new confirmed cases, including 8 in Beni, 1 in Vuhovi and 1 in Oicha;<br /> 11 new confirmed case deaths:<br /> 5 community deaths, including 3 Beni, 1 in Vuhovi and 1 in Oicha;<br /> 6 deaths in Ebola Treatment Center including 3 in Beni, 1 in Mabalako, 1 in Butembo and 1 in Katwa.

The cumulative number of confirmed / probable cases among health workers is 128 (5% of all confirmed / probable cases) including 40 deaths.

NEWS<br /> Ebola Virus Disease in Uganda<br /> The Ministry of Health of the Republic of Uganda announced that all index case contacts have completed their mandatory 21-day follow-up period without developing signs of the disease. As a result, Ebola transmission in Kasese District was interrupted. As a reminder, the index case was a 5-year-old boy who had traveled with his mother to the burial of his grandfather who died of Ebola in Aloya, in the health zone of Mabalako.<br /> Uganda has strengthened its border surveillance system. Thus, all travelers coming from the DRC or having traveled to the DRC during the last 21 days must go through the sanitary control at Entebbe airport and at the various road and sea entry points of the country.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo.

On 2019-07-23 17:47:15, user GuyguyKabundi Tshima wrote:

EVOLUTION OF THE EBOLA EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI

Monday, July 22, 2019

The epidemiological situation of the Ebola Virus Disease dated 21 July 2019:

Since the beginning of the epidemic, the cumulative number of cases is 2,592, of which 2,498 are confirmed and 94 are probable. In total, there were 1,743 deaths (1,649 confirmed and 94 probable) and 729 people healed.<br /> 272 suspected cases under investigation;<br /> 14 new confirmed cases, including 10 in Beni, 2 in Mandima, 1 in Oicha and 1 in Mutwanga;<br /> 6 new confirmed case deaths:<br /> 3 community deaths including 1 in Beni, 1 in Mandima and 1 in Mutwanga;<br /> 3 deaths at the Ebola Treatment Center of Beni.

Change in coordination of the response to Ebola Virus Disease<br /> A new arrangement of the Presidency of the Republic announced this Saturday, July 20, 2019, the establishment of a technical secretary under the direct supervision of the Head of State to coordinate the response against the Ebola Virus Dpsease in North Kivu and Ituri. This technical secretary is headed by Professor Jean-Jacques Muyembe, who was also chairman of the laboratory committee in coordinating the current response since August 2018.<br /> As a result, all communications related to the response will now be managed directly by the Presidency.

Source: The press team of the Ministry of Health.

On 2020-02-10 08:17:02, user zjuliu wrote:

Firstly I think this should be a milestone for 2019 NCov research because of the work from Academician Zhong and his colleagues. But one thing I need to point out: this paper included patients from Wuhan, Hubei (except Wuhan) and other cities except Hubei, but as we know, it is very different on epidemiological trend, mortality etc when compared with these cohorts. Therefore, I think it is worthy to share this trend on public database so that the scholars can better use these data for further study.

On 2020-02-10 20:08:48, user Marc Bevand wrote:

93.6% of cases (1029 out of 1099) are still in the hospital. Their outcome (death or recovery) is not known yet. This is why the case fatality rate observed (1.4%) so far is low..

For comparison, the two other studies with 41 and 99 cases had only 17% and 58% cases still in the hospital at the time of their writing. More cases had resolved, this is why their case fatality rate was higher (15% and 11%).

On 2020-02-11 06:09:14, user ) Bill Ford wrote:

They do not know how long it remains potent only they have found 24 days. What is the total? No one yet knows

On 2020-02-12 07:04:04, user Marc Bevand wrote:

Table S3 in supplemental data says there are 3665 confirmed patients with 2019-nCoV infections. All the numbers in the table add up to 3665. However the rest of the preprint claims 4021 confirmed patients. What explains this discrepancy?

On 2020-02-13 16:20:21, user Xiaolin Zhu wrote:

We are the authors. We have retrained our model with confirmed cases by Feb. 11. We updated our prediction results. The total infections in mainland China would be 72172 by March 12, 2020 under current trend. It will be 149774 in the worst situation.

On 2020-02-17 09:19:59, user Ellie_K wrote:

Keep this assessment of R0 values when making comparisons to other contagious disease, via the CDC in Atlanta https://wwwnc.cdc.gov/eid/a...

On 2020-02-20 09:21:38, user Linh Ngoc Dinh wrote:

Thanks for sharing your research.<br /> Just a small comment: In an introductory graph, you said "In jurisdictions outside China (and excluding Hong Kong, Macao and Taiwan) the CFR as detailed in the 13 FebruaryWHO Report [3] was 1/447 = 0.22% (95% confidence interval (CI) = 0.40% to 1.26%)."<br /> This is quite misleading statement, WHO has never mentioned an estimate of CFR outside China until now. I think what cite here is the information that there was 1 death and 447 confirmed cases outside China. You should make this point clear, because as a reader, I feel like the number 0.22% (95%CI: .4%-1.26%) is what WHO said. <br /> Also, I wonder how you arrived to that 95%CI as we have only 1 point estimate.

Thanks much!

On 2020-02-21 00:19:53, user Leon Yuan wrote:

The link for Supplementary material is not available, please update it, thanks!

On 2020-02-25 12:11:41, user Igor Nesteruk wrote:

Dear colleagues,

Unfortunately, the coronavirus epidemic in Italy is developing very much like we have seen in mainland China (details in my preprint).

http://dx.doi.org/10.13140/...

Only very strict quarantine and safeguards can stop the spread of the infection throughout Europe.

May be, this information could be useful for your investigations.

I have found today the accumulated number of cases in Italy – 229 - on the official site of Italian Health Ministry.

http://www.salute.gov.it/po...

This point was already in the figure from

http://dx.doi.org/10.13140/...

We need the correct and reliable information about the accumulated number of cases. Do you have any links?

Be careful and healthy!

Sincerely yours,

Igor Nesteruk,

On 2020-02-26 19:26:13, user ricwerme wrote:

Did I miss the exported case counts the paper used to determine the internal # of cases, or is it just "three" with assumed missed cases?

The abstract says "suggesting a underlying burden of disease in that country than is indicated by reported cases." Should that be "a greater underlying..."?

On 2020-02-28 02:55:16, user Art Enquirer wrote:

Will it be possible for an AI startup (actually a hackathon team) to secure access to your data? Will you share for the sake of the world? We are also running servers with AI algorithms and wish to pre-test your conclusion.

On 2020-02-28 19:16:00, user Antoine Jomier wrote:

Hello, i am running an ai algorithm start up company. Would it be possible to share your model or data set so that we distribue it in France. We would not do any commercial exploitation but make it available widely to the community. My contact antoine.jomier@incepto-medical.com<br /> Thanks

On 2020-02-29 18:15:11, user Per Carlbring wrote:

For reference #21 a correction was published in 2016: https://journals.plos.org/p...

On 2020-03-05 09:13:52, user Jørgen K. Kanters wrote:

Important paper but needs to be improved to be a High flier. First around 50 % had a hypertensive history. In an American population that would mean hypertension is protective. You need an age gender matched control population from the same area to compare with. Furthermore you miss a very important point. Which medicine prescriptions had the patients before admission? ACE Inhibitors and A2 antagonists as the most interesting. Again compared to a control population

On 2020-03-05 16:05:56, user Erik Kulstad wrote:

Thank you for this data. You mention that you excluded patients with mild symptoms who had been transferred to mobile cabin hospitals (as well as patients who had been transferred to other hospitals for advanced life support), but were any of the patients with mild symptoms then re-admitted, to then become patients that were included in your 109 total (or are you able to track)?

On 2020-03-06 15:54:48, user Steven Ge wrote:

Please let us me know if you have any questions or suggestions. Twitter @StevenXGe

On 2020-03-13 11:54:03, user Murat Von Marit wrote:

For what temperature is the time you set?<br /> 4C? 20C? 40?<br /> And what about Textile,Fabric,Clothes?

On 2020-03-25 17:42:51, user Rudolf Brüggemann wrote:

It is a bit irritating that version1 and version2 give different values for the half-lifes. Is there an error based on a factor ln10 somewhere? The half-lifes in Table 1 of the supplement of the published version are much smaller than those in Table 1 in the preprint version. E.g., half-life (median) for steel 13.1 hours in the preprint, median of 5.63 hours in Table 1 of the Supplement of the published version.

On 2020-03-14 08:04:28, user Stefano Gaburro wrote:

Minimal viral titer for infection: thanks for this great piece of work that allows governmental bodies to give suggestion. One question: the viral titer decreases over time meaning it could be detected but no longer infectious. Have you determined the minimal titer of virus to determine an infection?

On 2020-03-14 19:27:30, user Halmartin Brown wrote:

Covid-19 Question: What is the risk of the virus being transmitted on paper and mail in general and packages? Are mail and package carriers being tested and are they using gloves? I read it can survive up to a day on cardboard and cash doesn't allow viruses to survive as long.

On 2020-03-15 18:34:58, user tusitw wrote:

Are you also going to study as a function of humidity?<br /> Below LOD, do we know it is still capable of infecting? a question in the same theme as Stefano Gaburro...

On 2020-03-17 23:37:34, user RunningThrough wrote:

Given the study cohort of patients are all hospital admitted patients there in Wuhan, presumably are all in the 'severe' and 'critical' category of all COVID-19 patients per admission policies that we read, so does this present data suggests that the SARS-CoV-2 virus has a higher infectivity amongst blood Gp A patients or that blood Gp A patients are more likely to develop a more severe disease?

On 2020-03-20 17:02:20, user Kevin Hamill wrote:

An editing suggestion:<br /> This manuscript will be read by new media/journalists therefore I would encourage more careful use of the term "significant".

For example where it says "blood group A had a significantly higher risk for COVID-19 " if that was quoted then people would hear this as "blood group A had a much higher risk for COVID-19."

In the same sentence, I would write:<br /> blood group A had approximately 20% higher risk for COVID-19 (odds ratio-OR, 1.20; 95% confidence interval-CI 1.02~1.43, P = 0.02). <br /> [and equivalent changes with the other phrases].

Note that as you have already stated the p values, using the word significant has no added value, it only provides a source for ambiguity.

On 2020-03-26 07:52:06, user M.E.Valentijn wrote:

Has anyone been able to verify their source claiming 33% prevalence of Type O in the general population? I can't find the journal that's cited for that, and a newer article says 30.2% for Han Chinese, not the nearly 34% claimed here. Though that's Han Chinese in general, not just in Wuhan. Can't find their other sources for normal blood types in the area either.

On 2020-03-20 20:57:29, user Sylvie Vullioud wrote:

Could authors provide information to dissipate high risks of bias:

1. Manuscript was first published on mediterranee-infection.com website, not on medRxiv. On the manuscript on the website on mediterranee-infection.com, I can read 'In Press 17 March 2020 – DOI : 10.1016/j.ijantimicag.2020.105949'. It means that manuscript was already accepted by International Journal of Antimicrobial Agents at the time when the manuscript was deposit on the 20.03.2020 on medRxiv.

-> Pre-print on medRxiv is not a real pre-print to collect feed-back for manuscript improvement, as originally designed for. Moreover, medRxiv states: 'All preprints posted to medRxiv are accompanied by a prominent statement that the content has not been certified by peer review'.

-> There is an obvious potential conflict of interest, because last author Raoult is editor of the article collection COVID-19 Therapeutic and Prevention in International Journal of Antimicrobial Agents.

-> International Journal of Antimicrobial Agents is runned by Elsevier, suggesting 'If accepted for publication, we encourage authors to link from the preprint to their formal publication via its Digital Object Identifier (DOI)'.

1. Discussion on the controversy of main cited Chinese paper, ref 8 ?

2. According to paper, allocation of patients group was random but treated group is 51.2 years average and control group 37.3 years?

3. Article describes 3 conditions of patients: asymptomatic, low and high symptoms. Why?

4. Care to patients, biological and physiological sampling and analyses, and statistical analyses were not blinded. Why?

5. I think that no placebo was used. Why?

6. 6 patients on total of 42 were excluded from study: three patients were transferred to intensive care unit, 1 stopped because of nausea, 1 died. One left hospital. <br /> It is written :'study results presented here are therefore those of 36 patients (20 hydroxychloroquine-treated patients and 16 control patients). Why were dead, intensive care, and nausea patients not included in statistical treatment? <br /> -> This may be a selection bias? <br /> -> What about unwanted very worrying effects of the treatment?

7. 'The protocol, appendices and any other relevant documentation were submitted to the French National Agency for Drug Safety (ANSM) (2020-000890-25) and to the French Ethic Committee (CPP Ile de France) (20.02.28.99113) for reviewing and approved on 5th and 6th March, 2020, respectively'. Pre-print was posted on 20.03.2020. Time points on day 14 on patients.<br /> -> So recruitment and study started before approval of ANSM and French Ethic Committee? How is it possible?

8. How is it plausible that numerous authors (18!) participated equally to the work? Is it possible to add their respective contributions?

Thank you in advance for considering my questions. <br /> Regards, <br /> Sylvie Vullioud

On 2020-03-22 04:52:08, user Juan B. Gutierrez wrote:

In summary, provided that our Ro is correct, and we are certain it is, as we reused very long results from our recent peer-reviewed result, https://doi.org/10.1007/s11... Bulletin of Mathematical Biology (the premier venue for the discipline), then with the information that we have today, Ro cannot be close to 3.

By a suggestion of Dr. Jeremy Faust, MD, Brigham and Women's Hospital, @jeremyfaust, I modified the most uncertain parameters to produce an Ro of 3.These parameters are the mean infectious periods for symptomatic (lambda_yr) and asymptomatic (lambda_ar) subjects. If we consider the median of the other parameters to be correct (there is more data), then the mean infectious period of a symptomatic patient should be 4.9 days, and the mean infectious period of an asymptomatic should be 4.1 days. These numbers do not match what is happening on the ground. If we reduce alpha, the probability of becoming asymptomatic upon infection, to something less than 0.86, e.g. alpha = 0.5, then the mean infectious period of a symptomatic patient should be 3.7 days, and the mean infectious period of an asymptomatic should be 3.1 days.

The reality is that patients are infectious before the onset of symptoms, and the disease lasts more than 3 days in symptomatic patients. The necessary conclusion is that via a computational reductio ad absurdum, and with the information we have today, Ro cannot be close to 3.

On 2020-03-23 21:39:01, user Shayan wrote:

wondering what the 4000+ test results refers to with there only being 28 patients? looking at the distribution plots, there seem to be more than 28 data points per biomarker

On 2020-03-24 14:55:05, user syed aleem wrote:

Excellent paper! Any particular reason to express RBD as a secreted protein?

On 2020-03-25 22:43:03, user Sinai Immunol Review Project wrote:

Title: A serological assay to detect SARS-Cov-2 seroconversion in humans

Immunology keywords: specific serological assay - ELISA - seroconversion - antibody titers

Note: the authors of this review work in the same institution as the authors of the study<br /> Main findings: <br /> Production of recombinant whole Spike (S) protein and the smaller Receptor Binding Domain (RBD) based on the sequence of Wuhan-Hu-1 SARS-CoV-2 isolate. The S protein was modified to allow trimerization and increase stability. The authors compared the antibody reactivity of 59 banked human serum samples (non-exposed) and 3 serum samples from confirmed SARS-CoV-2 infected patients. All Covid-19 patient sera reacted to the S protein and RBD domain compared to the control sera.<br /> The authors also characterized the antibody isotypes from the Covid-19 patients, and observed stronger IgG3 response than IgG1. IgM and IgA responses were also prevalent.

Limitations of the study:The authors analyzed a total of 59 control human serum samples, and samples from only three different patients to test for reactivity against the RBD domain and full-length spike protein. It will be important to follow up with a larger number of patient samples to confirm the data obtained. Future studies will be required to assess how long after infection this assay allow to detect anti-CoV2 antibodies. Finally, while likely, the association of seroconversion with protective immunity against SARS-Cov-2 infection still needs to be fully established.

Relevance: <br /> This study has strong implications in the research against SARS-Cov-2. First, it is now possible to perform serosurveys and determine who has been infected, allowing a more accurate estimate of infection prevalence and death rate. Second, if it is confirmed that re-infection does not happen (or is rare), this assay can be used as a tool to screen healthcare workers and prioritize immune ones to work with infected patients. Third, potential convalescent plasma donors can now be screened to help treating currently infected patient. Finally, the recombinant proteins described in this study represent new tools that can be used for further applications, including vaccine development.

Review part of a project by students, postdocs and faculty at the Immunology Institut of the Icahn School of Medicine, Mount Sinai.

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:

1. 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.

2. Critical – any of the following: respiratory failure that requires mechanical ventilation; shock; other organ failure that requires treatment in the ICU.

3. 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 2020-03-26 16:57:03, user S Weeth wrote:

How about soap? Any testing on with soap?

On 2020-03-27 15:01:22, user Sinai Immunol Review Project wrote:

These authors looked at 17 hospitalized patients with COVID-19 confirmed by RT-PCR in Dazhou, Sichuan. Patients were admitted between January 22 and February 10 and the final data were collected on February 11. Of the 17 patients, 12 remained hospitalized while 5 were discharged after meeting national standards. The authors observed no differences based on the sex of the patients but found that the discharged patients were younger in age (p = 0.026) and had higher lymphocyte counts (p = 0.005) and monocyte counts (p = 0.019) upon admission.

This study is limited in the sample size of the study and the last data collection point was only one day after some of the patients were admitted.

These findings have been somewhat supported by subsequent studies that show that older age and an immunocompromised state are more likely to result in a more severe clinical course with COVID-19. However, other studies have been published that report on larger numbers of cases.

On 2020-03-27 20:04:02, user Sinai Immunol Review Project wrote:

The authors present a digital PCR (dPCR) diagnostic test for SARS-CoV-2 infection. In 103 individuals that were confirmed in a follow-up to be infected, the standard qPCR test had a positivity rate of 28.2% while the dPCR test detected 87.4% of the infections by detecting an additional 61 positive cases. The authors also tested samples from close contacts (early in infection stage) and convalescing individuals (late in infection stage) and were able to detect SARS-CoV-2 nucleic acid in many more samples using dPCR compared to qPCR.

The authors make a strong case for the need for a highly sensitive and accurate confirmatory method for diagnosing COVID-19 during this outbreak and present a potential addition to the diagnostic arsenal. They propose a dPCR test that they present has a dramatically lower false negative rate than the standard RT-qPCR tests and can be especially beneficial in people with low viral load, whether they are in the earlier or later stages of infection.

On 2020-03-28 18:07:46, user Ian Timaeus wrote:

I may be being very stupid, but isn't the ACFR formula given in the preprint wrong? Aren't you simply averaging the age-specific CFRs? So don't you want to multiply their sum by n/100, i.e. divide by the number of age intervals, not multiply by the width of those intervals? As an alternative, you could standardise the age-specific CFRs on the age-sex distribution of Italy, rather than on a uniform age distribution, so that the adjusted CFR equated to the CFR if incidence were constant by age and sex.

On 2020-03-30 15:27:50, user Sinai Immunol Review Project wrote:

Summary and key findings: Summary of clinical trials registered as of March7, 2020 from U.S, Chinese, Korean, Iranian and European registries. Out of the 353 studies identified, 115 were selected for data extraction. 80% of the trials were randomized with parallel assignment and the median number of planned inclusions was 63 (IRQ, 36-120). Most frequent therapies in the trials included; 1) antiviral drugs [lopinavir/ritonavir (n-15); umifenovir (n=9); favipiravir (n=7); redmesivir (n=5)]; 2) anti-malaria drugs [chloroquine (n-11); hydroxychloroquine (n=7)}; immunosuppressant drugs [methylprednisolone (n=5)]; and stem cell therapies (n=23). Medians of the total number of planned inclusions per trial for these therapies were also included. Stem cells and lopunavir/ritonavir were the most frequently evaluated candidate therapies (23 and 15 trials respectively), whereas remdesivir was only tested in 5 trials but these trials had the highest median number of planned inclusions per trial (400, IQR 394-453). Most of the agents used in the different trials were chosen based on preclinical assessments of antiviral activity against SARS CoV and MERS Cov corona viruses.

The primary outcomes of the studies were clinical (66%); virological (23%); radiological (8%); or immunological (3%). The trials were classified as those that included patients with severe disease only; trials that included patients with moderate disease; and trials that included patients with severe or moderate disease.

Limitations: The trials evaluated provided incomplete information: 23% of these were phase IV trials but the bulk of the trials (54%) did not describe the phase of the study. Only 52% of the trials (n=60) reported treatment dose and only 34% (n=39) reported the duration. A lot of the trials included a small number of patients and the trials are still ongoing, therefore no insight was provided on the outcome of the trials.

Significance: Nonetheless, this review serves as framework for identifying COVID-19 related trials, which can be expanded upon as new trials begin at an accelerated rate as the disease spreads around the world.

On 2020-03-31 19:02:12, user earonesty wrote:

It's an immunomodulator, it prevents some of the inflammation issues associated with COVID-19. Not a surprising result. There may be better ones, but since this is used to treat asthma, and other issues pulmonary inflammation, it's a good choice.

On 2020-03-31 22:51:51, user Ruth Etzioni wrote:

When will the actual description of the model be available?

On 2020-03-31 22:59:25, user Whiskers wrote:

Even more worrying if it is air spread, we have been led to believe that it is only really contact spread unless someone coughs directly over you.<br /> Perhaps this accounts for the prolific spread of this disease.

On 2020-04-01 14:34:11, user Sinai Immunol Review Project wrote:

Summary: ?Retrospective study on 97 COVID-19 hospitalized patients (25 severe and 72 non-severe) analyzing clinical and laboratory parameter to predict transition from mild to severe disease based on more accessible indicators (such as fasting blood glucose, serum protein or blood lipid) than inflammatory indicators. In accordance with other studies, age and hypertension were risk factors for disease severity, and lymphopenia and increased IL-6 was observed in severe patients. The authors show that fasting blood glucose (FBG) was altered and patients with severe disease were often hyperglycemic. Data presented support that hypoproteinaemia, hypoalbuminemia, and reduction in high-densitylipoprotein (HDL-C) and ApoA1 were associated with disease severity. ?

Limitations: ?In this study non-severe patients were divided in two groups based on average course of the disease: mild group1 (14 days, n=28) and mild group 2 (30 days, n=44). However mild patients with a longer disease course did not show an intermediate phenotype (between mild patients with shorter disease course and severe patients), hence it is unclear whether this was a useful and how it impacted the analysis. Furthermore, the non-exclusion of co-morbidity factors in the analysis may bias the results (e.g. diabetic patients and glucose tests) It is not clear at what point in time the laboratory parameters are sampled. In table 3, it would have been interesting to explore a multivariate multiple regression. The correlation lacks of positive control to assess the specificity of the correlation to the disease vs. correlation in any inflammatory case. The dynamic study assessing the predictability of the laboratory parameter is limited to 2 patients. Hence there are several associations with disease severity, but larger studies are necessary to test the independent predictive value of these potential biomarkers.?

Findings implications:? As hospital are getting overwhelmed a set of easily accessible laboratory indicators (such as serum total protein) would potentially provide a triage methodology between potentially severe cases and mild ones. This paper also opens the question regarding metabolic deregulation and COVID-19 severity.

On 2020-04-03 05:01:21, user Jacob G Scott wrote:

Please find our update, with HIGHER recommended exposure times for porous PPE, on our github repo: https://github.com/TheoryDi...

We expect another update in the coming days with filtration/fit testing results at these exposures, as well as biologic validation.

Please also see recent CDC guidelines: https://www.cdc.gov/coronav...

and a cooperative groups recommendation for N95 decontamination: https://www.n95decon.org/

Please stay safe and healthy.

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 2021-05-17 17:12:31, user Cathy Crowe wrote:

Thank you for this. In Toronto, Canada we've now had over 120 shelter outbreaks with over 1500 people infected. In fact we had to take the city to court to ensure at least 2 metre (6 feet) physical distancing would be ordered. Congregate shelters continue to have outbreaks, some are on their 2nd and 3rd. Post COVID the new model of shelter delivery must be one person per room, one couple per room, one family per room while they wait for housing and the housing has to be fast tracked.

On 2021-05-20 18:34:22, user Andrew wrote:

PEER REVIEWED UPDATED PAPER:<br /> https://clinmedjournals.org....

On 2021-05-21 20:59:55, user Harold Thimbleby wrote:

From harold@thimbleby.net

I mailed the author for correspondence constructively two weeks ago but I have had no reply.

I am a professor of computer science. The Excel spreadsheet is, sadly, appalling and needs a lot of professional work to be believable. There is no useful documentation about how it is intended to work. It is obscure and impossible to check. I doubt it is correct, unless evidence can be provided. In short, the results described in this paper cannot be relied on or used for any public health purposes.

Have any of the authors carefully reviewed the spreadsheet? Any independent parties? If so I think the paper should say so.

Best wishes, though, with this important work.

-Harold

On 2021-05-25 20:24:16, user Green Ranger wrote:

The results and conclusions of this study are wrong. The authors mistook the ivermectin and control arms of one of the RCTs that they included. Look at figure 2. The results from Niaee 2020 are dramatically misreported. The actual results for that study are as follows:

Control groups: 11 deaths out of 60 patients.<br /> Ivermectin groups: 4 deaths out of 90 patients.

When this is corrected, the results of this meta-analysis confirm what other meta-analyses have found. Ivermectin use is associated with approximately 66% reduction in Covid fatalities. And this result is statistically significant.

A source for this.

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 2021-05-26 14:43:55, user Donepudi Raviteja wrote:

Sorry Sir, but this article need so much more rigorous Multivariate statistics like PCS, MANOVA etc.,. The statistics are basic and also misleading to some extant. In correlation matrix (Figure 3) the correlation between deaths per million and sanitation parameters looks identical to correlation between age >65 years and sanitation parameters. This show that the confiding factor is age distribution. If a proper multivariate analysis is done this would have been easily identified and avoided as discussion point. A simple age adjusted death-rate correlation with sanitation parameters would also be sufficient.

On 2021-05-27 14:27:10, user Michael W. Perry wrote:

This study reinforces an earlier Danish one published in the Annals of Internal Medicine which found that the result of mask wearing had so little statistical significance it could be "compatible with a 46% reduction to a 23% increase in infection."

Results:<br /> A total of 3030 participants were randomly assigned to the recommendation to wear masks, and 2994 were assigned to control; 4862 completed the study. Infection with SARS-CoV-2 occurred in 42 participants recommended masks (1.8%) and 53 control participants (2.1%). The between-group difference was –0.3 percentage point (95% CI, –1.2 to 0.4 percentage point; P = 0.38) (odds ratio, 0.82 [CI, 0.54 to 1.23]; P = 0.33). Multiple imputation accounting for loss to follow-up yielded similar results. Although the difference observed was not statistically significant, the 95% CIs are compatible with a 46% reduction to a 23% increase in infection.<br /> https://www.acpjournals.org...

On 2021-05-29 01:46:20, user David Steadson wrote:

Why were respiratory issues etc not part of the symptoms studied? They are typically listed as frequent long covid symptoms. Ref 10 for example says -

"Insomnia (18.6%), respiratory symptoms (including pain and chest tightness) (14.7%), nasal congestion (12.4%), fatigue (10.8%), muscle (10.1%) and joint pain (6.9%), and concentration difficulties (10.1%) were the most frequently reported symptoms."

On 2021-05-30 06:25:34, user Allan Saul wrote:

I was struck by the difference in the efficacy estimates in this paper and the estimates from Israel for efficacy against the B.1.117 variant e.g., https://www.medrxiv.org/con...<br /> It would be useful if the authors could make some comment on the apparent differences in results.<br /> Also, I am a bit perplexed at the time frame for estimating efficacy following first vaccination with BNT162b2 vaccine. Paper says that the efficacy was measured "21 days or more after the first dose up to the day before the second dose" . Recommended time for the second dose IS on day 21 so how come there are ANY cases? Presumably, second doses were delayed. In view of earlier data that suggests that the BNT162b2 is substantially more effective in the 4th week following a single dose (in absence of a second dose) than in the third week, it would be useful if the authors can be more explicit about the observation windows.

On 2021-06-01 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 2021-06-07 12:30:38, user UNG wrote:

Covishield vaccine is essentially spike protein, while Covaxin was whole virus. I don't understand why the antibody titre was measured against only spike protein? also one should used multiple brand kit (at least two) to get the fair idea of the antibody titre.

On 2021-06-12 09:02:36, user Daksya Siddhi wrote:

One bit of information that I did not find is the data on the age of the Vitamin D measurements. Since these measurements can span from 2 weeks to 2 years prior to hospital admission, their predictive value for Vitamin D levels at time of infection can't be judged, or at very least, won't be consistent. Can this data be provided?

Additionally, it is mentioned that the most recent Vitamin D measurement is used. For those patients for whom multiple measurements are available, what is the trend - stable, increasing or decreasing? That could affect their imputed Vitamin D level at time of infection.

On 2021-06-13 17:13:57, user artpatronforever wrote:

Quote "No drug for prevention or treatment in earlier stages of COVID-19 are yet found;" In an alternate reality certainly that could be true. In that same alternate reality likewise it could be true that a dosage of a vaccine is properly the same for a 100 pound woman as for a 300 pound man. I choose not to place confidence in wisdom offered from that alternate reality where originates such lame disinformation. Truth has no agenda but disinformation certainly does have an agenda.

On 2021-06-14 19:15:42, user Alhaji Abubakari Sadiq wrote:

Can i please get the vaccine acceptance scale for my study. I will validate it anyway. Maybe provide some information about the it for me

On 2021-07-01 02:18:40, user akcita wrote:

I was hoping that there would be obvious conflicts of Interest. Alas, it seems the Authors barely reviewed their own study and left it to a technical editor, and the viewing public to fix their bad science...

On 2021-07-05 20:17:46, user Baljeet Singh wrote:

The trial data looks good and with sufficient details for analysis.

On 2021-03-20 03:55:01, user Jean Tyan wrote:

Very inspiring and socially relevant work! Regarding your DAG, social determinants of health appear to be confounders on both the pathways between biological aging and healthspan and between biological aging and biological aging* due to the directionality of the arrows. I’m not sure I quite understand the correct analysis approach in this situation when evaluating the potential relationship between biological aging and healthspan—should regression models adjust for social determinants, even though they are conceptualized as an upstream cause? In addition, do you have any thoughts on how social determinants and weathering may be linked differently to aging, depending on the type of health outcome measured? A recent systematic review (Forde et al., 2019) reviewed studies on weathering examining a range of different health outcomes (e.g., allostatic load, mortality, telomere length, etc.) and found conflicting results—I would be interested to hear what you think of how these outcomes may relate to biological aging. Many of these variables are also available in the HRS data and could definitely be interesting to explore using the mediation analysis methods you describe here!

On 2021-03-22 19:00:50, user Trash Trashisfree wrote:

57 patients in the placebo arm, yet using 54/58 surviving in the placebo arm. Something is wrong in the math it's either 57 patients in Placebo or 58 patients in placebo.

On 2021-03-26 21:03:47, user odevinyak wrote:

The authors should use Fisher's exact test on mortality data. This leads to p-value of 0.119. The overall mortality difference is non-significant.

On 2021-03-26 17:47:12, user ayman alqunneh wrote:

• This article represents one of the most robust, well-organized studies I have ever read and reviewed.
• Its large sample size gives the article a high level of reliability and trust.
• Although the results of this study did not significantly differ from other studies published in this field, its focuses on Arabic-speaking communities gives its uniqueness and makes it special.
• Elements assessed by the researcher and his colleagues were inclusive and well selected.
• Inclusion and exclusion criteria for individuals selected to be included in the analysis make research unbiased.

On 2021-03-27 21:56:08, user Jesse Knight wrote:

Please note the following correction to the posted article (including numbers in the abstract):

In the previous version of this work, the parameters theta = [alpha, beta] were calculated incorrectly because the Kullback-Leibler divergence was defined in the wrong direction. The impact on generation time parameters and statistics is as follows (original -> fixed): shape (alpha): 1.813 -> 1.633, scale (beta): 2.199 -> 2.498, mean: 3.99 -> 4.08, SD: 2.96 -> 3.19. The qualitative interpretation of results is unchanged, and the corrected version should appear soon on the Infectious Disease Modelling journal site. We are unable to edit the version posted here. We sincerely apologize for this error.

The error correction is shown here: https://github.com/mishra-l...

On 2021-03-28 05:46:23, user Kareem Choucair wrote:

This article really shows what it is like to be a medical student and the crucial role that partaking in research has in their training. I greatly believe that the implementation of an evidence-based approach in exploring this topic has been used excellently and it has really compounded how research participation is one of the most crucial parts of generating an outstanding physician. Hopefully compulsory research modules become more prevalent as a result within medical schools globally. Well done altogether.

On 2021-04-06 18:30:59, user John Simeral wrote:

A version of this manuscript has now been peer reviewed and accepted at IEEE Transactions on Biomedical Engineering. This medRxiv page will shortly link to that version, which is also indexed at PubMed:<br /> IEEE doi: 10.1109/TBME.2021.3069119. Online ahead of print.

On 2021-04-07 03:09:47, user Jesse Baker wrote:

I think Table 1 should list, for each age group and month, the actual number of cases and the number of people who were tested for Covid, as these quantities are needed to interpret the case fatality rates (CFR) given there. It is well known that the CFR generally decreases as more people are tested for the virus; the tests discover mild infections otherwise overlooked. If younger people were becoming less likely to seek testing while older people maintained their previous testing habits, this might explain at least part of the observed increase in the CFR and the bias toward younger ages.

I’m not an expert in such matters, and concern over the new strains P.1, B.1.351 and especially B.1.1.7 extends to the USA as they begin to circulate here. The April 6 New York Times noted an increase in hospitalization among Americans under age 50 during March, but it has yet to be reflected in case fatality rates for that group.

On 2021-04-07 23:31:13, user Risham S wrote:

What about therapies like entyvio? Can anyone shed some light on that? Many thanks for the study , a great help for CID patients like me. Appreciate it.

On 2021-04-27 20:11:38, user BenSahn wrote:

I'm one of those people. I had Rituximab infusions in November for IgG4-RD. In March I got the J&J COVID vaccine while on a low dose of prednisone. Last week, after a few weeks off prednisone, blood test showed I had no COVID anti-bodies.

On 2021-04-13 23:06:29, user disqus_pagO5NCOKq wrote:

From the abstract above: "After the second vaccination, 31.3 % of the elderly had no detectable neutralizing antibodies"... does that mean the vaccination offers NO benefit to 1/3 of the "elderly"?

On 2021-04-15 16:36:59, user AYUSH YADAV wrote:

I want to ask about the validity of Data on Spectacle Wear, which you obtained from reference no. 20 of your article {Sheeladevi et. al.}, the analysis of Spectacle use was not done in that research paper; it was based on refractive errors, and also the predominant region covered by the reference 20 's study {Sheeladevi et. al.} was of Southern India, while your study takes into consideration a North Indian population, as I shall quote from the study, "Fifteen studies were included from South India, one each from Western and Central India, and one study covered 15 states across India" , I think you need a much better method to assess the Spectacle use in general population.

On 2021-04-16 16:18:49, user S Wood wrote:

Since we first posted this a couple of results from direct statistical measurements have come out that are broadly in agreement with the paper's results on incidence and its timing relative to lockdown. Figure 1 of this REACT-2 report shows the reconstructed time course of symptom onset, which lags infection by 5 to 6 days. Reported ONS incidence reconstructions from statistical infection survey data tell a similar story. This paper, now published in Biometrics, also produces similar results on incidence and R, by a different approach.

On 2021-04-19 18:28:52, user lbaustin wrote:

Has this been submitted (or accepted) in a journal yet?

On 2021-04-22 07:09:12, user Dimitar Kolev wrote:

Can somebody please explain how they give results of 18-64 years already, but generally Sweden counties still not even call (or just started to call 60+ years old only). Or those are only risk groups, doctors, law enforcement and army? Or what?

On 2021-04-24 23:50:51, user Chucky2017 wrote:

I'm not sure where to post for an expert opinion, but I have been searching and still can't find an answer. Maybe someone here could be kind enough to direct me.

If you delay the Phizer second dose for 3 months (or even 2 months) we see a fall off in antibodies. When you get your second shot what happens? Does it become less effective than if you had it in the 21 days? So basically is there a study that has someone who had it in 21 days, take their blood and compare them to a person that got it 3 months later and see what level of antibodies they have compared to the person with 21 days.

Canada is delaying the phizer shot by 4 months, would a person be better off not getting the second shot and redue the schedule again.

On 2021-04-25 15:46:55, user Annette Toledano wrote:

Congratulations on advancing the understanding of the range of symptoms in hospitalized COVID-19 patients. I am surprised that pain was not a common symptom in the population you examined. I found elsewhere (Persistent neurologic symptoms and cognitive dysfunction in non-hospitalized Covid-19 “long haulers.” E. Graham, March 2021, Annals of Clinical and Translational Neurology), pain afflicted 43% of "long haulers." <br /> Are you aware of a review paper on the clinical manifestations of non-hospitalized COVIS-19 patients? <br /> It appears the Cov-Sars-2 virus affinity to target organs varies in different populations. In some people, the innate immune system's initial response in endothelial cells can lead to lung or vascular symptoms. In others, infected nerve cells can lead to pain.

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 2021-04-26 16:44:55, user Stuart Weisberg wrote:

This article has now been published with a revised title "Distinct antibody responses to SARS-CoV-2 in children and adults across the COVID-19 clinical spectrum". The PMID is 33154590

On 2021-04-27 06:36:03, user Miguel Pedrera Jiménez wrote:

Hi, I'm Miguel Pedrera, from Hospital Universitario 12 de Octubre in Madrid, Spain. Great job, very interesting manuscript. We have recently published an article about a methodology to obtain useful data for clinical research from EHRs, applied to COVID-19. I am sending you the link in case it is of your interest: https://www.sciencedirect.c...

On 2021-04-29 12:05:31, user Anders Julton wrote:

Correct me if I'm wrong, but the actual air rate change efficiency of the HVAC alone in the hospital room seems quite low. My calculations yielded about 30% using eq. 2.1.6 in a book (linked below, linking is weird) on air rate change measurements. The control room had roughly 100% efficiency.

I used C(t = 10)/C(0) ~ 0.5 for the hospital room and C(t=40)/C(0) ~ 0.2 for the control room.

https://www.aivc.org/sites/...

On 2021-04-29 19:23:16, user ohminus wrote:

No reference to the national testing strategy? The assumption that asymptomatic people are mainly treated unter OuS is questionable, given how many are tested under the national testing strategy.

The notion that 50% is a low predictive value is likewise questionable. If anything, given the low prevalence in an asymptomatic population tested without any reason to suspect COVID infection, 80% is a questionably high positive predictive value.

On 2021-05-06 20:18:47, user Murray Stein wrote:

Important, well-conducted study. Results are puzzling. Antidepressant effects of IV ketamine are nicely replicated. Anti-PTSD effects are not. PTSD symptoms were reduced substantially after first infusion in all 3 groups (albeit statistically significantly more in the standard-dose ketamine group), and then stayed low and drifted a bit lower over the remainder of the study. Response rates were high and non-significantly different between all 3 groups (~60% for each of the ketamine groups, ~ 50% for the placebo group). This high a placebo response would not have been anticipated -- particularly given the selection of participants to have failed at least one adequate SSRI trial (although one wonders if this qualifies them as "antidepressant-resistant"). Will require some rethinking about future PTSD trial designs, including possibility that we should be measuring PTSD outcomes differently (i.e., doing something other than asking repeatedly about the 20 symptoms in DSM-5). I would be interested in hearing what others think, particularly with regard to clinical implications.

On 2021-05-11 15:46:38, user Julie McMullan wrote:

This paper has now been published and can be found at: https://onlinelibrary.wiley...

On 2021-05-13 22:21:36, user Jason wrote:

This paper should be removed for the lack of data, incorrect use of equipment as stated earlier, and improper testing conditions. Information such as flow rates, particle size concentration, and thermal conditions should have been presented, however are left out. As stated above, every other study conducted on this matter (which was done with correct equipment) shows very different results. This paper further spreads misinformation about mask efficiency and lacks any supporting scientific evidence or results to support the claims made.

On 2020-11-19 20:34:46, user Hilja Gebest wrote:

Thank you for this study. The serum level aimed for of 30ng/ml is sufficient for bone health but the immune system needs higher levels, at least 40 if not 60ng/mL. More importantly, as the dose was late in the illness, in addition to being quite low. Single bolus doses of vitamin D3 are rarely effective as an intervention particularly when administered without the cofactors: (Mag, zinc, boron, vitamin B + K2 complex and Omega-3). Bolus doses of vitamin D3 start becoming effective up around 500,000 IU and there must be followup with a maintenance dose of at least 10,000 IU/day vitamin D3. The D3 group was disadvantaged by means of many values and risk factors, the three main ones known to us - hypertension, Diabetes II and COPD - by a factor of more than 4:3 vs the Placebo group.

On 2020-11-22 01:11:32, user Mahan Ghafari wrote:

Your phylogenetic analysis is flawed: you cannot estimate a unique TMRCA for two independent introductions like this. Your constructed phylogenetic tree (fig.4) is blatantly incorrect (what's going on with the branches on the red clade B1??). I suggest you retract the preprint immediately and correct the fatal flaws in your analysis. Also, you are not the first group to study the phylogenetics of Iran and you should appropriately acknowledge earlier contributions.

On 2020-11-23 07:29:44, user Andreas Haas wrote:

The peer-reviewed article has been published in JIAS: https://onlinelibrary.wiley...

On 2020-11-25 02:49:25, user Dr Bishnu Mohan Singh wrote:

This preprint has been published in peer-reviewed journal Hindawi: Advances in Preventive Medicine. DOI: https://doi.org/10.1155/202...

On 2020-12-07 14:47:02, user Mark S Perry wrote:

Although you looked for any correlation with self reported BMI I’m wondering if the possible F/M difference might be down to a weight related threshold for a nutrient/drug? As with low dose Aspirin, effective in 1ry prevention of IHD in women - but not (heavier) men

On 2020-12-08 21:22:40, user Michal Piják wrote:

DOUBTS ABOUT THE EFFECTIVENESS OF MASS TESTING OF ASYMPTOMATIC POPULATION FOR CORONAVIRUS (SARS-CoV-2) IN SLOVAKIA

Indeed, it might seem that the number of positive PCR tests / per day, per million inhabitants two weeks after the nationwide testing of the whole country in Slovakia has started to slowly decrease. However, this declining trend may be skewed by significantly less testing. For example data from Monday 9.11.20 show that if as many tests were performed in Slovakia as on Thursday 29.10.20 (when the highest number of positives in the second wave was reached), we should have about 3x times higher number of positives on Monday 9.11.20. cases, i.e. about 3150, instead of 1050.

The cause of the lower number of tests is not known and one of the reasons could be the lack of RT PCR tests or staff in other days. After extensive testing with antigen tests, we had a big problem in Slovakia. This is that so far we have evaluated the situation according to the positivity of PCR tests. However, antigen testing made this situation unclear to us because people tested positive for antigens fell out of the statistics. It should also be borne in mind that lower numbers of positive cases could also be explained by the tightening of epidemiological measures and also because most of the persons with positive antigen tests were quarantined and did not undergo PCR testing.

There is evidence that strategies based on a large number of tests may not produce the expected results. A good example is a comparison of the strategies used by New Zealand and Iceland.1-2 In both of these island countries, the first cases were identified at the end of February 2020, but each country took a different path. New Zealand was one of the few countries that openly announced a COVID-19 elimination strategy right at the beginning of the epidemic. This included a gradually strengthened system for monitoring and isolating contacts with the timely and consistent use of lockdowns and border controls. It should also be recalled that some EU countries, such as Belgium, the Czech Republic, Switzerland, France, Slovenia and the Netherlands, have had a progressive decline in the number of positives, despite the fact that they did not have any comprehensive testing of the entire country.

Unlike New Zealand and many other countries, Iceland's strategy did not include any lockdown period, no official border closure for non-residents and negligible use of quarantine facilities. The cornerstone of Iceland's strategy was easy access to testing and mass screening, along with quarantine and contact tracking. According to data from October 21, New Zealand had 6 times fewer deaths, despite 4.5 times fewer tests than Iceland. Similarly, Slovakia, despite more than 8 times lower number of tests, had half less deaths per million inhabitants than Iceland. It should be recalled that, despite the large number of tests in Iceland, this was not a full-scale test and PCR tests were used. Taken together these findings are further evidence that nationwide antigen testing in a country with low prevalence is ineffective.

References<br /> 1. Jefferies S, French N, Gilkison C. COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. Lancet Public Health. 2020;5:e612-e623

1. Murdoch, D, Gottfreðsson M. COVID-19 and small island nations: what we can learn from New Zealand and Iceland., The conversation, published, September 23, 2020, https://theconversation.com...