On 2020-04-05 22:12:33, user Soarintothesky wrote:

What delay was used for the time adjustment. A 10 day delay for cases>deaths in the Aneirin Bevan University Health Board in Gwent in South Wales shows a 22% CFR.

On 2020-04-04 18:15:08, user clem mcdonald Jr wrote:

If you or anyone has infection rates broken down by people in inner cabines only ventilated by the ships airconditioner verus those who had an open balconey-- where we could presume most of those people spent much time, we could ice the question as to whether the airducts were spreading the disease. It is well known that the airconsitioning systems have meager filtering ( only blocking large particles > 100 microns. (In contrast airplanes have HEPA filters ( which stop 3 micron and smaller particles). influenza eppidemics also have occured on shipd.

With detailed information about he kind of cabine (interior os seaside) we could know whether the airvcondiationing was the cupret. If it was would expect that the infection rate would be higher among inner cabin passengers.

Thanks<br /> h

On 2022-12-04 06:34:10, user Jianyu Lai wrote:

This manuscript is now published in Clinical Infectious Diseases: https://doi.org/10.1093/cid...

On 2020-02-27 04:02:53, user ShangShang Gao wrote:

They recruited 125 patients from Nanjing Second Hospital, of which 103 were patients with new coronary pneumonia. The official data till 2.27 showed a total of 93 confirmed diagnoses in Nanjing. How did this sample data come?

On 2020-05-21 23:33:25, user Jack A Syage wrote:

Very interesting analysis, but I have a counter argument to this. Most of these studies were conducted before the death rate peak. Deaths represent infections from about 2.5 weeks before whereas antibody measurements are current. So cases have grown by multiples by then. As a check I see the following trend in Table 3: the earliest dates show the lowest IFR's (since growing cases run way ahead of deaths) and latest dates show the highest IFR's (as cases are subsiding and catching up to deaths). So I plotted this and there is a distinct upward dependence for IFR vs. date with a Pearson coeff of 0.61 (pretty strong) and a 2-tailed, paired t-value of a staggering p = 0.00003.

I suspect continued antibody tests for populations well past the death rate peak will start to converge on a higher value of IFR, e.g., about 1%.

I have been doing modeling and interested in views: please check out:

https://www.medrxiv.org/con...

and

syage-covid19-assessment.com

@jacksyage<br /> https://twitter.com/jacksyage<br /> https://twitter.com/medrxiv...

On 2020-05-23 11:05:15, user John Jacobson wrote:

It would be great to see more of a discussion on how many of the recorded deaths from COVID-19 are directly attributable to Sars-CoV-2. Is the current ~96K US deaths the true death toll of direct deaths from Covid-19 or does this number include significant number of deaths from other causes, but list Covid-19 in death certificate (e.g. a patient with renal failure or end-stage liver cancer or head trauma picks up nosocomial infection and is recorded as part of covid burden)? This would surely reduce IFR estimates if accurately known. Is testing for Covid-19 more widespread than for routine influenza A or B infections? Seems likely in the current climate in the midst of a pandemic, which might be another caveat when comparing. Important to factor in both of these points to get a greater appreciation and context of the current pandemic?

On 2022-01-06 14:06:14, user Maker innovations Tom maker wrote:

Notice they do not list the non pharmaceutical therapies

On 2022-01-01 14:56:50, user Jeffrey_S_Morris wrote:

Nice study! For completeness, it would be nice if table 3 included the transmissibility odds ratios for vaccination statuses stratified by variant

On 2022-01-27 21:10:24, user Siguna Mueller, PhD, PhD wrote:

I find it difficult to see how many individuals were in each group. I may, or may not, be able to guess some proportions. For instance, Fig. 4 suggests that there were not many in the booster group, if any at all. (This is because boosters obviously were only rolled out not too long ago). Is the small peak at approx. 35 days since injection attributable to the booster group? If so, this makes me wonder if they were sufficiently many to be statistically relevant. Again, I find it hard to infer exact numbers of participants in the different groups. This info would really be helpful. Thanks.

On 2020-04-03 14:56:00, user Pavel Valerjevich Voronov wrote:

Study must go forward as a priority. Nobody else gives any other clue about who is quietly transmitting, who is getting seriously sick, who (if) totally immune. There must be a system. I do afraid that it won't continue. Any guess is better in current circumstances then no guess at all. If you have any better idea - please share.

On 2021-06-02 19:11:57, user Tim Winton wrote:

Would it be possible to have the Simulink and Matlab code + diagrams?

On 2021-08-05 18:41:36, user Ultrafiltered wrote:

With the probability of a PCR match of 1 with any sample comparison to a reference given 8 billion genotypes against strands of 30 to 50 mRNA, as DNA is expressed in any and all cells, the study only shows how many in the population are expressing a gene similar to a COVID phenotype, thus why the CDC has pulled its support of the PCR tests and going back to the process of isolation and identifying cells discovered through patient exam, similar to current Influenza like analoques. The basis of this paper goes to show that if you're sick with disease, you are sick with the disease and shed components, just like any other virus. The idea this effect is novel in this paper is superceeded by years of virology and research.

On 2020-05-19 04:00:43, user Sinai Immunol Review Project wrote:

Main Findings:<br /> During the unprecedented COVID-19 pandemic, identifying patients at high risk for mortality is critical so as to guide clinical decisions on early intervention and patient care. To identify factors associated with risk of death from COVID-19, the study developed a secure and pseudonymized analytics platform, OpenSAFELY, that links the UK National Health Service (NHS) patient electronic health records (EHR) with COVID-19 in-hospital death notifications. This platform enabled the rapid analysis of by far the largest cohort to date from any country, comprising 17,425,445 multi-ethnic adults and 5,683 COVID-19 deaths. The analyses were based on hazard ratio generated by cox-regression and were adjusted for demographics and co-morbidities.<br /> Increased risk of COVID-19 hospital death was associated with male gender, older age, certain clinical conditions (uncontrolled diabetes, severe asthma, other respiratory diseases, history of haematological malignancy or recent non-haematological cancer, obesity, cardiovascular disease, kidney, liver, neurological diseases, autoimmune conditions, organ transplant and splenectomy). Notably, the association of asthma with higher risk of COVID-19 related death is contradictory to previous findings of no increased risk of death or even protective association. This effect was even stronger with recent use of oral corticosteroids (i.e. more severe asthma). In addition, people of lower socio-economic background (i.e. deprivation) or black and Asian origin were identified at high risk. However, this association could not be entirely attributed to pre-existing health conditions or other risk factors, which warrants further exploration into drivers of these associations. The open source analytics code is available at OpenSAFELY.org.

Limitations:<br /> 1) There are few drawbacks in data source and collection. The study did not account for COVID-19 deaths in false-negative/ untested individuals, relied on EHR from specific software and dealt with incomplete EHR information. <br /> 2) Additional discussion regarding the reasons behind the associations would be insightful. Specifically, recent studies have shown that risk factors including asthma, hypertension, and diabetes impact the expression of ACE2 gene, which is the entry receptor for SARS-CoV-2. However, while asthma with type 2 inflammation has been associated with lower ACE2 expression and thereby potentially protective effect, this association has not been observed for nonatopic asthma in these studies. In the current study, asthma is only categorized in terms of severity (recent oral corticosteroid use vs not). Further categorization in terms of subtype would have been helpful.<br /> 3) Understanding the underlying causes of high risk in people of black and Asian origin is important for public health and mitigation of the spread. In this study, the most common assumptions of high burden of underlying comorbidities and lower socio-economic status are shown to contribute only partly to the risk. However, other factors, such as occupational exposure, neighborhood and household-density and possible influence of genetic or other biological factors still need to be explored. <br /> 4) The study suggests increased risk in former smokers and slight protective effect in current smokers. More in-depth analyses into whether the protective effect of current smoker status is an artifact of over-adjustment, selection protocol of healthy controls or a true correlation are needed. <br /> 5) It would be helpful to have the p-value along with the reported hazards ratio and 95% confidence intervals.

Significance: <br /> Overall, the OpenSAFELY platform allows secure and real-time analyses of clinical data stored in situ. As this global pandemic progresses, outcomes and data are expected to expand, revealing more insights to the effects of medical treatments and less common risk factors on COVID-19 infection, spread and death. This approach can help better identify additional factors that affect disease severity and immune response. Finally, this rapid and massive study was only possible because of the detailed longitudinal data already available through General Practitioners within the UK National Health Service (NHS), replication of which at a similar massive scale would be daunting within the highly fragmented healthcare system of the USA. While even within UK NHS many data integration issues remain, the findings from this study is a testament to the global model we need to follow to increase our power to rapidly answer crucial questions related to COVID-19 epidemiology. Such an approach will also open new avenues for increased understanding of other diseases.

Reviewed by Myvizhi Esai Selvan as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

On 2020-04-07 01:06:33, user Cristian Reyes P. wrote:

In Chile BCG vaccine has been mandatory since 1949. Everybody is vaccinated. Over 90% of the population. You can study us. We still have the lowest mortality in the region.

On 2020-04-10 00:26:48, user Brothers in arm wrote:

Curious to know why the BCG vaccination last only about 20 years. I have had mine as an infant, without any further boosters. Still my skin tuberculin test remains reactive after almost 50 years. The reaction subsides before follow up check on day 3. This is read as negative for active TB. I assume the slight reaction as due to having had BCG, and I still have immunity. People who never had it do not get any reaction or erythema. Maybe any immunization can confer some cross immunity?

On 2020-05-04 00:02:15, user Kyriakos wrote:

https://www.linkedin.com/pu...

On 2020-07-21 16:14:03, user Kamran Kadkhoda wrote:

Baes on the current estimates, the sero-prevalence in Idaho is around 4% at most; such high percentages are most likely false positives; I refer authors to the study just posted here on medrxiv from China showing sero-prevalence of 2% or less in Wuhan! They used PRNT to confirm the results. That's the right way. <br /> Abbott is clear in their IFU by saying they did NOT use samples from cases with confirmed infection with common CoVs…<br /> Despite publications using "convenience samples" specificity shows its shortcoming while used large scale in the field...here's one example!

On 2020-04-14 10:56:53, user Prof Page wrote:

It would be good if there were parenthetical sample sizes noted for these studies. Some are exceedingly small.

On 2020-05-20 08:57:29, user rob martens wrote:

This very interesting study could perhaps be supplemented by the relationship between COVID19 and air pollution. This relationship is now getting attention and is relatively well founded. https://www.sciencedirect.c... <br /> But what is usually not seen is that exposure to UV light (thus vitamin D) could also here be the underlying cause. Air pollutants strongly block the amount of UV light reaching the earth's surface. https://www.researchgate.ne... <br /> For example, it could explain the big difference in COVID19 lethality between northern Italy (where smog is common) and the rest of the country. Or the Wuhan district and other regions. By taking air pollution into account, the picture outlined could become even more precise.

On 2020-05-09 17:47:17, user Kai Londenberg wrote:

Will you make dataset and your causal models available somehow? This would be really helpful. Thanks for that interesting and work.

On 2020-12-21 18:08:41, user Michael Schrader wrote:

Very helpful study.<br /> Just a short comment on table: With 35 patients, precision is not better than +- one patient, corresponding to +-2.9 %. It is thus confusing to claim percentages with 4 digits like 97.12 %.

On 2020-05-20 14:45:39, user Benny Borremans wrote:

For supplementary information, R code, and data, please follow this link: https://osf.io/evy4q/

On 2024-02-20 21:32:15, user Wally Wilson wrote:

It would be handy if the authors could get the abbreviations for Borderline Personality Disorder (BPD) and Bipolar Disorder (BD) corrected

On 2020-04-22 04:34:52, user Paul Hue wrote:

What are they using as a gold standard to confirm pos or neg results?

On 2020-04-19 12:52:03, user mendel wrote:

I agree with the maths. A prevalence of 0.15% means the cases underestimate infections by a factor of 3-9 (unweighed-weighed), and results in an Infection Fatality Rate of 1.2%-3.4%. That is thoroughly unexciting.<br /> For comparison, with 12 reported deaths by March 24th, the unadjusted IFR for the Diamond Princess stands at 12/712=1.7%.

On 2020-04-25 07:19:42, user John Smith wrote:

1. A local website (SFGate, I think) mentioned a person who emailed many friends about the free test and this selected wealthier people who might have more exposure to international travel. This would boost the percentage with antibodies above a population sample that had more poor people in the sample. It did mention the team tried to correct for this email by recruiting from other areas of the county. 2. Santa Clara county has more international travel than most other areas of the USA that have fewer immigrants, so people who are saying other areas of the USA might have the same higher level of recovered patients would be wrong.

On 2020-04-20 01:18:02, user Americo Lobo wrote:

Hi Dean I would like to learn more about your approach or code.

Best regards from Brazil!

On 2020-04-22 09:32:28, user Mazyar Javid wrote:

Comparing the reactions to this study vs. another study published on the same MedRxiv site by Dr. Murray which is behind the IHME COVID19 model is an eye opener. Neither paper has undergone peer review yet. While no study is perfect, even though the results from this study have already been corroborated by a few other studies in independent populations, most of my colleagues here appear to be zealously obsessed with a mission to tear it apart and prove it to be worthless. On the other hand, the projections based on the other study on death toll of COVID19 have already been proven to be off by large margins and had to be revised a few times yet the amount of negative reaction and criticism is nowhere near what seen here. This makes me concerned since as scientists, we are supposed to have open minds and look at data without bias and before forming opinions, not to form opinions first, and then look at the data through the window of our opinions and try to find reasons to prove us right.

On 2024-03-14 13:13:14, user Rune Wilkens wrote:

This is a very interesting study! Why not discuss and highlight that 20% of the "cirrhosis" patients have PBC? One of the big drivers of the difference between IBD and non-IBD looks like being PBC ("immune-mediated") or even viral. This provides a different picture.

On 2022-05-05 12:39:51, user Robert Clark wrote:

The data shows efficacy against infection becomes NEGATIVE after one month. Imperative to found out if at longer times this also happens for hosp./deaths. Review the data to found out.

Robert Clark

On 2022-04-13 23:10:02, user D.R. wrote:

This study unfortunately missed an opportunity. It purports to have “[ established a Phase 3 pragmatic trial to evaluate the effectiveness of a test-and-treat approach to identification and treatment of vitamin D insufficiency for prevention of COVID-19 and other ARIs in U.K. adults” ].

When the trial failed to determine a statistically significant prevention of ARI with the dosing followed, the authors say that “ultimately, however, this trial was designed to investigate the effectiveness of a pragmatic ‘test-and-treat’ approach to boosting population vitamin D status, rather than biologic efficacy of vitamin D to prevent ARIs, and our findings should be interpreted accordingly,” essentially abandoning the prevention of ARIs.

In any trial there has to be a target serum concentration of the drug under study.<br /> Participants should be determined as having reached it, IF sufficiency for prevention of ARI is to be assessed. This trial did not appear to have a target, other than the presumption that over 75nmol/l was sufficient which has no scientific basis.

As regards the “pragmatic test-and-treat approach to boosting population vitamin D status”, the strategy has to get the participants to a target within a short period of time as it will take many weeks to become effective. A fixed dose across six months and across all BMI types is ill-advised. The approach should at the start reflect current primary care practice to get the participant to a target level within 5 weeks, and confirm that it was reached as a prerequisite for observation of effectiveness at prevention of ARIs. The difference here should have been to then assess whether participants has achieved the required level and not just abandon the effort as currently practiced by primary care in the UK. <br /> More than 50% of participants were over-weight or obese, and would struggle to get to a target within 5 weeks unless calcifidiol was used, or a specifically tailored regimen employed. This absorption challenge was known in advance of design. So not only was the evaluation of a pragmatic approach compromised, but the foundation for the proper assessment of prevention of disease was too.

The data on the effectiveness of the dosing by BMI should be detailed in the paper, and raw data made available.

In any event, the trial found that a mean of 102.9nmol (s.d. 23.6) did not produce a preventive effect. The failure to have a prerequisite specific serum level in a timely fashion for the observation of prevention, when the virus was most prevalent, most likely compromised the outcomes of the trial.

The conclusion would be better worded to state that “Among adults with a high baseline prevalence of apparent vitamin D insufficiency, implementation of a test-and-treat approach to vitamin D replacement using a maximum uniform 3200IU/d over six months for all participants, regardless of BMI type, did not reduce risk of all-cause ARI or Covid-19.”

On 2020-04-30 16:31:03, user Dr SK Gupta wrote:

Authors have reported the high prevalence of Mycobacterium Tuberculosis infection in Covid19. Authors have tried to portray not only the Higher prevalence of MTBI but also more severe and rapid progression of disease. However, since their findings are not in tune with observational data on the subject all across the world. Rather corona infection has been found to be low in South East Asia, Africa and other places where the tuberculosis is rampant. Also burden of Covid-19 has been highest in United States and Europe where the prevalence of Tuberculosis is low.

These Observations have prompted the scientist to look for the role of BCG vaccination/ past TB infection in prophylaxis and treatment of Covid 19.

Authors have erroneously relied upon use of Interferon gamma release assay (IGRA) to diagnose MTB Infection using a kit X.DOT-TB kits (TB Healthcare, Foshan, China). Not much has been described in article about the methodology used in these kits, but as the name suggests probably it is T-spot test which measures the number of IFN-?-secreting T cells via an enzyme-linked immunospot (ELISPOT) assay.<br /> Two types of IGRAs available, the QuantiFERON-TB Gold In-Tube test and the T-SPOT.TB blood test. Though both these tests are approved by the Food and Drug Administration as indirect tests for TB infection (including active disease) when used in combination with other medical and diagnostic evaluations. Since aging leads to a decline in the strength of immune responses, it is also argued that these tests loose their sensitivity with advancing age.

Overall Interferon gamma release assay (IGRA) has a poor sensitivity and specificity for the diagnosis of Tuberculosis.<br /> In patients with Non Tuberculous Mycobacterial Disease specificity of only 74% for infection and a relatively high indeterminate rate was found for QuantiFERON®-TB Gold(QTF) test assay with a sensitivity of 81.7 %. Hence the test is not able to discriminate between tuberculosis(TB) and non-tuberculous mycobacterial (NTM) disease with high degree of specificity.

The problem gets compounded even more in countries like China and India where the prevalence of TB is high and use of Tuberculin Testing and BCG vaccination is a routine and such cases have all the likelihood of being labelled as positive despite no active disease.<br /> Contrary to current practice Authors have also not used the available gold standards to define active TB based on either a positive Mycobacterium culture or a positive TB polymerase chain reaction/Gene expert/CBNAAT.

Not only that present investigators have also not describe any base line x-ray lung findings like cavitation, fibro-infiltration, lymph node enlargement, Spirometry based poor Lung function suggestive of tuberculosis in patients with positive MTBI or active tubercular disease which may have contributed to the rapid progression of superimposed pneumonia of Covid 19 in these patients.

In Covid-19 disease pathogenesis initially it is the role of Innate immunity mediated by Neutrophils Macrophages which mount a protective response. In tuberculosis Cell mediated immunity or the adaptive immunity involving T cells and B cells is at work. This has prompted world scientists to look for the role of BCG in treatment and prophylaxis of Covid-19. BCG Vaccine for Health Care Workers as Defense Against COVID 19 (BADAS) (NCT04348370) in USA and Brace trial by an Australian University are such attempts.

The current study needs the support of larger data which doesnot seem to be coming from other countries like India where TB is rampant. Till now the observations don’t support the hypothesis of increased susceptibility of TB patients for Covid-19 nor are there any indicators of more severe/ rapid progression of disease in patients with TB infection.

Dr S K Gupta <br /> Senior Consultant Physician <br /> Secretary Community Health Care Foundation<br /> Dr Prabhat Prakash Gupta <br /> Dr Mrs Praveen Gupta

References:<br /> 1.Comparison of the Sensitivity of QuantiFERON-TB Gold In-Tube and T-SPOT.TB According to Patient Age Won Bae,Kyoung Un Park,Eun Young Song,Se Joong Kim,Yeon Joo Lee,Jong Sun Park,Young-ae Cho,Ho Il Yoon,Jae-Joon Yim,Choon-Taek Lee,Jae Ho Lee <br /> Published: June 3,216 https://doi.org/10.1371/jou...<br /> 2. Sensitivity of the QuantiFERON-TB Gold test in culture-verified NTM disease and TB in a Danish setting Thomas Stig Hermansen, Vibeke Østergaard Thomsen, Pernille Ravn <br /> European Respiratory Journal 2012 40: P426; DOI:<br /> 3. https://clinicaltrials.gov/...<br /> 4. COVID-19: a recommendation to examine the effect of hydroxychloroquine in preventing infection and progression Dan Zhou , Sheng-Ming Dai and Qiang Tong J Antimicrob Chemother<br /> doi:10.1093/jac/dkaa114<br /> 5. Covid-19 coronavirus pandemic. https://www.worldometers.in...<br /> 6. 1. Mehta P. Mc Auley DF, brown M et al. Covid-19, consider cytokine storm syndromes and immunosuppression. Lancet. 2020. doi.org/10.1016/S0140-6736(...

1. Roitt I, Brostoff J, Male D. Immunology (Fifth Edition). Philadelphia: Mosby; 1998. ?

2. Wang L, Cai Y, Cheng Q, Hu Y, Xiao H. Imbalance of Th1/ Th2 cytokines in patients with pulmonary ?tuberculosis. Zhonghua Jie He He Hu Xi Za Zhi. 2002; 25 (9) : 535-537. ?

3. Collins FM. Cellular antimicrobial immunity. Crit Rev Microbiol. 1979;7:27–91. ?

4. Bretscher PA. An hypothesis to explain why cell-mediated immunity alone can contain infections by ?certain intracellular parasites and how immune class regulation of the response can be subverted. ?Immunol Cell Biol. 1992;70:343–351.

On 2021-09-22 17:13:20, user dansari wrote:

The authors do not describe their methodology for comparing incidence of myocarditis/pericarditis to background rates, and thus whether they have been disproportionately reported following vaccination, nor whether this comparison has indeed been performed. Reference (8) does include this detection.

As mentioned in other comments, the "Vaccinated in Ottawa" data (is this the dataset that was used?) indicate that 838,442 vaccinations were given during the time frame of this study. This would yield approximately 4 cases per 100,000, a figure more in line with currently understood vaccine statistics.

On 2021-09-23 11:48:38, user Arturo Tozzi cns wrote:

This is the problem with Preprints. <br /> This manuscript lacks the true denominator for the number of vaccinations, therefore it is useless. In order to get published, it requires huge improvements. <br /> However, it will the same go through the press worldwide and will become a must for No-Vaxes.

On 2020-07-30 22:09:35, user Tanya Bondar wrote:

Now published in Blood Cancer Discovery! The peer-reviewed, revised version of the article is free to read @BCD_AACR: bit.ly/2XbLhXf

On 2021-07-16 20:44:43, user temporalista wrote:

I reviewed the spreadhseet and replicated the computations in different software for both analysis (R) and visualisation (PowerBI, Tableau). In all cases the formulae seems correct and consistent.

I've been able to follow the modelling reasoning (is not complicated) and check the referenced sources. It seems consistent.

On 2021-11-15 18:45:58, user Yves POTARD wrote:

I just check your facebook calculation, i see an issue in using cfr form Reuters which mix vaccinated and unvaccinated people, we must use the cfr for unvaccinated people , i haven't find it but a gross calculation for Russia give around 3% if we take the ratio death and case, OMS cfr for Alpha was above 3 Delta is considered as more dangerous. , so if we take only 2% we find a correction of 303 which give zero infection with vaccinated following your calculation. It absurds of course but it means that this bias may be really substantial and cannot be ignored

On 2021-08-30 08:54:05, user Martijn Weterings wrote:

So, to compare the efficacies of convalescent and vaccine-induced immunity at staving off all infections, we'd have to look at their reductions in infections relative to those with naive immune systems.

'naïve immune systems'

This is not the point of the article.

Of course vaccination prevents infections and especially among people that have naïve immune systems.

However a matter that is currently important for society is how to reduce restrictions and social distincing rules. Currently people with vaccinations get more rights than people without vaccinations. A question is whether previous infection should be treated differently from vaccination. This article provides data that indicates it can be considered as equal.

On 2021-08-29 17:43:03, user Edison Wong wrote:

I looked at the raw #s. If you take model 1, the break thru infection rate for the twice vaccinated was 1.46%. This is actually a much higher rate of efficacy vs clinical trials abd other studies I have seen. When you look at breakthru infections for previously infected, this is 0.12%. I do not see this mentioned anywhere else. A 13-fold greater risk of infection does becomes less meaningful if the higher risk group is closer to 1% than 10%.

Perspective is important to determine how much of a public health response is reasonable. If the risk is for 100 people vs 1000,000 in a nation of 6 million, that should figure into any decision for lockdown & mask mandates.

On 2021-08-30 00:15:54, user chris amos wrote:

An important paper and carefully conducted study, but it would be useful if the authors would provide a figure or table starting with the overall cohort size, indicating the total numbers of events according to vaccination versus infection or first vaccination among infected. Given the data that are provided I do not know how to accurately calculate a positive predictive value of having been vaccinated, which is another statistic that is of interest. Also, when the authors refer to the analyses as 'multivariate', I think the more accurate way to refer to the analyses is "multivariable". Multivariate would mean that multiple outcomes (vaccinated only, infected only or vaccinated and infected) are jointly modeled, but it seems like the comparison groups are analyzed in separate analyses.

On 2021-09-15 03:48:48, user David Epperly wrote:

PART1<br /> While mRNA and other vaccines may create a very diverse polyclonal antibody response, encountering the virus often results in more diverse immune response because the mRNA usually does not create proteins for all aspects of the virus to include all of the S/RBD, N, E proteins. Most mRNA vaccines are designed to create a currently-thought best set of proteins to stimulate immune response. For example, the Moderna and Pfizer vaccines approved in December 2020 encode the entire spike that includes the highly important S/RBD proteins. These mRNA vaccines do not encode the Envelope or Nucleocapsid proteins and thus antibodies to those are not developed. With antigen level and all other things being equal, the RBD neutralizing effectiveness would likely be equal between natural infection and vaccine response. However, all other things being equal, the natural infection response would tend to be more protective because the more diverse immune response would be more likely to "tag" the virus for phagocytosis and other complement immune response..

PART2<br /> If the antigen level profile over time was held identical between vaccine and natural infection, natural infection would have a more diverse and thus more protective result. For natural infections where more antigen developed during exponential replication before adaptive immune response than is the case with vaccine, it is likely that a stronger immune response and better protection would develop as a result of natural infection. In the case of a natural infection exposure with lower antigen levels than that provided by vaccine, the greater natural infection immune response diversity would be offset by a lower overall level of antigen providing activation of adaptive immune response, and would likely result in lower protection than the vaccine response.

PART3<br /> Said another way, it is likely that asymptomatic or lightly symptomatic natural infections that have symptoms more mild than the typical 1 day dose 2 side effects of myalgia, fatigue, chills/fever, etc., will result in lower protection than the vaccine. Natural infections with greater symptoms than the dose 2 side-effects are likely to have stronger protection than the vaccine. And, with all of this, there is also some bias in favor of natural infection due to the more diverse immune response. This will not always be the individual case, but over a broad population, this correlation would likely exist.

The finding in this epidemiological study is consistent with what would be expected given immunological understandings.. Given the typical symptoms that follow a personally observed and/or clinically diagnosed mild infection, most asymptomatic infections, which may result in less protection than vaccine, are typically not observed / diagnosed and therefore the individual is unlikely to make a claim of natural infection, which further strengthens the case that observed / diagnosed natural infections would most often lead to better protection than the vaccine.

On 2021-09-08 13:34:31, user Chewbacca wrote:

Nobody is advocating for unvaccinated people to get covid on purpose rather than the vaccine. This study just shows that natural protection is better than vaccine protection, so the people who already got covid should be considered fully protected.

On 2021-08-30 00:23:58, user Bob Robertson wrote:

The conclusions in the results section regarding relative efficacies of convalescent immunity and vaccine-induced immunity are incorrect because they are based on improper comparisons.

Using the same improper comparisons, one would conclude that a 98%-off coupon saves the purchaser twice as much as is saved by a 96%-coupon.

What should instead be compared are the magnitudes of the reductions in symptomatic infections relative to naive immunity, and reductions in hospitalizations relative to naive immunity, as well as deaths, and maybe total infections too (though this one's a bit tougher).

If you have access to Israel-based numbers, all the better, but, for now, I'll assume that the percentage of symptomatic infections in Israel is comparable to that in the US... where 85% of pre-vaccinated, first infections are symptomatic, where 5% of infections lead to hospitalizations, and where 0.64% of infections lead to death. https://www.cdc.gov/coronav...

As a rough estimate, about 540 unvaccinated people who'd not previously been infected would've gotten infected out of a group of 16,214 (based on an assumption that 20% of people get infected per year... divided by 6 to represent those likely to be infected in a two-month span).

So, to compare the efficacies of convalescent and vaccine-induced immunity at staving off all infections, we'd have to look at their reductions in infections relative to those with naive immune systems.

540-19 gives us 521 infections avoided via convalescent immunity.<br /> 540-238 gives us 302 infections avoided via vaccine-induced immunity.<br /> From there, <br /> (521-302)/302 = 0.73, <br /> so, convalescent immunity against all infections could be said to be 73% better than vaccine-induced immunity, or 1.73 times better.

Of the 540 estimated infections expected from 16,214 naively immune, 85% would've been expected to experience symptomatic infections; so that's 457 estimated symptomatic infections.

457-8 gives us 449 symptomatic infections avoided via convalescent immunity.<br /> 457-191 gives us 266 symptomatic infections avoided via vaccine-induced immunity.<br /> From there,<br /> (449-266)/266 = 0.69,<br /> so convalescent immunity against symptomatic infections could be said to be 69% better than vaccine-induced immunity, or 1.69 times better.

Of the 540 estimated infections expected from 16,214 naively immune, 5.16% would be expected to end up in the hospital; that's 28 hospitalizations.

28-1 gives us 27 hospitalizations avoided via convalescent immunity.<br /> 28-8 gives us 20 hospitalizations avoided via vaccine-induced immunity.<br /> From there,<br /> (27-20)/20 = 0.35,<br /> so convalescent immunity against hospitalization could be said to be 35% better than vaccine-induced immunity, or 1.35 times better.

Deaths were the same, so, they each seem to be 100% effective against death (compared to the 4 deaths that would've been expected in the naively immune.

Given that 20% more of the vaccinated group had comorbidities than did the previously infected group, and that there were more than 2.5 times the number of immunocompromised and more than double the number of individuals with cancer, all results should be weighted by reasonable estimates related to comorbidities' impacts on outcomes. I find it quite surprising that comorbidities would've proven to have no impact on your results, and assume there's some mistake there too.

<edit><br /> I've since been informed that the metric about which I'm whining is called relative risk, that relative risk is totally a standard metric, and that I should separate my concerns about public misunderstanding from expert publication... it'd still be cool if y'all chose to add additional metrics for plebs like myself.<br /> </edit>

On 2024-06-02 22:52:33, user Hellen Paskaleva wrote:

p. 10, line 13: "Opposing", not "apposing".

On 2024-01-04 18:43:48, user John Beach wrote:

As a non-doctor with no opinion on ivermectin, I read<br /> "Effect of Ivermectin 600 ug/kg for 6 days vs Placebo"<br /> to learn.

My questions:

1.a.) What is being compared to what?<br /> Placebo isn't identified in the paper or the Supplemental material. Where can I look up the substance used as placebo?

1.b.) Does the placebo contain folic acid or folate? Or a placebo containing Magnesium, Sodium, or Potassium in some form?

Reason for question 1: is it possible the study assumed the placebo was inert, but fortuitously discovered instead another treatment that works against COVID with efficacy equal to ivermectin?

2.a.) Is there a supplement-to-the-study that shows the outcomes sorted by Cytochrome polymorphism?

Reason for question 2:<br /> CYP polymorphisms can affect how our bodies respond to virus.<br /> Some CYP polymorphisms can prevent a patient from even metabolizing the treatment.<br /> Furthermore, CYP3A4 mediates metabolism of ivermectin, but COVID-19 reportedly decreases metabolism of drugs by CYP3A4

e.g. via GoogleScholar, I found<br /> https://tandfonline.com/doi... <br /> "The impact of COVID-19 infection on cytochrome P450 3A4-mediated drug metabolism and drug interactions"

Back to my questions...

2.b.) Is there a way to determine if ivermectin was metabolized by a study participant, and if the molecule was circulating in their system? If so, was there any variability in study outcomes when those outcomes were sorted by Cytochrome polymorphism?

3.a.) Has any past research shown that the placebo ingredients interfere with any of the following?

• Phospholipase PLA2
• Lipid pathways
• Glycolysis, e.g. <br /> PKM2 (pyruvate kinase muscle isoform 2)
• lactate dehydrogenase
• pyruvate kinase
• glyceraldehyde phosphate dehydrogenase

If so, evidence suggests that these, and therefore the intended placebo that affects these, change human biology relevant to the COVID disease process.

It looks like a lot of good work was put into the Duke study, and so I am trying to learn from it. Any answers you can provide are much appreciated.

On 2021-12-16 21:16:38, user tshann wrote:

A little confused at this statement from the article:

While variants and waning efficacy are relevant, SARS-CoV-2 vaccines reduce the risk of infection, transmission, and severe illness/hospitalization in adults

Someone'll likely correct me here, but to my dim memory, Walensky, CDC and Faucci himself have admitted the vax's don't stop transmission or prevent infection. Am I wrong?

Peace

On 2021-01-30 15:14:58, user Joe Psotka wrote:

Autoimmune diseases to increase with covid-19?

On 2021-09-06 02:45:16, user William Brooks wrote:

The authors claim that if only high-mortality SA countries like Peru had maintained 90+ GSI throughout 2020, their cumulative deaths would have been lower. However, Fig.1 doesn't show that countries with higher GSI for longer had fewer cumulative deaths; if anything, it shows the opposite since low-mortality Uruguay clearly had the lowest GSI while high-mortality Argentina maintained one of the highest GSI.

Also, the fastest increase in deaths in the seven high-mortality countries was during the Southern Hemisphere winter when they all had GSI around or above 80 similar to the low-mortality countries except Uruguay. This lack of correlation between government policy and mortality outcomes means it's impossible to say "If only country X had locked down earlier/harder, they would have had fewer deaths."

Also, the authors use cumulative deaths, which can only go up regardless of whatever the GSI does. However, if Fig. 1 showed the reported deaths per month rather than cumulative deaths, readers would see that deaths tended to stay flat or decease as winter turned to spring, contradicting the authors' claim about lowering the GSI leading to higher mortality. The authors ignore this obvious seasonality, but it can explain why hard lockdowns in autumn didn't decrease Covid deaths in winter better than changes in GSI.

On 2020-11-19 12:41:34, user Bruno Gualano wrote:

Thank you, Dr. Gareth Davies. <br /> 1. I agree with your concerns with P-values. In this study, main outcomes are also expressed as CI95%. While this study was shown to be well-powered for the primary outcome, we recognize that it might be underpowered for secondary outcomes (please see limitations).

1. This time between the symptoms onset and randomization is quite typical in studies involving critical patients admitted to a quaternary hospital. This, however, does not affect the study objective, which was investigating the efficacy of vit D in severe, hospitalized patients. Of course, the findings are confined to this population. It is important to note that vit D supplementation was effective in increasing 25(OH) levels, including in those with vit D insufficiency (sensitivity analysis). We do recognize, however, that early vit D remains to be tested in the context of COVID-19 (please see limitations).

2. Despite ~90% of our sample comprised overweight and obese patients, vit D supplementation was able to increase vit D levels, irrespective of 25(OH) status at baseline. Due to the nature of the study design (ie. a trial involving hospitalized, severe patients in a quaternary hospital), it is possible to speculate that the treatment could have been “too late”, but it is unlikely that it could have been “too little”, based on the effective correction of vit D deficiency through the supplementation.

On 2020-08-24 15:34:57, user Eva Lendaro wrote:

Hello,<br /> I question regarding what does the vector beta account for. it supposedly includes policy dummies of businesses, restaurants, movie theaters, and gyms being allowed to reopen but in practice it is not very clear how these are accounted for. Is the capacity at which they were allowed to reopen considered? are the categories considered separately?

I would also like to point out this systematic review on this exact topic published on may 26th, 2020 on bmj that is nowhere mentioned in this article but would be rather important to include for completness.

https://www.bmj.com/content...

Best Regards,<br /> Eva

On 2021-05-18 18:17:37, user happydog wrote:

NOTE from the authors: The Brazil data used in the analysis (the data associated with the Hallal et al. (2020) serostudy; see https://doi.org/10.1016/S22... ) must be reevaluated in light of new results about the sensitivity of the Wondfo antibody tests (Silveira et al.; see: http://dx.doi.org/10.2139/s... ). We are currently working on a revised analysis with this in mind.

On 2023-01-06 03:26:21, user Danuta Skowronski wrote:

The peer-reviewed publication of this paper with the Canadian Medical Association Journal (CMAJ) is freely available as of December 5, 2022, at the following link : https://www.cmaj.ca/content...

On 2021-02-27 07:05:42, user jeromee5000 wrote:

published

doi

10.22034/jzd.2021.43762.1097

On 2021-02-23 02:00:44, user vinu arumugham wrote:

Parenterally administered vaccines never prevent infection.

"Administration of parenterally administered vaccines alone typically does not result in potent mucosal immunity that might interrupt infection or transmission"

SARS-CoV-2 Vaccines: Much Accomplished, Much to Learn

www.acpjournals.org/doi/10....

On 2020-09-05 17:49:00, user DFreddy wrote:

You assume masks reduce infectious transmission by 50%. Most of this evidence comes from observational studies with the known biases. Including such uncertain data in your model seems to me multiplying uncertainty, which goes against the idea of science: reduce uncertainty. Your conclusion lacks carefulness and may cause harm to the population. I advice you to prominently report on any uncertainties and their repercussions. Finally craft a new well balanced conclusion.

On 2020-12-26 19:39:53, user Sam Smith wrote:

TreatEarly / promising drugs.<br /> https://www.treatearly.org/...<br /> A large multi-center observational study with hospital inpatients in France showed that SSRIs in general were protective against covid. The drug with the greatest sigma-1 activation in the study (Fluoxetine in that case) had the greatest protection. The hypothesis of the researchers at Washington University is that the protection comes from the activation of Sigma-1. S1R is known to inhibit a cytokine, a chemical in our body that gets activated in certain infections, such as Covid-19.

On 2021-03-13 17:08:08, user truthful melody wrote:

Honest question seeking a good faith answer:

Does anyone know why the CDC is not reporting the variant cases that were widely reported in the media from the paper?

The headline on multiple outlets was, “Houston has all the variants”.

However, these are the current numbers from the CDC:

Texas (March 11, 2021)<br /> B.1.17 Variant: 140<br /> P.1. Variant: 0<br /> B.1.351 Variant: 1<br /> Source: US COVID-19 Cases Caused by Variants | CDC

The CDC is still reporting zero P1 cases in Texas and only one B.1.351.

Since Houston is a city in Texas, and I see from the comments section here that the cases are included in GISAID, what is the discrepancy here?

On 2020-12-20 20:25:16, user Pablo Pablo wrote:

I can't explain why this result is in contradiction with this other one.eurosurveillance<br /> Could it be because of the Simpson paradox?

On 2020-04-06 19:55:46, user Sinai Immunol Review Project wrote:

Clinical Characteristics of 2019 Novel Infected Coronavirus Pneumonia:A Systemic Review and Meta-analysis

The authors performed a meta analysis of literature on clinical, laboratory and radiologic characteristics of patients presenting with pneumonia related to SARSCoV2 infection, published up to Feb 6 2020. They found that symptoms that were mostly consistent among studies were sore throat, headache, diarrhea and rhinorrhea. Fever, cough, malaise and muscle pain were highly variable across studies. Leukopenia (mostly lymphocytopenia) and increased white blood cells were highly variable across studies. They identified three most common patterns seen on CT scan, but there was high variability across studies. Consistently across the studies examined, the authors found that about 75% of patients need supplemental oxygen therapy, about 23% mechanical ventilation and about 5% extracorporeal membrane oxygenation (ECMO). The authors calculated a staggering pooled mortality incidence of 78% for these patients.

Critical analysis:<br /> The authors mention that the total number of studies included in this meta analysis is nine, however they also mentioned that only three studies reported individual patient data. It is overall unclear how many patients in total were included in their analysis. This is mostly relevant as they reported an incredibly high mortality (78%) and mention an absolute number of deaths of 26 cases overall. It is not clear from their report how the mortality rate was calculated. <br /> The data is based on reports from China and mostly from the Wuhan area, which somewhat limits the overall generalizability and applicability of these results.

Importance and implications of these findings in the context of the current epidemics:<br /> This meta analysis offers some important data for clinicians to refer to when dealing with patients with COVID-19 and specifically with pneumonia. It is very helpful to set expectations about the course of the disease.

On 2020-09-18 17:53:41, user harrie geenen wrote:

1/ Consider air, fog or airpollution, all airborne . They can only remain airborne if enough more or less comparable particles or gasmolecules are available. if you compare the flightduration , you have to consider:<br /> The particle itself or attached to locally widely available host particle eg moisture, the microdroplets of many other persons in the party etc.<br /> 2/ the original droplets spread experiment adopted by the WHO is false because it neglects the liftsupporting of other particles.<br /> 3/ It is not difficult to do real tests of particle distribution in real life and it is also possible to take airsamples mimicing a persons load on a party evening or in other social areas. Using an infected person at least 3 meters away fom the crowd., (completely safe according health autorities). or using another marker. or giving the crowd safe air through a nose tube.<br /> Or measuring the particle distribution and copy this to a test area with an infected person.<br /> 4/ RIVM or other parties could monitor air in many places to get an impression of covid particles in an aerosol form.

5/ The virusload resonse reaction may vari very much between individuals. As long as this is the case you cannot accept a low level acceptable dose.

harrie geenen

On 2021-10-10 03:44:51, user kdrl nakle wrote:

The results on T-cells is quite murky here, without much explanation. You really need a bigger sample to be able to see this better, your sample of 46 is too small.

On 2021-06-08 03:28:15, user mengmeng Wang wrote:

one standard deviation (SD) is equal to ? natural log transformed random glucose mmol/L?

On 2025-04-10 16:27:18, user Epidemiologist wrote:

This is a phenomenally bad study, which contains stark evidence of its bias in the Figure purportedly supporting its conclusions. To summarize:<br /> 1. They compare two groups of employees who received a trivalent, inactivated influenza vaccine. Those who received the vaccine (82%) and those who sought an exemption (18%).<br /> 2. As hospital employees, they are aware of the extent to which their work puts them at risk of exposure but the investigators make no effort to determine differences between these groups beyond very crude categorizations.<br /> 3. They find that, after 100 days, they see higher influenza rates in the vaccinated.<br /> 3. They provide no plausible explanation as to how the inactivated vaccine puts one at increased risk of influenza 100 days after vaccination.<br /> 4. That means the ONLY plausible explanation for a significantly higher risk in the vaccinated is a significantly higher exposure risk in the vaccinated. Ergo, the sample is biased.<br /> 5. It is notable that the infection rate among the vaccinated was only 2.5% in a high risk setting for infection. <br /> 6. In sum, the best explanation for their results is that the vaccine was very effective and their sample was biased.

On 2021-03-15 07:47:08, user Pete Austin wrote:

These seem the kind of symptoms that would be caused by living indoors too much, due to inadequate exercise/ventilation and perhaps putting on weight. Publicity for the pandemic will cause people to be more alert to health symptoms, especially following a positive PCR test. I can't find any mention that the same analysis was done for any control group (I searched for "control" and "group"). I wonder if this is a pattern that you would find with other sub-groups of worried well.

On 2021-10-20 03:04:28, user Noriaki Kurita (????), MD, PhD wrote:

This preprint has been published by BMC Health Services Research. <br /> https://bmchealthservres.bi...

On 2020-11-03 20:27:05, user SteveH wrote:

Vitamin D levels do not decline quickly enough to explain the October surge. https://www.hindawi.com/jou...

On 2020-06-08 17:06:04, user Johann Holzmann wrote:

Dear authors,<br /> Thank you for making the pre-print accessible, I read it with great interest.

How do your findings regarding the presumptive false-positive rate of SARS CoV2 detection using RT-PCR relate with the very low RT-PCR positive rate as currently seen in many countries or regions with a very low prevalence of SARS CoV2?<br /> For example Australia runs between 30.000 to 35.000 PCR test daily for the last month and only gets around 10 positive assays per day. <br /> Other examples with a ratio of PCR assays per day to posiive assays of around 600-2000:1 are Iceland, Greece, Croatia, Thailand and certain parts of Germany (eg Sachsen-Anhalt, Mecklenburg Vorpommern) or Austria (eg Tirol).<br /> Wouldn't these data indicate a much lower false-positive rate than the one suggested in your manuscript?<br /> Thank you again for making your research accessible<br /> kind regards

On 2020-04-16 03:51:54, user Rhodes wrote:

Adding supplements? zinc, vitamin c, vitamin d, copper?

On 2020-04-15 14:24:47, user Greg Potter wrote:

Any data that looks at HCQ efficacy in COVID-19 patients before they reach the severity of pneumonia?

On 2020-04-15 12:57:31, user ZStat wrote:

When will the studies be done for patients who get HCQ right after diagnosis of covid19 ? Anybody doing these studies with a control group ? We need to know if HCQ effective if given early.

On 2020-01-25 10:47:13, user stucash wrote:

I am not sure if this is due to the "preprint" nature of paper, but a few points that look a bit suspicious:<br /> 1. The actual data set used to conduct the estimation was not disclosed in paper;<br /> 2. The research method for estimation was also not disclosed in paper<br /> 3. Reasoning for the employed assumptions and not others? Reasoning for the employed transmission model and not others? Apparently this should be part of research method elaboration yet there's none. <br /> 4. Do all med papers come in this short?? This paper is just too descriptive and only estimation results were presented.

I'd really wait for a full-fledged version, I am reluctant to call this research.

On 2020-05-07 12:01:23, user Alex Cranberg wrote:

The authors’ conclusion about being far from herd immunity is based on the questionable assumption that the population is homogeneous. If population has variation in susceptibility herd immunity may be close or even reached. https://www.medrxiv.org/con...

On 2020-08-03 14:07:24, user Monica Sidén wrote:

I am a nurseryschool teacher in Sweden. My bloodgroup is AB+.<br /> As I can understand it is a rare bloodgroup and I can receive blood from any other bloodgroup( since I don´t have any antivirus against any other bloodgroup). Now I am very wooried that I am likely to be at a high risk. I would be very pleased if someone can explain.

On 2020-08-18 20:34:55, user Lauren Call wrote:

I found this study through a link in a CNN article, along with the quote: “Gommerman said since scientists have not seen a record of re-infection, even with as widespread as the pandemic is, that strongly suggests the body's immune system is working well against this threat, and re-infection is less likely.” I am surprised they haven’t “seen” a re-infection, because I’ve had 2 positive COVID-19 tests, separated by 3 months, with a negative antibody test in between. Both times I had classic coronavirus symptoms, but they were distinctly different cases.

On 2021-07-17 21:41:27, user David Timmons wrote:

Do we have an estimate of when this article will be peer reviewed and move out of the preprint shadow? Many skeptics ignore anything listed as a preprint, no matter how much work has been done. Including those unvaccinated who test positive for Covid 19 antibodies would put the US population with some form of immunity at about 75%. That means our vulnerable population (unvaccinated with no prior Covid 19 infection) at 25% or less.

On 2022-01-23 11:48:37, user Michal wrote:

So this study took place between Dec 16, 2020 and May 2021 (5 months). Is there any newer study covering possibility of reinfection with longer period of time included? I relied on this pretty much, believing natural immunity should last longer, but here I am - first infection 20th April 2021 and now 22nd January 2022 and tested positive, third day in bed, wondering if anything would be different if I was vaccinated. After first infection the complications were as follows: depression (2 weeks), after waking up - feeling of liquid in lungs (1 month), short heart palpitations (2 months, every day), brain fog (up until now). 33 years old male, other than that I've always been as right as rain, so just felt like sharing, maybe there's more people like me.

On 2020-05-13 10:37:19, user Keith baker wrote:

PCOS females in ageing would be a interesting sub group. The genetics of AR and ACE2 play a role in their conditions when excess testosterone, gives rise to risk factors DBII, obesity and specifically male like adipose patterns, on torso and heart.

On 2022-03-04 05:05:06, user Satwant Kumar wrote:

The research community has gained valuable data from this multi-site project. In comparison to ATLAS v1.2, ATLAS v2.0 represents a significant upgrade.<br /> However, a quantitative evaluation of the level of disagreement between human raters in segmenting lesions of different sizes should be provided. <br /> Rationale: We recently trained segmentation models on ATLAS v2.0 and examined their predictions and errors. We found that the model is both over- and under-predicting lesions. We believe the error arises from inconsistent manual labeling in the ATLAS v2 dataset. Below are a few examples. If the labels are in fact inconsistent, the model cannot explain beyond a certain upper bound. If the disagreement between human raters is reported, we could estimate that upper bound and report the "explained explainable variance" for our segmentation models. <br /> Examples of model (a deep convolutional network) prediction on the validation set (5-fold CV). Before training and testing, the brains were extracted (skull-removed). Images are plotted in MNI152 space and 8 mm spacing is used to visualize axial (transverse) sections:

https://www.dropbox.com/s/f...<br /> https://www.dropbox.com/s/0...<br /> https://www.dropbox.com/s/o...<br /> https://www.dropbox.com/s/q...

Satwant Kumar, MBBS, PhD,<br /> UT Austin

On 2024-12-09 20:43:18, user Louis El Khoury wrote:

Usually methylation changes in response to an environmental factor are slow. How is it possible that there is enough methylation change during the course of a single match to reduce the epigenetic age, and then return to baseline 24hrs later? This is not clear in the discussion section.

On 2020-06-18 12:11:32, user Marcus wrote:

Hi, I had the error using my data:<br /> Maximum number of function evaluations exceeded: increase options.MaxFunctionEvaluations.

Can you help me?

On 2020-10-31 09:30:37, user Paolo Benna wrote:

In a meta-analysis related to EPHX1 polymorphisms, Gui-Xin Zhao et al. [1] used the Newcastle-Ottawa scale (NOS) [2] for assessing the quality of the case series to be included in the study. The same Authors in this meta-analysis [3] use, for the evaluation of other polymorphisms, some of the series already included in [1]. Nevertheless, they attribute a different NOS score to these in the two meta-analysis. In detail:<br /> Hung CC (2012): 8 [1] and 6 [3]<br /> Yun W (2013): 5 [1] and 8 [3]<br /> Zhu X (2014): 5 [1] and 8 [3]<br /> Daci A (2015): 8 [1] and 6 [3]<br /> I think a clarification in this regard is appropriate, since the discrepancy is not easy to understand.<br /> Yours sincerely,<br /> Paolo Benna

References<br /> [1] Zhao G, Shen M, Zhang Z, Wang P, Xie C, He G. Association between EPHX1 polymorphisms and carbamazepine metabolism in epilepsy: a meta-analysis. Int J Clin Pharm. 2019; 41: 1414–1428. https://doi.org/10.1007/s11...<br /> [2] Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomized studies in meta-analysis. The Ottawa Health Research Institute. 2013. http://www.ohri.ca/programs...<br /> [3] Zhao G, Zhang Z, Cai W, Shen M, Wang P, He G. Associations between CYP3A4, CYP3A5 and SCN1A polymorphisms and carbamazepine metabolism in epilepsy: a meta-analysis. medRxiv 2020.03.03.20030783. https://doi.org/10.1101/202...

On 2020-08-25 21:29:56, user Chris Raberts wrote:

I am not sure how the authors can use a study that speaks of N95 and 12-16 layered cloth masks and come to a conclusion like this. (reference 31).

In a recent comment (https://www.thelancet.com/j... "https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30352-0/fulltext)") the same authors speak of a range of 6% to 80% of mask benefits regarding reduction in transmission. I wonder what percentage was used in this paper, but given the results I'd assume it is on the higher end. Also that paper does not speak to schools, mostly to health care settings.

More transparency would be great but overall this paper looks like agenda and not science :/.

On 2021-11-24 21:54:37, user Jens Happel wrote:

If the calculation and assumptions would be correct there would be a huge surge of Myocarditis during the Covid19 waves.

But that is clearly not tbe case.

https://jamanetwork.com/jou...

During the Covid19 waves the number of Myocarditis and Pericarditis was more or less constant.m, compared to 2019.

The surge started according to cited paper above in February, when most of the wave was over but vaccination rate started to pick up speed and was changing from elderly to the next younger groups where Myocarditis is more likely.

I guess your assumption about not detected Myocarditis is terrible over estimating that factor.

The charts in cited paper above show clearly that your paper has substantial flaws.

On 2022-04-15 03:46:27, user the paper link wrote:

P values from multiple comparison post ANOVA, should be adjusted

On 2020-10-28 21:36:35, user IJ wrote:

The SNPs were found using a GWAS that controlled for vitamin D supplementation, and thus measured the genetic association with unsupplemented vitamin D levels. However, the question we are interested in is the relationship of COVID-19 outcomes with actual vitamin D levels, including supplementation for those who are already taking supplements. Since the decision to supplement is affected by unsupplemented vitamin D levels, this study needs to account for supplementation.

In particular, those with low vitamin D levels are more likely to be advised to take supplements. Could it be that those with genetic predisposition towards low levels might be taking supplements that raise their vitamin D levels, on average, more than is needed to compensate for the genetic predisposition? In that case, genetic predisposition for low vitamin D could _negatively_ correlate with actual vitamin D level, which would reverse the interpretation of the results.

On 2023-12-07 19:39:10, user Chihua Li wrote:

The study has been published: https://bmcmedicine.biomedc...

On 2021-07-08 05:59:35, user Dr.G.R.Soni wrote:

Comments on preprint medRxiv publication entitled "Efficacy, safety and lot to lot immunogenicity of an inactivated SARS-CoV-2 vaccine (BBV152) : A double blind, randomised, controlled phase 3 trials" reg.

This is regarding indigenously developed inactivated SARS-CoV-2 vaccine by M/s Bharat Biotech, Hyderabad by using vaccine strain NIV-2020-770 containing D614G mutation. The conventional vaccine consists of 0.5 ml volume having 0.6 ug of virus antigen and results of phase 3 clinical trial available on preprint of medRxiv publisher suggest that the vaccine is of good quality if not best//excellent and the vaccine in my opinion can be used by many under developed and developing countries. However, following are the further comments:

1. Results claimed for vaccine efficacy against severe symptomatic, asymptomatic and delta variant like 93.4%(95% confidence interval CI 57.1-99.8), 63.35( CI 29.0-82.4) and 65.2% (CI 33.1-83.0) respectively are statistically highly insignificant because of lesser precision and wide confidence interval. Even over all vaccine efficacy reported to be 77.8% (CI 65.2- 86.4) is not highly significant. This may be due to known and unknown variations as well as lesser number of participants involved in clinical trials. Of course the study was designed to obtain a two sided 95% CI for vaccine efficacy with lower limit greater or equal to 30% but it is only applicable when vaccines of high efficacy are not available. Whereas now fact is that the efficacy of Moderna, Pfizer, Johnson & Johnson, Sputnik etc. vaccines has been reported to be more than 90.0% with better precisions.

2. Again vaccine efficacy reported for elderly patients viz. 67.8% (CI 8.0- 90.0) shows very poor precision and widest CI means high uncertainty and least confidence in data.

3. No vaccine efficacy data submitted after first dose of vaccine administration and reason furnished by the sponsor is because of low number of Covid-19 cases reported and this is not very convincing. Comparison of vaccine efficacy between two doses is important to know the progress of efficacy.

4. GMT was reported to be higher i.e.194.3 ( CI 134.4-280.9) in vaccinees who were seropositive for SARS-CoV-2 IgG at base line than in those who were seronegative 118.0 (104.0-134.0). The difference is not even two fold whereas in other studies including Pfizer more than 4-6 fold increase in immune response has been reported even after single dose of vaccine under such conditions. This may be due to killed nature of present vaccine which is in general less immunogenic than live vector and m-RNA based Covid-19 vaccines.

5. The immune response of three vaccine lots of vaccine in terms of GMT 50 is 130, 121.2 and 125.4 Vs 13.7 in placebo which seems to be optimum but much higher immune response has been reported in other internationally approved vaccines.Why the IgG GMT titre after two doses of vaccine studied by ELISA has not been reported separately for each lot of vaccine rather overall titre against S1 protein, N protein and RBD has been reported i.e. 9742 EU/ml, 4161 EU/ml and 4124 EU/ml respectively? Since RBD is part of S1 protein therefore S1 titre includes RBD titre also. It means ELISA IgG antibody titre of these viral proteins are roughly equal hence this needs clarification. Anyway unless the titre of these neutralizing and ELISA IgG antibodies are compared with sera of asymptomatic, symptomatic and severely recovered covid-19 patients or immune sera of WHO, US, EU approved vaccines available in market it is very difficult to say whether the immunogenicity of present vaccine is at par or not with these standards?

6. The lesser efficacy of present vaccine than other approved vaccines by the US, WHO and EU may also be due to lack of T cell mediated cytotoxicity response; this is why this response is not measured in the present study.

7. When Covid-19 disease is known to affect men and women differently so separate clinical trial data are required to be submitted by the sponsors. However in the present study no marked difference in GMT,s for neutralizing antibodies at day 56 was found when assessed based on age and gender. This is very surprising and difficult to believe because age definitely and gender also are known to affect the immune response of any viral vaccine.

8. Reasons for contracting Covid-19 disease by some vaccinees are to be given specially when immune-compromised and immunosuppressed etc. patients have already been excluded from the study.

9. As per WHO requirement the minimum level of protecting antibodies should be there up to six months therefore sponsors have to continue the study and then can claim the actual efficacy of vaccine.

Indigenous development of SARS-CoV-2 killed vaccine by conventional method using Indian isolate in the country is an excellent attempt for controlling the Covid 19 disease. The vaccine so far seems to be good based on the results of controlled clinical trials and its effectiveness will be further come to know over the time after its massive use in vaccination program. It is nice that the said vaccine has been exported to many other countries. Let us hope for its early approval by WHO for emergency use.

Dr.G.R.Soni

On 2021-06-13 21:34:07, user thomas wrote:

natural immunity would be inferior to induced immunity

Is this a typo, did you mean to say "induced immunity would be inferior to natural immunity"?

On 2021-07-21 16:34:01, user Pete wrote:

Does the vaccine continue to provide immunity or do you need a booster shot?

On 2021-08-31 10:15:07, user Isatou Sarr wrote:

Excellent paper,

the route of therapeutic administration usually plays a pivotal role in immune cells activation, type as well as robustness. Mucosally induced immunological tolerance has become an attractive strategy for diagnostics and treatment of diseases, although there is a need to fully understand the dynamics of mucosal-tolerance immunotherapy as well as efficient antigen delivery and adjuvant systems.

Additionally, the genetically diverse human subjects who also differ significantly in their mucosal flora, nutritional status and previous immunological/environmental exposure, all of which are factors that can been affect mucosal vaccine efficacy.

On the brighter side of life :)))), if practical assays for assessing mucosal immune cells reactivity in research settings are developed as well as methods for predicting efficacy of candidate mucosal immunotherapeutics, harnessing the therapeutic potentials of the<br /> mucosal immune pathway can be a reality.

Thank you.

On 2024-04-27 19:47:53, user Rebecca L. Roop wrote:

Thank you for the work and dedication to create this article for publication. I suffered through TSW for 24 months after only using various classes of topical steroids for 12 months. That period of my life was absolute hell.

On 2024-05-07 17:04:04, user Katy Ross wrote:

There is a current flawed premise that ‘adherence’ to steroid treatment is a good idea and anything querying whether that’s a good idea is deemed to be a phobia, as opposed to a clear, rational and well reasoned concern.

I am out the other side of topical steroid withdrawal syndrome, and have lost a lot to the trauma it brings (in all matter of ways). Sufferers like me are desperate for science-backed research, and it’s great to see progress here. It will be fantastic when a diagnostic code has been approved and further research is available to differentiate TSW from other skin conditions so that patients can be acknowledged and treated appropriately.

The former Chair of the British Dermatology Council said that as many as 1 in 10 patients may have TSW and it may be seriously under-diagnosed, and the hashtag #TSW has racked up more than one billion views on TikTok - I applaud anyone who’s working towards ending this huge amount of unnecessary suffering.

On 2020-08-17 06:34:30, user Jesper Markmann wrote:

There are several studies examining whether AC units can transpot virus. There are also studies examining the effect of climate.

If temperature and humidity can have an effect outside, it would have a more pronounced effect in a confined space.

Have you considered the possibility, that the problem isn't that AC is transporting the virus. The problem is that the colder air allows the virus to stay active in the air for longer, increasing the risk that it will infect other people.

This would explain why the virus is much more predominant in meat factories, on cruise ships, and in countries where it is common to cool confined spaces.

On 2020-05-18 18:56:00, user Guest wrote:

Wrong Irv.

"It was shown that increasing 25(OH) vitamin D levels are associated with

decreasing CRP levels, with a stronger effect in the inflammatory

disease group compared to the non-inflammatory disease group."

https://www.ncbi.nlm.nih.go...

On 2021-10-27 02:28:10, user Chad Bousman wrote:

Article published in Psychiatry Research, Volume 305, November 2021, 114247 (https://authors.elsevier.co... "https://authors.elsevier.com/a/1d%7E27bZg7EYbm)").

On 2021-10-04 17:19:09, user Bearwallow wrote:

Thank you for this interesting study. Well done! Has it yet been peer reviewed yet? or are your conclusions too easy and cost effective to implement? The world medical industry seems to prefer complicated expensive patented experimental "solutions". I so hope your study receives the attention it deserves. <br /> The dose of honey seems quite large. 1gm/kg/day- just asking- just making sure, is this correct? I saw one other study used 0.5 gm/kg/day. Thank you and keep making your useful discoveries.

On 2021-09-06 17:33:34, user Shannon Marie BScPharm (she/he wrote:

Very curious to know what this means for patients who suffer from mast cell disease prior to COVID19 infection. Limited research to date shows that if MC disease is well controlled, outcomes are no more severe than in general population. But as the mother of a child with yet uncontrolled mast cell disease, I shudder at the thought of her system ramping up even more during a SARS-CoV-2 infection. Hoping for more details on this as the science rolls in, regardless of her vaccinated status. Thanks to these researchers for their efforts to identify cellular mechanisms of COVID19 sequelae.

On 2025-04-28 08:39:14, user Tom Hähnel wrote:

Published version available under:<br /> https://www.nature.com/articles/s41531-025-00923-2 <br /> DOI: 10.1038/s41531-025-00923-2

On 2021-09-29 11:30:07, user kdrl nakle wrote:

Good example of making a paper focused on useless graphics instead on self-explanatory data.

On 2020-05-26 17:03:03, user Sinai Immunol Review Project wrote:

The main finding of the article: <br /> Recent studies have diverged as to weather conditions are allied or not with the spreading of Covid-19. Through random-effects meta-regression analysis, this work aimed was to determine if elements linked to meteorology can influence SARS-CoV-2 incidence and the speed of its propagation. The number of Covid-19 patients and meteorological conditions at each Japanese prefectural capital city from January to April 2020 were collected. <br /> The results demonstrated a negative association between Covid-19 incidence and monthly mean air temperature (C) (coefficient -0.351), sea level air pressure (hPa) (coefficient -0.001) and the monthly mean daily maximum UV index (UV) (coefficient -0.001).

Critical analysis of the study: <br /> The manuscript would benefit from a more thorough introduction and discussion of the results in the context of previous studies. The authors could explore more the results of the supplementary table 1 (wind speed, relative humidity and sunshine). The figure caption should be better detailed, explaining the characteristics of each graph.

The importance and implications for the current epidemics: <br /> The transmission dynamics of SARS-CoV-2 depends on different factors, such as population density, demographic and clinical characteristics of the population, hygiene, local ventilation, etc., and the seasonality of SARS-CoV-2 is not yet known.<br /> The data of this manuscript suggest that higher air temperature, air pressure, and ultraviolet are associated with a lower incidence of Covid-19. Certainly, this study is a step in identifying which environmental factors can favor viral transmission.

Reviewed by Bruna Gazzi de Lima Seolin.

On 2020-09-22 20:58:09, user Daniel Corcos wrote:

1) The hazard ratio between twice weekly and once weekly HCQ have been inverted in the abstract as compared to the figure.<br /> 2) If there is no statistical difference between twice weekly and once weekly, why don't you pool the results to observe a statistical difference between HCQ and placebo?

On 2020-03-16 16:32:43, user Bill Keevil wrote:

Although not peer reviewed yet, this work is not surprising because we showed long term survival of the similar coronavirus 229E on plastics, ceramics, stainless steel and glass for 4-5 days; the virus was inactivated on copper in just minutes and its RNA destroyedhttps://mbio.asm.o.... Another group showed SARS survived 5 days on stainless steel. We and others also showed flu survives several days. Implications are that in a closed environment a potentially infectious aerosol of small particle size can remain suspended in air for some time before landing on surfaces – hence being outdoors or opening windows is probably a good thing. It might question whether the 2 metre gap between people is sufficient in a confined space. As I have said before, survival of coronaviruses for days on touch surfaces (not the 2 hours cited by some advisers) is a hygiene risk, and it is difficult to avoid touching door handles, stair rails, public touch screens etc. It re-emphasises the need for good personal hygiene such as washing hands rigorously throughout the day, or using an alcohol hand gel, and avoid touching the eyes, nose and mouth.

Because this is a pre-print it is difficult to know exactly what they have done. Clearly they are using a different virus and culturing in Vero-E6 kidney cells while we used MRC-5 lung cells. An important difference may be that in their 2003 MERS paper they used 100ul culture onto unspecified size surfaces (“washers”) –McMaster-Carr, USA); for the new paper where they say they used 50ul of virus then we know that this can take a long time to dry out. Copper alloys kill bacteria and viruses when dry due to the inactivation mechanisms we have published. Our method to simulate hand contact uses 20ul onto 1 square cm, spread over the surface and then dries out in several minutes; sometimes we use 1ul when we have high concentrations of pathogen available.

Perhaps more importantly, our cells were maintained in minimal essential medium (MEM) supplemented with 1mM GlutaMax-1*, nonessential amino acids, and 5% fetal calf serum and incubated at 37°C and 5% CO2. Their cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM; Sigma) supplemented with 2% fetal calf serum (Logan), 1 mM L-glutamine (Lonza), 50 U/ml penicillin and 50 µg/ml streptomycin (Gibco).

*(GlutaMAX™-1 (Gibco), L-alanyl-L-glutamine, is a dipeptide substitute for L-glutamine. GlutaMAX™-1 can be used as a direct substitute for L-glutamine at equimolar concentrations in both adherent and suspension mammalian cell cultures with minimal or no adaptation. GlutaMAX™-1 is highly soluble, heat-stable, and improves growth efficiency and performance of mammalian cell culture systems. GlutaMAX™-1 eliminates problems associated with thespontaneous breakdown of L-glutamine into ammonia during incubation, allowing for longer lasting cultures. )

Importantly, glutamine binds copper while it also spontaneously breakdowns at physiological pH to ammonia which reacts with copper to precipitate light blue Cu(OH)2. This would give a partial passivation effect, making the copper surfaces less antiviral while our GlutaMAX-1 would not; hence explaining their longer time for copper inactivation.

This is one of the reasons we decided GlutaMAX-1 was the better option to avoid subsequent potential copper binding problems in the surface contact experiments.

On 2021-02-10 17:17:13, user bert jindal wrote:

could you provide me with more clarity on the parameters being measured to service the algorithm. As a clinician important diagnostic indicators include the history and presentation .does the system use patients symptoms age sex an ethnicity to derive its predictive value?

On 2024-06-08 00:47:24, user Renzo Huber wrote:

The manuscript entitled “Laminar multi-contrast fMRI at 7T allows differentiation of neuronal excitation and inhibition underlying positive and negative BOLD responses” is a methods paper that estimated metabolic changes (CMRO2) across cortical layers.

The subject matter is relevant for the field. (layer-)fMRI suffers from the interpretability challenge of ‘only’ capturing an indirect measure of neural activity. This study aims to estimate neural energy demand more directly with a newly re-implemented multi-contrast sequence of CBV, CBF, and BOLD.

The method is benchmarked on previously established tasks (finger tapping) and applied on visual retinotopic stimuly.<br /> The study is clearly described and the results on positive responses look robust and convincing.<br /> The results on negative responses are weak and less clear and less convincing, though. <br /> One advantage of this study compared to previous laminar CMRO2 studies is that it does not rely on a Grubb coefficient that relates CBV and CBF. Instead, the study at hand measures both parameters concomitantly.

There are some model assumptions that are not really justified (detailed below).

I recommend the editors to publish this manuscript given the authors make a few small revisions.

Detailed comments are below:

1.) The Davis model on CMRO2 estimation is based on many assumptions that might not be valid for the spatial scale of laminar fMRI with GRASE. I believe the authors could spell out the assumptions that they are making and discuss if and how much they matter for the conclusions.

1a) The Davis model is based on the Fick’s principle. This assumes that delivered oxygen (via CBF) is either (i) sitting in the voxel -CBV , (ii) metabolized -CMRO2 or (iii) drained away - BOLD. Its a mass-balance principle. This assumption is valid for conventional 3mm voxels that cover the entire vascular tree. But for laminar resolution this is not valid anymore. The exchange (CBF) is happening in different layers than the draining (BOLD). So in superficial voxels, when there is a BOLD signal change without any CBV or CBF change, the Davis model results in unphysiological results.

1b) The Davis model is solely parametrizing venous CBV that is contributing to the BOLD signal. The Davis model does not include arterial CBV. In the study at hand, the authors take VASO and it’s estimation of total CBV, in the equation that is meant for venous CBV only. Given that venous CBV is weaker, slower, and has a different sensitivity to superficial layers [Huber 2014 10.1016/j.neuroimage.2014.04.022], this can result in skewed estimations of CMRO2. Previous studies on laminar CMRO2 have used a scaling factor to account for this [Guidi 2016 10.1016/j.neuroimage.2016.06.030]. The study at hand does not account for the mismatch between total CBV and venous CBV.

1c) The power law that equates BOLD signal changes with oxygenation changes is originally estimated based on a supralinear effect: “a linear large vessel component is combined with small vessel contributions, which tend toward a quadratic effect on relaxivity according to the Luz-Meiboom model for diffusion-mediated exchange on the capillary scale” (Davs paper 1998). In my understanding, this has always been applied with gradient echo BOLD. In the study at hand, the authors apply the same relationship to GRASE BOLD. Based on modeling work in [Scheffler 2021, https://doi.org/10.1002/mrm...], the vessel sensitivity and the relationship between intra and extravascular BOLD is dependent on vessel radius and flip angle. This is different from GE-BOLD which does not have these dependencies. This makes me wonder if it's justified to use an universal beta value in the Davis model for GRASE BOLD. Maybe beta varies a lot across layers and areas?

2.) The study by Bohrhaus et al 2023 also used laminar CBV, CBF and BOLD to estimate CMRO2 with a layer peak that seems much more superficial (monkeys) than the results shown here. The authors could acknowledge that this study exists and include it in the reference list?

Bohraus, Y., Merkle, H., Logothetis, N.K., Goense, J., 2023. Laminar differences in functional oxygen metabolism in monkey visual cortex measured with calibrated fMRI. Cell Reports 42, 113341. https://doi.org/10.1016/j.c...

3.) It seems that the profiles in Figs. 3,4 are group results. It is not clear if the corresponding maps are single participant maps. Are the inflated brains in Fig. 4 averages in FS-average?

4.) It is not clear to me to which experiment the results in Fig. 3 refer to. The heading suggests its experiment 1. The figure caption seems to suggest it refers to experiment 2.

5.) I think it would be helpful to add a zero line in Fig. 5d. It's not clear if the author hypothesizes that the superficial layer sees negative changes or if the deeper layers see positive changes.

6.) I found Fig. 8 a bit misleading. The scanner plots are mixing many different sources of variance. The spread across points might contain true spatial patterns as well as intersubject variability e.g. different fMRI gain due to different venous baseline oxygenation [Lu,et al., 2008. https://doi.org/10.1002/mrm...]. So it’s not clear what a higher correlation means. In the Davis model, CBF dominates the estimates of CMRO2. Thus, any thermal noise in CBF will be expected to translate to noise in CMRO2 estimates; Making them not independent parameters. Thus, I am not sure if the higher correlation in CBF-CMRO2 is an excelent measure to investigate which parameter is most closely related to CMRO2. But it also doesn’t hurt to keep the figure in there.

7.) In the discussion, the authors discuss their beta value with respect to the literature. I think it would be helpful to mention that beta is not solely a tissue property constant. It is expected to be different across field strength, TE and BOLD contrast (GE-SE).

8.) Typo in discussion “rang from 0.9…”

On 2020-03-31 18:56:14, user Igor H. wrote:

I would suggest verifying the calculations. Data for Colorado do not fit.<br /> Here is the comparison of actual reported hospitalizations and your prediction for 3/18-3/29:

First column after date are actual hospitalizations (not new per day but all covid hospitalized patients on the day) reported by Colorado Dept of Public Health - https://covid19.colorado.go... - and the right column is your predicted "allbed_mean" which is supposed to be “Mean covid beds needed by day” (I assume that you mean number of beds needed on the particular date, not a cumulative number from the beginning – patients get discharged or die)

3/18/2020 26 158<br /> 3/19/2020 38 186<br /> 3/20/2020 44 268<br /> 3/21/2020 49 323<br /> 3/22/2020 58 455<br /> 3/23/2020 72 573<br /> 3/24/2020 84 716<br /> 3/25/2020 148 882<br /> 3/26/2020 184 1069<br /> 3/27/2020 239 1294<br /> 3/28/2020 274 1542<br /> 3/29/2020 326 1841

When I look closely, Allbed_mean on the day is the sum of (admis_mean) from the beginning to that day.

This is how you project ***new*** admissions (admis_mean) for the same time period:

69<br /> 28<br /> 88<br /> 56<br /> 137<br /> 124<br /> 149<br /> 178<br /> 209<br /> 242<br /> 278<br /> 317

This is also hugely overestimated and the numbers more resemble TOTAL number of hospitalized patients on the day.

Also, spotcheck for New York State does not match. See attache https://uploads.disquscdn.c... d images (prediction and actual reported number this morning)<br /> https://uploads.disquscdn.c...

It appears that (Allbed_mean) is only correct if 100% of cases need hospitalization, which is not the case in the US (it was the case in China). So, actual number of beds needed seems to be 20% of the predicted number, which much more closely corresponds with reported data.

Igor Huzicka

On 2020-04-05 15:41:10, user Art Mills wrote:

This was supposed to be updated yesterday and had a notification it was to be, then after the daily press conference, suddenly was not updated and the notification it was to be updated was removed. Why?

The ICU and Vent numbers in the model to support the 93K deaths by Aug. 4 are off by a factor of 10 (meaning they have modeled far too high). The update yesterday should have brought those lines down. Why would we not update to reflect a line closer to the actual numbers?

On 2021-03-03 22:58:04, user Dan Elton wrote:

The data from Phase, I, II, and III and our prior scientific knowledge on vaccines like this one indicate this vaccine is very safe and effective. It also appears to be our best weapon against the B.1.351 variant. US taxpayers have already footed the bill for 110 million doses and it's very likely millions of doses have already been produced. The US FDA should ask Novavax to submit all the data they have collected so far and their EUA application immediately and then the FDA should work overtime to approve it within a week in order to save lives. The FDA should also allow the vaccine to be pre-distributed to ensure the vaccine gets to at-risk groups as quickly as possibe. With 1,000+ people dying every day, we must act quickly to save lives! The status-quo is dangerous - the vaccine by contrast is very safe and will save lives!

On 2021-06-10 17:58:43, user Aliya Amirova wrote:

A blog post: https://aliyaamirova.medium...

On 2025-11-30 07:05:45, user Ali Rahimi wrote:

Dear authors,

I have read your interesting article. I think the following revisions would strengthen the article:

Abstract<br /> Clarify that the 72.4% and 38.2% figures come from patients reporting barriers, not from the full sample, so the denominator is clear.<br /> Keep wording aligned with the design: change “barriers limit uptake of cataract surgery in Bangladesh” to “barriers were commonly reported among patients undergoing cataract surgery in Bangladesh.”<br /> Make the main statistical result consistent with the Results: state that education, income and prior surgery were associated with the number of barriers (Adj R² = 0.138).

Introduction<br /> A few sentences are long and repetitive around “accessibility” and “health inequities”. Tighten these into one concise paragraph without changing meaning.<br /> Where you describe evidence as “scarce”, add 1 sentence that positions your study among Bangladeshi work (rural children, Rohingya, etc) and makes clear that prior studies were population specific.

Methods<br /> In “Participants and data collection” clarify in one sentence that 595 patients consented, but analyses of barriers use 583 due to item non-response.<br /> Briefly describe how the “fear score (0–5)” and “barrier count” were constructed (number of items, response scale, direction).<br /> You model a count outcome with linear regression. Add one line acknowledging that barrier counts were approximately normal and that this approach was chosen for simplicity; alternatively mention that Poisson or negative binomial regression would give similar interpretation.

Results<br /> Ensure mean age is reported consistently (Abstract uses 62 years, Table 1 has 61.3). Choose one rounding rule and use it everywhere.<br /> Replace approximate p notation “p ? 0.003” and “p ? 0.221” with standard “p = 0.003” and “p = 0.221”.<br /> Table 3: the p value shown as “1” for “Afraid of surgery” under gender should be reported as “1.000” or the exact test result.<br /> Table 4: the p values for “Number of reported barriers” currently read “> 0.001”; this should be “< 0.001”.<br /> In the text for section 3.3, “The geographical barrier of transportation is predominant in our study” is misleading because cost is clearly highest. Rephrase to “an important barrier” rather than “predominant”.

Discussion<br /> Soften causal phrasing. Examples:<br /> “Patients who delay seeing an eye doctor are more likely to postpone surgery and show up with advanced cataracts” could be “Patients reporting delays in seeing an eye doctor often present with more advanced cataracts.”<br /> Any sentences that link barriers directly to “prolonging the waiting period” or “contributing to disability” should be framed as association, not cause.<br /> When you describe gender norms and decision making, keep language neutral and clearly signpost what comes from your data versus from cited literature.<br /> Consider one short sentence acknowledging that your barrier profile reflects people who ultimately accessed surgery and may under-represent those who never reach services.

Limitations<br /> Add explicit mention that the cross-sectional design and the hospital-based sample (only patients scheduled for surgery) limit causal inference and generalisability to all people with cataract in Bangladesh.<br /> You already mention possible social desirability bias; make that sentence more direct and link it to self-reported barriers.

Conclusion<br /> Tone down strength of generalisation: instead of “The study's strength lies in its inclusion of a diverse population, thereby increasing its generalizability” use “The inclusion of patients from hospital clinics and outreach camps provides some diversity, although findings still reflect one service network.”<br /> Rephrase recommendations as suggestions: “could help improve access” or “may help bridge the knowledge gap” rather than “can facilitate” or “will improve”.<br /> Keep the ending sentence tightly tied to your data: emphasis on cost, transport, time, fear, and gendered escort constraints.

Tables and Figures<br /> Check that the labels in Figure 1 and Figure 2 match exactly the barrier wording used in the questionnaire and in the text (for example “hospital too far / no transportation”).<br /> Consider adding “multiple responses allowed” to the figure legends for barriers.

On 2020-10-12 13:15:33, user Anechidna wrote:

Vitamin D, which one? The assumption is D3 but D2 is the most commonest form of supplemental vitamin D in the belief that it is converted into D3 which it isn't. Very sloppy work to talk about Vitamin D when you were performing your research on a specific form. The research also indicates elevating levels of D2 in an attempt to drive up D3 results in suppression of D3 levels. So either get the required skin sunlight interaction allowing for skin tone or get D3 in its proper supplemental form as D3.

On 2021-02-25 14:32:20, user Haitham kussaibi wrote:

Dear valuable readers,

The current manuscript has been peer reviewed and published by<br /> the saudi medical journal<br /> DOI: <br /> https://doi.org/10.15537/sm...

Thank you for your interest.<br /> The Authors

On 2020-09-19 14:10:12, user kdrl nakle wrote:

Given the low infection rates you most likely have many false positives and the adjustment is probably of uncertain quality. Basically, it is not very accurate to use serosurveys in low infection areas.

On 2021-12-13 18:28:05, user Kristen wrote:

I just stumbled across this and I wonder what impact the Mullen's norms have to do with this drop. The Mullen norms are over 20 years old and many of the VR stimuli are very outdated and are not recognizable to children born in recent years. I always prefer to give the Bayley or WPPSI if I can given this issue. It looks like there has been an overall downward trend in Mullen scores in your sample. I know you wouldn't be able to go back and compare as easily, but I wonder how the COVID-19 babies would fare on a measure with more updated norms. Bayley-4 has been freshly updated and would capture those born during the pandemic.

On 2021-08-17 11:58:29, user Pasco Fearon wrote:

Hi Sean,<br /> Yes, hopefully other groups will come with with more data, particularly with follow-up once normal testing has resumed. I do think a mask could be pretty distracting, particularly for the youngest ones, where you are able to relying least on language-based instructions. Interacting with strangers could also be a factor I guess too. I wondered whether parent report data could also help triangulate this if you had any (e.g., milestones, language inventories). Some people have had success with parent-administered tests, which if you were running a similar study could help address these issues. I agree that if it is an administration issue that amount of time in pandemic conditions should not be a predictor - it'd be neat to check that out. Certainly, infants who were tested just shortly after restrictions came in should not have been impacted developmentally, but would receive the 'non-standard' testing, so you might even be able to use regression discontinuity analysis to test that.

On 2021-08-12 19:48:21, user Pasco Fearon wrote:

Hi Sean, and colleagues. Fascinating paper, but the scores are so low I worry something might not be right. Could the testing have been affected by the pandemic measures directly - e.g., mask wearing during testing? I could imagine some impacts but these are extreme, which leads me to worry that it's an administration issue. Can this be checked or ruled out somehow? I get asked this question a lot (how much have babies been affected by the pandemic), hence why I'm keen to be pretty confident in what I say.... Thanks!

On 2020-06-07 01:37:32, user vinu arumugham wrote:

Oral famotidine/cetirizine, mast cell stabilizers, etc. avoid risk of de novo autoimmunity associated with biologics. They also have a broader effect instead of targeting IL-6 alone.<br /> Immunological mechanisms explaining the role of IgE, mast cells, histamine, elevating ferritin, IL-6, D-dimer, VEGF levels in COVID-19 and dengue, potential treatments such as mast cell stabilizers, antihistamines, Vitamin C, hydroxychloroquine, ivermectin and azithromycin<br /> https://doi.org/10.5281/zen...

COVID-19: Famotidine, Histamine, Mast Cells, and Mechanisms<br /> www.researchsquare.com/arti...

Vaccines and Biologics injury table based on mechanistic evidence – Feb 2020 Covering over 125 conditions<br /> https://doi.org/10.5281/zen...

On 2020-03-24 15:03:38, user Sinai Immunol Review Project wrote:

Title: Clinical Features of Patients Infected with the 2019 Novel Coronavirus (COVID-19) in Shanghai, China

Summary: This single-center cohort study analyzes the clinical and laboratory features of 198 patients with confirmed COVID-19 infection in Shanghai, China and correlated these parameters with clinical disease severity, including subsequent intensive care unit (ICU) admission. 19 cases (9.5%) required ICU admission after developing respiratory failure or organ dysfunction. Age, male sex, underlying cardiovascular disease, and high symptom severity (high fever, dyspnea) were all significantly correlated with ICU admission. Additionally, ICU admission was more common in patients who presented with lymphopenia and elevated neutrophil counts, among other laboratory abnormalities. Flow cytometric analysis revealed that patients admitted to the ICU had significantly reduced circulating CD3+ T cell, CD4+ T cell, CD8+ T cell, and CD45+ leukocyte populations compared to the cohort of patients not requiring ICU admission.

Limitations: The limitations of this study include the relatively small sample size and lack of longitudinal testing. The authors also did not assess whether respiratory comorbidity – such as asthma or chronic obstructive lung disease – in addition to immunosuppression affected ICU admission likelihood.

Relevance: COVID-19 has already sickened thousands across the globe, though the severity of these infections is markedly diverse, ranging from mild symptoms to respiratory failure requiring maximal intervention. Understanding what clinical, laboratory, and immunologic factors predict the clinical course of COVID-19 infection permits frontline providers to distribute limited medical resources more effectively.

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

On 2020-04-16 14:40:22, user David Steadson wrote:

The paper reports "Observed cumulative death counts are illustrated as circles (O) and amount to 62 by the 25th of March, 2020"

As of April 15 2020, the Swedish Public Health Authority is reporting cumulative deaths of 106 as of that date.

On 2022-04-29 17:03:47, user Madhava Setty, MD wrote:

Very interesting study. From where did the data on viral copies come from? Also, the odds of seroconversion in placebo vs treatment, stated as 13.67 at a given viral copy level, doesn't seem to be reflected in the corresponding plot (B).

On 2022-04-28 18:16:46, user Darwin's Monkey wrote:

The conclusion seems to be contradictory with the "agreed scientific consensus". If the Negative Efficacy in the vaccinated group is because of behavior differences, leading to acquiring and spreading the disease, then how can more vaccines be the answer? It's illogical and contradictory. Surely someone in the research group recognised this!

For example, the vaccinated are getting tested more (which is logical since they are more likely to be concerned about covid than the unvaccinated). However, the prevailing narrative suggests that vaccines reduce symptoms. Therefore the behaviour of vaccinated (with reduced or no symptoms) would logically lead to more risky behaviour and more spreading of the disease. So efficacy of the vaccine is great because it reduces symptoms, according the authors. Then they say. behavior is the reason there is low efficacy. But logically it's also the reason they are catching and spreading it more, so the vaccine isn't so great is it?

You can't have your cake and eat it if you are going to claim behaviors is a significant variable but only for the variables that show the vaccine is great, otherwise we completely ignore behaviors.

These behavior claims appear to always err on the side of the vaccinated (as with the UK Vaccine Surveillance Report which makes the same claims without evidence).

Negative efficacy rates appear to be increasing for the vaccinated week by week (as confirmed by the UK Vaccine Surveillance Report over the last few months). So for these "behavior" claims to be true, you would have to be able to prove that the behavior of the vaccinated has changed dramtically, but the unvaccinated behavior has changed very little. Why would this be? A previous study showed that levels of concern and consequential behaviors were quite different for the two cohorts with the vaccinated being more concerned. But why has this changed so dramatically? Are the vaccinated getting more concerned OR are the unvaccinated getting less concerned?

This flawed and unexplained analysis is driving the conclusion for more vaccine rollout, without considering the possibility that this is NOT the full story and it could more plausibly be some kind of loss of immunity in the vaccinated or another reason

On 2022-01-08 23:20:51, user Joshua wrote:

From the study: “1. The negative estimates in the final period arguably suggest different behaviour and/or exposure patterns in the vaccinated and unvaccinated cohorts causing underestimation of the VE. 2. This was likely the result of Omicron spreading rapidly initially through single (super-spreading) events causing many infections among young, vaccinated individuals.”

Let’s discuss the sentence I labeled 1.

1a) Is any data available which supports the author(s) hypothesis that the vaccinated cohort engaged in riskier behavior when compared to the unvaccinated? My anecdotal evidence from my lived experience with those in my circle is that the unvaccinated are living a much riskier life as it pertains to covid infection. But don’t take my word because that is not how science works, instead, consider this KFF survey of 1,527 adults aged 18+ conducted in July 2021 indicating the opposite reality: “ Majorities of vaccinated adults say news of the variants has made them more likely to wear a mask in public (62%) or avoid large gatherings (61%), while fewer unvaccinated adults say the same (37% and 40%, respectively).”

1b) Is there any explanation why this alleged confounding variable of riskier behavior by the vaccinated did NOT appear during the studies surrounding delta?

1c) Is there any explanation why this alleged confounding variable of riskier behavior by the vaccinated only appeared during the 91-150 days time period for the omicron variant?

Let’s discuss the sentence I labeled 2.

2) I found this statement in the Methods section of this study: “VE was calculated as 1-HR with HR (hazard ratio) estimated in a Cox regression model adjusted for age, sex and geographical region, and using calendar time as the underlying time scale.” That means the authors accounted and controlled for age, yet they claim age as a confounding variable. Talk about having your cake and eating it too!

On 2021-11-14 13:53:40, user Marc Middleton wrote:

I don't even have to read the whole (not yet peer-reviewed and thus questionable) article to see that the conclusion, which anti-lockdownists like to draw from it, is faulty. It's already stated in the abstract that "efficient infection surveillance and voluntary compliance make full lockdowns unnecessary". People of the studied population obiously had enough common sense to contrain their contacts, which OF COURSE reduces viral spreading without the need for lockdowns! But as we have seen in several countries, not all people are as smart as the Danish...

On 2020-05-24 08:44:09, user Raúl H. Sánchez wrote:

There is an erratum between lines 146-149 (HLHF instead of ~~HLLF~~ in the stratification).

The correct statements for the stratification are:

a. Audiometric group-a: HLHF < 50 dBHL, and HLLF < 30 dBHL.

b. Audiometric group-b: HLHF > 50 dBHL, and HLLF < 30 dBHL.

c. Audiometric group-c: HLHF > 50 dB HL, and HLLF > 30 dBHL.

d. Audiometric group-d: HLHF < 50 dB HL, and HLLF > 30 dBHL.

On 2020-05-15 10:17:49, user Jean-Michel Boiron wrote:

Hi,<br /> I think you may consider writing "Compared with the control group, HCQ with or without azithromycin (AZI) showed no benefit in viral clearance of SARS-CoV-2 (odds ratio (OR) 1.95, <br /> 95% CI 0.19-19.73) or reduction in progression rate (OR 0.89, 95% CI0.58-1.37)" instead of "Compared with the control group, HCQ with or without azithromycin (AZI) <br /> showed benefits in viral clearance of SARS-CoV-2 (odds ratio (OR) 1.95, <br /> 95% CI 0.19-19.73) and a reduction in progression rate (OR 0.89, 95% CI <br /> 0.58-1.37), but without demonstrating any statistical significance.", which is misleading.

On 2020-04-29 19:27:09, user Kevin McKernan wrote:

Very much commend the open and rapid peer review people are contributing in these times.

On 2021-06-24 15:13:53, user dami Onifade wrote:

In paragraph describing table 3. "these results would indicate effectiveness of 45% and 76% respectively for B.1.617.2" How have you derived these estimates?

On 2021-07-19 14:49:56, user Julia R. Gog wrote:

A final version is now published at Royal Society Open Science:<br /> https://doi.org/10.1098/rso...

(Since medRxiv V2: Expanded detail in the methods section, new section in SI on sensitivity to higher d, fixed typos and minor changes for clarity throughout)

On 2025-11-11 14:27:16, user Evolutionary Health Group wrote:

We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.

Here are our highlights:

Outlines a research roadmap connecting climate-driven stressors such as heat, flooding, and water scarcity to hygiene behavior and infection risk.

Emphasizes the absence of temporally aligned datasets linking climate exposure, human behavior, and microbiologic outcomes.

Proposes a coordinated data infrastructure for longitudinal monitoring of climate-hygiene-health interactions. This roadmap focuses on behavior and adaptation, grounding climate health in everyday lived contexts rather than abstract exposure models.

It demonstrates how human factors research can complement environmental surveillance to guide intervention design.

On 2020-05-10 20:55:36, user knbizz wrote:

Would be nice to see the data itself.

It is not the same, for example, DM2 with 6.8 and with 10 or DM1. Given the lactate it is not the same if the DM is treated with metformin or insulin.

It is not the same high blood pressuse130/80 and 160/100. Will be good if data are provided as is.

On 2020-09-07 14:44:00, user Thomas P. Dooley wrote:

The article has been published in Pulmonary Pharmacology & Therapeutics:

https://doi.org/10.1016/j.p...

On 2021-02-22 19:53:12, user Alexander Porter wrote:

What does: '8,041 individuals received two doses of a COVID-19 vaccine and were at risk for infection at least 36 days after their first dose.' mean? Were these individuals exposed to SARS-CoV-2 directly every day?

Were all PCR methods run with the same cycle threshold before and after administration?

On 2022-06-17 09:43:38, user Will wrote:

A persistent link to the supplementary materials can be found here: https://doi.org/10.5281/zen...

On 2022-02-01 14:15:41, user Richard Reynolds wrote:

There is another reason, the most likely one, why immune cell aggregates were not found in the meninges in this study, which is due to the nature of the tissue used. This study used formalin fixed paraffin embedded tissues which are suboptimal for studying the delicate meningeal compartment. Cells are lost from the meninges at every stage of the embedding, cutting and section mounting stages, when compared to using snap frozen blocks. When you float FFPE sections on to a water bath before mounting them on slides you can actually see with the naked eye parts of the meninges floating away from the sections. Presumably this would also results in losing various components of the meninges, including immune cells. We dont find nearly as many immune cell aggregates in the meninges when we use FFPE sections and in order to see them in the FFPE sections we needed to change out protocols substantially to much milder procedures in order to better preserve the cellular components of the meninges.

On 2022-05-30 22:36:58, user Stuart Turville wrote:

Now published within this manuscript here:

Congratulations<br /> Dear Stuart G. Turville

We are pleased to inform you that your article has just been published:

Title<br /> Platform for isolation and characterization of SARS-CoV-2 variants enables rapid characterization of Omicron in Australia

Journal<br /> Nature Microbiology

DOI<br /> 10.1038/s41564-022-01135-7

Publication Date<br /> 2022-05-30

Your article is available online here https://doi.org/10.1038/s41... or as a PDF here https://www.nature.com/arti....

On 2020-04-21 22:10:28, user Marv Goosen wrote:

The problem with this study is that it is retrospective and as the authors state in their discussion, patients in worse shape may have been put in the HC group which would also account for higher mortality. Unfortunately until a prospective study with severity matched controls is done, no conclusions can be made.

On 2020-03-31 07:38:25, user Candido Hernandez Lopez wrote:

The 15 co-infected patients were treated for the chronic hepatitis B? this is an important aspect to clarify

On 2020-01-31 12:47:48, user Jonathan Li wrote:

Hi, how can I download this article? Very interested to read details because my prediction is it reaches peak point around 6th March then ends on early June. Thanks.

On 2020-07-18 05:45:16, user Peter Lange wrote:

Those last 2 #covid symptom clusters associate strongly with frailty... seems frailty and covid are associated with delirium and poor outcome. Not sure structuring as "symptom clusters" helps

On 2021-09-24 06:20:12, user Ella Lively wrote:

Where can I get the questionnaire for thus study please?

On 2025-08-10 22:18:50, user Ashebir Gurmessa wrote:

This study is an outstanding and timely contribution to HIV care in Ethiopia. Your rigorous mixed-methods approach offers critical insights into the risk factors for virological failure among second-line ART patients, which is invaluable for clinicians, policymakers, and program implementers. The finding that *loss to follow-up* and *regimen changes* significantly increase the risk of virological failure highlights the urgent need for patient-centered adherence strategies and continuity of care.

Thank you, Bekelch Bayou and team, for shedding light on this vital yet underexplored issue. Your work not only fills a crucial data gap but also provides a strong foundation for targeted interventions that will improve outcomes and quality of life for people living with HIV.

I am grateful for your dedication and the ethical rigor with which you conducted this study.

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

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

On 2020-06-22 23:00:16, user Marcus Quintilian wrote:

Some iportant words are missing: "Our machine learning analysis also showed that the two groups were linearly separable. .....incubation of COVID-19 along with previous statistical analysis. "