On 2020-02-28 01:21:33, user RQ wrote:

It was an easy method to calculate the true T value and CFR without any indigestible mathematical formulas or models requiring severe calculating conditions. Actually, when different T was assumed, if it was smaller (bigger) than the true T, calculated daily CFRs would gradually increase (decrease) to infinitely near the true CFR with time went on. Left of true T is decreasing, right is increasing,so T could be easily determined, then the true CFR could be calculated. The calculated true CFR had accurately predicted the death numbers more than two weeks continuously

On 2020-03-02 16:40:24, user Abed Ghanbari wrote:

We estimated that 18,300 (95% confidence interval: 3770 – 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing.

How did you reach to these numbers?

On 2020-03-07 17:48:10, user fuyutao wrote:

Figure2 has Leucine and Serine codons swapped.

On 2020-03-08 05:57:17, user James Nokes wrote:

Highly informative paper. Thank you. A few points/questions:

1. Table 1 indicates it is contact-based surveillance with higher proportion male than female contrary to the results text.

2. How was temperature measured and what was the definition of fever?

3. How were nasal samples collected (eg nasopharyngeal swab, per-nasal swab, aspirates). Did the method differ for contact and case-based surveillance?

4. Assessing severity status - (i) can you clarify if moderate required all three of fever, respiratory symptoms, and radiographic evidence of pneumonia? What is included in 'respiratory symptoms'? (ii) How did you measure oxygen saturation?

5. Table S1. It would be useful to include the proportions with fever. The proportion of cases from symptom-based surveillance with shortness of breath (4%) or difficulty breathing (3%) is remarkably low.

On 2020-03-16 13:08:45, user Karl Milhon wrote:

I have been pushing for people to investigate the role of children in transmission very hard since the Chinese CDC first put out their descriptive epi piece. there are numerous articles and quotes pointing toward this element of Covid 19 transmission but it appears that no one is truly trying to get at the problem. Simply utilizing serology testing to do quick and dirty seroprevalence studies would provide some insight. Singapore has developed and utilized some decent serologic tests. I do not understand why this is not being more aggressively pursued.

On 2020-03-11 10:09:13, user Bob Phillips wrote:

Needs the units for bilirubin and ALT, and a very clear description of WHEN these lab tests were taken ('predicting' severe disease when a child has severe liver dysfunction on an ICU isn't that useful)

On 2020-03-13 23:55:07, user Cadence C wrote:

Singapore health ministry stated that pre-symptomatic transmission is not a prominant mode. How did the authors conclude that 40% to 80% there are asymptomatic transmission ?

On 2020-03-14 16:58:24, user J Belcar wrote:

RE immune imprinting, has anyone looked at attack rate or severity of illness in relation to previous (or recent) influenza vaccine?

On 2020-03-17 19:20:48, user RealConspiracy wrote:

And what about "-" Rh-factor?

On 2020-03-20 11:57:18, user Romain G. wrote:

No data about hypertension and diabetes mellitus in these patients, which increase risk for COVID-19 infection and severity. Should be interesting to cross the informations. Here, it is pure speculations. Has to be reviewed, but many other parameters have to be included.

On 2020-03-20 15:00:35, user Jane Ford wrote:

What about those of us with blood type B?

On 2020-03-21 17:15:33, user escabatum wrote:

There's no mention of rates of smoking, underlying lung disease, or other major risk factors between groups. This seems like a pretty useless study.

On 2020-03-20 16:15:46, user Juan B. Gutierrez wrote:

Supplemental material, data, and source code: https://tinyurl.com/USA-COV...

On 2020-03-23 16:03:24, user Ing. Alessandro Zivelonghi wrote:

no ambient temperature is reported in the study... ?????!!!!!

On 2020-03-24 14:01:51, user Sinai Immunol Review Project wrote:

Main findings<br /> The authors collected data on 25 COVID-19 patients (n=11 men, n=14 women) using standard laboratory tests and flow cytometry. All patients were treated with antibiotics. Twenty-four of the 25 patients were also treated with anti-viral Umefinovir and 14 of the patients were treated with corticosteroids. 14 patients became negative for the virus after 8-14 days of treatment. The same treatment course was extended to 15-23 days for patients who were still positive for the virus at day 14. <br /> The authors found a negative association between age and resolution of infection. Patients with hypertension, diabetes, malignancy or chronic liver disease were all unable to clear the virus at day 14, though not statistically significant.<br /> Elevated procalcitonin and a trend for increased IL-6 were also found in peripheral blood prior to the treatment.<br /> A trend for lower NK cell, T cell and B cell counts in patients was also reported. B cell, CD4 and CD8 T cell counts were only increased upon treatment in patients who cleared the virus. NK cell frequencies remained unchanged after treatment in all the patients.

Limitations of the study<br /> 73% of the patients who remained positive for SARS-CoV2 after the 1st treatment, and 43% of all patients who cleared the virus were treated with corticosteroids. Corticosteroids have strong effects on the immune compartment in blood{1}. The authors should have accounted for corticosteroid treatment when considering changes in T, NK and B cell frequencies.<br /> Assessing if IL-6 concentrations were back to baseline levels following treatment would have provided insights into the COVID-19 cytokine storm biology. Patients with higher baseline levels of IL-6 have been reported to have lower CD8 and CD4 T cell frequencies{2}. Correlating IL-6 with cell counts before and after treatment would thus have also been of interest.<br /> The report of the laboratory measures in table 2 is incomplete and should include the frequencies of patients with increased/decreased levels for each parameter.<br /> Correction is needed for the 1st paragraph of the discussion as data does not support NK cell restoration upon treatment in patients who cleared the virus. NK cells remain unchanged after the 1st treatment course and only seem to increase in 2 out of 6 donors after the 2nd treatment course in those patients.

Relevance<br /> Previous reports suggest an association between disease severity and elevated IL-6 or pro-calcitonin concentrations in COVID-19 patients3,4. IL-6 receptor blockade is also being administered to patients enrolled in clinical trials (NCT04317092). This report thus contributes to highlight elevated concentrations of these analytes in COVID-19 patients. Mechanisms underlying the association between viral clearance and restoration of the T cell and B cell frequencies suggests viral-driven immune dysregulation, which needs to be investigated in further studies.

References

1. The CHI Consortium et al. Effects of Systemically Administered Hydrocortisone on the Human Immunome. Sci Rep 6, 23002 (2016).
2. Zhao, Z. et al. Clinical and Laboratory Profiles of 75 Hospitalized Patients with Novel Coronavirus Disease 2019 in Hefei, China. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.03.01.20029785.
3. Chen, X. et al. Detectable serum SARS-CoV-2 viral load (RNAaemia) is closely associated with drastically elevated interleukin 6 (IL-6) level in critically ill COVID-19 patients.<br /> http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.02.29.20029520.
4. Lippi, G. & Plebani, M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clinica Chimica Acta 505, 190–191 (2020).

Review by Bérengère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-25 03:52:12, user Renee Chan wrote:

Hi Dr Liao, Prof Zhang and Prof Zheng, May I know if you have done any fixation of the cell isolated from BAL before doing the downstream procedure using 10X genomics?

On 2020-03-26 20:05:32, user Pedro Perez wrote:

Hemoglobinas could escape for diferent PH with cloroquina

On 2020-03-29 17:21:35, user Sinai Immunol Review Project wrote:

Title: A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

Keywords: disease severity – clinical data – Neutrophils/lymphocytes ratio – CRP – D-dimer

Main findings:<br /> 377 hospitalized patients were divided into two groups: severe and non-severe pneumonia. The laboratory results of their first day of admission were retrospectively analyzed to identify predictors of disease severity.<br /> After adjusting for confounding factors from chronic comorbidities (such as high blood pressure, type 2 diabetes, coronary heart disease, and chronic obstructive pulmonary disease), the independent risk factors identified for severe pneumonia were age, the ratio of neutrophil/lymphocytes counts, CRP and D-dimer levels.<br /> To further increase the specificity and sensibility of these markers, they showed that their multiplication [(Neutrophil/lymphocyte count) * CRP * D-dimer] was a better predictor of disease severity, with higher sensitivity (95.7%) and specificity (63.3%), with a cutoff value of 2.68.

Limitations:This study included 377 hospitalized patients. Among them, 45.6% patients tested positive for SARS-Cov-2 nucleic acid test results, and others were included in the study based on clinically diagnosis even if the molecular diagnosis was negative. Thus, additional studies are needed to verify this on a larger number of covid-19 certified patients and the cutoff value might be adjusted. Also, all the patients that did not have the clinical characteristics of severe pneumonia were included in the non-severe pneumonia group, but usually patients are also divided into moderate and mild disease.

Also, studying different subset of lymphocytes could lead to a more specific predictor. Another study showed that the neutrophils to CD8+ T cells ratio was a strong predictor of disease severity [1]. Another more precise study showed that the percentage of helper T cells and regulatory T cells decrease but the percentage of naïve helper T cells increases in severe cases [2]. Taking these subpopulations into account might make the predictor more powerful.<br /> Other studies also noted an inverse correlation between disease severity and LDH [3] or IL6 [4] levels, but the authors here do not discuss LDH nor IL6 levels, although this could help to strengthen the predictor.

The study is based on the results obtained on the first day of admission, studying the dynamic of the changes in patients might also be interesting to better predict disease severity.

Relevance:This study confirms that the neutrophil to lymphocyte ratio can be a predictor of disease severity as shown by many others [2], [5], [6]. The novelty here is that they show that a combination with other markers can enhance the specificity and sensibility of the predictor, although the study could be improved by taking into account sub-populations of lymphocytes and more biological factors from patients such as LDH and IL6.

References:<br /> 1. Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 2. Dysregulation of immune response in patients with COVID-19 in Wuhan, China | Clinical Infectious Diseases | Oxford Academic. https://academic-oup-com.do.... Accessed March 29, 2020.<br /> 3. Clinical findings in critical ill patients infected with SARS-Cov-2 in Guangdong Province, China: a multi-center, retrospective, observational study | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 4. Mortality of COVID-19 is Associated with Cellular Immune Function Compared to Immune Function in Chinese Han Population | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.<br /> 5. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. - PubMed - NCBI. https://www-ncbi-nlm-nih-go.... Accessed March 29, 2020.<br /> 6. Neutrophil-to-Lymphocyte Ratio Predicts Severe Illness Patients with 2019 Novel Coronavirus in the Early Stage | medRxiv. https://www.medrxiv.org/con.... Accessed March 29, 2020.

Review written by Emma Risson as part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-30 13:40:15, user Viktor Szathmáry wrote:

This study has fundamental flaws, both in the chosen metrics as well as the stats, plus misleading visualizations.

Details: https://www.facebook.com/53...

On 2020-03-31 15:52:10, user Pedro Thompson wrote:

Is it valid to compare an Italy with a destroyed health system against a Brazil just beginning the problem? I mean, is the mortality rate a constant, in the same country during all the epidemic?

On 2020-04-01 15:52:32, user Peter Hansen wrote:

Comments.<br /> This is a very noisy signal, because data are changing very much from week to week. Current country statistics are more influence by: <br /> 1) The time where COVID-19 began to spread in each country<br /> 2) The degree of preventive measures (e.g. compare Denmark and Sweden).<br /> So all figures in the paper might look very different 2 weeks from now.

Smaller comments.<br /> Would be nice to have a table with actual data for each country, rather than just highlighting selected countries in the figures. BTW.

Where is France in Figure 2? <br /> Number of deaths per capita in France is relatively large (higher than Denmark). <br /> The BCG was mandatory in France for school children between 1950 and 2007, and for healthcare professionals between 1947 and 2010. (Whereas Denmark stopped BCG in the mid 1980s).

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

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

On 2020-11-25 05:08:10, user ArthurConanDoyle wrote:

Layman here, w/Covid. Wondering why you don't use sputum for greater accuracy?

Of course, it's not as easy or ubiquitous as saliva, but maybe a sample option?<br /> The major point being accuracy is almost everything, other factors count, but...

On 2020-11-27 19:55:21, user John Butler wrote:

There seems to be either something wrong with the risk calculator or the paper text. If I choose White Female age 70-74, no comorbidities, it says 18.9% higher. I take it that means "multiply the base rate time 1.189". If I switch that to "male" it reports 119.1% higher, which would, to be consistent with the female, mean "multiply the base rate times 2.191". However, if I select Hispanic Male, 80-84 with Chronic Kidney Disease, it report 601.8%, the text reports "6 times higher". All this suggests that the White Female 70-74, as an example, is inconsistent with the form of the others.

On 2020-12-01 04:48:42, user Sandeep B wrote:

Another one !

https://www.statnews.com/20...

On 2020-12-02 11:30:50, user Rafael Moraes wrote:

This preprint has been published in a peer-review journal:

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242251

On 2020-12-02 20:32:21, user Roberto Etchenique wrote:

Great work !! We had done the same procedure, but a bit cruder, for City of Buenos Aires, Argentina. Our results for our country (not published) are coincident with these ones. https://www.medrxiv.org/con...

On 2020-12-10 00:06:10, user Amandeep Goyal wrote:

What kind of scores or testing was used to diagnose polyneuropathy ?

What tests were used if polyneuropathy was due to Diabetes or Amyloidosis ?

On 2020-12-10 17:00:23, user Susan Bewley wrote:

Thanks Russell & team. Would it be possible for you to share the following on #medRxiv too? (i) the research information leaflet you describe, (ii) the informed consent form, (iii) the statistical plan mentioned as S2. Best wishes

On 2020-12-14 08:49:55, user Patrick Schmidt wrote:

Interesting connection between network models and standard inference on contact tracing data.

I have a paper showing that the tendency of superspreading can be estimated without contact tracing data from aggregated surveillance data alone. In line with the results here, I estimate a dispersion of 0.61 (95%-CI: [0.49, 0.77]) for Germany in spring 2020.

https://arxiv.org/pdf/2011....

On 2020-12-20 22:06:38, user Sam Smith wrote:

Is it possible to begin this treatment too early or too late, or to continue for too long?

On 2020-12-29 00:37:11, user Olga Matveeva wrote:

Several recent preprints support some of this manuscript findings.<br /> 1. Authors from Sweden and China in a study entitled “Pulmonary stromal expansion and intra-alveolar coagulation are primary causes of Covid-19 death” demonstrated that “The virus was replicating in the pneumocytes and macrophages but not in bronchial epithelium, endothelial, pericytes or stromal cells. doi: https://doi.org/10.1101/202...<br /> 2. Researchers in Brasil investigated SARS-CoV-2 infection of PBMCs and found that in vitro infection of whole PBMCs from healthy donors was productive of virus progeny. They also found that “SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from COVID-19 patients, and less frequently in CD4+T lymphocytes” The preprint is entitled “Infection of human lymphomononuclear cells by SARS-CoV-2”. <br /> doi: https://doi.org/10.1101/202...<br /> 3. SARS-CoV-2 infection of macrophages and some other immune cells in deceased patients was suggested in other autopsy related preprint entitled “Broad SARS-CoV-2 cell tropism and immunopathology in lung tissues from fatal COVID-19” doi: https://doi.org/10.1101/202... The study was done by US researchers from Pittsburgh. <br /> 4. Researchers in France demonstrated “that SARS-CoV-2 efficiently infects monocytes and macrophages without any cytopathic effect.” Their findings are reported in the preprint entitled “Monocytes and macrophages, targets of SARS-CoV-2: the clue for Covid-19 immunoparalysis” doi: https://doi.org/10.1101/202...

On 2020-12-29 21:12:44, user Meerwind7 wrote:

It seems these evaluations assume that all non-pharmaceutical intervention and prevention measures (e.g. masks and “lockdowns”) would be abolished once the vaccinations start. In a different approach, these measure would be upheld for a while, for example such as to limit prevalence to a certain level for some time, or to limit the number of overall deaths. One such target could be called “partial herd immunity”, which is achieved when the combination of partial vaccination and some amount of precaution measures would in combination be sufficient to assure the reproduction factor not to exceed 1 or to achieve quick shrinking of infection numbers. The combination of some vaccine plus non-pharmaceutical interventions thus would have an effect similar to full herd immunity that is achieved when recurrent infection is avoided with fully “normal” life.

If there was only one type and scope of non-pharmaceutical intervention, an objective could be formulated as “how to minimize the duration of that intervention, when a certain maximum number of deaths (or of severe illness) shall be maintained”, taking into account vaccine doses become available slowly or other restrictions apply. A further objective should be to minimize or cap the number of vaccinated persons that are exposed to virus, because each such expose gives mutants an opportunity to break the barrier from vaccination, just like an evolutionary training.

It would also be possible to optimize vaccine distribution and non-pharmaceutical intervention while setting a target for a particular age group. Even if an upper limit of the death count of, for example, people of 75+ years was a binding target, and some non-pharmaceutical intervention is available, it may be better to vaccinate younger people first, to reduce overall transmission more quickly and then be able to “open” the society quicker, than if 75+ obtain vaccine first.

In further modeling, the extent (effect strength) of the non-phamaceutical measure could be gradually increased, while maintaining a goal like low nomber of overall deaths, lost lifetime or deaths of old people. I believe there could be some point where the results suddenly switch from vaccines for the old to vaccines for the young, and that beyond that point, the duration of the intervention could suddenly be reduced in a non-continuous way while upholding aggregate goals.

On 2020-12-30 01:49:12, user Franko Ku wrote:

Perhaps you should start over based on others' comments..<br /> Only one dose? Should be calcifediol?<br /> What were measured levels of Vit, D in those that received placebo?<br /> Many other studies show those with very low hormone (not a "vitamin" D have much more risk of dying.<br /> https://www.researchsquare....<br /> https://link.springer.com/a...<br /> https://www.sciencedirect.c...<br /> https://www.ncbi.nlm.nih.go...<br /> https://medium.com/microbia...

Needed for Prevention - your paper will prevent some from supplementing as Dr Fauci said he does.:<br /> https://www.healthline.com/...

On 2020-11-18 16:18:54, user LB wrote:

Please add, in the Limitations, a comment about the fact that, "The mean time between the onset of symptoms and randomization was 10.2 days." It is quite possible that by the time the vitamin D levels were raised, the "cytokine storm" was already well underway. Thank you!

On 2020-11-19 15:54:00, user Lorenzu Borsche wrote:

Hello, this sentence:

Subsequently, we calculated sample size assuming a 50% between-group difference in hospital length of stay (considering 7 days as a median time of stay, with an expected variability of 9 days).

to me is not quite clear: do you mean, that you preset a desired length of stay to 7 days and the grouped the data so that both groups fit these 7 days? Thus did you mean a 50:50 distribution wrt the 7 days? If so, this cannot be done without distorting the data. If not, please explain, TIA Lorenz Borsche

On 2020-11-28 13:16:44, user Angie wrote:

The description of the amount of vitamin D used doesn't account for the mistake made in calculating vitamin D needs, nor is that mistake discussed in the article. In addition, making active forms of VitD from what is ingested is not an instant magic process. A body under attack may lack the energy to carry it out. Maybe it's just giving something by a pill is ineffective right now. What if you did transdermal? That would avoid the stomach/gut which is a place we know the virus attacks. Also vitamin D doesn't act alone. A person in ICU may not get a lot of vitK and may even be on anti-K blood thinners if they are a stroke risk. How many patients were on Lovenox vs something that thins blood via the vitamin K route? A daily exposure to a UV lamp may be more efficient for providing Vitamin D.

Anyway, the point is, I am not convinced that this test was properly done with reference to vitamin D. It takes weeks to normalize vitamin D in tissues where it is needed. Just testing the blood level after you gave a bolus pill is lying to yourself. It's like adding dye to water and saying, look, the sand at the bottom of the river turned all blue, we can assume it goes deep. What's the vitamin D status of hepatocytes after the one pill you gave? How much enzyme activity was there in the kidney to activate the D you gave?

Giving someone a vitamin is not like giving them a drug. The vitamin has to go to the tissues and do its work. You're thinking far too simplistically. VitaD affects thousands of reactions in the body and is not actively excreted as if it were an invader. That's nothing like a drug. Vitamins aren't drugs, that goes double for the fat soluble ones.

On 2020-12-24 23:19:01, user Matthias Fax wrote:

By design, this study could only fail to meet its hypothesis. It only proves what was to be expected. They used inappropriate dosage in oral form. They accepted an inappropriate delay after onset of symptoms. They didn't mention the significance of 25OHD sufficiency for patient outcome, indicating that the oral dosage was given too late to be of any immunological use.

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

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

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

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

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

On 2021-01-05 22:41:19, user Troy Richlen wrote:

An important variable that this and other studies have not been able to adequately incorporate into this analysis is the effect of comorbidities on life expectancy of COVID-19 deaths which is due to a lack of appropriate statistical information.<<<br /> This is calculating the delta of the age of death due to Covid versus the average age of death for the population not the population of people who average 2.6 comorbidities. People who are obese, have diabetes and other significant health issues also will have a negative offset from the average age of death.

On 2021-01-15 14:43:05, user Rafael M wrote:

I would like to know the false positivity of PCR compare with the result published by the press .

On 2020-09-11 14:12:07, user Kamran Kadkhoda wrote:

Great paper but it is pivotal to highlight that correlate of protection is ONLY inferred from prospective vaccine efficacy trials instead of from convalescent cases... <br /> The inflation in MBC population shown here may very well partly be from the common CoVs

On 2020-09-13 07:31:07, user OxImmuno Literature Initiative wrote:

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

On 2020-09-14 11:45:41, user Andrew Boswell ???????????? wrote:

"We found that an increase of only 1 ????g/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate"

Is your COVID evidence actually reflecting a more general extreme sensitivity to PMs across underlying respiratory conditions which has been detected through the lense of the COVID research. I found this tweet (https://twitter.com/AliNour... "https://twitter.com/AliNouriPhD/status/1296554508684754945?s=20)") where Dr Ali Nouri says that the same effect has also been observed for other respiratory viruses like Influenza and SARS-1, and reflects the impacts of PMs to the underlying respiratory and cardiac system.

Have you looked into this with your research?

Is there other studies out there which suggests COVID is a lense to see other more underlying effects?

On 2020-09-16 21:36:10, user Qunfeng Dong wrote:

An updated version of this manuscript is now accepted for publication at JAMIA (Journal of the American Medical Informatics Association)

On 2020-09-17 11:50:43, user Brian Kennedy wrote:

Semi - comment, also a question.

I am an economist living in Bangkok, Thailand, so this is pretty far out of my area of expertise. Thailand was the first country to get the virus outside China, but it never took off here, at this date still less than 4,000 total cases, and 60 deaths. I think there were a variety of things that led to this, but clearly early and near universal mask usage was part of it.

Your paper looks very interesting, but I could not really follow all the math. So I will trust you on it :)

My question is one of emphasis by public health officials in the U.S. Why has there not been a push on the issue of Viral Load? It seems to me that it is very important concept - even if using the mask doesn't always prevent you from getting infected, it will still reduce the viral load, giving your body more time to deal with the virus, significantly increasing your bodies chances of fighting it off.

Why has this issue been (it seems to me) largely absent from the public sphere, and from the arguments public health officials use to promote mask usage? Note if it has been there and I am missing it from far away, just say so.

Thank you for helping a laymen in your field understand :)

On 2020-09-22 20:47:31, user Scandinavian Journal wrote:

Outpatient treatment has not been the focus of almost any clinical study and this may be among the first that examine the side effects. <br /> Since the doses are very low for the hydroxychloroquine in early treatment it is no surprise that side effects is less of an issue. <br /> In fact a popular treatment is where zinc is the active component that slows virus replication and where the role of hydroxychloroquine is to act as a so called zinc ‘ionophore’ where it works to increase zinc uptake into cells. Early treatment really show so much potential and where the alarms of danger seems based on improper data for outpatient use.

Those hospital studies that gave overdose treatments to seriously ill patients showed several side effects and was incorrectly taken to represent the risks for ALL Covid-19 usage.

On 2020-09-24 14:24:26, user Uri Goldsztejn wrote:

The source code is available on Github:<br /> https://github.com/uri-gold...

On 2020-09-28 09:44:58, user markd wrote:

• very low copies number for air samples, would like to know the LOD for the detection method(s)
• 300l/' x 10' = 3mc, 50l/' x 10' = 0.5mc - why didn't you keep analysis volume the same across sites/samplers?
• can you report on the state/condition of ventilation in the sampled rooms?

On 2020-09-29 09:17:54, user Carlo Wilke wrote:

The article has been published in EMBO Molecular Medicine:

Wilke, C. et al. Neurofilaments in spinocerebellar ataxia type 3: blood biomarkers at the preataxic and ataxic stage in humans and mice. EMBO Mol Med, 2020.<br /> https://doi.org/10.15252/em...

On 2020-09-30 15:25:51, user Sinai Immunol Review Project wrote:

Very interesting paper!

Main Findings<br /> In this preprint, Zietz and Tatonetti explore the relationship between blood type and risk of SARS-CoV-2 infection, disease severity, and mortality. Using data from the electronic health records (EHR) of 1,559 patients who presented with suspected COVID-19 (with only 682 who tested SARS-CoV-2 positive) at New York Presbyterian Hospital (NYP), they analyzed four outcome pairs. Two pairs were used to test risk for infection: i) positive for infection vs negative for infection and ii) positive for infection vs general patient population. Another pair was used to test for infection severity: iii) positive patients intubated (179) vs positive patients not intubated. The last pair was used to test risk of infection-related death: iv) deceased vs surviving patients. As a measure of exposure, they used ABO blood type (A, B, AB, or O) alone or with Rh factor. In total, they generated eight contingency tables, two for each outcome pair, one with Rh and one without. Blood type was found to be significantly associated with SARS-CoV-2 infectivity after chi-squared analysis of positively vs negatively tested patients (p=0.006 for ABO system and p=0.031 for ABO+Rh system). To identify specific blood types that may predict viral test outcomes, they specifically tested each blood type against those of a different blood type for all four outcome pairs tested in the chi-squared analysis. Fisher-exact test showed that a significantly higher proportion of patients with the blood type A tested positive for the virus, and a lower proportion of patients with O and AB tested positive (p=0.009, 0.036, 0,033 respectively). When the Rh factor was included in the analysis, Rh-positive patients with the blood type A were at a 38.2% higher risk for testing positive (p=0.004), while those with the blood type O were at a 21.0% lower risk (p=0.024). Furthermore, they performed a meta-analysis by pooling data from NYP and Zhao et al’s data from China, which substantiated the findings on blood types A and O in a random-effects analysis that compared the positively infected patients with the general populations of NYP (USA), Wuhan, and Shenzhen (China) (OR=1.164, p=0.0291, for A, and OR=0.7252, p=0.0012 for O). This analysis also revealed a new increased risk of testing positive for those with blood type B (OR=1.1101, p=0.0361). Logistic regression models confirmed that although other risk factors such as diabetes, age, and obesity correlate with certain blood types, adding the blood type as a variable to the model significantly strengthens the prediction for SARS-CoV-2 positive versus negative outcome. On the other hand, blood type was not found to be a risk factor for disease severity or mortality in any of the analyses.

Limitations<br /> The study should be considered in the context of its limitations. Firstly, blood type-disease association was significant when comparing patients who tested positively for COVID-19 to those who tested negative, but the result was not replicated when comparing positively-tested patients to the general patient population. As the authors note, only a specific population received testing for COVID-19 while the majority of the patients in the EHR system were never tested, which could explain the discrepancy. Another related limitation is that the sample meant to be representative of the general population consisted only of people in NYP’s EHR database, which may be biased toward a specific population, and it is therefore unclear if the results would replicate in another cohort. Additionally, the finding that AB blood type is associated with lower risk of infection can only be taken as preliminary; the sample size was quite small (only 4.4% of the cohort had that blood type), and the result was not replicated in the meta-analysis with the data from China. Furthermore, the analyses that included Rh factor, the sample sizes for all Rh-negative subtypes were also small, and there were no patients with AB-negative blood who tested positive for the virus. This highlights the necessity for larger cohorts from multiple sites, but the preliminary results are promising.

Significance<br /> This preprint on NYP patients supports the previous results by Zhao et al on Chinese Wuhan and Shenzen patients that showed that individuals with the blood type A are at a greater risk of testing positive while those with type O are at a lower risk. As the authors reported, blood type distribution is different in NYP than in China, this substantiates their results and indicates it may be replicable in other geographical and ethnic populations despite blood type heterogeneity. Furthermore, they provide a more detailed picture through a meta-analysis with both NYP and China data, and include the Rh factor in the NYP analysis. Notably, they introduce new findings: a decreased risk for testing positive for those with AB blood in the NYP-only analysis, and an increased risk in those with blood type B in the pooled analysis. With the use of convalescent serum as a disease therapeutic, the knowledge that those with A-positive and B blood types may be at an increased risk of contracting COVID-19 can help ensure that sufficient amounts of plasma donors are compatible with that blood type. Finally, the study shows that blood type is not a significant predictor of disease prognosis in those infected with SARS-CoV-2, highlighting the need for other immunological and serological predictors of disease severity and mortality.

Credit<br /> Reviewed by Miriam Saffern as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-09-30 16:10:46, user Dan Housman wrote:

The paper is interesting although I would have expected to see some associations between genetic SNPs/genes etc, and certain 'eating types'. There is only one mention I saw of a single gene. Is that type of analysis in the works? I'd be interested to see such an association study.

On 2020-10-06 12:01:01, user Jani Ruotsalainen wrote:

I'm teaching systematic review methods on two occasions this autumn and I'm using this article as an example to be evaluated using the AMSTAR II tool. And yes, I'm doing this even though the authors do not in fact identify their work as a systematic review. My point is to show how a meta-analysis (MA) without all the supporting structures of a thorough systematic review does not really make much sense. AMSTAR II has 16 domains that one scores Yes or No. In some cases it also allows also Partial yes and some items might not be applicable when a review does not include MA. However, in the end there is no overall sum score. Anyway, according to my assessment, the manuscript as it is now scored two instances of Yes and fourteen instances of No. In other words, it didn't do too well. The biggest cause of problems is, in my opinion, the lack of a protocol published a priori that would have established the methods to be used in sufficient detail. Now it is impossible to tell if the authors deviated from their original plans along the way and what effect this might have had on their findings. Other problems include not using a satisfactory technique to assess the risk of bias of results extracted from included studies or its possible effects on results obtained with MA. The description of included studies is minimal and the description of excluded studies is nonexistent. There are also issues with the MA itself (proficiently examined by Jesper Kivelä on Twitter: https://twitter.com/JesperK... "https://twitter.com/JesperKivela/status/1291697936842338305)") and more. I'm happy to share my full assessment with the authors.

On 2020-10-06 15:42:18, user T_Rogers wrote:

How do we know that NR was the effective factor?? Perhaps it was the other ingredients in the mixture. Also, only 71 patients with mean age of 35 and limited to no co-morbidities. IOW, exactly the profile that would be expected to recover. So, good result, but inconclusive as to efficacy of NR.

On 2020-10-07 11:50:31, user Zed wrote:

it is confirming this meta-analysis published in CMI: https://www.clinicalmicrobi...

Maybe authors can discuss the main differences and/or similarity?

On 2020-10-09 12:40:32, user Zed wrote:

ooh nice paper ! <br /> The conclusion is pretty much similar to this meta-analysis: https://www.clinicalmicrobi...

On 2020-10-14 20:35:53, user BannedbyN4stickingup4Marjolein wrote:

If "A fraction of the population may also already be intrinsically resistant to infection as a consequence of high functioning innate immunity" as the paper claim, how is it that infection rates of c. 85%, with the potential to rise further upon further exposure (for example https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.08.13.20173161v1)"), have been observed in homogenous populations?

Such phenomena should surely be referenced in the authors' discussion, without which an entirely theoretical model such as that produced is perhaps an unreliable basis upon which to formulate policy prescriptions.

There is also a recent paper commenting on cross-immunity (https://www.nature.com/arti... "https://www.nature.com/articles/s41577-020-00460-4)") the conclusions of which the authors should carefully consider.

On 2020-10-16 12:16:12, user Torbjørn Wisløff wrote:

I would seriously consider revisiting the analyses. In Figure 2c, the RR of 1.00 seems not to correspond with the somewhat diverging curves. In Figure 2a, on the other hand, the RR is 0.95, but the curves follow each other very closely.

On 2020-10-17 15:54:52, user Dan Myers wrote:

The results make sense, actually. Antivirals are usually not as useful unless used early.

On 2020-10-18 07:14:28, user Robert Clark wrote:

A key comparison was left out of the report, the effect of HCQ on patients specifically under invasive mechanical ventilation. This is a key category beyond just being “ventilated”. This is when the lung inflammation is so severe the patients have to be intubated, i.e., given a breathing tube inserted down the throat.

HCQ is a highly effective anti-inflammatory. Then it would be this case where it would be most effective for hospitalized patients. Note that the study was primarily focused on these drugs anti-viral effects. HCQ is the only one of them that also has an anti-inflammatory effect. Indeed it may be the only drug among all those being considered for COVID-19 that has both characteristics.

Note that in the RECOVERY trial it was specifically for THIS type of ventilation that the steroid dexamethasone was found to cut mortality, via its anti-inflammatory effect.

But in Table S1 of the Supplementary file to this SOLIDARITY report it states the “ventilation” discussed includes both invasive and non-invasive types:

Table S1

The authors need to add to the report the effect of HCQ specifically for patients under invasive mechanical ventilation.

BTW, the report does show the result of Remdesivir on invasive mechanical ventilation patients in Fig. 3 of the report itself, showing null effect. But this was not a useful set of data anyway because Remdesivir does not have an anti-inflammatory effect.

The more important and relevant case of HCQ was not shown.

Robert Clark

On 2020-10-16 12:17:32, user Criticical Opinion wrote:

Unfortunately it is not clear if there was a difference between a diagnosis of OSA vs treatment for OSA, severity or degree of OSA, or type of treatment for OSA. Without this information, the utility of these findings is questionable at best.

On 2020-10-18 04:17:11, user C Jones wrote:

My family has used the self-administered oral swab at an LA City Covid testing site each time we've tested.<br /> My son (19 yrs) tested last Friday 10/09 & received Negative result.<br /> He was out late on Saturday & I had him retest on Monday 10/12, and he received a Positive result.<br /> He tested again yesterday 10/16 and received a Negative result.<br /> His father & I both tested on 10/09, 10/14, and 10/16 - all results Negative.<br /> Very confusing. How do we proceed?

On 2020-10-20 17:57:35, user Dinofelis wrote:

It is strange to conclude that one observes statistically significant "<br /> PCR negativity in intervention and control groups were (day 7, 182 (52.1%) vs. 54 (35.7%) (P value = 0.001)"and concludes that there is no effect.

Let us remember that statistical non-significance of rejection of H0 is not equivalent to proof of absence of effect. It simply means that the test didn't have enough power to prove anything.

In order to prove absence of effect, one needs to reject with statistical significance the hypothesis that the effect is larger than a given threshold.

I have seen many papers confusing "statistical absence of significance" with "proof of H0".

On 2020-10-31 12:07:38, user Scandinavian Journal wrote:

One issue not talked about much is that a normal HCQ dose as per on the package is not considered lethal, has been around for 50 years with good reliability and costs a few dollars for a package. If I caught the virus would I decide that this is useless because some study say so while others say it is effective ? I would of course put myself under this treatment.<br /> If it is useless well what damage did it do. If it was effective it may have saved my life or made the disease progression milder. For a few dollars. Easy choice.

On 2020-10-21 14:08:08, user Darren Brown; HIV Physiotherap wrote:

The EUROQoL EQ-5D-5L self-reported health related quality of life (HRQOL) measurement tool has been used for statistical purposes, however this baseline data of EQ-5D-5L scores across 5 domains (health status) and index value are not reported. This would be useful data to understand the HRQoL of the sample, with respect to population normative data (https://euroqol.org/eq-5d-i... "https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/population-norms/)").

On 2020-10-21 19:18:46, user Alan Wilson wrote:

I would recommend the authors to check other recently published ASRM abstracts in the topic:

https://www.fertstert.org/a...

https://www.fertstert.org/a...

On 2020-10-23 11:44:52, user Alexander Samuel wrote:

Dear authors,

I fully agree with your introduction, discussion, and everything is done correctly in this paper. After scientific misconducts from Gautret et al. in Didier Raoult's IHU Marseille, and its transfer to USA through J. Todaro followed by Zev Zelenko's strange comments, there is clearly a situation that went out of control about hydroxychloroquine.

My comment on your work is that Recovery + Solidarity weight almost for 90% of the results, In a meta-analysis, I expect a significant effect of all (or most) studies, here it seems like the results are a new read of Recovery + Solidary, with comments on very low weighted unpublished or published clinical trials. Of course, authors mention that there is still no effect in the absence of Recovery, indirectly (published vs unpublished, high dose vs low dose). I think it would be important to not just make a "second read of recovery data" (exagerated statement, sorry for the way it is said). The discussion on the difference between high / low dose is what interested me most in your paper, and would be worth more comments or even analysis.

I would suggest more theoretical molecular biology bibliography (molecular effects of HCQ might reduce the immune reaction more than affect viral cell entry), more introduction elements on in vitro data (which clearly did not favor HCQ that much) for the next effort mentioned in this paper !

Anyways, this is a good paper since it is very honest and shows data properly, congratulations for this work.

Best regards.

On 2020-10-26 00:13:04, user Mahdi Rezaei wrote:

The accepted and peer-reviewed version of the article is now available via open-access Journal of Applied Sciences. DOI: https://doi.org/10.3390/app...

On 2020-10-27 03:12:46, user Critical Dissection 2 wrote:

Dear authors,

First I just want to say I think that it was great you pursued such an important topic. There were a lot of good things about your article like your clear abstract that very well laid out the different parts of the paper and the main summary of each section. I also like that you laid out the limitations of your study and how they should be solved for in further investigation of this topic. However, there is still some room for improvement in this paper. I thought that the introduction could use more background to contextualize the issue and put some scope into it to explain why people should expand upon your results and see if the data is helpful in the future. I also think that the figures need more explanation in the results section, unless a highly experienced physician is reading it, it is a little hard to tell what we are supposed to be looking at and drawing from the figure that supports your hypothesis. There was also an emphasis drawn between the two patients whose ablation was done with a little more targeting of certain factors compared to patients who underwent standard ablations that was only mentioned in the discussion but is a great point that I think should be brought up earlier maybe somewhere in the results section. I think with these changes you will have a good paper.

On 2020-10-27 16:42:24, user Kamran Kadkhoda wrote:

Again no good panel of confirmed common CoVs to exclude cross-reactivity especially to an Ab like IgM with poor affinity maturation.

On 2020-10-28 08:35:30, user Rasmus Skov Husted wrote:

The final peer reviewed article is published open access - please follow this link: https://bit.ly/3mrLlvZ

On 2020-10-28 16:35:38, user Edsard wrote:

I think we have a chicken and egg issue here. Your pollen theory is pretty good but also the reason why scientist always say: Correlation is not causation. Your pollen is the result of the weather (temperature and humidity, which has explained seasonality of the flu for 10 years already). Here is our paper. https://www.medrxiv.org/con...

On 2020-10-28 17:53:21, user Sam Wheeler wrote:

It it so that non-vaccinated hospital personnel are forced to wear a mask almost all the time to prevent flu, so the protection of flu vaccine is even greater than this study tells?

On 2020-10-30 17:10:52, user gatwood wrote:

I suspect there could be a strong corellation between vaccination status and following a strict adherence to all COVID anti-infection guidelines, PPE etc... Experienced and medically trained Drs and nurses more likely have been vaccinated and also are more likely to follow PPE wearing and careful anti-infection routines. Support staff (food service, assistants and claening staff) with less formal medical training and understanding of infection are probably less likely to be vacinnated and also may be less likely to carefully employ all technical anti-infection measures. Would this account for the vaccinated folks having less COVID infection?

On 2020-10-31 07:52:03, user Robert Eibl wrote:

This looks interesting, although there are a few caveats mentioned in the paper. Nevertheless, it should be possible almost everywhere, and even restrospectively, to check the vaccination status of Covid-19 patients, not only for influenza - and compare this with the average vaccination status of a whole country. Then it should be immediately clear, if there is a major benefit.

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

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

On 2020-11-03 23:04:46, user David Markun wrote:

Nice study. Copy edit: " If we instead look after September 1, 2002" should refer to 2020.

On 2020-11-04 14:41:47, user Rodrigo Quiroga wrote:

Are´t these results expected regardless of children´s proneness to infection and infectivity? Up until August, the time periods with open schools were also periods with low viral propagation in the UK.

Wouldn´t the interesting period to observe with such an analysis be precisely August-November, with open schools and increasing case numbers?

On 2020-11-05 12:26:14, user Sandra Chydé wrote:

Dear authors,

You are citing one of my papers (reference 15) in a misleading way here : " There are concerns that the use of e-cigarettes in never-smokers may increase the probability that they will try combustible tobacco cigarettes and go on to become regular smokers, particularly among youth and young adults [13-15].".

First, our methodology focused ONLY on ever-smokers aged 17 having experimented with e-cigarette.

Second, we found that in this population of 17 yo, among ever-smokers, those who declared having ever used e-cigarettes were LESS likely than those who did not to transition to daily smoking at 17: RR =0.62 95 %CI [0.60 – 0.64].

This analysis is strongly robust and relies on a sample of 21,401 respondents.

Best,

Sandra Chyderiotis, Pharm.D, MPH

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

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

On 2020-11-14 02:00:10, user Melimelo wrote:

Hi, very interesting article. Which software did you use for initial qualitative coding and subsequent text mining? are there particular commands or functions in a given software package that were useful? are you sharing your code anywhere (eg github?)

On 2020-11-14 04:23:20, user Rich Kibbee wrote:

Error, this pdf lists a Centricon 70 Plus 100 kDa filter ...the first version lists it at 10 kDa.

On 2020-11-15 21:48:01, user Ands Hofs wrote:

We in germany do re-testing of positives on a regular basis, and the result is that false-positive diagnostic findings that are actually filed to the patient are in the range of 0,001 %. Even if testing activity of healthy subject was high up to September, the number of people that had a wrong test result is something like a handful a week and totally acceptable in the face of the alternative. Especially since one does a second test some days later.

But right now we have positive testing of 25% of samples in Frankfurt (Main),e.g., just mentioning this to get the perspective right, water is rising above neck to the lips...

A few people (like 1-5%) mentally infect 30% insecure anxious people here, damaging our wakefulness to keep our viruses for ourselves, prohibiting smart distancing to be practice in private contexts, behind closed doors in companies and among friends and neighbors all the same, and this is making the 2nd lockdown necessary.

And causing thousands of deaths not necessary when they would obey the democratic decision: we do not want to do triage. We want to keep the numbers low. We want to keep our viruses to ourselves. We do not want to have unnecessary lockdowns burning away existences, jobs, money... But what choices do we have?

Since we wasted the summer where we had the chance to get incidence real low.

Now the only thing that can save our neck is a (pre-) test that is really free for everyone, and MIT has one: https://digitalreality.ieee...

Every one writing about false positives should weigh his words thoroughly.<br /> Not the rate of one single test method is what people want to know. <br /> They want to have approved quality testing and numbers for "their" lab.

These numbers are there in every German lab, since they are obliged to certify every test they offer and to take part in Ring Tests where labs and their certified tests are tested. This is done by sending a lab unknown but specially prepared samples that each lab has to let run through the lab on a regular basis. This also is done to get quantitative tests to comparable levels between labs.

Comparable Levels for Covid-19-Infected patients:<br /> It is a pity that we do not let some piece of human DNA normally found from throat swabs run together with the Sars-CoV2 Test on a regular basis, resulting in viral units per human DNA count, because this would enable us to estimate the viral load at the place where the sample was taken. It would outrun many variabilities that occur when taking samples that affect the amount of material gained in the sampling process, and one could monitor viral loads across the time line for each infection with high therapeutic value. <br /> I'm so curious if someone has done this with the gargling method for probing, since here the local variability in infection density is not playing any role any more, as is the case for the question how infectious one could be in a certain state of the infection.

Boston children hospital has done this in their study on viral loads in children, where for the first time it was found that children, regardless if having symptoms or not, have viral loads like heavily ill adults. <br /> Since their lungs are smaller proportional to their age and development, of course the net amount of aerosols produced by a small child e.g. up to 8 or 10 years is smaller ( - but proportional to the loudness of their voices ;)) <br /> Still - starting with 11 or 12 years, it starts to reach adult levels, meaning we must do DIY patchwork air ventilation with heat recovery mechanisms out of vapor barrier film and 2 vents in schools or let the pupils sit in the cold of fresh air or 8hrs / day under some masks that muffle sound (many innovative ideas for DIY masks are asked here for).<br /> I like the nordic approach either to do home schooling or do classes under the trees for the younger ones, leaving a lot of space in the school for elderly pupils, especially in classes wanting to have their final exams ;)

Andi

On 2020-11-20 16:49:15, user Jean Kaweskars wrote:

Hello,

Your figures are not consistent.<br /> If you have 11% IFR for 80-90-year-old subjects who represent 6,3% of France population (> 80 years old), it does translate into a minimum of 0,69% overall IFR. If you apply your IFR rates by age, you would actually get an overall 0,91% IFR at minimum.

Regards

On 2020-11-25 15:49:17, user James Wyatt wrote:

In the discussion of weaknesses, you failed to mention that you eliminated approximately 1/3 of the cases for lack of complete data. Did you study these cases to see if their exclusion could possibly have biased your results? What was the crude death rate among these cases?

In Table 1, the disparity between mortality rates per 100k population is solely a function of the difference in incidence rates. That's significant, for sure, but the fact that CFR is the same for whites and blacks is also significant. In its rawest state, that indicates that, once someone is sick, race seems not to matter in the outcome. Doesn't that bear some discussion?

How did you determine cause of death? Covid-19 is rarely the sole cause when death certificates are completed competently and there is some judgment required to clearly identify a covid death. As follow-ups, were there non-Covid-19 deaths in your data? How were they identified? If there were none, can you justify that?

In mortality studies, the key question often is: How did you calculate the exposure? That is, how did you determine the denominators for your ratios? You reference some models, but you give no details.

The paper needs a lot more work, don't you agree?

On 2020-11-25 19:54:26, user Puya Dehgani-Mobaraki wrote:

Interesting data, which are also seen on our study were the persistency of the antibodies were detected and persisted during 8 months.

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

I do would like to have more informations in regards of the patients selection for the 8 months analysis.<br /> Our cohort was based in patients resulted positive for Sara-Cov-2 early days of March, Italy. As far as my knowledge, very few cases were reported in Australia at that time.<br /> Puya Dehgani-Mobaraki

On 2020-12-04 13:43:42, user Ben Finn wrote:

Why does the paper make no mention at all of the large risk differences between sexes & races? (Men, Black and most Asian people have 2+ times COVID mortality of others.) Straightforward to model. Without considering this it can’t claim to be an ‘optimal vaccination strategy’.

On 2020-12-04 20:45:12, user Sam Wheeler wrote:

Is the full text possible to read as HTML somewhere? I did find the PDF.

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

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

On 2020-12-08 11:30:27, user Abdur Rahman wrote:

This preprint is accepted for publication, and the new link is: <br /> https://onlinelibrary.wiley...

On 2020-12-09 12:20:53, user Vladimir Gusiatnikov wrote:

The authors appear to treat viral loads quantified in transfer media as viral concentrations in mucus, however there typically is a 1.5 to 2 order-of-magnitude dilution as material is eluted from swab into media. As a result, the authors' estimates for the copy generation rate and copies per infectious quantum may be 1.5 to 2 orders of magnitude too low.

On 2020-12-09 16:34:09, user Livia Dovigo wrote:

The elegebility criteria (that lead to only 5 studies to be included) has not be clearly described... Searches returned more than 90 entries, the authors needed to inform the methodology for studies selection. Otherwise, results may not be reliable.

On 2020-12-11 00:07:54, user Peter Novák wrote:

PARTICIPANT CONSENT?

Authors claim, cite: "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived."

I find this proclamation highly dubious.

I'm not sure how many of the the mass tested people have signed a form of informed consent, and I'd like to see how would the authors prove that. I personally have asked several participants and they insist they did not sign anything. But even in the case some, or even the majority signed something, what weight would that have under circumstances?

The people subjected to the mass testing (that technically means biological material extraction) with consideration, that those who do not subject, will be quarantined for week or two - that means, forced to stay home under threat of penalty as high as 1650 EUR (average monthly income in Slovakia is 1100 EUR brut), with few exceptions (e.q. nearest grocery store, drugstore and necessary health care), but certainly denied the access to workplace with no lost worktime salary compensation at all, neither from state nor employer.[1] A proposition effectively resembling a home prison in my opinion, and what's even worse in situation of economical crisis - with published threats from some employers that untested employees may lose their job eventually, thus undermining the public confidence in freedom of choice furthermore.[2]

It is known that criminal complaints on the accounts of possible coercion, health care law violations, human rights violations etc have been filled to the public prosecutor office. These are yet to be resolved.<br /> I acknowledge that some of the people have attended the testing voluntarily indeed, probably a minority though, as indicated by low compliance (15%) to the third round of testing where quarantine threat was removed.

Nevertheless, I doubt anyone could assume the actions under such circumstances constitute a "participant consent" by standards of any possibly existing ethical guidelines.

Or maybe I just read the citation wrong and the authors did not mean the 3,6 million people undergoing the biologic material extraction to be the subject of the "necessary patient/participant consent"...?

[1] Public Health Office Edict No. 16 from 30.10.2020. Government Bulletin vol. 30 no. 12. http://www.minv.sk/swift_da...

[2] Dobrovolne nasilu? Niektorým ludom bez testovania hrozia výpovedou. Pravda, 26.10.2020. https://ekonomika.pravda.sk...

On 2020-12-13 13:26:48, user Sam Smith wrote:

A concern about the Córdoba study is that 25OHD serum levels were not measured, so we do not know if the treatment was associated with a benefit only in patients who were deficient. A randomized controlled trial will be needed to determine whether calcifediol will benefit hospitalized COVID-19 patients who are not deficient.

Sadly, calcifediol is sold in some European countries, including Finland?

On 2020-12-15 10:43:49, user NK wrote:

Re: article pre-published at https://www.medrxiv.org/con...

There are several methodological problems in this study.

1. Findings that suggest increased ORs among primary school teachers, child care workers and secondary education teachers are not properly presented and discussed

The summary states: "Teachers had no or only moderately increased odds of COVID-19". This finding is mentioned several places in the text of the article. Teachers are repeatedly referred to as having a low risk, even when the results for teachers show a significant increase in admissions and borderline significant increase in infection rates. Quotes: «First, our findings give no reason to believe that teachers are at higher risk of infection», and in the conclusion: “Teachers had no increased risk to only a moderate increased risk of COVID-19”. We wonder why the authors find it important to repeatedly mention this the result for teachers when the results for the last period does not exclude a substantial increased risk for teachers, whereas occupational groups with lower risk than teachers are not mentioned in the summary.

The part of “Supplementary table 1” shown below does not provide a basis for such a conclusion that teachers are a low risk group.

The OR (95% CI) for 1) primary school teachers 2), child care workers and 3) secondary education teachers were 1.142 (0.99-1.32), 1.145 (1.02-1.29) and 1.095 (0.82-1.47) respectively. The upper confidence limits are does not exclude 29 % to 47 % increased ORs, which represent substantial increases.

Concerning the results on the risk of admission, it is stated: «None of the included occupations had any particularly increased risk of severe COVID-19, indicated by hospitalization, when compared with all infected in their working age (Figure 3, S-table 2), apart from dentists, who had 7 ( 2-18) times increased odds ratio, and pre-school teachers, child care workers and taxi, bus and tram drivers who had 1-2 times increased odds ratio”.

This finding is not discussed or mentioned in the summary, even if the findings were statistically significant for pre-school teachers as well as for child care workers.

1. The study periods include periods when the schools were closed and include no period with high infection rate among children and youths.

It is not to be expected that teachers have higher infection rates than the average working population in periods when school are closed and when the infection rates are low in the age groups 0 - 9 and 10 -19 years. This problem is not discussed in the paper. Schools were closed from 12 March to 27 April. For a majority of the schools, holiday started from Friday 19 June.

The first study period lasted from February 27 to July 17. Thus, schools were closed for over 70 days of the first study period of 139 days. The infection rates in children at school age in the first study period were rather low (3.6 per 100 000 children per week between in the age group 10 -19 in week 19, 1.1 per 100 0000 children per week in week 25). In the last study period, the infection rates varied between 7 to 17 per 100 000 per week in the age group 10 - 19. Even if these rates are much lower than later weeks that were not studied (after week 42), the results from this second part of the study suggest an increased risk for teachers.

Thus, the infection rates among children started to increase from week 43, after the end of the study period. By not including this period, the study design excludes the possibility to detect if these high rates among pupils could be related to increase infection rates among teachers.

It is a problem that the results from this pre-published study has been quoted in the media and referred to as if teachers have no excess risk, or even possibly a reduced risk at the time that several municipalities were to decide what type of restrictions at schools should be introduced to reduce the risk of transmission among school children, see https://www.barnehage.no/korona/ny-forskning-nei-barnehagelaerere-har-ikke-okt-risiko-for-smitte/211143

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 2021-01-05 09:55:32, user Disqus wrote:

In addition to the previuos comments I read, page 7 "SARS-CoV-2 positive incidence rates were calculated for staff and students attending an educational setting, irrespective of whether the infection was acquired within or outside the educational setting."

thus it is evident that if the incidence is higher among teachers than the general population, <br /> schools are not the safest places, with a perhaps low transmission rate among students but a <br /> greater transmission rate from students to teachers

On 2020-12-22 08:44:55, user Maxime Baud wrote:

Study published in Lancet Neurology, now available at https://www.thelancet.com/j...

On 2020-12-28 23:51:01, user ErikCarter wrote:

You really ought to test for infectious virus, rather than just RNA. Otherwise you can't truthfully claim that the variant results in higher "viral load"...you've not measure viral load, you've measured RNA load. These are not the same thing.

On 2021-01-08 15:09:29, user Kevin McKernan wrote:

Can the authors explain the mess in Table 2? This dilution series is non-linear and any student delivering such data would be told to repeat it. If it is in 6 replica's, you should share the dispersion in that data. There should a clear 3.3 Ct shift in each 10X dilution. If you dont have linearity in your dilution series how can you make a Ct cutoff? The non-linearity is non-concordant across different amplicons? It is frightening this is being used as a diagnostic test. Are there any internal controls in the test to ascertain the sample prep variance? Dahdouh et al demonstrates 10-16 Ct variance in RNaseP signals (human gene) suggesting tests that lack internal controls to normalize for swab and sample prep variance are random number generators. Table 2 also looks like a random number generator.

On 2020-12-29 01:55:20, user F Alexander Diaz-Quijano wrote:

This article is now published in the Journal of Clinical Epidemiology.

Share Link:<br /> https://authors.elsevier.co...

On 2020-12-31 14:23:25, user Don Wheeler wrote:

Interesting. Additional research with a larger sample size featuring a broader cross section of the population will be most beneficial. Let's see where this leads.. Great work! @ComaRecoveryLab #covid19

On 2021-01-05 03:55:19, user mahejibin khan wrote:

Though air transmission of the virus has been suspected , swab samples collected in unsterile conditions for RT-PCR screening of human subjects continues to be a practice in many regions/countries. <br /> Two mass scale nasopharyngeal swabs of employees of an establishment in Mysore, Karnataka, India, collected under unsterile conditions in their premises, by seating them in an open ground and screened for SARS-CoV-2 infection by RT-PCR, identified a large number of asymptomatic SARS-CoV-2 positive cases. Thus the establishment forced a two-day campus lockdown, on both the occasions, in order to sanitize and break the virus transmission chain. Since most of the infected subjects remained asymptomatic through their home quarantined period, they were certified for fitness to resume work. Since reports have shown patients fighting SARS-CoV2 infection developing IgM and IgG antibodies between 6–15 days after disease onset. Blood samples of two RT-PCR positive asymptomatic subjects after 17 day home quarantine were analysed for the presence of IgM and IgG antibodies. Absence of detectable titres of antibodies to SARS-CoV-2 virus in the two blood samples suggested lack of acquired immunity due to asymptomatic patients unexposed to the virus. Nasopharyngeal swabs positive for the virus by RT-PCR inferences establishment of the Covid-19 pathogen infection in the host. Absence of prodromal symptoms for the disease in these subjects and some of them testing negative in a second Rapid Antigen Detection Test (RAD) opinion, when swabs were sampled in designated hospital rooms, suggested occurrence of air borne virus and swab contamination during sample collection under unsterile conditions. <br /> Droplets that are sneezed or coughed behave differently in the open air, according to environmental conditions like temperature, humidity, ventilation, and the amount of virus deposited.<br /> My observations on plausibility of air borne SARS-CoV2, RT-PCR determining their fairly high numbers and prevalence of asymptomatic subjects living in that environment provides leads for studies with reference to herd immunity from the purview of viral attenuation due to environment and/or innate immunity initiation through pattern recognition receptors

On 2021-01-05 15:56:38, user Ti wrote:

You write that "the best performing method is XRAI (AUPRC = 0.224 ± 0.240)". Meaning that the AUPRC ranges from -0.016 to 0.464. Surely, you cannot have a negative area under the curve.

On 2021-01-05 16:49:15, user Ania Lorenc wrote:

Hi, I was wondering - have you excluded known factors contributing to severity - sex + age?

On 2021-01-09 15:11:56, user Derek Enlander, MD, MRCS, LRCP wrote:

The "long Haul Post Viral" SARS 2 Covid19 effects, Fatigue, Myalgia, Cognative defect, insomnia etc are reminiscent of the symptoms reported historically by Melvin Ramsay in 1955 when he reported these symptoms in a cohort of young doctors and nurses in the Royal Free Hospital in London. He termed the outbreak as Post Viral Fatigue, renamed Myalgic Encephalomyelitis (ME) and later Chronic Fatigue Syndrome (CFS).

On 2021-01-10 22:17:22, user Wayne Griff wrote:

Single dose vaccine efficacy is not 90%. It's less than 50%, and that's after only 3 weeks. It would be even less effective at 6, 9, 12 weeks or more. More importantly, at 3 weeks the neutralizing ability of 1 dose is only 1/5th as much as Convalescent Plasma (NEJM)

On 2021-01-11 23:14:43, user Chaitanya wrote:

Excited to read the paper since its amazing that the authors have both in vitro and patient sample data. I a curious to read more with regard to false positive/negative and the role of NASBA amplification

On 2021-01-13 11:39:34, user carina brehony wrote:

hopefully a full published paper will acknowledge the laboratories, public health departments and the Health Protection Surveillance Centre that collected, validated and provided the data which was then made available publicly

On 2021-01-15 16:00:23, user Martin Reijns wrote:

Congratulations on this work. One comment though: I know it's difficult (if not impossible) to keep up with all the literature on SARS-CoV-2, but I just wanted to say that the statement "Currently, no test combines detection of widely used SARS-CoV-2 E- and N-gene targets and a sample control in a single, multiplexed reaction" is incorrect. Our paper on this has been on medRxiv since June:

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

and was recently published in PLoS Biology

https://journals.plos.org/p...

All the best, Martin

On 2021-01-18 05:38:05, user Benjamin Seng wrote:

This paper has been published in General Hospital Psychiatry (journal) and is available at the following link: https://doi.org/10.1016/j.g...

On 2021-01-21 09:05:28, user Dominik wrote:

The conclusion drawn here is simply wrong: "suggesting that current SARS-CoV-2 vaccines will protect against the 20B/501Y.V1 strain" when in fact they didn't check for all 17 epitope changes of mentioned strain but only N501Y which was never thought to be immune evasive. The same erroneous conclusion was drawn in the paper of Uni Texas which also only tested against N501Y but not all mutations.

On 2021-01-24 20:42:28, user Thomas Arend wrote:

Just some short remarks to table 5:

(35-5)/35 is 85.71% and not 82.86%

If you round 32/23 = 91.4285.. you get 91.43 not 91.42

Typo? 34/35 = 97.1428 ~ 97.14 ... not 97.12

And I agree with Michel Schrader comments to the presicion of 4 digits.

The hypothesis H0: specifity = 94% / Ha: specifity <> 94% you can only be rejected for AgPOCT V. p=0,0168.

H0: Specifiy = 92% cant be rejected for any AgPOCT.

You should calculate a CI for the specifity.

On 2021-01-26 23:19:50, user Janet Aisbett wrote:

The analysis as presented does not appear to support the conclusion that “individuals discharged from hospital following COVID-19 face elevated rates of multi-organ dysfunction…..”. We can conclude that these individuals have elevated rates of multi-organ dysfunction, but we have no way of knowing whether these were ‘new-onset’ events after discharge or were factors contributing to the severity of the individual’s COVID episode. This is because ‘new-onset’ events are defined with respect to HES APC and GDPPR extracts over the ten years prior to 2020. It would help if counts of ‘new-onset events’ were provided, broken into those which first appear in 2020 but before discharge (e.g., as secondary diagnostic codes alongside the COVID primary) and those which first appear post-discharge.

Forgive me if I have missed something, but I also have concerns about the 1:1 matching of the COVID cases to controls. Supplementary Table 1 suggests very coarse matching criteria. The use of an age category 70+ is particularly striking, given the comparative age distributions of COVID versus all hospitalisations. The matching of clinical characteristics also deserves further explanation. As presented, it appears that an episode of skin cancer eight years ago could allow a match to an individual with metastasised tumours requiring palliative care. Since more serious comorbidities may be a factor in COVID hospitalisation, matching on coarse clinical characteristics may tend to select a healthier control group. Presenting frequencies of selected sets of ICD-10 codes by age for the COVID cohort versus the control group would help resolve this question. Also worthy of explanation are the decisions not to include dementia in the matched histories, and not to consider previous hospitalisation.

Finally, the Supplementary Table 3 shows quite different outcomes for controls matched to ICU COVID cases compared with controls matched to non-ICU cases. These differences are not reflected in the COVID cohort. Although numbers are small for the ICU control group, the discrepancy is worthy of comment.

On 2021-01-27 14:15:18, user Antonio Beltrão Schütz wrote:

I think that this article is important, considering that in spite of does not proof by mean RT-PCR test that ivermectin can turn negative viral load in patients with increased viral load of Covid-19, it decreased the mortality (4/112) patients. This data extrapolated to 100.000 or 1,000.000 cases is significant.

On 2021-02-04 14:21:22, user Peter Ray wrote:

The suggested reason for the increased case rates for the first 10 days or so after injection is a possible change in behaviours to being less cautious.

Another possible reason is the dramatic increase in Covid prevalence occurring in Israel generally at the start of the vaccination program (late December). Given that the positive case data is available in public it might be worthwhile including a comparison of general population case rate on the daily incidence chart.

On 2021-02-05 13:36:40, user Marie wrote:

Could you, as required by law, please declare your conflict of interest?

On 2021-02-06 02:50:56, user Crystal Sonia wrote:

How accurate and reliable is this SIR model? With the mco arent the cases still increasing? How are the beta and gamma estimated? What's the sensitivity and specificity of this model?

On 2021-02-15 15:10:33, user Paul Wolf wrote:

Near the end of the abstract, you say the data is "suggesting parallel evolution of a trait that may confer an advantage in spread or transmission." Why would this mutation be occuring in different parts of the US and nowhere else in the world? That doesn't suggest convergent evolution, but a common origin.

On 2021-02-16 20:47:03, user jiver wrote:

NHS are obsessed with over simplifying race. Why ask people to self report in only 3 categories? And two are skin colour but the other is geographical. Why on earth? What did you hope to achieve by only going this far? Already people are using this to discredit non whites. And NHSP have done it before, exactly the same, 3 categories allowed only.

On 2021-02-19 20:04:10, user Jacqueline J Clarke wrote:

Have you confused the symbols lesser than and greater than ? The occupying sentences don't make sense ?

On 2021-03-09 09:36:30, user Raph Ohuru wrote:

Published here https://doi.org/10.1093/cli...

On 2021-03-20 14:23:19, user Alexander Mathioudakis wrote:

This study has now been published here:

https://www.mdpi.com/2075-1...

On 2020-11-16 06:38:23, user whitecat31 wrote:

At the 39th replicant when the exponential phase is basically over? Am I understanding that correctly? Seriously? Did you guys run a comparison standard curve with 39 points? Something like the 39th replicant would be considered below the limit of detection and LOQ. So yeah.. your sample was contaminated.

On 2021-06-02 11:15:56, user Philip Ward wrote:

The authors of the paper have now published a second study that states their earliest positive sewage sample is from January 15th 2020 and does not make any reference to this preprint:

https://journals.asm.org/do...

On 2021-06-01 09:39:20, user Facundo Muñoz wrote:

Very nice paper.

I'd just like to point out a minor mistake in the text. At the top of page 6 it is stated that alpha_lockdown has a Gamma prior of mean 0.1667 and standard deviation 1.

This didn't match the stated 50/50 chance for decreasing/increasing effects. And indeed, thanks to your open-science approach to publishing I could verify that in the code [1] you used a Gamma prior with shape and rate parameters of values 0.1667 and 1 respectively.

Best wishes.

[1] https://gitlab.in2p3.fr/bou...

On 2021-08-15 11:06:56, user Dorian Dale wrote:

The most obvious flaw is depending on honest self-declarations of educational status. Go to LinkedIn for innumerable examples of resume inflation. The huge disparity between masters at 8.3% and PhDs at 23.9%. We are now seeing much analysis of how pervasive are dishonest responses to polling. If one is an anti-vaxxer, why not claim PhD status to add cred at the expense of over-educated elite?

On 2021-06-01 18:07:54, user japhetk wrote:

This research is problematic. <br /> First, this clinical trial's primary outcome measures were as attached below. And authors did not mention 2 out of 3 primary outcome measures. These are not good omissions apparently.

Second even the primary outcome measure they used was not specified before the study. Authors used ct cut value of 30, which was arbitrary. Authors explain why they did not use 40, but they must have used 40 if that leads to the good results. And that is not a clinical trial.

They did not correct for multiple comparisons across three primary endpoints, either, which they should have done as there are three primary endpoints.

Second as the figure 2 and the table shows the ct values of two groups before the intervention are close to statistically significant differences (p = 0.10).<br /> And as the figure 2 shows this group difference did not show even a hint of change at day 6! The two groups were almost statistically different from the beginning and that did not change visually at day 6 apparently. I can't see any hints of effects of IVM from this study.

Lay persons are watching this study, and they say they love IVM and hate vaccine and let's use IVM instead of vaccine based on this study's result. We hope authors do the proper research. They should provide three primary endpoints, correct the preexisting differences, and correct multiple comparisons, and should provide apparent conclusions.

Primary Outcome Measures :

Viral clearance at day 6 [ Time Frame: Outcome will be determined till 6 days post intervention ]

The primary outcome will be the viral clearance at day 6 in the intervention group compared to placebo.

Viral shedding duration [ Time Frame: Outcome will be determined till 14 days post intervention ]

Secondary outcomes: viral shedding duration (time between first positive PCR to last of two consecutive negative tests)

Symptoms clearance time [ Time Frame: Outcome will be determined till 14 days post intervention ]

Time between drug treatment and symptoms resolution

On 2021-06-02 13:20:34, user Robert Clark wrote:

Thank you for this report. In fact, several earlier studies on HCQ failed to report the effectiveness in their own data of HCQ specifically for the case of patients on mechanical ventilation.

Since this was one of the cases with the highest fatality rates, it was truly unfortunate that this option for treatment was not presented to doctors treating ventilated patients.

See discussion here:

Rapid identification of effective treatments for COVID-19.<br /> https://exoscientist.blogsp...

Robert Clark

On 2021-06-25 10:48:30, user ScottK wrote:

Since this was an observational study with a small number of survivors, the fear is that you draw conclusions that are tied to survival and not to treatments. Simply put...<br /> MDs won't prescribe HCQ/Zithro to certain risk pools.<br /> Cumulative dose may rise with survival, not the other way around.<br /> You've got 15% of the total population in the study on this regimen and 20% of the population survived. If the MDs picked 10-15 of the 'least contraindicated' patients to do anything with, chances are that you'd see higher survival.

Double blind, controlled or explain the heck out of the pooling and selection criteria.

On 2021-06-08 00:40:20, user Daniel Bastian wrote:

"The available pharmacokinetic data from clinically relevant and excessive dosing studies indicate that the SARS-CoV-2 inhibitory concentrations are not likely to be attainable in humans."

Say it with me now: cell culture studies != controlled clinical data.

On 2021-06-11 03:08:36, user Kel Sigmund wrote:

1. Do you know if the relative lack of severe manifestations among the vaccinees with breakthrough covid 19 was associated with the occurrence of prior Covid 19 infection before undergoing vaccination?
2. Do you have data on the demographics of the vaccinee recipients, especially risk factors for severe disease and age because it would be interesting to know if the vaccine truly mitigated the severity of disease or the vaccinated population is younger and healthier than most being health care workers.

On 2021-06-15 23:51:09, user Drew wrote:

Anyone know an estimate as to when the article will be peer-reviewed? Thanks in advance!

On 2021-06-16 18:24:27, user David McAllister wrote:

Given that randomisation to budesonide was stopped in March, are we expecting the updated findings soon?

On 2021-06-16 21:14:35, user Amanda wrote:

hi - i think there is a typo! This says 8/174 -- yet earlier it said 175.

The overall prevalence of persistent symptoms was 1.7% (80/4678 children; 95% CI 1.4%, 2.1%), and 4.6% (8/174 children; 95% CI 2.0%, 8.9%) in children who had a history of SARS-CoV-2 infection before persistent symptom onset.

Also ages 2-11 were overrepresented versus 12-17

On 2021-06-19 01:13:24, user suraj senjam wrote:

Now published in Indian Journal of Ophthalmology

On 2021-06-19 11:30:14, user Will Turner wrote:

Is this small changes in most subjects, or large changes in a smaller subset of subjects? You could get insight into this by showing the distribution (histogram) of (xbefore - xafter) where x is any brain measure found to significantly decrease. Figure 1 is very helpful, but it’s unclear what percent of subjects stay the same, increase slightly, decrease slightly, or decrease dramatically.

A second question your data should be able to address but the paper doesn’t: what are the % changes here? I get that fig 1 necessarily shows an index on the y axis for comparison purposes. But shouldn’t it be possible to construct this index with an absolute 0, because any length or volume measurement can be compared to 0. Then you could understand the difference in means in terms of not just statistical significance but magnitude, which is important for understanding what kind of effects this could have.

On 2021-06-22 13:45:29, user ibamvidivici wrote:

Both, age and BMI is highly correlated for the risk of Covid-19. Can you add the data about Median-age and Median-BMI for the groups:<br /> - positive SARS-Covid-19-Test<br /> - negative SARS-Covid-19-Test<br /> for both (LTCF and HCW) cohorts?<br /> This would be necessary to measure the margin of error for this study.

On 2021-06-22 21:24:02, user Noriaki Kurita (????), MD, PhD wrote:

This manuscript has now been available from Journal of General <br /> Internal Medicine. <br /> https://link.springer.com/a...