On 2020-04-02 22:35:05, user Yuri Utsunomiya wrote:

The companion app to this manuscript can be accessed at: https://www.theguarani.com.br/<br /> The source code is available at: https://github.com/adamtait...

On 2021-06-12 20:06:10, user Bill Wilson wrote:

The sample size could be larger but it is the best “science” we have so far.

On 2021-11-07 06:10:27, user Ina wrote:

I haven't really understood what happens when a recovered subject is taking the vaccine. I mean there are already neutralising antibodies which will come in contact with the Spike protein leading to destruction of the cells that express it... right? Is that happening in the muscle or it has a more extensive degree? <br /> Those recovered from Covid are more likely to have adverse reactions after the jab, says a Harvard study... what is the explanation, what do we know about the molecular mechanisms ?<br /> I would love to know more about this and I would appreciate very much your opinion on this.

On 2021-06-09 21:22:25, user Al Smith wrote:

The study indicated zero re-infection. Unless they never left the house again that would be nearly impossible to avoid.

On 2020-04-24 14:50:11, user BR wrote:

This study compares a group of patients with "more severe disease" that needed medication ("many times as a last resort"-VA) and where they "expected, increased mortality"----with a group with milder forms of the illness that didn't need medication. I'm not sure what the value is in making such a study.

On 2020-04-25 08:48:51, user Huan Mo wrote:

Of course the patients who were treated with hydroxychloroquine were sicker at the baseline. The no-hydroxychlroquie patients have tons of missing values in basic labs and apparently they are mostly mild and outpatients.

This study is like saying pneumonia treated in tertiary medical center has worse outcome than treated in an outpatient clinic.

On 2020-03-30 22:55:25, user Pau Corral wrote:

None of the scenarios take into account immunity against second infection, or do they?

On 2020-07-30 14:03:32, user DFreddy wrote:

Scientific poor practice: conclusion not based on its research findings. Finding= no good evidence of effect (in any direction), yet the authors conclude using findings from a different study "<br /> Based on observational evidence from the previous SARS epidemic included<br /> in the previous version of our Cochrane review we recommend the use of <br /> masks combined with other measures." Sad.

On 2021-04-12 13:32:42, user H Arnold wrote:

Fantastic paper! What makes me a bit wonder is the discordance to the publications by Yost et al 2019 and Wu et al. 2020. Both report the replacement of T cells in the tumor (different entities) from external sources upon successful ICI.

Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C, Kageyama R, McNamara KL, Granja JM, Sarin KY, Brown RA, Gupta RK, Curtis C, Bucktrout SL, Davis MM, Chang ALS, Chang HY. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat Med. 2019 Aug;25(8):1251-1259. doi: 10.1038/s41591-019-0522-3. Epub 2019 Jul 29. PMID: 31359002; PMCID: PMC6689255.

Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O'Gorman WE, Au-Yeung A, Takahashi C, Goldstein LD, Poon C, Keerthivasan S, de Almeida Nagata DE, Du X, Lee HM, Banta KL, Mariathasan S, Das Thakur M, Huseni MA, Ballinger M, Estay I, Caplazi P, Modrusan Z, Delamarre L, Mellman I, Bourgon R, Grogan JL. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020 Mar;579(7798):274-278. doi: 10.1038/s41586-020-2056-8. Epub 2020 Feb 26. PMID: 32103181.

On 2023-11-08 17:24:49, user Paul Auer wrote:

Great paper. Very interesting work. One question about calculating dilution across multiple studies. Suppose one study used say the 1000 Genomes reference panel for imputation and another study used the TOPMed reference panel for imputation. If we compare the summary statistics between these two studies, might we see systematic differences in effect sizes if the imputation quality is quite different? i.e., might differences in imputation be confounded with phenotypic misclassification as estimated by PheMED?

On 2020-04-18 02:30:01, user Don Phan wrote:

This does not appear to be random sampling. If you are using volunteers, then the sampling is not representative of Santa Clara country. What have you done to correct this? And I am not talking about demographics. The people who suspected that they had the virus would be the most likely to risk the shelter at home, drive to location, and participate.

On 2020-04-18 12:39:09, user Tomas Hull wrote:

I will try to be the devil's advocate:

80% of population infected with COVID-19 have very mild symptoms or none.<br /> 75% of population with COVID-19-like-symptoms tested negative.

What's the likelihood of anybody from these 2 groups responding to the facebook ad, and participating in the study?

On 2020-04-17 22:27:44, user Julie Larsen Wyss wrote:

Have the participants been informed if they were positive yet?

On 2020-04-23 19:04:09, user CP wrote:

Cuomo of NY announced two hours ago (4/23) results of antibody study of 3,000 in 19 NY counties. Result showed average infection rate of 13.9% - higher in NYC, lower in rural areas...

On 2020-04-21 10:02:48, user Tomas Hull wrote:

I don't ever remember a time where the morgues are over filled where they are actually using refrigerated trucks to store the body's. If you remember a time please advise us because it seems a lot more lethal then the seasonal FLUE.

"The U.S. government estimates that 80,000 Americans died of the flu and its complications last winter (2018)— the disease’s highest death toll in at least four decades"**

WHY didn't we hear about 80, 000 flu related deaths in 2018 in th U.S. ? <br /> I guess news and social media didn't find this as interesting news?

https://www.cnbc.com/2018/0...

On 2020-04-21 14:48:12, user Comfrey's Gone wrote:

I'd like to restate my comments in a manner that should be comprehensible to those who didn't dig into the statistical appendix, and in language that is less technical.

Many commenters have observed (correctly) that the uncertainty in false positives (cases in which the antibody test registers positive when no antibodies are present) are large enough to preclude exclusion of the possibility that nobody in the sampled population had Covid, i.e. that with 95% confidence, one cannot infer that more than 0% of the population had Covid. However, the results of the paper indicate that with 95% confidence, at least 2.49% of the population had Covid. This raises the question of what error or errors were made by the authors in computing their confidence levels.

Many of the comments have noted that the authors assume that errors are normally distributed when determining the final confidence interval (2.49% to 4.16% of Santa Clara county infected), and that this assumption is invalid, and will cause an understatement of the width of the lower bound of the confidence interval. This is absolutely correct, and is a significant problem, but is not large enough to explain the large discrepancy between the lower bound for infections of 2.49% and the observation that this lower bound should be zero.

The authors made a larger and more elementary error that is responsible for their overstatement of the lower confidence interval bound; they incorrectly scaled by the wrong 'sample number' in computing their errors. Here is an explanation:

Given a sample of finite size, e.g. the manufacturer has tested 371 pre-Covid blood samples with their antibody test, so N = 371, the uncertainty in the result (e.g. the number of false positives) is proportional to the reciprocal of the square root of N. This is, for instance, the 1/square root of N in the usual formula for standard deviation.

The authors of the paper compute the standard error of the infection rate by aggregating the errors due to the finite size of the sample of respondents (3,330 people were tested), the sample used to determine the number of false negatives given by the antibody test, and the sample used to determine the number of false positives (371 tests run by the manufacture, plus 30 additional tests run by the Stanford lab). When they perform this calculation, they do not rescale by 1/square root of N until the very end, when they divide the error by 1/square root of 3,330, the number of respondents.

This means that the authors have effectively rescaled the uncertainty due to the number of false positives by 1/sqrt(3,330), while they should have only rescaled it by 1/sqrt(371) (considering only the manufacturer's testing) or 1/sqrt(401) (adding on their 30 additional tests). They have therefore incorrectly reduced the contribution of the false positive error by sqrt(3,330)/sqrt(371), which is about a factor of 3. Since this is the main source of their error, they have understated the entire standard error by nearly a factor of 3, and likewise understated their confidence interval width by about that amount.

This is a basic, straightforward 'math' error, and should not be the subject of any controversy.

On 2020-04-19 19:05:33, user Isaac barr wrote:

If so many are immune do we need strict closures?

On 2020-04-07 07:38:54, user dreich wrote:

Every place in the US is significantly under-testing.<br /> This means that the infection rate is underestimated.<br /> And because there aren't enough tests almost no post mortem tests on performed for any deaths.<br /> This has the "advantage" of under-counting the number of fatalities which makes the organized incompetence (intentional or otherwise) seem less bad. The bigger the number the worse it looks for the guilty politicians.

On 2020-04-04 03:15:45, user Charles Baker wrote:

Why are some states peaking in the model almost 30 days behind all the states around them? If you look at virginia and then all the states around them they peak almost a month after. How or why would that be?

On 2025-10-15 20:48:18, user jpirruccello wrote:

The updated version of this manuscript has been published; please see https://pubmed.ncbi.nlm.nih.gov/40175013/

On 2025-05-19 13:07:08, user Jspr Saris wrote:

I am pleasantly surprised by the work described, its methods and results. <br /> At base I don't have strong ideas on these. However, I do wonder whether age and or severity should be included in the clinical follow-up.

Regarding the discussion; I am missing the point above in the discussion (i.e. line 431 & 440), although line 444 hints at it slightly.<br /> In line 453 I am missing a reference or the like, e.g. the Dutch VKGL community variant sharing database.

Regarding line 642 and further, work done by the twin study center in Amsterdam could be of help, specifically the work of J. van Dongen on the epigenetic mark found in mono-zygotic twins with known age. This mark diminishes with age, but is present and detectable up to 55 y.

A suggestion for further work would be to compare the synthetic dataset to age versus severity -ratio or weighting.

On 2023-09-12 09:30:17, user Chris Iddon wrote:

This paper conflates SARS-CoV-2 genome copies (ie RNA) with viable virions on page 14. Data from the human challenge studies suggests that there are between 100 to 10,000+ RNA copies to a viable virion and thus the authors are overestimating the transmission potential and infection risk. Please see Killingley et al doi 10.1038/s41591-022-01780-9 and Zhou et al doi 10.1016/S2666-5247(23)00101-5<br /> Also what is the limit of detection for the RT-PCR assay? How much of the total eluate was used in a single assay?

On 2020-06-11 12:46:39, user Jennifer Punt wrote:

This publication may be of interest: Glycobiology, Volume 18, Issue 12, December 2008, Pages 1085–1093, https://doi.org/10.1093/gly...

On 2021-07-02 14:30:43, user Joe Psotka wrote:

I don't understand why they do not report vaccination rates. Did no one participate in a clinical trial?

On 2024-11-04 08:17:51, user Oren Miron wrote:

Short video summarizing the findings: https://www.youtube.com/watch?v=NFVkE_nwSEk

On 2021-01-25 14:58:28, user ad4 wrote:

There is also a study by Simon Wood that also suggests that lighter NPIs were having a noticeable impact on R before lockdown. https://arxiv.org/pdf/2005....

On 2022-03-13 07:22:25, user Hanjun Lee wrote:

Dear Dr. Colson,

Thanks a lot for sharing this preprint and for your work regarding the virology of SARS-CoV-2. I would like to ask regarding the possibility of Amplicon-related artifacts in the Nanopore sequencing data. One of your key results that surprised me the most was the identification of a recombination event at p.156–179 of the SARS-CoV-2 Spike protein. However, looking into the Artic amplicon panel (ARTIC nCoV-2019 Amplicon Panel v4.1) that has been utilized during the amplification step, I have a fear that the recombination event at p.156–179 may be the consequence of primer set #73's preference toward a specific variant. The Artic amplicon panel that has been utilized has 99 different primer sets. While many regions are covered by more than two primer sets, some regions are covered by a single primer set. If a region is covered by a single primer set, this makes it harder to distinguish true hybrid "deltamicron" or "deltacron" genomes from a co-infection or co-incorporation of delta and omicron genomes. Spike protein's p.125–178, which has a very great overlap with the p.156–179 region that has been identified by the authors, is one such region; it is covered by ARTIC's 73rd primer set, but is just outside of the regions covered by the 72nd and 74th primer sets. Do the authors think it is possible that the 3' end of the recombination event (p.178 or 179) might be the starting site of the forward primer of the 74th primer set, not a bona fide recombination locus? Or do the authors think there is enough evidence that can outlaw such a possibility?

Again, thank you so much for your work, and stay safe.

On 2023-04-27 15:33:25, user Anshu Varma wrote:

Dear Vincent Auvigne

I hope this e-mail finds you well.

My colleagues and I at the World Health Organization are<br /> intrigued by your study in France on the vaccine effectiveness of bivalent boosters compared to monovalent boosters against symptomatic SARS-coV-2 disease, among adults aged >60 years. Your work is timely, so we would be highly appreciative if you could help us improve our understanding of the study.

We acknowledge that baseline characteristics did not differ<br /> between groups, but we wonder if the recommendation for bivalent boosters and<br /> monovalent boosters may have been different and would like to know your<br /> thoughts on that.

Do you know why the bivalent booster was offered concurrently with the monovalent booster between 03/10/2022 and 06/11/2022 in the study area?

Do you know who the bivalent booster was recommended to<br /> between 03/10/2022 and 06/11/2022 in the study area?

Do you know who the monovalent booster was recommended<br /> to between 03/10/2022 and 06/11/2022 in the study area?

Thank you very much in advance and looking forward to hearing from you.

All the best

Anshu Varma

Technical Officer,<br /> COVID-19 Vaccine Effectiveness, Impact<br /> Department of Immunization, Vaccines & Biologicals (IVB)<br /> Universal Health Coverage/Lifecourse Division<br /> World Health Organization, Geneva, Switzerland<br /> varmaa@who.int

On 2022-12-12 14:29:12, user Biomedizinische Technik, BMT wrote:

https://www.frontiersin.org...

On 2021-04-09 21:24:21, user disqus_LHZMcrKY6P wrote:

Are the PCR results confirmed by a second test before reporting as per guidance for low prevalence

On 2020-05-07 20:33:43, user Walter Langel wrote:

The fit and extrapolation of the total infection number by a logistic function has been continued till first week of May and extended to several countries. Now the excel file containing all data and fits is available at researchgate:<br /> https://www.researchgate.ne...

On 2020-10-22 15:04:33, user BB_Aragon wrote:

Table 6 in the supplementary material appears to be another copy of the text. Could the author please replace this with the actual Table 6?

On 2020-05-13 15:14:17, user Fred Douthwaite wrote:

Nearly all of the comorbidities in those who contract Covid-19 are associated with zinc deficiency. Furthermore, the SARS-CoV-2 virus robs the body of some of its zinc, further reducing immune response. If zinc plus a zinc ionophore (hydroxychloroquine) works as a rescue therapy, a federally directed program of targeted zinc supplementation for vulnerable groups seems sensible.

Is a single nutrient capable of resolving this pandemic? The single nutrient iodine resolved past goiter epidemics. The single nutrient vitamin D resolved past rickets epidemics. The single nutrient thiamine resolved past beriberi epidemics. Zinc conceivably could resolve this present pandemic and prevent future pandemics.

On 2021-08-26 14:07:19, user Holger Lundstrom wrote:

The relative risk reduction is never what matters, because a relative reduction from 0,0001% to 0,00005% may be 50%, but as anybody can see will be practically meaningless overall. In fact, such a treatment will be detrimental if there is any chance of side effects which is higher than that and a monetary cost associated with the treatment.

According to the original Pfizer study, the relative risk reduction is 94,6%, whereas the total risk reduction is only 0,73% (within that study, from 0,77% to 0,04%). It is still a significant reduction, however that will also diminish with certain age groups, especially children.

https://www.nejm.org/doi/fu...

And now you shouldn't even begin arguing with the "risk over a certain time span", such as multiple years, because then you'd also have to take into account that a virus may develop immune evasion and/or booster shots will be necessary.

On 2021-08-24 07:21:17, user Red wrote:

This paper is missing one very crucial piece of information: 6-month adverse event followup. Table S3 still reports only adverse event counts up to 1 month after the second dose, but nothing about longer followup periods. This is a violation of a commitment from the study's protocol where it was stated that 6-month safety data will be reported (section 9.5.1). And the only reason I can think of why such a data was not reported is because it suggests the treatment is not as safe as it is claimed.

On 2022-03-09 21:24:14, user Les Funk wrote:

Figure 1 caption contains the false statement, "Disposition is presented for all enrolled participants..." There are 1841 participants not included in the figure after dose 2: 1258 from the BNT162b2 group and 583 from the placebo group. What happened to them?

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

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

On 2020-10-20 09:17:45, user Anne Hartmann wrote:

Dear Prof. Kähler, <br /> thank you very much for your study. <br /> Unfortunately, we think that some aspects are not considered correctly and some mentioned conclusions therefore can not be drawn. We summarized our comments in a statement on our blog (statement is available in German as well as English)<br /> https://blogs.tu-berlin.de/....<br /> Kind regards<br /> Anne Hartmann, member of the research group of Prof. Kriegel

On 2025-04-10 20:11:03, user Jeffrey_S_Morris wrote:

This study's conclusion of -27% negative effectiveness does not seem to be supported by the study, given they did not account for testing bias, which happens to also be 27%, with vaccinated testing on average a 27% higher rate than unvaccinated.

To their credit, the authors acknowledged this in the following plot:<br /> https://uploads.disquscdn.c... <br /> Here it can be seen in my replotting of their Figure 1a scatterplot on the log y axis (after extracting the data by applying AI tool to their scatterplot image), with the 27% increase being the (geometric) mean testing rate (vaccinated/unvaccinated) over the days they plotted.

https://uploads.disquscdn.c...

Incidentally, taking simple means (or fitting linear regressions) for a sample of ratios is not good statistical practice since the <1 and >1 parts are asymmetric, so instead the geometric mean (averaging on the log scale) should be used. For example, if one day is 4x higher for vaccine and one day 4x lower, they should average to be equivalent. The average on the raw scale (4 + 0.25)/2 = 2.125 would imply a mean 2.125x increase, which is incorrect, while a geometric mean (averaging on the log scale and then exponentiating) would get the correct result. 2^{log_2(4)+log_2(0.25)} = 2^(2 + -2) = 2^0 = 1. That is why I used geometric mean in the plot above and plot in the log scale, and think the authors should do the same in their paper.

While they acknolwedge the increased testing rate, the text of the paper dismisses it as a potential source of bias by claiming the test positivity rate is equivalent in vaccinated and unvaccinated. I agree with their logic that if test positivity were identical in vaccinated and unvaccinated, then the 27% higher testing rate could simply be a result of a 27% higher infection rate, and not from testing bias.

However, the analysis they present to support this assumption is not justified and seems flawed. They perform a linear regression of the ratio of testing positivity (vaccinated/unvaccinated) by day over time, and because the confidence bands intersect zero they conclude the test positivity is no different between vaccinated and unvaccinated, and thus the difference in testing rate is not a bias, but from the negative effectiveness that they conclude is true.<br /> https://uploads.disquscdn.c... <br /> However, this analysis is problematic for numerous reasons:<br /> 1. It is not clear why a regression over time should be done to answer this question, and not clear why one would assume any time trend is strictly linear. It would make much better sense to compute a (geometric) mean over time, or if wanting to model time trends to use a smooth nonparametric function.<br /> 2. Computing means or modeling time trends on ratios should not be done on the raw scale, but the log scale, for the reasons discussed above.

Plotting these numbers on the log scale (again, after using AI tool to extract it from their scatterplot image in the paper), I computed the geometric mean test positivity, and find it to be 0.80, meaning the "average" test positivity over time is 20% lower in vaccinated than unvaccinated, certainly not the same.

https://uploads.disquscdn.c... <br /> This lower test positivity is obscured in their original plot on the raw scale, since the ratios <1 got compressed and ratios>1 expanded.

If you have a situation with vaccinated having 1.27x the testing rate and 0.80x the test positivity, this would correspond to an infection rate that is 1.27 x 0.80 = 1.016x higher infection rate. This would correspond no difference in infection rate, certainly not a 27% increased infection rate in vaccinated.

While not a formal analysis, this demonstrates that vaccinated having a 27% higher testing rate along with a 20% lower test positivity rate could result in a 27% higher rate of confirmed flu infections even if the infection rate was equivalent between vaccinated and unvaccinated.

In that case. the 1.27x increased testing rate would be a testing bias that produces a spurious 1.27x confirmed infection rate even if the infection rate were not higher in the vaccinated.

Based on this, one cannot tell from the study whether the 1.27x increased rate of confirmed flu infections is from negative effectiveness (as claimed), or from the testing bias (which is not adjusted for in the analysis).

The authors cannot rule out the possibility that their results are caused by the testing bias, which is not accounted for in their analysis.

Thus, I don't think the conclusion of -27% VE is valid.

At most, they could say there is no evidence of any vaccine effectiveness vs. infection, but cannot conclude a significant negative effectiveness because of failure to account for the testing bias.

Of course, there are designs to adjust for this testing bias -- test negative designs -- but the authors eschew this design, seemingly because it gives odds ratios rather than relative rates which they express concern that they are not as intuitive to grasp.

To me, that seems like a relatively minor issue relative to testing bias of sufficient magnitude to drive spurious results.

If I were reviewing this paper, I'd require them to adjust for the testing bias, and ideally perform a test negative design, even if considered a secondary analysis.

Of course test negative designs have their own limitations and potential biases, but at least considering it as a secondary analysis would be useful to see if they obtain equivalent results using that design and, if not, should raise questions on whether they should boldly conclude negative effectiveness in this study, or instead more carefully conclude a lack of evidence of vaccine effectiveness in their cohort.

These concerns are also summarized in an http://x.com thread

On 2020-12-18 09:01:57, user Frank Conijn wrote:

For as to why in the latter version(s) the analysis regarding face masks was deleted, see the comments under version 2 on https://www.medrxiv.org/con....

On 2022-01-08 08:27:05, user Menno Schaap wrote:

Significant contribution! Self-tests are promoted for testing oneself before an event or visiting relatives. But in case the subject has no symptoms, reliability is only 22.6%. Therefore perceived feeling of safety is questionable. People should realize that.

On 2024-07-27 16:25:36, user Mahalul Azam wrote:

This article now have been published in an peer reviewed journal "The Southeast Asian Journal of<br /> Tropical Medicine and Public Health"

https://journal.seameotropmednetwork.org/index.php/jtropmed/article/view/1001

DOI: https://journal.seameotropmednetwork.org/index.php/jtropmed/issue/view/26

On 2020-12-26 04:49:01, user Peter Tomasi wrote:

Correction: If we assume a latency of 28 days, a substantial amount of samples REPRESENTING A POINT IN TIME WHEN THE level of the exposure in the environment was not yet as high as the one the authors draw their conclusions for, could have been collected.

On 2021-08-14 13:55:30, user Don Elbert wrote:

Nice work. There is a typo saying that p < 0.5 is significant. Are there any barriers to applying a GLM with age and time elapsed from 2nd dose as continuous covariates (ANCOVA) on the whole dataset?

On 2021-08-07 01:35:26, user MamaDoc2012 wrote:

Older people are probably more likely to have symptomatic infections than younger people and therefore would get tested more frequently. Or am I misunderstanding known facts about COVID?

On 2020-07-28 14:55:48, user Alessandro Turrini wrote:

The probability of getting infected on a plane P in this model is independent on whether this particular passanger is flying on plane with a total of 2 or 2000 passengers. it seems that a possible flaw comes from the fact the the probability Q of having a passenger who is infectious on board should be computed not as the probability that a generic individual in the population is infectious but as the probability that the plane contains at least one infectious individual, i.e., 1 - the joint probability of not having not even one infectious individual, obtained under independency as 1-Q^S where S is the number of non empty seats in the plane

On 2021-09-05 08:03:43, user Michael Tomlinson wrote:

Why does Figure 2B in the paper show hospitalizations for the unvaccinated group peaking on 1 May, when the CDC's own Covid Data Tracker shows all hospitalizations peaking on 9 Jan concurrently with infection rates as you would expect: https://covid.cdc.gov/covid... .

Figure 2B in the paper shows these rates curving over the same period almost inversely to population infection rates, which were dropping from 748 per million to 149 per million over these months.

Meanwhile, hospitalization rates for the vaccinated group are completely flat over the period, and show no response to the sharply varying infection rates.

How can this be?

On 2020-05-18 04:13:11, user Alex Backer wrote:

A similar result to this was previously reported in https://papers.ssrn.com/sol...

On 2020-05-01 23:43:12, user Sinai Immunol Review Project wrote:

Title A single-cell atlas of the peripheral immune response to severe COVID-19<br /> Wilk, A.J. et al. MedRxiv ; doi:10.1101/2020.04.17.20069930

Keywords<br /> scRNAseq; Interferon-Stimulating Genes (ISGs); Activated granulocytes

Main Findings<br /> The authors performed single-cell RNA-sequencing (scRNAseq) on peripheral blood from 6 healthy donors and 7 patients, including 4 ventilated and 3 non-ventilated patients. 5 of the patients received Remdesivir.

scRNAseq data reveal 30 gene clusters, distributed among granulocytes, lymphocytes (NK, B, T cells), myeloid cells (dendritic cells DCs, monocytes), platelets and red blood cells. Ventilated patients specifically display cells containing neutrophil granule proteins that appear closer to B cells than to neutrophils in dimensionality reduction analyses. The authors named these cells “Activated Granulocytes” and suggest them to be class-switched B cells that have lost the expression of CD27, CD38 and BCMA and acquired neutrophil-associated genes, based on RNA velocity studies.

SARS-CoV2 infection leads to decreased frequencies of myeloid cells, including plasmacytoid DCs and CD16+ monocytes. CD14+ monocyte frequencies are unchanged in the patients, though their transcriptome reveals an increased activated profile and a downregulation of HLAE, HLAF and class II HLA genes. NK cell transcriptomic signature suggests lower CD56bright and CD56dim NK cell frequencies in COVID-19 patients. NK cells from patients have increased immune checkpoint (Lag-3, Tim-3) and activation marker transcripts and decreased maturation and cytotoxicity transcripts (CD16, Ksp-37, granulysin). Granulysin transcripts are also decreased in CD8 T cells, yet immune checkpoint transcripts remain unchanged in both CD8 and CD4 T cells upon SARS-CoV2 infection. The frequencies of memory and naïve CD4 and CD8 T cell subsets seem unchanged upon disease, though gdT cell proportions are decreased. SARS-CoV2 infection also induces expansion of IgA and IgG plasmablasts that do not share Ig V genes.

Interferon-signaling genes (ISGs) are upregulated in the monocyte, the NK and the T cell compartment in a donor-dependent manner. ISG transcripts in the monocytes tend to increase with the age, while decreasing with the time to onset disease. No significant cytokine transcripts are expressed by the circulating monocytes and IFNG, TNF, CCL3, CCL4 transcript levels remain unchanged in NK and T cells upon infection.

Limitations<br /> The sample size of the patients is limited (n=7) and gender-biased, as all of them are men.<br /> The activating and resting signatures in monocytes should be further detailed. The authors did not detect IL1B transcripts in monocytes from the patients, though preliminary studies suggest increased frequencies of CD14+ IL1B+ monocytes in the blood of convalescent COVID-19 patients[1].<br /> Decreased NK cells, B cells, DCs, CD16+ monocytes and gdT cells observed in peripheral blood might not only reflect a direct SARS-CoV2-induced impairment, but also the migration of these cells to the infected lung, in line with preliminary data suggesting unchanged NK cell frequencies in the patient lungs[2].<br /> The authors identified platelets in their cluster analyses. Recent reports of pulmonary complications secondary to COVID-19 describe thrombus formation that is probably due, in part, to platelet activation[3, 4]. A targeted characterization of the platelet transcriptome may thus benefit an increased understanding of this phenomenon.<br /> The transcriptome of the Activated Granulocytes should be further detailed. As discussed by the authors, IL24 and EGF might be involved in the generation of the Activated Granulocytes, though these cytokines are poorly represented in the blood of the patients. The generation of these cells should therefore be further investigated in future studies.

Significance<br /> The authors show a SARS-CoV2-induced NK cell dysregulation, in accordance with previous studies[5]. Alongside the upregulation of ISGs in NK cells, these findings suggest an impaired capacity of the NK cells to respond to activating signals in COVID-19 patients. The unchanged expression of immune checkpoints on CD4 and CD8 T cells suggest distinct SARS-CoV2 dysregulation pathways in the NK and the T cell compartments. In particular, the downregulation of transcripts encoding for class II HLA but not for the HLA-A, -B, -C molecules in monocytes suggest an impaired antigen presentation capacity to CD4 T cells, which should be further investigated.<br /> The authors provide preliminary results suggesting an age-related activation of the monocytes in the COVID-19 patients. Future studies will be needed to evaluate if the age impacts the involvement of the monocytes in the cytokine storm observed in COVID-19 patients.

References<br /> 1. Wen, W., et al., Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing. MedRxiv, 2020.<br /> 2. Liao, M., et al., The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. MedRxiv, 2020.<br /> 3. Giannis, D., I.A. Ziogas, and P. Gianni, Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J Clin Virol, 2020. 127: p. 104362.<br /> 4. Dolhnikoff, M., et al., Pathological evidence of pulmonary thrombotic phenomena in severe COVID-19. J Thromb Haemost, 2020.<br /> 5. Zheng, M., et al., Functional exhaustion of antiviral lymphocytes in COVID-19 patients. Cell Mol Immunol, 2020.

Credit<br /> Reviewed by Bérengère Salomé and Zafar Mahmood as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

On 2021-12-29 09:29:29, user Željko Serdar wrote:

Although some of us do not want to listen to good advice, I still give it to you, and it is up to you to decide whether to listen to it and apply it. These are the results of an analysis of more than 42 million people and can be considered reliable, which means that preliminary studies that said that young men have a higher risk of myocarditis after infection than after vaccination were wrong. Vaccination of the elderly and at-risk is justified because it significantly reduces the risk of severe disease. Vaccination of young and healthy people carries a significant risk of myocarditis and any form of forcing young people to get vaccinated is irresponsible.

On 2021-07-06 09:34:12, user David wrote:

Just 188 participants were seropositive. So if long covid would occur in 1% of patients one would statistically expect 1,88 long covid sufferers in this group. Obviously, this sample size is too small to quantify the long covid risk. It could probably be anywhere between 0 and 3% according to this study. Assuming this survey covers long covid symptoms well which it obviously does not as already mentioned in the 2 previous comments.

On 2020-04-01 01:59:42, user T2000q wrote:

Very interesting analysis of possible correlation between COVID-19 mortality rates and countries vaccination policies. However, it would be much more convincing to see proportion of BCG-vaccinated and non-vaccinated patients, who died of COVID-19. Not sure if such vaccination data exists in different countries.

On 2020-04-01 17:13:32, user Hikmat Ghosson wrote:

I do not understand why Lebanon is considered as a country with high rate of COVID19-related deaths. Actual data (01/04/2020) do not demonstrate this assumption:

14 deaths (2 deaths in 1 M of population), vs. 43 recoveries (0.33 of deaths-to-recoveries ratio).

Meanwhile for Italy:<br /> 13155 deaths (218 deaths in 1 M of population), vs. 16847 recoveries (0.78 of deaths-to-recoveries ratio).

For The Netherlands:<br /> 1173 deaths (68 deaths in 1 M of population), vs. 250 recoveries (4.69 of deaths-to-recoveries ratio).

For Belgium:<br /> 828 deaths (71 deaths in 1 M of population), vs. 2132 recoveries (0.39 of deaths-to-recoveries ratio).

For The U.S.:<br /> 4394 deaths (13 deaths in 1 M of population), vs. 8698 recoveries (0.50 of deaths-to-recoveries ratio).

Otherwise, how other determinant factors potentially influencing infection and death rates (e.g. age medians, healthcare systems, population concentrations, social traditions, screening test numbers, crisis management policies) can be assessed and then excluded from the correlation models?

Thanks in advance.

(data source: https://www.worldometers.in..., 01/04/2020 - 4:45 PM GMT update)

On 2020-04-22 13:05:27, user Gv wrote:

To #2. Over 1/3 of Panama are made up expatriates that are non-native to Panama. It’s a haven for people that come from outside of Panama. Universal BCG vaccination applies to to the native population only.

On 2020-04-02 07:42:02, user japhetk wrote:

I don't know why, but medrxiv keeps deleting my warning comments.

So, I write brief comments again.

This study doesn't control important variables as kept suggested in comments and probably the findings are due to spurious correlations.

The one of uncontrolled important variable is "when the infection spread in the country". This study should have used the measure like "number of deaths or patients 10 days after 100th patients were detected in the country". Other analyses are doing that.<br /> The second uncontrolled important variable is "how long the country advanced BCG measure". UK, for example, advanced the BCG measure for more than 50 years. So, majority of nations are experienced with BCG.<br /> The third uncontrolled important variable is GDP. You can see the most of nations without BCG is Western rich countries which can do more tests, which are popular from tourists.<br /> I did analyses controlling these variables, and all the correlations between the length of BCG measure with coronavirus data (how fast the 100th patients were detected in the country, number of patients or deaths ten days after the 100th patients were detected in the country) are all insignificant. They did not even show the statistical tendencies.

Many people have wrong ideas how effective BCG is though this preprint. Somebody has to give warnings. Please do not delete this warning.

On 2021-08-08 00:42:33, user vinu arumugham wrote:

If a virus infects the lungs, T cells that recognize those viral proteins are programmed to move (homing) to the lungs. With an intramuscular vaccine administration, the "infection" is in the deltoid muscle/skin. So T cells that recognize the spike proteins made by the muscle/skin tissue are programmed to move to the skin/muscle.<br /> Mechanisms of T cell organotropismhttps://pubmed.ncbi.nlm.nih...

On 2020-10-29 15:18:11, user bljog wrote:

In the results you mention "A cluster of sequences in clade 20A has an ad- ditional mutation S:A222V colored in blue" but the Figure 1 has an annotation in blue for S477N.

On 2020-04-16 01:47:35, user 777Rampage wrote:

I have the following questions and issues on their testing of UVC LED.<br /> 1. In the article it states that General Tools UVAB digital light meter was used. Is that the UV513AB meter? If so, that monitoring probe is only able to measure UVA&B, not C. <br /> 2. If using General Tools UV512C meter, that probe's spectral range is 220 to 275 and it is used to measure low pressure mercury UVC lamp, not UVC LED.

On 2024-04-21 00:21:38, user Alan Olan wrote:

This article has been published in the journal of “Biology and Medicine” as “Method of Combining Multiple Researches to Determine Non-Infectious Disease Causes, Analysis of Depression and Celiac Disease Causes” and available at - www.walshmedicalmedia.com/o...<br /> The appendix of this article is available at www.walshmedicalmedia.com/s...`

On 2023-02-02 06:20:07, user Dr. Albert wrote:

Thank you for such an amazing paper! Your paper provides novel insight into non-invasive COVID-19 detection method, which has the potential to be implanted worldwide. To strengthen this paper even more, I would suggest some edits for your introduction/discussion section. It would be great to incorporate the advantage and disadvantage of recently used detection methods followed by how your model overcomes the caveats of pre-existing detection methods. Also, a very recent preliminary paper from University of Toronto hypothesized the application of Raman scattering along with fluorescence resonance energy transfer to detect COVID-19 using the breath! It might be worthful for you to mention about it in the paper as one of the emerging technique along with its pros and cons relating to your detection method.

Here is the link to that paper: https://www.tmrjournals.com...

On 2021-06-10 04:44:14, user John Jay wrote:

Sorry if I missed it, but was there a discussion of how the demographics (ie age, co-morbidities, status before treatment, etc.) differed or didn’t differ between the 3,000mg HCQ + 1,000 AZM group and the rest of the patients?

On 2021-06-09 16:25:49, user livefreeinTX wrote:

It's heartbreaking to me that all those hundreds, perhaps thousands, of NYC patients in the spring of 2020 were left to sit around at home until they could barely breath and were then hospitalized and put on ventilators, and were denied HCQ, which may have saved hundreds of lives. Truly sickening, imo.

On 2020-03-20 14:58:48, user David C. Norris, MD wrote:

1. The pink-highlighted NEG measures in Supplementary Table 1 (see image below) would seem to be false negatives (FN), as they are followed by positive results on subsequent days. A reanalysis with these FN's imputed as POS seems indicated.

https://twitter.com/davidcn...

1. How comparable are the outside-institution RT PCR results with those from the treated group? How fragile are the apparently positive results from this study, to plausible systematic between-institution differences in test sensitivity?

On 2021-12-02 08:51:52, user koen wrote:

This publication makes a number of hard claims, with a title that insinuates as such. These claims are based on a model that is proposed by the authors without proper validation and verification of the model. One of your claims is that your models shows that with a vaccination uptake of 80% of the total population the reproduction number r remains at 0.86 in the current situation in Germany. These claims could be verified by applying the model to the COVID situation in different countries (with higher and lower vaccination uptake). Furthermore, contact tracing results should be used in part to validate claims about the source of infection. Based on the comments above and the discussion in the article the subjective title seems inappropriate and suggestive to person viewpoints of the authors.The best of luck in publishing this article in the current state!

On 2021-12-01 22:49:09, user Tom wrote:

Since this model is used to show the main drivers, it would only show that the main drivers are unvaccinated people since no one is vaccinated.

On 2021-10-26 03:27:22, user Rahi Brahmbhatt wrote:

Dear authors,

Firstly, thank you to the authors for sharing this commendable work. The manuscript is well written and signifies an important contribution to the field. Furthering research on the effects of the gut microbiome on bile acid metabolism is groundbreaking for the understanding of many metabolic and immune disorders. The introduction and discussion sections were the absolute powerhouses of this manuscript. They both displayed tactics that will help reach a broad audience, such as detailed background information and the connection of bile acid regulation to several high profile metabolic disorders. Furthermore, the specialization of the gut microbiome effects on bile acid regulation on type one diabetes offers great hope in further understanding the pathogenesis of T1D. Both the introduction and discussion sections were clear and easy to follow, and I greatly appreciated the acknowledgement of limitations and areas to improve in future studies.

However, a few minor points to consider in the methods, figures, and results section. In the experimental design conducted, is there any standardization done to account for the disproportionate sample sizes? Could the inclusion of more subjects that progressed to single or multiple autoantibodies impact the findings for the P2Ab group? In the comparisons between the P2Ab group and the controls, how this gap in sample size (nearly double the amount of CTR subjects) accounted for? Furthermore, a combination of the clinical study setting of the methods section and first section of the results may lead to a clearly understanding of the experimental design as a whole. Currently, the first result seems to repeat the initial study set up than indicate a result from the set up. Also, there is a section in the results that seems to have incorrectly identified 2C as 2D in the written section. Lastly, if possible a condensification and simplification of figures 4 and 5 may allow for a clearer understanding of how bile acid profiles change the regulation of bile acid metabolism in progression to islet immunity. Perhaps a figure correlating the changes in the profile and it's direct change in the metabolism pathways as displayed in figure 3 would help in this understanding.

Finally, this manuscript opens up many pathways for further gut-microbiome-bile acid regulation studies. The suggestion of incorporating lifestyle factors such as diet and environmental factors that impact the gut microbiome allows for a coherent and logical flow of how research in this field may progress. Once again the paper was an excellent concept and more studies should be done to further our understanding of the gut microbiome's contribution to islet autoimmunity. I look forward to seeing this paper post publication and thank you for your contribution to science!

On 2024-02-24 08:49:54, user Dr Michael Turner wrote:

This article has been accepted for publication - Feb 2024

On 2025-03-15 18:00:20, user amin Abdorrashid wrote:

By singling out Iran for its alleged populist actions, the paper ignores the global prevalence of unscientific practices during the COVID-19 crisis, rendering its argument one-sided and lacking in broader contextual insight.

On 2021-11-14 09:40:17, user disqus_1lj0sBLhKD wrote:

No P values are given.

It would be very useful if the specific ARBs used and the various doses used were given, and correlate this with the risk of getting COVID-19 and dying. There was a study done in Argentina:<br /> https://www.dropbox.com/s/t...<br /> using 180 mg Telmisartan (80 mg BID) which showed excellent results. Another study, using Losartan at a dose of only 50 mg per day, did not show any advantage for Losartan. See: https://www.medrxiv.org/con...<br /> The people who did the Telmisartan study chose Telmisartan because of its reputed superior binding affinity, longest half life, high tissue concentrations, superior insurmountably, and superior activation of the PPAR gamma receptor. See page seven of the Argentina study.<br /> It would be very useful for the selection and design of future studies if any additional data could be shown that would shed light on this.

On 2020-04-15 18:47:50, user Y H wrote:

Over thirty years ago, we found that chloroquine and related chemicals block muscarinic agonist binding. Similar findings were reported afterwards. This effect may link to a cause for cardiac arrhythmia induced by chloroquine. YH

Antimuscarinic effects of chloroquine in rat pancreatic acini<br /> Yoshiaki Habara, John A Williams, Seth R Hootman<br /> Biochemical and biophysical research communications 137 (2), 664-669, 1986

On 2020-04-02 02:05:24, user Jesus Bejarano wrote:

Hi, all how can I compute (or derived) the R0 from this model?

On 2020-04-15 11:57:13, user ?????? ??????? wrote:

! conflicting studies : https://academic.oup.com/jt...

On 2021-06-25 20:02:29, user das-pikle wrote:

They did, and COVID is many, many times more infectious than the flu. Comparing mask mandates is useless, comparing mask compliance with adjustments for individual daily travel, individual daily interactions, population density, etc. would be a more thorough study - but a bajillion well-designed studies showing masks worked didn't convince you before, I doubt another one would convince you now.

On 2021-08-15 15:07:13, user Patrick De Ville wrote:

Robert, would appreciate your responding with a link to the body of data that shows that masks in general (not just N95) are effective. Thanks in advance.

On 2021-08-11 14:00:33, user Holger Lundstrom wrote:

You did not read the conclusion correctly. It clearly states:

"Although no statistically significant difference in SARS-CoV-2 incidence was observed, the 95% CIs are compatible with a possible 46% reduction to 23% increase in infection among mask wearers."

This means that the supposedly positive effect is a matter of interpretation, not actual statistics.

On 2021-05-27 06:20:33, user Mike Stevens wrote:

Well, it’s not whether there is a mandate in place, is it?<br /> It’s whether the mandate is adhered to.<br /> And when people actually comply, and wear the masks, Covid spread declines.<br /> https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0249891

Funny how a disease spread primarily through droplet spread can be halted by methods that stop droplet spread, isn’t it?<br /> ...Who would have thought it?

On 2021-12-23 14:40:32, user Margalit wrote:

Totally feel vindicated as I (like many others) suggested taking Vitamin D in April 2020. https://theprepared.com/blo...

However, as outlined there, and by many others, e.g. the UK bio bank, there has been a link between race and severity in many countries. In the UK study, the effect of Vitamin D disappeared once ethnic background was taken into account.

I am glad you controlled for SES as a crude 3 step factor. But it may not be enough. Also in Israel, people with dark skin are both discriminated against, experience lower SES, and are hence predicted to be at lower Vitamin D levels. Is there a way to subdivide by ethnicity better than Ultra-orthodox, general and Arab, but account in "general" for Ashkenazi, Sephardi, Oriental, and African origin? I just think part of the effect - like in the UK and US - may be due to skin color darkness, discrimination, SES and Vitamin D deficiency being totally confounded.

On 2023-06-07 15:49:25, user Donald R. Forsdyke wrote:

These results are supported by a case history currently available at the Qeios preprint server<br /> Preprint

On 2020-07-31 11:36:15, user NetDoc wrote:

Why are Tables 1 and 2 missing ages 15-24?

On 2020-04-15 20:31:22, user Jim Thompson wrote:

It will be interesting to see if they break out the degree of symptomatic improvement. Profound reduction of CRP suggests a substantial anti-inflammatory effect. Right now the data seems to be pointing toward early use with the positive endpoint being reduction in progression to more severe disease. That would be a powerful benefit. C19 is overwhelming us with the ones who get really sick; for the rest it's fairly trivial as diseases go...

On 2020-04-18 16:52:45, user novictim wrote:

Also worth considering is the timeline for treatment. HCQ proposed mode of action is not just its anit-inflammatory properties but its ability to act as a zinc ionophore. Zinc ions then interfere in viral replication. So you have to use Hydroxychloroquine early in the infection to see the maximum benefit. If you give it after lung epithelium and T-cells are already compromised, the benefit is less significant.<br /> I look forward to the trial results involving prophylaxis with HCQ and the use of it at the first signs of COVID-19.

On 2020-05-10 16:29:06, user Puddin'Head wrote:

Given A) the prevalence of conspiracy theories running through the <br /> internet regarding the cause and treatment of covid-19, and B) the <br /> abundance of quacks selling vitamin mega-dosing as a reliable cure, I <br /> hope the authors plan to make clear just how tenuous their conclusions <br /> are in this manuscript. Drawing cum hoc conclusions based on the <br /> possible correlation of a correlation (that's not a typo) with a <br /> surrogate marker that requires rough estimation of nearly every <br /> pertinent variable (e.g. case numbers, mortality rate, vitamin D status)<br /> doesn't yield a particularly compelling result.

It might be better to look at aggregate US states which more closely approximate the<br /> land area and population density of the UK, for instance, if <br /> comparisons are going to be made. The mortality rate in New York, New, <br /> Jersey, and Massachusetts are all higher than that in the UK, so how <br /> does that affect the observed trends? The overall mortality rate in the <br /> US as a whole is lower on account of a variety of circumstances, likely <br /> including the low population density through much of the mid west and <br /> northern mountain west states. Sun exposure, and thus presumably vitamin<br /> D status, is also not as uniform across the US as it can more <br /> reasonably be expected to be across the UK. Lots of variables.

It's also worth pointing out that the observations of low vitamin D <br /> correlating with high CRP and high CRP correlating with severity of <br /> covid-19 do not allow for the conclusion that low vitamin D causes (or <br /> even correlates in a meaningful way with) more severe covid-19 cases or <br /> worse outcomes, despite the ability of equation 4 to generate a number. <br /> In other words, the observations that:

A) consuming 4 drinks correlates with my having a headache and <br /> B) my having a headache correlates with getting kicked in the head by a mule

...don't lead us to the conclusion that:

C) consuming 4 drinks is going to cause me to get kicked in the head by a mule.

On 2020-05-09 21:37:30, user Brandy Wootten McManus wrote:

Is the math right?

On 2021-08-29 03:38:56, user julie kemp wrote:

I've heard many different reports , and most agree , that if you recover from Covid 19 your immunity is greater than a vaccinated person. A lab test would prove it. I had the vaccines, my friend had the Covid 19 virus, and doesn't want the vaccine. Why can't she just have a lab test to check her immunity ,and that should suffice.If she's immune. why force her to take a vaccine.??

On 2021-09-09 13:13:01, user Wolfgang Birkfellner wrote:

sorry, the comment referred to another paper ... my bad ...

On 2021-11-18 23:35:19, user Emily wrote:

Does anyone know if this study has been published yet? Don't they usually prioritize data on current health threats to get them through peer review a little faster?

On 2021-09-01 06:52:27, user Jeffrey Bachant wrote:

The key model rests on ~260 total cases out of ~65,000 individuals between the cohorts. <br /> Cohort studies are biased towards statistical artifacts if the outcome of interest is rare. Thus, the main finding should be that delta infections in both cohorts is infrequent. The confidence intervals for the way cases bin between acquired immunity primed by viral infection vs vaxx is statistically only applicable to their cohort group. Whether it has predictive value in a larger setting is unexplored. It's clearly written in way that, just from what I've bumped into, has allowed it do be heavily talisman linked by the anti-vaxxer crowd. Its unfortunate.

On 2022-01-26 19:05:09, user Charles R. Twardy wrote:

Looks like this was published in Mayo Clinic Proceedings.

On 2020-10-28 03:32:39, user David Epperly wrote:

Was any genomic study done? Since the patients are all co-located, this could help identify whether or not there may be a genome relationship to the activity identified.

On 2021-10-17 09:12:32, user TheTiger wrote:

It might be a crucial finding which could save many people.

On 2021-10-26 18:07:58, user Daniel Lidstone wrote:

Hi, very nice paper. Was the anti-correlated DMN-DAN edge showing the mediation effect an anterior or posterior DMN parcel? I didn't see specific labels in the preprint.<br /> -Daniel

On 2020-05-06 18:09:14, user Olujimi A-Williams wrote:

I was wandering if the writers can look for any Vitamin D levels in this cohort. Any level found in the EMR will do. It sure will be interesting to see

On 2021-09-01 02:33:45, user Andrea Boggan wrote:

"Survival of the Flattest." This new variant will wait it's turn until Delta is through delivering its blow, and when Delta is done, it and the others waiting in the wings will step forward and compete for fuel.

On 2020-07-13 22:42:36, user Lutz Froenicke wrote:

Hello authors, could you help?<br /> The primer sequences seem to be missing? The methods supplement states<br /> "The sequences for primers used are shown in Supple Table 1."<br /> The only available table lists samples, but not primer.<br /> Thanks in advance!

On 2020-11-07 10:32:09, user Ivan Ivanov wrote:

They will never share the primer sequences as the test is being commercialized already. The idea is interesting however I cannot imagine the price for 500ul LAMP reaction. Also what's the point to put DNase and carrier DNA together in the mastermix.

On 2020-09-02 08:21:04, user Roberto Piva wrote:

Still no sequences of primers used:<br /> please authors, share infos!!

On 2024-04-28 02:04:59, user Ian Myles wrote:

Small correction, the Clinical trial number is NCT04864886

On 2021-07-27 14:07:32, user VailShredBetty wrote:

Additionally, the only conclusion I can see that this study and subsequent studies illustrate is the vaccine is stopping ore severe infections....but at what cost??

On 2021-02-28 21:19:31, user Ana Christoff wrote:

Now published in PLOS ONE<br /> Swab pooling: A new method for large-scale RT-qPCR screening of SARS-CoV-2 avoiding sample dilution.<br /> https://doi.org/10.1371/jou...

On 2020-07-07 00:26:09, user AR wrote:

Is Supplementary Data 1 missing from this article?

On 2021-06-21 23:41:51, user ciudadano_py wrote:

Not a good point, the authors already answer that question. CT was chosen to be 30 because of time and there is evidence that it works as well as CT of 40 and is cited in the document, they did not arbitrarily remove individuals that had CTs above 35 and they explained it. For more info check this link https://bit.ly/2TND1Oi

On 2021-10-10 19:52:00, user Markus Falk wrote:

The study seems well performed. However, although randomized, result may be related to different starting values. It seems that the placebo group had a marginally higher viral load at beginning. A Wilcoxon-test for the matched pairs of day 0 and 6 may give some insights. Decrease of viral load from day 0 to 6 seems to be paralleled and only offset by starting values. Adding starting values to the logistic regression may correct for this.

On 2020-04-06 18:50:52, user Sinai Immunol Review Project wrote:

Main Findings: Currently, the diagnosis of SARS-CoV-2 infection entirely depends on the detection of viral RNA using polymerase chain reaction (PCR) assays. False negative results are common, particularly when the samples are collected from upper respiratory. Serological detection may be useful as an additional testing strategy. In this study the authors reported that a typical acute antibody response was induced during the SARS-CoV-2 infection, which was discuss earlier1. The seroconversion rate for Ab, IgM and IgG in COVID-19 patients was 98.8% (79/80), 93.8% (75/80) and 93.8% (75/80), respectively. The first detectible serology marker was total antibody followed by IgM and IgG, with a median seroconversion time of 15, 18 and 20 days-post exposure (d.p.e) or 9, 10- and 12-days post-onset (d.p.o). Seroconversion was first detected at day 7d.p.e in 98.9% of the patients. Interestingly they found that viral load declined as antibody levels increased. This was in contrast to a previous study1, showing that increased antibody titers did not always correlate with RNA clearance (low number of patient sample).

Limitations: Current knowledge of the antibody response to SAR-CoV-2 infection and its mechanism is not yet well elucidated. Similar to the RNA test, the absence of antibody titers in the early stage of illness could not exclude the possibility of infection. A diagnostic test, which is the aim of the authors, would not be useful at the early time points of infection but it could be used to screen asymptomatic patients or patients with mild disease at later times after exposure.

Relevance: Understanding the antibody responses against SARS-CoV2 is useful in the development of a serological test for the diagnosis of COVID-19. This manuscript discussed acute antibody responses which can be deducted in plasma for diagnostic as well as prognostic purposes. Thus, patient-derived plasma with known antibody titers may be used therapeutically for treating COVID-19 patients with severe illness.

Reference:

1. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019

doi: https://doi.org/10.1101/202...

On 2020-11-08 21:44:53, user Redouane Qesmi wrote:

This preprint is now published in Chaos, Solitons & Fractals doi: 10.1016/j.chaos.2020.110231 with a new title which is "Impact assessment of containment measure against COVID-19 spread in Morocco".

On 2022-01-14 21:37:58, user Rich Condit wrote:

Authors: Please include controls in which negative NP samples are spiked with different amounts of virus and processed for quantification of FFU. In this way you can determine a limit of detection for the assay, and report negatives as "less than xxx FFU/ml"

LIsten to TWiV 854.

On 2020-07-20 17:17:47, user JayTe wrote:

In the Annals of Internal Medicine, the authors demonstrated in the research article, Cloth Masks and Surgical Masks Ineffective in filtering SARS-COV-2 in COVID-19 Patients, Oberg and Brousseau demonstrated that surgical masks did not exhibit adequate filter performance performance against aerosols measuring 0.9, 2.0 and 3.1 um in diameter. As well Lee and his colleges showed that particles 0.04 to 0.2 um particles can penetrate surgical masks. If we assume that the SARS-COV2 particle has a similar size to the SARS-COV1 particle even surgical masks are unlikely to effective filter the virus. In this study the primary means of the spread was via coughing. Now the author of this article claims that it is primarily via contact or droplets!?! If a person has respiratory issues due to covid-19, would it not make sense that they would be coughing? If you have persons that are not ill or asymptomatic then given the existing evidence, there is little to no chance of an individual becoming ill by the transmission of the SARS-COV2 virus. Doing a meta-study where one chooses studies that conform to their particular perspective and avoids the probability of transmission via coughing doesn't help the debate on the value of masks.

It also excludes the negative effect of wearing masks which in a 2015 study in the BMJ: “A cluster randomised trial of cloth masks compared with medical masks in healthcare workers“ highlighted that not only are these masks 100% ineffective at reducing the spread of COVID-19, but they can actually harm you since the moisture retention, reuse of cloth masks and poor filtration may result in increased risk of infection.

On 2021-09-15 11:40:15, user japhetk wrote:

Although this study is about analyzing time series data, the research method is inappropriate because it uses ordinary multiple regression analysis without analysis for time series analysis. Here is a page that explains the basics of analysis for time series data.<br /> https://logics-of-blue.com/...

Based on this preprint that spread on social media, people have the misconception that delta variants have nothing to do with the spread of infection and that vaccines spread infection. I think the authors should refer to it and revise the manuscript as soon as possible.

Also, in the data I have, from April to the end of August, the share of daily delta variants, the number of days elapsed, and the raw data of vaccination rates each show a correlation of 0.95 or higher. These highly correlated variables can cause multicollinearity, which can lead to strange results. Simply put, these variables cannot be included in the same model.<br /> I also prepared my own data for Tokyo from April to the end of August, but when I used the same simple multiple regression analysis as the authors did, including human flow, the number of days elapsed, and these variables (which is an inappropriate method of analysis for a number of reasons, as explained above), the vaccination rate of tokyo (+ 14 days) was negatively correlated with rt (- 14 days) and the delta variants share was positively correlated with rt, and the authors' results could not be replicated.

I referred to the apple mobility data (transit and walking) for the population flow.<br /> The following page for RT (-14 days) of Tokyo.https://uub.jp/cvd/cvd.cgi?T=5&Y=21...<br /> Delta variant share for owid data. (-21 days, Blank days were complemented by weighted averages.)

On 2021-12-26 13:57:47, user bat9991 wrote:

This is a faulty study, and will be either corrected or retracted once peer reviewed!<br /> You cannot exclude "Previously SARS-CoV-2 PCR-positive individuals" and correlate VE against the same cohort you just removed a significant percentage of them!

You are skewing your test data significantly towards the unvaccinated cohort (which has much higher rate of PCR positive than the vaccinated cohort)

If negative VE was not a clue for the faulty calculations, I don't know what would be!

On 2020-12-08 09:28:26, user Neville Calleja wrote:

Obviously the people receiving the influenza vaccine are (a) the most at risk of dying with COVID-19, either as an underlying cause of death, or as a contributory cause, and (b) more likely to be coming from affluent countries wherein identification of deaths as a COVID-related death is much more likely due to enough resources permitting testing of all patients. The latter could have been corrected by using excess mortality figures rather than reported COVID-19 deaths, which is highly dependent on the countries' capacity to test. Nonetheless, the first clearly explains the findings completely wherein countries with high life expectancy and therefore high proportion of elderly population with co-morbidities who are typically protected in winter using the influenza vaccines, could not be protected from COVID-19. This could be considered a correlational study at best.

On 2020-03-30 14:54:38, user Emma Cairn wrote:

Data is not an object in itself. Data is sometimes profoundly affected by the political, social and economic environments.

1.There are differing political criteria for selecting who is tested in different countries. Some select only those with severe symptoms and some sample test over their country. Some tests are inaccurate and sometimes not enough test kits are distributed. Some testing centres are inconvenient and sometimes multiple tests to the same person are negative until nucleic acid high enough.

1. There is political variation in what constitutes a corona death. It is possible that some are only reporting it as corona if there is no underlying condition. Or in other words if some have a heart attack it is being listed as that rather than Corona. If some die out of hospital untested this is also not a Corona death.

3<br /> I suggest that if people in countries knew the true number there would be widespread panic and disruption, economic turmoil and political unrest.

Conclusion Unless standardisation of political, economic viewpoints there will be no standardisation of empirical data. Virus will only slow down when it has learnt how to live with us harmoniously.

On 2022-01-02 21:25:37, user madmathemagician wrote:

In its 4th revision, part of the title changes from an unsubstantiated suggestive question "Are COVID-19 data reliable?" which the article fails to answer, to "Applying Benford’s law to COVID-19 data: ...". The article mentions the conditions of applicability of Benford’s law, but does not test these assumptions on the source data, and "compensates" by making weak but suggestive claims, and recommending further research.

I'd recommend the author not doing any further research.

On 2020-04-29 22:11:46, user Sinai Immunol Review Project wrote:

We would also like to point out a brief report by Gioia et al. (2005; https://journals.sagepub.co... "https://journals.sagepub.com/doi/pdf/10.1177/039463200501800312)"), describing the existence of SARS-CoV-1 reactive T cells in healthy donors, an important observation that is very likely due to cross-reactivity with endemic coronaviruses and now seems to be confirmed by the findings in this current preprint.

On 2021-02-25 19:18:34, user Colin Pawlowski wrote:

Thank you for this feedback. We have updated the discussion section to note the potential "healthy-user bias" here.

On 2020-07-14 12:41:27, user David Simons wrote:

There are ongoing questions about a potential protective effect of nicotine. You report tobacco use and the risk of hospitalisation which includes both current and former tobacco use. There are multiple confounders associated with these groupings (i.e. those who have increased comorbidities are more likely to be former compared to current smokers). Could you consider exploring this in further versions of your work?

On 2021-09-10 16:16:11, user bee researcher wrote:

To clarify, this work is not comparing the risk of myocarditis in vaccinated individuals with the risk of hospitalization in similarly aged COVID-positive individuals, but rather an age-matched demographic regardless of COVID infection status. Is that correct?

This seems misleading in terms of risk assessment, because it's comparing the risk after a specific event (vaccination) with the background level of risk over certain periods of time. Yet active spread of COVID appears likely to continue for at some level for years, and the risk of hospitalization in COVID-positive individuals in this age group is much higher than the risk of vaccine-related myocarditis. Indeed the risk of COVID-related myocarditis is higher in this age group than the risk of vaccine-related myocarditis. If eventual infection by a now-endemic COVID-19 is incredibly likely, than it seems more informative to compare the risks associated with such an infection with the risks of vaccination.

On 2021-09-12 02:15:10, user MotherGinger wrote:

I'm not sure why the fact that these vaccines can't prevent spread is so little known, but it's a shame. The CDC Director has confirmed that they fail to prevent transmission, which means that the vaccinated person protects only herself, and no one else around her.

On 2020-08-05 08:21:05, user Ed Rybicki wrote:

This is a really good validation of plant-made antigens for this purpose, and a great paper. Well done, all!

And yes, I'm horribly biased: some day all good antigens will be made this way...B-)

On 2021-01-15 10:11:06, user Martijn Weterings wrote:

This research shows an interesting significant difference between the groups. The contingency table <br /> 3, 2, 8, 1 <br /> 22, 23, 17, 24 <br /> is an indication for a significant dependency.

However this is likely caused by age differences (given the abundant information that indicates the relationship between age and risk of death). It is mostly the 3rd group with the highest number of deaths (8 deaths) and the highest estimated adjusted risk ratio (RR 2.18). This is also the group with the highest age.

It is very problematic that there is no clear dose response relationship.

Because this lack of a monotonic relationship between O3l and risk, it seems arbitrary to make a comparison between the 4th quartile and the first 3 quartiles. The observed effect is mainly due to the 3rd quartile having a high risk. One might just as well make a comparison between the 1st quartile and the last 3 quartiles and find a similar (though slightly less) significant result.

Besides other potential confounding variables it is the age distribution among the four quantiles which is remarkable and likely seems to be a strong influence on the statistical relationship. This means that the adjustment must be done with great care. I personally believe that currently the adjustment might be biased due to the binning of the continuous O3 levels into 4 quartiles and age might need to be included as a polynomial and not just a linear effect (it is actually unclear what sort of model has been used).

The problem with binning is that correlation between age and O3 levels might not be captured smoothly. The relationship is not linear (rather it is something exponential or logistic). For each increase 10 years increase in age there should be something like a 2 fold increase in risk of death (in this research the odds ratio is only 1.33 for a decade increase which is odd). This means that a group of 80 year olds and 60 year olds, with a mean of 70 years, are not comparable to a group of only 70 year olds. One might get peculiar results when the distribution of age in the different quartiles is not evenly distributed. (and possibly there could be some sort of Simpson's paradox due to the way that age is distributed within the 4 quartiles, if the 3rd quartile happens to have many people of 'very' old age then this might interact with the age effect, resulting in a reduced risk rate for the increase of age and an increased risk rate for the 3rd quartile)

I would suggest to provide a scatter plot of age versus O3l (along with some colour or markers for death vs no-death) which allows a more clear view of the structure in this data set and allows to see a more clear relationship with the O3l. It could also be interesting to see the output of a logistic model where O3l is treated as a continuous variable (potentially with some non-linear relationships like polynomials or interaction terms). Such a model would not have to treat the levels O3l levels as categorical, and would have less problems with non-homogeneous age distributions within the categories.

I would not be surprised to see some sort of clusters in the scatter plot of O3l versus age (and potentially deaths occur might occur more often in particular clusters). A more exploratory analysis of such structures might reveal more useful insights to generate hypotheses to be studied in future research.

On 2023-03-15 10:27:28, user Chinh Quoc Luong wrote:

Dear The medRxiv Team and Readers,

Our article has been published in BMJ Open.[1]

Thank you very much!

Best regards,<br /> Chinh Quoc Luong

[1] Do SN, Dao CX, Nguyen TA, et al. Sequential Organ Failure Assessment (SOFA) Score for predicting mortality in patients with sepsis in Vietnamese intensive care units: a multicentre, cross-sectional study. BMJ Open 2023;13:e064870. doi: 10.1136/bmjopen-2022-064870.

Online publication is available at: https://bmjopen.bmj.com/con...

On 2020-06-06 12:35:34, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2021-03-08 16:10:37, user Alberto wrote:

5 out of 25 (20%) healthcare workers in the control group developed COVID-19 vs 0 out of 25 (0%) in the intervention arm.

The number of participants is small, yes. But it sounds like the researchers expected better results! Not very enthusiastic about this small feat.

On 2022-01-07 16:35:19, user Toby Koch wrote:

No adjustments for age, vaccination status, and variant for risk of hospitalization and death?

On 2020-04-06 12:58:13, user Maria wrote:

A very accurate study, which explains the high rate of spread of Sars-CoV2. I would repeat it in dark and cold rooms, keeping air samples also in the dark, since UV light and heat damage the virus. This could reveal why only nude RNA is found.

On 2021-08-24 10:14:29, user Mikaela Olsen wrote:

How I wish it was possible to contact the authors to ask a simple question. The study compares two groups but which groups? One group contains those who survived a SARS-cov-2 infection the second group contains vaccinated people who would and would not survive an in fection. Is it really possible to compare these two groups? What would waning of antibodies have looked like if it was possible to exclude those who would not survive an infection from the vaccinated group?

On 2020-03-17 03:45:06, user God Bennett wrote:

I foresaw this from February 9th, having started an ai based ct scan initiative:

https://github.com/JordanMi...

On 2020-04-22 20:47:32, user John Stevens wrote:

Hope this is widely understood - each tiny bump in efficiency makes differences in daily KPIs

On 2021-12-19 11:46:40, user Kjell Krüger wrote:

Tables and figures in the study point out that some 50% of the selection have status "unv." <br /> and "not born in Norway". Statistics from the study also marks out that some 80% of the total selection comes from the South-East region of Norway. Finally some 35% of unv. are marked with virusvariant "unknown", which we may suppose is other than omicron, as the study was done in the period up to october? It could be of interest to se some more deviation analyzes made on these parameters. Amount of beds i Norwegian hospitals are stated by SSB to be some 11500 beds, of which now some 400 are occupied with cov patients. I suppose all these parameters also should be interesting indata for future planning for how to manage future epidemic crises in Norway. Maybe new studies also will highlight possibilities that some regions should be set up with more capacity and competence than others, with the possibility to also transport both personell and patients between regions? I think questions and answers on these matters will be of big interest for politicians in both locally, regionally and nationally area one day when this crisis fade out - and preparation for the next one begins.

On 2020-05-15 01:01:11, user Timeisrelative wrote:

This is not my field of study but I hope my comments are helpful to you. Thank you for publishing this important work.

The name "SD" for your metric is confusing for three reasons. 1) Standard deviation which is also used in the paper is commonly abbreviated as SD. 2)Recently less travel has *increased* what people commonly refer to as "social distancing", however your metric "SD" tends to *decrease*. 3)Mobility is only one aspect of the common definition of social distancing. Other aspects are not attending mass gatherings, standing at least 6 ft apart, not shaking hands, etc.(https://hub.jhu.edu/2020/03... "https://hub.jhu.edu/2020/03/13/what-is-social-distancing/)") These other aspects are not captured by your metric so again I think it's confusing to call it a "social distancing ratio" and use the abbreviation SD. Better names might be "Mobility Reduction" or "Relative Mobility".

Further, according to Wikipedia: "During the COVID-19 pandemic, the World Health Organization (WHO) suggested favoring the term "physical distancing" as opposed to "social distancing", in keeping with the fact that it is a physical distance which prevents transmission; people can remain socially connected via technology." (https://en.wikipedia.org/wi... "https://en.wikipedia.org/wiki/Social_distancing)")

Your metric SD is based on "the assumption that when individuals make fewer trips, they physically interact less." But you are not looking at the number of trips directly, instead you look at the deviation from normal levels of trips. Why not look directly at the number of trips? Different areas my have widely varying baseline numbers of trips and one would expect infection rates to vary correspondingly. By measuring the correlation between the actual number of trips and infection rates we could see if that is in fact true.

I'm having trouble understanding the calculation of GR. You state "A GR equal to zero indicates no new confirmed cases were reported in the last three days" However, plugging 0 into the all three Cj in the numerator of the GR calculation leads to log(0/3+0/3+0/3). The result is undefined(negative infinity) not zero. You also state " a value below one means that the growth rate during the last three days is lower than that of the last week" and testing some sample data does not produce this result. Perhaps I'm misinterpreting your formula?

FIG 3 What is the "Raw Date" line? In your description of GR you say "We use 3-day moving averages to smooth volatile case reporting data." Does that statement refer to the 3-day summation in the numerator of "GR" or is there an additional 3-day moving average taken after GR is computed?

The GR calculation itself introduces a lag due to averaging the previous 3 days of data in the numerator and previous 7 days of data in the denominator. This distinction is important as you state that the value of the 9-12 day lag "reflects the time it takes for symptoms to manifest after infection, worsen, and be reported." In fact the lag from the calculation itself is also a factor.

It's also unclear if your source data is the date a positive test was taken or the date the lab results came back. When we are talking about a lag on the order of 10 days, a 1-3 day delay for results could be significant. Further, source data including the date of symptom onset is available in some states and would be more useful as it would eliminate part of the lag which could be affected by test availability and speed.

Why are only the top 25 counties are analyzed? I would be interested in seeing the metrics calculated in other lesser affected areas. In other words, could mobility reductions result in the prevention of outbreaks or just in the reduction of major outbreaks?

The metrics you've chosen (SD and GR) follow very similar paths among all 25 counties analyzed. All 25 counties saw sharp drops in SD between March 10th and March 20th. All 25 counties saw sharp drops in GR a few weeks later. However, adding counties that didn't have a sharp reduction in SD during that time period would be revealing. Also adding counties that had GR paths that either dropped over different time periods or that grew much slower and steadier would also help reveal if GR and SD are correlated in wider situations.

Caption to Fig 2 has redundant text "(vertical dashed red lines)"

"King County, Washington is excluded because it precedes widespread social distancing and was driven by an infection source that differs from other outbreaks in the US." Previously you demonstrated that the SD metric is not well correlated with dates of implementation for local and state social distancing directives. King County shouldn't be excluded just because it precedes widespread social distancing. Also how is it known that the "infection source" is different from the outbreaks at the top 25 counties chosen?

"Last, the data used in this analysis does not differentiate amongst sociodemographic groups, and therefore may not representatively capture all groups such as the elderly, low income families and underrepresentative minorities, for whom social distancing may not be an option, or may not have cell phones." Everyone in those groups with a mobile phone and that has the apps and permissions required for teralytics to track them is expected to be included in the dataset. The dataset may not be representative of the population at large but that is not *because* the dataset doesn't differentiate between sociodemographic groups.

Conclusions: "In conclusion, our results strongly support the conclusion that social distancing pays dividends in the vital reduction of load on hospital systems in the United States." I think this conclusion is too broad. You show no data on load of hospital systems. Your data is on the reduction in reported cases correlating to reduced number of trips in severely affected areas not social distancing as a whole.

On 2020-05-03 17:48:53, user Sinai Immunol Review Project wrote:

SUCCESSFUL MANUFACTURING OF CLINICAL-GRADE SARS-COV-2 SPECIFIC T CELLS FOR ADOPTIVE CELL THERAPY

Leung Wing et al.; medRxiv 2020.04.24.20077487; https://doi.org/10.1101/202.... 20077487

Keywords

• SARS-CoV-2 specific T cells

• Adoptive T cell transfer

• COVID-19

Main findings

In this preprint, Leung et al. report the isolation of SARS-CoV-2-specific T cells from two convalescent COVID-19 donors (n=1 mild, n=1 severe; both Chinese Singapore residents), using Miltenyi Biotec’s fully automated CliniMACS Cytokine Capture System: convalescent donor PBMCs were stimulated with MHC class I and class II peptide pools, covering immunodominant sequences of the SARS-CoV-2 S protein as well as the complete N and M proteins; next, PBMCs were labeled with a bi-specific antibody against human CD45, a common leukocyte marker expressed on white blood cells, as well as against human IFN-?, capturing T cell-secreted IFN-? in response to stimulation with SARS-CoV-2 peptides. Post stimulation, IFN-?+ CD45+ cells were identified by a mouse anti-human IFN-? antibody, coupled to ferromagnetic dextrane microbeads, and magnetically labeled cells were subsequently isolated by positive immunomagnetic cell separation. Enriched IFN-?+ CD45+ cells were mostly T cells (58%-71%; CD4>CD8), followed by smaller fractions of B (25-38%) and NK cells (4%). Up to 74% of T cells were found to be IFN-?+, and 17-22% of T cells expressed the cytotoxic effector marker CD56. Very limited phenotyping based on CD62L and CD45RO expression identified the majority of enriched CD4 and CD8 T subsets as effector memory T cells. TCR spectratyping of enriched T cells further revealed an oligoclonal TCR ß distribution (vs. a polyclonal distribution pre-enrichment), with increased representation of Vß3, Vß16 and Vß17. Based on limited assumptions about HLA phenotype frequencies as well as estimated haplotype sharing among Chinese Singaporeans, the authors suggest that these enriched virus-specific T cells could be used for adoptive cell therapy in severe COVID-19 patients.

Limitations

This preprint reports the technical adaptation of a previously described approach to isolate virus-specific T cells for targeted therapy in hematopoietic stem cell transplant recipients (reviewed by Houghtelin A et al.: https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5641550/pdf/fimmu-08-01272.pdf)") to PBMCs obtained from two convalescent COVID-19 patients. However, not only is the title of this preprint misleading - no adoptive cell transfer was performed -, but this study also lacks relevant information - among others - on technical details such as the respective S epitopes studied, on the precise identification of immune cell subsets (e.g. NK cells: CD56+ CD3-?), data pertaining to technical stimulation controls (positive/negative controls used for assay validation and potentially gating strategies), as well as on the percentage of live enriched IFN-?+ CD45+ cells. Generally, a more stringent phenotypical and functional characterization (including coexpression data of CD56 and IFN-? as well as activation, effector, proliferation and other markers) would be advisable. Similarly, in its current context, the TCR spectratyping performed here remains of limited relevance. Most importantly, though, as noted by the authors themselves, this study is substantially impaired based on the inclusion of only two convalescent donors from a relatively homogenous genetic population as well as by the lack of any potential recipient data. In related terms, clinical criteria, implications and potential perils of partially HLA-matched cell transfers are generally not adequately addressed by this study and even less so in the novel COVID-19 context.

Significance

Adoptive cell therapy with virus-specific T cells from partially HLA-matched third-party donors into immunocompromised recipients post hematopoietic stem cell transplantation has been successfully performed in the past (cf. https://www.ncbi.nlm.nih.go... https://www.jci.org/article... "https://www.jci.org/articles/view/121127)"). However, whether this approach might be clinically feasible for COVID-19 therapy remains unknown. Therefore, larger, more extensive studies including heterogeneous patient populations are needed to assess and balance potential risks vs. outcome in the new context of COVID-19.

This review was undertaken by V. van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-05-13 22:04:02, user Steve Condie wrote:

The study concludes that the Infection Fatality Rate (IFR) in Santa Clara County is 0.17%. Currently New York State has a fatality rate from Covid-19 equal to 0.14% of the entire population of that state. (PFR) In New York serological studies have found less than 20% of the population has been infected. That puts a floor under the IFR of 0.7+% (People are still dying.) The authors brush that 400+% discrepency off as being due to overwhelmed hospitals (?)

New Jersey's PFR is over 0.1%. Five more states, the District of Columbia and many nations in Europe have PFR's over 0.04%. There is no reason to believe that any of those states or nations have infection rates higher than that of New York. That necessarily implies that the IFR in those states and countries is also far higher than that which the authors have calculated for Santa Clara County.

Is Santa Clara County a "special case?" Or is there a problem with the study?

On 2020-05-05 14:08:15, user buzzbree wrote:

Beyond the seroprevelance conclusions of the study which are widely discussed, another very important issue that needs to be clarified by the authors is if the study fully adhered to Good Clinical Practice (GCP) standards. It is highly concerning that in the rush to publish the study- the authors may have not done so, and there is never an acceptable reason to do this- nor would an IRB agree.

To be fully compliant with GCP the Stanford IRB really needed to be informed of the the email Jay Bhattacharya's wife (https://www.buzzfeednews.co... sent to potential subjects. The email had several erroneous statements- that the test was FDA approved (Its not) and they would know if they were now immune from COVID-19 and would know that they were <br /> free from getting sick and could no longer spread the virus. These statements could have impacted subject safety by encouraging riskier behavior (i.e. ignoring social distancing) from the study subjects if they believed that the test was FDA approved and a positive result was <br /> definitive proof of protective immunity.

In the Buzzfeed article Dr. Bhattacharya has stated that he did not know about the email or <br /> approve of it, but he still had an ethical duty to report it to the IRB when he found out. There is only one line in manuscript stating that IRB approved the study- how the IRB addressed this email should be expounded upon in final manuscript given these new issues that have come to light. If the email was kept from the IRB, and instead the authors just capped enrollment from certain areas I do not see how that is compliant with GCP. These issues as they pertain to subject safety are not discussed in the manuscript- and they really should be.

Relevant GCP sections:

"3.3.8 Specifying that the<br /> investigator should promptly report to the IRB/IEC:(b) Changes <br /> increasing the risk to subjects and/or affecting significantly the <br /> conduct of the trial (see 4.10.2).

4.10.2 The investigator should <br /> promptly provide written reports to the sponsor, the IRB/IEC (see 3.3.8)<br /> and, where applicable, the institution on any changes significantly <br /> affecting the conduct of the trial, and/or increasing the risk to <br /> subjects.2 2

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Reply

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On 2020-11-18 12:16:47, user Robin Whittle wrote:

I did not see any mention about how long after supplementation the 25OHD levels were tested. D3 takes some days to be converted in the liver to circulating 25OHD.

Since the intervention was with already-hospitalised patients, on average 10 days after their symptoms began - and with 25OHD levels rising over a period days, with the average length of stay about 7 days, this intervention may have been too late, and perhaps too little.

In the Cordoba trial (Castillo et al. https://doi.org/10.1016/j.j... "https://doi.org/10.1016/j.jsbmb.2020.105751)") 0.532 mg 25OHD calcifediol would have raised 25OHD levels within a few hours, probably above 100ng/ml on average - if one extrapolates from the curve shown for 0.266mg (a single Hidroferol capsule, of which two were used in Cordoba) in this patent: https://patents.google.com/... This greatly reduced the need for intensive care and eliminated deaths.

Since hospitalised COVID-19 patients have an extremely urgent need for raised 25OHD levels, so the autocrine signaling systems of their immune cells and many other cell types can function properly (McGregor et al. https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.07.18.210161v1)"), a combination of 25OHD calcifediol with bolus D3 may prove more effective than either treatment alone. The bolus D3 would sustain 25OHD levels for weeks, and the D3 itself may protect the endothelium (Gibson et al. https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pone.0140370)").

On 2025-10-20 19:15:26, user Alastair Howcroft wrote:

This work has now been published in the British Medical Bulletin. The final peer-reviewed version is available here: https://doi.org/10.1093/bmb/ldaf017

On 2021-08-24 14:14:10, user Lisa Donlan wrote:

I am sorry the authors did not reply to this pertinent question.<br /> Given the April publication date I'd say the Delta was not a factor in this study. Senator Rand Paul in the US is using this study/data to fight vaccine mandates and encourage "personal responsibility" despite more recent data gathered among his own constituents that clarifies the increased risk of reinfection with natural immunity over vaccination. <br /> https://www.courier-journal...<br /> I hope the authors can update their data to include the newer variants quickly. Lives may depend on it!

On 2020-09-05 11:35:05, user Tricia Young wrote:

Thank you for providing statistics that are more consistent with what is really happening. Will this article be published?

On 2020-06-05 17:17:20, user Helmuth Haslacher wrote:

There is an error in the abstract, as manufacturer names have been confused. A revision has already been submitted. Many apologizes. The sentence should read as follows:

However, at low seroprevalences, the minor differences in specificityresulted in profound discrepancies of positive predictability at 1%seroprevalence: 52.3% (36.2-67.9), 77.6% (52.8-91.5), and 32.6% (23.6-43.1) for Abbott, Roche, and DiaSorin, respectively.

On 2022-06-03 09:49:21, user Rodger Moore wrote:

Dear author et al. This statistical research under a population of 800.000 suggests a different conclusion. https://www.mdpi.com/2077-0...

On 2024-08-22 14:41:40, user Gabriel Baldanzi wrote:

Hi. This paper is now published in Circulation https://doi.org/10.1161/CIRCULATIONAHA.123.063914

On 2020-03-22 17:24:40, user Jacob Madzan wrote:

The low potassium could be caused by a continual use of IV saline

On 2022-01-25 09:20:10, user Petter Jakobsen wrote:

Now published in PLOS ONE doi: 10.1371/journal.pone.0262232

On 2020-06-14 20:56:36, user Marm Kilpatrick wrote:

Thank you for this very nice synthesis of the literature on this topic and for your careful thoughts on shortcomings of the available data. One small suggestion is that in Fig 2 you propose a few options for the "Hypothetical distributions of S ARS -CoV-2 viral load". One option that has been proposed and fit to data in one of your cited papers (He et al 2020 Nat Med) is a gamma distribution that starts before symptom onset. In that paper they suggest it starts 2.3 d before symptom onset. <br /> Also, there are now several papers linking viral loads to infectious virus. Several can be found in this thread:<br /> https://twitter.com/Disease...

On 2021-03-30 07:17:34, user Eunji Lee wrote:

This is a good study to supplement the results of previous studies that showed the high false-positive rate of PET in early cervical cancer for pelvic lymph node detection. In particular, it is impressive that this cause was evaluated by correlating with inflammatory changes after conization. However, it would have been better if other imaging evaluations, such as CT and MRI, were added to the analysis to provide a way to supplement this limitation of PET.

On 2025-08-06 05:14:00, user Iulian Emil Tampu wrote:

A journal publication updates this preprint.

Tampu, I.E., Bianchessi, T., Blystad, I., Lundberg, P., Nyman, P., Eklund, A. and Haj-Hosseini, N., 2025. Pediatric brain tumor classification using deep learning on MR images with age fusion. Neuro-Oncology Advances, 7(1), p.vdae205.

DOI: https://doi.org/10.1093/noajnl/vdae205

On 2021-01-13 13:34:01, user Tavpritesh Sethi wrote:

Great work. Please do have a look at our work from June, 2020 on similar lines using explainable models https://www.medrxiv.org/con...

On 2020-03-15 09:12:21, user fuyutao wrote:

Wow, this paper may be a historical one when the findings are verified. I would encourage the authors to refine grammar and stick with accepted virology terms. For example "<br /> HKU-1 and OC43 (the source of FCS sequence-PRRA) caused influenza" is an easy target. But, the content of the paper does fill in several important pieces of the SARS-CoV-2 puzzle. It took so long for this boot to drop, I am surprised social media hasn't jumped on this yet :)

On 2019-11-27 15:46:04, user Guyguy wrote:

EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT 25 NOVEMBER 2019<br /> Tuesday, November 26, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,304, of which 3,186 are confirmed and 118 are probable. In total, there were 2,199 deaths (2081 confirmed and 118 probable) and 1077 people cured.<br /> • 392 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths among confirmed cases;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;

NEWS

NOTHING TO REPORT

VACCINATION

• Despite the tense situation of the city of Beni, a vaccination ring was opened around the confirmed case of 24 October 2019 in the Kanzulinzuli Health Area of the General Reference Hospital;<br /> • 724 people were vaccinated with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two Health Zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,215 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

MONITORING AT ENTRY POINTS

• Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 120,825,670 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
2. If an acquaintance from an epidemic area comes to visit you and is ill, do not touch him/her and call the North Kivu Civil Protection toll-free number;
3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
4. If a person dies because of Ebola, follow the instructions for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.
5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.