On 2020-07-18 11:46:21, user Benjamin Kirkup wrote:

Could you address the observations [ie. https://www.medrxiv.org/con...] of viral loads up to 10^11 viruses per ml saliva?

On 2020-07-18 12:58:04, user Anand Srinivasan wrote:

I understand that the risk calculations are done with the assumption of 1E8 RNA copies per mL of viral load in the saliva. I would like to know whether the risk estimates are directly proportional to the viral load (whether linear or non-linear dependency). Also, if the viral concentration in the saliva is lower by two orders of magnitude (1E6 RNA copies per mL), then what will be risk for the same conditions described in this pre-print? <br /> Thanks and Regards.

On 2020-07-19 15:32:00, user Helene Banoun wrote:

Prior infection by seasonal coronaviruses does not prevent SARS-CoV-2 infection and associated Multisystem Inflammatory Syndrome in children

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

June 30, 2020

This June 2020 study shows how difficult it is nowadays to admit that antibodies in viral infections are only a witness of the infection and do not mean much about the protection conferred.

The authors acknowledge this in the text of this multi-disciplinary study, but it does not appear in the abstract, the conclusion or the title.

Almost 800 children were tested.

Only humoral immunity was tested.

In children who tested positive for SARS-CoV-2 (there is no mention of Rt-PCR or other confirmatory tests), 55% had neutralizing antibodies (in vitro); in children with Multi Inflammatory Syndrome "Kawasaki like", 70% had "neutralizing" Ac. There is no correlation with traces of previous HcoV infection (detected by the presence of anti S and anti N Ac). The authors wonder whether the MIS could be explained by the presence of facilitator Ac (low or non-neutralizing Ac or cross-reactive to HcoV and SARS-Cov-2).

Clinical aspect: 70% of the seropositives did not present a specific Covid syndrome (only headache, nasopharyngitis and shortness of breath). This percentage is comparable to that found in adults.

This confirms the low rate of children with clinical Covid syndrome.

The prevalence of seropositivity in children is comparable to that found in adults (between 10 and 15% of the population). All seropositives present neutralizing Ac but these appear with a delay of several weeks compared to the first antibodies. The "neutralizing" Ac appear earlier and at a high rate in patients with severe Covid.

This confirms previous studies that correlate the level of Ac to the severity of the disease. Therefore, neutralizing Ac are not correlated with protection.

The seroprevalence of HcoV infections is 100% in adults. Children are finally as much infected by Covid as adults, present an asymptomatic picture as often as adults and therefore there is no reason to explain a lower level of damage in children not a higher level of cross-immunity with HcoV.

The authors admit that Ac are only a control for infection and are not correlated with protection against disease.

They also admit that the relevance of neutralization tests performed with pseudoviruses can be questioned because they do not involve the ACE2 receptor.

In addition, helper T lymphocytes reactive to SARS-CoV-2 epitopes detected in healthy subjects do not recognize the spike binding domain (SBD).

In contrast to the results of cellular immunity studies, here antibodies against Hcov and cross-reactive to SARS-CoV-2 do not confer protection against Covid.

Profiles of children with MIS show that this syndrome is due to a non-specific inflammatory response. The data collected do not imply that previous Hcov infections would facilitate SARS-CoV-2 (and MIS) infections by ADE

Therefore, this study cannot conclude that there is no cross-immunity with HcoVs since it only measures humoral immunity (and for some antibodies only). The papers by Grifoni, Braun and Le Bert showed this cross-immunity at the cellular level.

Braun et al., 2020-1, https://www.medrxiv.org/con...

Grifoni et al., 2020 https://www.cell.com/cell/p...

Le Bert et al;, 2020 https://www.biorxiv.org/con...

All this reinforces my belief that antibodies (in viral infections) are only a witness and not a sign of protection. On the contrary, immunity is mainly cellular (innate in a primary infection and adaptive in a re-infection); innate humoral immunity also intervenes rapidly via non-specific factors (such as interferon1 for example). The role of antibodies in reinfections can be discussed: protection or facilitation?

On 2020-07-21 02:10:17, user Paul Gordon wrote:

Hi, thanks for posting this. I see that this is in press at JCM, congratulations. Might it be worth noting that the described mutation occurs not just in the 8 described genomes in the manuscript, but also these 7 in GISAID?

Belgium/rega-0423297/2020 <br /> Belgium/rega-0423298/2020<br /> Belgium/rega-0423299/2020<br /> Belgium/rega-0423300/2020<br /> Belgium/rega-0423301/2020<br /> Belgium/rega-0423302/2020<br /> Belgium/rega-0423303/2020

Or is this a resampling of some of those same genomes? Thanks for any clarification you can provide.

On 2020-07-21 11:21:52, user Hagai Perets wrote:

This study: https://arxiv.org/abs/2007.... might explain these results. If a preceding strain provided immunity and began elsewhere in China, one would expect a correletion with two positions - one direct (Wuhan) and one inverse with the origin of the preceding strain.

On 2020-07-21 21:34:31, user Deborah Verran wrote:

Interesting development. Although a systematic review on this topic may be of interest the constraints posed by resorting to summarising the already published literature may limit it's utility in practice. Other groups of professionals are now undertaking the process of developing and posting guidelines in order to assist clinicians who are being faced with making decisions on such patients during the pandemic https://journals.lww.com/jb...

On 2020-07-22 09:51:25, user Peiying Hong wrote:

what was the spiked SARS-CoV-2 in the recovery test? Is it the gene product or actual SARS-CoV-2? Given that the wastewater may contain SARS-CoV-2, how can recovery efficiency be determined without accounting for those SARS-CoV-2 that are already present in the sample?

On 2020-06-23 08:22:43, user Julii Brainard wrote:

108-102 = 6. 6/108 rounds to 6%, so OR 0.94 is correct as change in risk from no exposure to exposure (exposure = wearing masks). We checked all the raw case/sample numbers using ITT and the numbers are correct so the OR & 95%CI are correctly calculated for primary prevention RCTs. -Dr. Julii Brainard, UEA

On 2020-07-23 19:58:56, user hlasny.j@centrum.cz wrote:

I believe that magnesium (Mg) is the most important component of DMB. Mg is pathologically involved in heart diseases, diabetes, and neuronal diseases. Mg is essential for regulation of muscle contraction (including that of the heart), blood pressure, insulin metabolism, Mg is important for optimal nerve transmission and neuromuscular coordination. It is most of these health problems that complicate the course of Covid-19 in the elderly. Similarly, magnesium is important in the diet of humans in the prevention of Alzheimer's disease, recently been pointed out, see www.researchgate.net/public... .

On 2020-07-30 23:36:10, user Ralph London wrote:

How did that abstract get published??? 'magnesium 150mg OD and vitamin B12 500mcg' - in what form was the magnesium and what vitamer: cyanocobalamin, hydroxocobalamin, adenosylcobalamin or methylcobalamin, or combo? It matters.

On 2020-07-24 03:22:06, user Paul McGehee wrote:

Any updates on where this is in the peer review process and when and in which journal it may be published?

On 2020-06-24 20:42:23, user Ece Demirbas wrote:

TzanckNet will be a useful method in clinical applications by not only providing high accuracy, sensitivity and specificity but also lowering the cost of diagnosis for erosive-vesiculobullous diseases. Congratulations to the authors for such promising research. Ece Demirbas ,MD

On 2020-07-26 18:05:01, user Marm Kilpatrick wrote:

In re-reading the study, I've found a small error. In the 3rd paragraph of Intro you write: "In a report by the Centers for Disease Control and Prevention (CDC) 3, only 291 of 2572 children who were infected with SARSCoV- 2 were symptomatic, though this may be due to poor reporting."<br /> In fact, this number 291/2572 are the number cases for which CDC had any data at all on symptoms, not the number that were symptomatic. The article states:<br /> "Data on signs and symptoms of COVID-19 were available for 291 of 2,572 (11%) pediatric cases and 10,944 of 113,985 (9.6%) cases among adults aged 18–64 years (Table)."<br /> Earlier the paper makes it clear that the other cases had *missing* data for symptoms for both children and adults:<br /> "At the time of this analysis, characteristics of interest were available for only a minority of cases, including hospitalization status (33%), presence of preexisting underlying medical conditions (13%), and symptoms (9.4%)."

On 2020-07-26 22:23:24, user Chris Barker wrote:

an editorial point. The citations numbering seems confusing. I finally found the citation to the principal components method for longitudinal data (Li). The major points. The data appears to be over a five day period. This seems to be a very artificial and inherently biased dataset . How much of an improvement are 5 days of data over say the last available measurement or first available measurement? . the inclusion of data in the analysis appears to be defined as "from disease onset until hospitalization or beginning of recovery". The authors should move the "subject selection" from the appendix to the main text. Do the authors require that using the method in current clinical practice that patients must present with precisely the identical "subject selection criteria" as the manuscript? The requirement for "imputation" seems may be especially difficult to implement in routine medical care of a covid19 patient. . How heterogenous are the "between patient" characteristics at time of disease onset? If the authors method were to be applied to actual clinical practice are future patients likely to have the same or similar values of patient characteristics at onset? For example is an elderly or adolescent or infant data applicable? A seriously concerning issue is the authors appear to "re-use" the validation set, rather than have a separate validation dataset for each re-use. if so that would guarantee a bias toward the authors preferred model and would not represent an independent validation. there are important multiplicity considerations for the analysis. the authors should account for multiplicity, using a method such as Bonferroni or false discovery rate. The authors need to define their criteria for claiming the model "validates". ONe option may be to use a mahalanobis distance between the test and training dataset outcomes (for example in table 1). The authors should also clarify whether the type or variable "numeric (ratio scale)", categorical, ordinal or binary may all be included in the method Mc2PCA.

On 2020-07-27 14:30:35, user Antonio Mattos wrote:

As most of countries have no testing, the best measurement would be death cases... Exemple like this, Brazil would just have coletive immunology when had approxiemely 2 milion deaths or hospitalizations... So, a tragetic cenarium. Seems that coletive immunology seens so far out of the reality.

On 2020-07-27 19:02:55, user GreenEngineer wrote:

The authors acknowledge several weaknesses to the study, which are all valid.

Another weakness, which was not acknowledged, was failing to assess the condition of the filters and filter racks.<br /> The condition of the filter racks is critical to effective filtration. If the clips which hold the filters in place, or the seals between filters, are missing or broken then substantial air will bypass the filters. Likewise if the racks themselves are damaged, if the filters are incorrectly installed, etc.

I know nothing about the condition of these particular AHUs. They may be in great condition, but the average condition of AHUs in my experience suggests that this should not be assumed.

The relevance is this: If the filters/racks are not in good condition, unfiltered air will bypass the racks. Increasing the MERV value in that case will not help and may actually make thing worse: as the pressure drop through the filter increases, more air will bypass around them.

Knowing how effectively a MERV 14 filter removes viral RNA in a realistic, as-found AHU condition is definitely relevant. But interpreting these results would be much easier if we had a sense of the condition of the equipment.

On 2020-07-09 17:58:50, user Jerry Lamping wrote:

This comment is about supply air grilles that are located at the end of ducts In the rooms. The exit dampers that are located in AHUs are not subject to this concert. Be careful about stating that the supply grilles were contaminated by virus that pass thru the air filters . You should discuss with a grille designer the possibility that the grille can entrain some room air as it passes the supply air over the louvers.<br /> I have found many dirty supply grilles that were depositing dust from the room on the louvers.<br /> Gerald Lamping<br /> Mechanical Engineer & IAQ Investigator

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

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

On 2020-07-28 15:02:30, user Liam Golding wrote:

In regard to your sample size, do you think running in triplicate may answer some of the unknowns with UVGI?

On 2020-07-28 15:25:40, user Maxim Sheinin wrote:

This is an interesting analysis. It would be important to discuss the impact of long-term care facilities in the discussion section,as in many countries about 50% of Covid deaths originated in these facilities.

On 2020-07-28 21:14:56, user Dude Dujmovic wrote:

Why not to put the name of the serology test in the abstract instead of requiring a reader to fish it out in the paper?

On 2020-06-29 05:59:53, user Andrew Craigie wrote:

Many of the confounders listed are known or suspected to be associated with low vitamin D, including ethnicity, obesity, diabetes, old age and deprivation. Adjusting for these will therefore incorrectly mask out the relationship between Covid-19 severity and low vitamin D. It's a bit like concluding that high sugar intake is not associated with early death after adjusting for confounding factors like obesity, diabetes, heart disease & tooth decay.

Using 10 year old vitamin D level data also renders the data meaningless. More relevant is what each patient's vitamin D level was at the point of diagnosis, and this is the data we should urgently be gathering to evaluate the relationship.

On 2020-07-02 17:57:05, user Dr Gareth Davies (Gruff) wrote:

This study is methodologically flawed in the following ways:<br /> 1. This study used vitamin D serum data taken 10 to 14 years prior rather than of levels on admission to hospital. We cannot infer anything about levels on admission from them. Indeed, if anyone test deficient it's very likely they would have been recommended to take D3 supplements.<br /> 2. It applies a grossly flawed statistical analysis using the full biobank data set numbers for N instead of the matches and therefore reports a misleadingly-low unjustifed p-values<br /> 3. The BAME COVID-19 positive test matches were just 32 Black people and 19 south Asian (N =51). Making statements about entire ethnic populations based on these data is not justified.<br /> 4. You should never adjust for confounders without first knowing the causal relationship to the other study variables. You introduce bias if you control for a collider and you don't know which variables may be colliders.

These flaws render the entire analysis invalid.

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

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

On 2020-06-30 06:03:47, user Verde wrote:

This model suffers from an incomplete data problem resulting in an order of magnitude error. Easily resolved by either using all cause death data, or seeding the time series upfront with a large number and adding a constant to each day’s new deaths.<br /> https://drive.google.com/fi...<br /> https://drive.google.com/fi...

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 2020-08-01 15:51:47, user Jennifer Hollowell wrote:

The authors appear to have only considered live births, yet it seems plausible that some women eg those with PPROM, and even women in Preterm labour, might delay presenting to hospital during the COVID lockdown. Can the authors provide some comparable data on stillbirths by gestational age?

On 2020-08-01 19:54:49, user Irwin Jungreis wrote:

This manuscript refers to ORF3b, which is ambiguous because the name ORF3b has been used to mean two different SARS-CoV-2 ORFs, namely the 22 amino-acid ORF with coordinates 25814-25882 orthologous to the 5’ end of SARS-CoV ORF3b, and the 57 amino-acid ORF with coordinates 25524-25697. There is a growing consensus, approved by the ICTV Coronaviridae Study Group, to refer to the 57 amino-acid ORF as ORF3d. Please specify which ORF3b you are talking about by giving the length and coordinates (and preferably switch to ORF3d if it is the 57 amino-acid one).

On 2020-08-02 21:33:37, user MS wrote:

Interesting and promising, of importance what is the limit of detection in cp/ml and the average cp/ml of viral RNA in saliva samples from symptomatic and asymptomatic patients? Probity analysis using quantified standard would also benefit to understand at the limit of detection(based on viral load of saliva ) that would likely be detected or undetected using this method as compared to upper respiratory swab viral load and RT detection . LAMP techniques offer many advantages including ability to test at scale, low cost, speed to results, reduction in reliance on propriety reagents.. Saliva collection as a self sample is an attractive option compared to obtaining acceptable self administered high nasal and pharynx sampling. It would be useful to triangulate the combined impact on Lod using LAMP and Saliva compared to a professionally sourced sample tested by established RT-PCR and a self samples by RT PCR all of which could be modelled to demonstrate the likely reduction in new cases by each method.

On 2020-08-04 10:47:13, user Tomasz Marczyk wrote:

As I understand children 0-14 years of age are responsible for 1,27% spread of the disease (11 of 890 cases).<br /> I really don’t understand why there is no such conclusion in this paper.

On 2020-08-10 16:49:09, user John Earls wrote:

Interesting paper. If I am reading it correctly it seems like this paper says high cholesterol makes you have less risk for severe COVID. I would be interested in seeing the results after adjustment for statin usage.

On 2020-08-13 03:24:12, user sjh007 wrote:

As a health care professional I have read this interesting article and find this approach to be extremely beneficial. Time is of the essence, as we all know.. I hope that the proper time and effort are given to review this so that there won't be any delays in getting a "good thing" into the public stream.

On 2020-08-13 16:38:14, user SonOfAnOnion wrote:

If you look at just look HIGH RISK patients in this study, you will see that the medications cut the risk of a bad outcome in half.

So despite the given conclusion, when you drill into the ACTUAL DATA, this study supports the use of the medications.

On 2020-07-10 00:50:41, user Savage Henry wrote:

The phrase "No aplicable as not human samples are used." has several typos, and should instead be written "Not applicable as no human subjects were used."

On 2020-07-10 14:38:58, user Tina Black wrote:

Has anyone checked into the deaths of those who were already in anticoagulants before contracting Covid? Although claimed to be viral, what about the use of anti inflammatory, anticoagulants, and heavy antibiotics together for a pharma regimen? Has this idea been tried in any cases?

On 2020-07-10 15:17:28, user Dimy Fluyau wrote:

The paper presents quantitative data on the efficacy of some pharmacological classes of drugs( medications) to manage or treat benzodiazepine( BZD) withdrawal. BZD withdrawal is a life-threatening condition, and its treatment requires the immediate use of BZDs. However, beyond the use of BZD for the management of BZD withdrawal, other drugs( medications) can also manage or treat the withdrawal. Some of them present less risk of withdrawal, tolerance, or dependence. Thus, their use may be recommended.

On 2020-07-11 20:42:32, user Andrew Rasmussen wrote:

A far more likely explanation <br /> https://medium.com/@ageitge...<br /> The problem is that this weekly cycle is fake. It’s an artifact of how the data is collected and reported.<br /> Once a day, each medical facility reports its total number of deaths to a central authority. The overall rise in deaths reported by the UK is the sum of those numbers minus yesterday’s sum.<br /> This causes two important side effects:<br /> The sum for a single day can be (and usually is) incomplete. If any medical facility fails to report a number in time or under-reports, those deaths will be missing from the overall UK total and will eventually get lumped into a future day’s total when that facility catches up.<br /> There is a 1-day lag between each facility reporting and the UK-wide sums being reported to the public.<br /> The explanation for the weekly cycle is simple. Hospitals don’t all have full staffing on weekends, so they don’t have the bandwidth to perfectly report their numbers in time. Slow reporting causes a drop over the weekend and then a corresponding rise after the weekend. And because of the one-day lag in reporting, that shows up in the data as a drop on Sunday and Monday instead of on Saturday and Sunday.

On 2020-07-13 14:47:31, user Donald R. Forsdyke wrote:

Antibody responses are an example of humoral immunity and the authors correctly point out that there is also cellular immunity, mediated by T cells, which they have not studied. They do not clarify the distinction between primary and secondary immune responses, be they humoral or cellular. This has led to media headlines such as "Immunity to covid-19 could disappear in months, a new study suggests" (MIT Technical Review, July 13th). It would be important in the final manuscript the point out that primary responses usually prime. They prime the patient for a greatly expanded secondary response to even low subsequent exposures to the virus. In this context, it would be interesting to compare the response to new vaccines of naïve subjects and those who have recovered from a primary infection.

On 2020-07-15 06:52:23, user Philipp Berens wrote:

The paper makes strong claims about the decline of antibody levels and neutralizing antibody titer. While no specific recurrence is made to the trendlines shown in Fig. 1 and Fig. 2A, these seem to underline the message promoted in the media, that antibody levels/titers go down over time and therefore there may not be immunity for a prolonged period of time.

For a paper making such far reaching statements, the statistical part is extremely thin. The trendlines are loess fits obtained with R using a span parameter of 1.5. This produces a fit which is quite obviously off. I extracted the data from the figure and recreated the plot using other parameter settings (see here for twitter post). For span parameters <1, the fit looks much more reasonable and I am sure also formal model comparison would confirm that. In particular, these fits do not predict declining antibody/titer levels after a certain period, albeit with high uncertainty.

On 2020-08-23 21:48:21, user Sui Huang wrote:

Thank you for this study. As other commenters have said - this is an very useful dataset. But alas - the numbers are difficult to interpret (see questions of Sally). The method section is scant. For instance: can you in greater detail explain how you convert Ct to viral particle concentration? It is not as simple as a linear scaling. Same concentration of viral RNA in different absolute volume can result in distinct Ct values. Also the description in the method is not very satisfactory: You just say: "... following equation derived from RNA quantification was used: -0.27Ct+13.04".<br /> First, this is NOT an equation!!! An equation must contain an equal sign, and tow expressions on either side. DO you mean:<br /> "viral RNA/mL = -0.27Ct+13.04"<br /> Second: How did you determine the parameter values"? The calibration should be shown.

Thank you very much!

On 2020-08-27 13:14:41, user Fernanda Di Genio wrote:

This preprint brings the same results of the preprint of Watanabe previously published more than one month ago:<br /> https://arxiv.org/abs/2007....

On 2020-10-05 15:02:11, user Kamran Kadkhoda wrote:

One out of 7 IgG-positive cases had positive RNA result; unless confirmed by PRNT, the remaining 6 can very well be false positive.

On 2021-04-12 15:40:57, user Philip Machanick wrote:

I wonder how the authors define mild to moderate when 4 patients died.

On 2021-09-12 06:33:29, user kdrl nakle wrote:

Very interesting, so mixing is the way to go. I want to see now J&J with AZ.

On 2021-09-12 09:56:05, user Rae Phillips wrote:

The initial results seem promising. I have 2 questions: 1. It's 18 months on from the beginning of the Novavax trial, are the original participants suffering any negative long term issues?<br /> 2. The spike protein in the Novavax vaccine pass through the blood brain barrier causing any detrimental effects on organs?

On 2021-09-13 23:40:58, user Tom Wenseleers wrote:

Regarding the line "Ad hoc methods to estimate the relative transmissibility of particular SARS-CoV-2 lineages are a computationally efficient alternative (1–3), but have typically relied on models in which one or two lineages of interest are compared to all others and cannot capture the complex dynamics of multiple co-circulating lineages.": this is not quite accurate - ref (1) - Davies et al. Science 2021 (https://science.sciencemag.... "https://science.sciencemag.org/content/372/6538/eabg3055)") actually also uses multinomial (mixed) models to model the competition among >2 co-circulating lineages and estimates the pairwise transmission advantages. Likewise, Campbell et al. Eurosurveillance 2021 also used multinomial models, https://www.eurosurveillanc..., as did Vohringer et al., https://www.medrxiv.org/con.... Best to rephrase this part to make it more accurate, and adequately cite previous work that used multinomial models to estimate the growth rate advantage of different lineages. I also wouldn't call such a model "as hoc", as it's the analytical solution expected with several competing lineages with different transmissibility in an asexual population. Also worth mentioning perhaps why https://www.nature.com/arti... did not succeed in identifying any major mutation under selection (I presume this is due to low statistical power of that phylogenetic RoHO test statistic used).

On 2021-09-14 09:00:20, user Alberto wrote:

"Incidence rates were estimated in the 28 days post first-dose vaccine, 90 days post-COVID-19". I think that this study will be much more informative once you can gather the data to estimate incidence rates between the first dose and 90 days post-second dose of the vaccines.

On 2021-09-19 05:15:21, user Les Smith wrote:

The background rates between 2017 and 2019 are not a valid basis for comparison. The rates of GBS, Bell's Palsy, Neuralgic Amyotrophy, and other such disorders have been significantly suppressed during the pandemic by efforts such as masking and isolation.

On 2021-09-15 03:22:12, user William Brooks wrote:

"The increase in mg household secondary infections could easily be the result of prior infections (before lockdowns) as a source of infection."

I agree. Once infections have become widespread, forcing infected people to stay at home increases the risk they'll infect other members of the household, especially in crowded living conditions.

"It could have easily happen, perhaps even worse without lockdown."

The results of this study suggest high-risk people in poorer areas of NYC would've actually been better off without lockdown.

Also, numerous states in the US have been through Covid waves while keeping schools and businesses open (Florida, Texas, South Dakota, Georgia, etc.), and in all cases, the curve flattened lower than in NY. Also, NY had large numbers of outbreaks in hospitals and care homes, which lockdowns don't prevent (see Belgium, Italy, Spain, UK, etc.)

"And indeed the hospitals in NYC, Brkln and Qns were overflowing with patients."

A few hospitals were very busy, but overall the hospital system never ran out of beds, which is why the hospital ship Trump pointlessly sent was hardly used.

"But when you have political slant it is hard to think, isn't it?"

Apparently so.

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 2021-09-09 09:00:48, user Mc wrote:

Wouldn't these results look different if the previously infected who died were included? i.e. you can't know they wouldn't have a deadly reaction again which would drive the immunity of the "previously infected" category down considerably.

On 2021-09-10 11:41:51, user maury779 wrote:

It is very difficult to tell what variant infected those who were sick prior to February 2021. We know that the Pfizer vaccine was made early in 2020, not using the delta variant. There is now questioning that the efficacy of the Johnson & Johnson vaccine may have been lower because the delta variant was already present during the trials. Furthermore a Kentucky recent study still shows that natural immunity did not protect as well as the previously sick who were then vaccinated. We need to sort out all those studies.

On 2021-09-01 16:05:27, user 4qmmt wrote:

The phrase "previously infected" is not accurate. According to the study, they were previously positive per PCR test, even though they had info on symptoms.

(2) unvaccinated previously infected individuals, namely MHS members who had a positive SARS-CoV-2 PCR test recorded by February 28, 2021 and who had not been vaccinated by the end of the study period;

Per MoH reports, Israel runs PCR at Ct up to 40. The level of false positives must therefore be taken into account. Since they are unknown, the best estimation, and the only one which makes any sense, is previously positive and symptomatic. Maccabi knows this data. This is even stated in the study:

information about COVID-19-related symptoms was extracted from EMRs, where they were recorded by the primary care physician or a certified nurse who conducted in-person or phone visits with each infected individual.

But the study says

unvaccinated previously infected individuals, namely MHS members who had a positive SARS-CoV-2 PCR test

The fact that they did not take that into account in the study tells you that the numbers of previously PCR + and symptomatic is lower than just positive PCR. In fact, the tell you that of 19 previously PCR+ in the recovered cohort, only 8 were symptomatic.

So, of those 8, Maccabi knows who was symptomatic before, but the paper does not discuss that. Why? Look at the Cleveland Clinic study which found 0 reinfections in their > 1300 previously PCR positive and symptomatic unvaccinated workers.

From page 7 of that study

"The health system never had a requirement for asymptomatic employee test screening. Most of the positive tests, therefore, would have been tests done to evaluate suspicious symptoms."<br /> What this means is that this Maccabi data study is actually setting the lower limit for the multiple of protection of natural immunity over vaccinated, i.e., at least 27X better, and likely orders of magnitude greater than that.

On 2021-09-09 09:59:21, user Patricia wrote:

This is one of a many studies that confirm what we already know, and that the CDC has confirmed, with >50% infected not knowing they are infected and being asymptomatic. Of those with symptoms, 80% are mild with cold-like symptoms and 80% of deaths are elderly. The US has a 1.6% death rate with SARS2, far below Peru's 9.2% or Mexico's 7.8% or Sudan/Syria 's 7.4% - out of 200 countries, the USA ranks #85/86 in the world. That is most likely due to the CDC refusing to support any safe COVID treatments successfully used around the world, the Media and Politicians condemning and smearing safe treatments (even banning use in multiple states), and the DO NOTHING APPROACH of : if you are really sick and need medical attention, quarantine and do nothing until you are on death's doorstep and need an ambulance & ventilator. Physicians that defied the nonsense & political hatred, by saving lives and treating COVID with very safe FDA approved drugs - with the common practice of off label use to treat the symptoms - show extremely low deaths rates. Excluding the CDC's push to use the very expensive Gilead's Remdesivir intravenously in Emergency settings that has a 2/3 success rate and ZERO STUDIES or Vanderbilt's monoclonal antibodies - There are over 1,000 studies with over 35 easily accessible drugs, that ALL PROVE INCREASED IMPROVEMENT. So why did the CDC and Doctors discourage any treatment and push the DO NOTHING UNTIL YOU ARE ALMOST DEAD PROTOCOL? In 21 months, since the first death in January 2020, 12% of the US Citizens have tested positive. >80% have mild to moderate symptoms. We have no idea how many had SARS2 because we have no idea how many more were asymptomatic. However, the CDC estimates 114.7M Americans have had SARS2 and have natural immunity. According to far left Media sites, the worst states to manage COVID are those with >200 cases per million, NY 349, NJ 284, SC 273, NV 237, DE 221, DC 209, MA 207 per million population. The most heavily populated states were the first states to be afflicted with COVID - 39M CA & 30M TX hovered around 97 per million, despite having highly criticized opposite approaches of CA Strict Lockdowns versus Texas Open with common sense guidelines. Since that revelation, COVID tracking was suspended by many or dates cherry picked when it became popular to smear successful states to deflect from the fact that the vaccine do not work and hence - are not a vaccine. They never said it would stop the disease, just somehow promised people wouldn't have as severe symptoms. Like treating flu with medicine to ease the symptoms for recovery. The US dismisses the chaos around the world, MSM refuses to inform us on major protests around the world that have been occurring for months. Why isn't the Oxford Director of Vaccines statement on every paper and news segment? He confirmed the vaccine isn't stopping SARS2 and herd immunity with it is "unachievable" and "mythical". What we do factually know today - is very detailed tracking in Iceland and Israel, who were also very PRO-VACCINE. Vaccinating ~90% of their populations, only to see a far worse spike of cases in SARS2 outbreaks that appeared in July August 2021. The inventor of the new mRNA vaccine publicized problems with using the vaccine, warning of manipulating antibodies that would results in weaker SARS2 variants become more virulent and slipping past the VAX'd immune system, creating a huge rebound of illness. And this summer, we are witnessing his expert analysis come to fruition.

On 2021-09-27 15:22:10, user Mitch Crimson wrote:

Can the authors release more detail on how they got their denominator value wrong?

For such a big error, may as well publicize HOW it happened since it could be educational to other studies and researchers who will want to avoid similar mistakes.

Thanks -Mitch

On 2021-10-03 09:50:51, user kdrl nakle wrote:

Interesting. Authors spent only three sentences to speculate why would this be the case. But this would also imply that vaccination induced immunity is more effective than natural immunity and I don't think this is yet conclusively proven.

On 2021-10-09 23:12:07, user kdrl nakle wrote:

So the least likely to get infected are the most likely to vaccinate. Not surprising.

On 2021-09-05 10:18:52, user Jessie Abbate wrote:

The authors need to explain what (precisely) they mean by their discussion statement that "the virus becomes more contagious as it is screened through the vaccinated population, eventually to become the dominant strain to infect the entire population." The following sentence and reference #12 (which is just discussing the presence of breakthrough infections) do not support this statement. I would argue to remove this statement entirely, given what I believe they mean and the (thus far) unsupported and controversial nature of the sentiment that high vaccination rates will drive evolution of escape mutants. In the history of vaccination for improving public health, this sentiment has never once helped nor been supported by the data; on the contrary, the data support the immense success of vaccines to control the spread and negative impacts of infectious diseases. While it's true that pathogens can adapt to persist when hosts become increasingly unavailable (such as with influenza pandemics), it is not an inevitability (look at smallpox), does not require that the adaptations will also lead to higher severity in vaccinated people, and above all, is not supported by any current data for COVID-19 as the emergence of the Delta variant had nothing to do with high vaccination rates. It emerged under low vax rates, and has dominated globally irrespective of vax rates.

On 2021-09-08 04:46:33, user Anni wrote:

Quite normal for any vaccine to affect immune responses to unrelated pathogens <br /> https://www.sciencedirect.c...<br /> ... not a bad thing, and Comirnaty has been shown to activate antibodies from past viral infections, all good signs.

Also, Don't forget to check the conflict of interest statement of the study we are commenting on.

The employer, "Trained Therapeutix and Discovery", with the motto "Don't fight the wave, ride it"!! is advertising their treatments to "dampen the ruinous hyperinflammation" in severely ill, hospitalized COVID patients.

It's clearly in their interest to reduce confidence in vaccines, as vaccinated individuals seldom need their treatments, vaccination mostly prevent the aforesaid ruinous hyperinflammation issue ...

Personally, unless you are a pharmaceutical company poised to profit off severely ill covid patients, I'd strongly suggest fighting the wave of infections with a good vaccine.

On 2022-12-08 22:41:55, user Dr. Kate wrote:

It's interesting to see what has become of this paper after peer review. A lot of the conclusions that made it on the front pages in German media are now significantly toned down. It should be a warning to all of us how quickly media jump on certain conclusions that are popular at the time, even if they do not hold up to scientific scrutiny. Also, neither media nor public opinion tend to look back and check which part of the initial report held up to peer review and which ones had to be significantly reworked.

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

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

On 2022-01-09 23:14:33, user Neil Bogdanoff wrote:

So why no mention about how people can test positive with both antigen and PCR tests for Covid-19 for up to three months after infection? And how might that portion of the population impact the results of the study? Also, is there some analysis as to why the different type of vaccines might play a role? And of the population studied, which vaccines did they receive--and what percentage had received booster shots? When will the study be peer-reviewed?

On 2022-01-13 08:19:03, user PierreLouis wrote:

Nice study. I disagree with your conclusion, though. We can’t deduce from the data that dogs sensed persistance of viral infection. They may sense the smell associated with the so-called long Covid syndrome. Which may not related to viral persistance but to post-infection metabolic chronic disturbance. Interesting controls would be patients with non infection-related depression and patients with chronic fatigue after another infection (cured bacterial sepsis for example). Anyway, a very original and provocative study!

On 2022-01-14 03:44:42, user Mike B wrote:

Although your result shows that analytical sensitivity is similar for both variants, more critically it also echos by analytical evaluation (" 5 of 11 (45%) Omicron samples were negative despite having levels of virus above the LoD." ) the numerous clinical complaints that significant false negative results suggest that this test is unreliable as a screening tool to identify infected and contagious individuals or as a negative screening tool via serial testing in a specific populations. (travel, school, theatre arts, music, live performance). This study is significant because it's sample set comes from asymptomatic and almost entirely fully vaccinated donors. <br /> There is good concordance with a recent clinical study (Adamson, Sikka, https://doi.org/10.1101/202... ). In that study they confirm infectious transmission on day 1, with day 0 being Limit of Detection via RT-qPCR. In their study antigen tests did not turn positive until day 3 with viral loads via RT-qPCR somewhat lower than days 1 and 2. This study confirms a false negative issue and eliminates sampling technique as the source because RT-qPCR was run from the same sample as the antigen test. The clinical significance of this issue is of primary importance in context of the rapid rise in viral load and infectious transmission of Omicron as highlighted in the clinical study. The cause of the false negative issue needs urgent exploration. Thanks for a great job and continued hard work. Valuable information.

On 2022-01-14 21:44:55, user JJ wrote:

The results are interesting and valid - but in my opinion, this is true only until the moment when the testing strategy started to differ for vaccinated and unvaccinated individuals (since about the end of summer 2021, vaccinated individuals did not have to test even if in close contact with a PCR-positive person while unvaccinated had to do so). This naturally lead to the increased difference between the numbers of unvaccinated than vaccinated PCR-positive individuals, which is a major source of bias that should be, in my opinion, removed.

On 2022-01-17 00:08:52, user Satvinder Singh Bawa wrote:

The key findings of this study are identical to the South California study. Vaccines are effective for Delta. Not for Omicron. Infection rates are actually highest in the boosted. But hospitalizations are lowest in boosted.<br /> https://www.medrxiv.org/con...

On 2022-01-18 15:04:19, user JJackson wrote:

Figure 1a shows about a 10 day difference between the onset dates for delta and omicron cases with delta averaging mid Dec and Omicron nearer Christmas. Given that the date on the paper is 11th Jan this just is not giving adequate time for disease progression in the Omicron cases to expect ICU admissions and deaths. For a fair comparison numbers of hospitalisation, ICU admissions and deaths for Omicron should be take 10 days later than for Delta.

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

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

On 2022-01-29 14:34:02, user Alberto wrote:

Thank you for highlighting this problem. It's amazing that indoor air quality has been so ignored apart from some "open the windows" advise.

On 2021-10-17 02:07:31, user X Basch wrote:

Xu, Katherine et al. “Elevated NGAL is Associated with the Severity of Kidney Injury and Poor Prognosis of Patients with COVID-19.” Kidney international reports, 10.1016/j.ekir.2021.09.005. 8 Oct. 2021, doi:10.1016/j.ekir.2021.09.005

On 2022-02-01 02:14:51, user Sulev Koks wrote:

The manuscript has been accepted for publication in Experimental Biology and Medicine.

On 2022-02-03 22:33:14, user Maksim wrote:

The manuscript is now accepted for publication <br /> ( Coronaviruses, Bentham Science) <br /> https://www.eurekaselect.co...

On 2022-02-26 15:01:29, user Rogerblack wrote:

The paper investigates symptoms remaining after 12 weeks. 15 weeks ago, 11M/46M of the UK population was boosted. (3 weeks for immune response and paperwork)

While perhaps smaller numbers than we might like, this would lead to approximately 63 (294(2-dose)*0.22) boosted in your cohort developing LC, if there is no change in risk from doubly vaxxed.<br /> This is not good enough to show if the protection from LC is slightly increased, but it is certainly enough to exclude an OR of (say) 0.1 or 0.2, which would be valulable to report.

It is my understanding that the CIS also contains questions related to employment/role.

What fraction of those reporting activity limitations have had changes on those metrics?<br /> An impatient advocate.

On 2022-02-27 04:15:03, user RonG wrote:

Dr. Bredesen most certainly does have a "conflict of interest". He uses this article to promote his company and sales of his "bestselling" book. For me, this lack of transparency casts grave suspicion on the results. Many scientists and doctors work with corporations to publish research, but they honestly acknowledge that association.

On 2022-03-25 20:53:31, user kpfleger wrote:

I didn't notice mention of the date range over which events were considered (did I miss it?). We know it takes time (many weeks) for 25(OH)D to rise after initiation or increase in oral daily D3 intake. A jump from zero to 800 IU/d will reach steady state relatively quickly, but a jump from 0 to 3200 will take maybe a month to reach 1/2 to 2/3 of the way to the new steady state 25(OH)D and ~2 months get most of the rest of the way. See for example Fig 1 (looking at the 125ug line) of "Human serum 25-hydroxycholecalciferol response to extended oral dosing with cholecalciferol" Heaney et al 2003:<br /> https://academic.oup.com/aj...

A well enough funded study could have done interim 25(OH)D tests, but I realize that was not possible here. Without such tests, if analysis for the 3200 vs no-offer was not limited to the range of dates for which the 3200 offer arm should reasonably have been expected to have fully increased their serum levels, then any effect may have been significantly diluted (due to mixing in infections in the offer arm(s) before the supplements could have done much good).

To the extent to which vaccination over the course of the 6 months of the trial caused a drop in infection events as each subject became vaccinated, that could have exacerbated the dilution by leaving fewer months between achievement of new raised 25(OH)D levels and increased protection from vaccination. How many of the infection events happened if the final 1-2 months?

I hope this issue can be addressed before publication. It would be interesting to see a graph with the full 6 months of the trial on the x-axis and number of infections per week for each of the 3 arms plus some measure of the cumulative % of each arm vaccinated by that point in time.

On 2022-03-27 12:21:47, user helgarhein wrote:

To bring my immune system into best working order I would want to be vitamin D replete for some months or years. (Similar to the finding that it took 5 years of 2000 IU daily to reduce the incidence of auto-immune diseases, Hahn et al BMJ, Jan 2022.) This trial lasted only 6 months, but, crucially, we are not told when the optimal blood level was reached. Only once the optimal level for immune health has been reached should an assessment happen. This is 75 nmol/l. Some individuals might need more than 3200 IU to reach it, some might even reach it in the 800 IU group. Could this subgroup be assessed: Those who achieved the defined sufficient 25(OH)D level across all 3 groups? Did this ‘replete’ group have reduced incidence of Covid infections?

On 2021-11-02 11:31:33, user Tom Jefferson wrote:

The review has now been published in Clinical Microbiology and Infection: https://doi.org/10.1016/j.c...

On 2021-11-14 16:21:34, user Isaac Núñez wrote:

This article has been published in the journal 'Medicina Intensiva' and is available at the following URL: https://www.sciencedirect.c...

On 2022-07-12 03:17:06, user Souradeep Chowdhury wrote:

The article has been accepted for publication; please find the link attached:

https://pubmed.ncbi.nlm.nih...

Please review and link to the published version. Regards

On 2021-05-16 16:45:43, user Richard Vallée wrote:

This is exactly as expected because of GIGO issues with medical records. As a result most medical records dealing with Long Covid symptoms are invalid, are missing most of the relevant data and feature too much arbitrary speculation.

There was no coding until a few months ago. A LC coding requires a positive test. Most long haulers did not get a positive test, more than half from surveys and research, as most people were denied a test for most of the pandemic. Most physicians are still not able to recognize LC, let alone from the start. Many physicians who may recognize some of the more typical cases would not use this coding as they don't believe in it. Most long haulers had multiple experiences of gaslighting and having a mix of anxiety, depression and vague "psychological issues" on their record, there is no chance that medical records accurately reflect reality. Many are simply not going back to see a medical professional because their experience was too awful, they lost all trust in medicine, at least for the time being, and anyway reality was not recorded at the time and there's nothing they can do to change that.

This is like having a security system where cameras are active but most of them are pointed at a wall and none are recording anyway, it writes straight to /dev/null. Once the event that needs to be analyzed has occurred, it's too late, nothing was recorded. A system has to be switched on in order to work. It was explicitly kept off. So now the whole first year was entirely wasted because no one was actually paying attention and it's an active process, it requires people to pay close attention, react and adjust to new information, which still has not happened.

There was an opportunity to do this correctly from the start. Many people predicted Long Covid, as early as April 2020, as there is a lot of precedent for chronic illness following infections, old and familiar. Medicine dropped the ball hard here. It's not too late to start doing the work but to rely on invalid data will only fuel more failure moving forward. I'm not even a long hauler and this stuff is obvious. Please do better.

On 2023-02-04 12:23:54, user Lucija wrote:

Interested work based on the latest advances in ablation strategies. Nevertheless, there are a few things to note. PFA has now been successfully performed in humans in several clinical trials (and in regular clinical practice since the last year when Farapulse catheter got its CE mark) so the obvious disadvantage of this study is that it is not an in-human study. Follow-up for lesion reassessment has a wide range - 3 weeks to 3 months. Three weeks is too short of a period for any relevant conclusions as we aim for arrhythmia freedom in our patients - 3 weeks would not be satisfying in clinical surroundings. Longer follow-up is warranted - most studies now aim for at least 3 months and ideally 1 year. Also, single-shot catheters are repositioned during the ablation, so to replicate clinical conditions this should be taken into consideration. These findings confirm the safety of the PFA, which is the main strength of the study, especially when you consider brain MRI + histology findings (you can not do that in humans if you have successfully performed the procedure :) !). Finally, endoscopy for esophageal lesions should have been performed as it has been in most studies confirming PFA's safety for adjacent structures).

On 2020-12-07 04:14:29, user Luis Ricardo Illescas wrote:

I think it would be important to separate the group that died in the street from the rest of the group that includes dying in houses, accommodations, shelters. They reflect a different level of tragedy

On 2023-06-01 13:18:49, user Ning Zhang wrote:

Please link this to the journal version. This manuscript has been accepted by the Global Health Journal (https://doi.org/10.1016/j.g... "https://doi.org/10.1016/j.glohj.2023.05.001)"). https://www.sciencedirect.c...

On 2021-12-16 16:22:57, user itellu3times wrote:

While I understand the place for these calculations given the social and government actions around the world, even the necessity for someone to do it, and it comes out on the correct side of things, I must point out that it is also socially deranged and mathematically extremely vague. "The unvaccinated" do not even exist as such in US, EU, UK, and other places where the pandemic has run now for two years, almost all will now have contracted and recovered from COVID, have natural immunity, and be no more at risk of getting or giving COVID than any of "the vaccinated". I would like to see studies of just where these "unvaccinated" patients today are even coming from - are they all immunocompromised, or vitamin D compromised, or are the figures cooked by authorities who use biased procedures "for reasons of public health", to bias the results? We know "the vaccinated" transmit the virus too, is that supposed to comprise the "base rate" here? How about reversing this, what if the vaccinated are excluded, what's the NNT then? But, whatever the calculation here, forward or reverse, the odds calculated are for one event, and when there are N such events even small odds are enough to propagate. Now, perhaps this factor is already comprised in this "base rate", I guess it is, but then the real odds for a single event may be much lower. You are then talking about socially exclusionary processes that even the article states are really for reasons of coercion, for event numbers that are truly tiny. Is this rational? Oh, and the SAR numbers must be considered plus or minus 80%, instances of any type event may be vastly better, or worse. Very hard to use such things to base any serious decisions on at all. Though, in science, I guess someone has to give it a look, as seems to have been done reasonably here.

On 2020-12-24 10:34:39, user Aroop Mohanty wrote:

The review is one of the first of its kind at a national and international level. It is good to see that mobile health intervention found effective in improving maternal health outcomes. The review and meta-analysis did very well and need of hours for developing country including India. the methodology and search strategy used very clear and crisp and elaborated in detail. I am impressed that the use of Mhealth intervention improved maternal outcomes. <br /> The use of the PICO approach and exclusion of research done in developed countries have a direct implication of the work in low and middle-income countries like India.<br /> The research question is clear and concise<br /> I am highly recommending such kind of work to improve maternal and child health indicators in developing countries.

On 2020-07-27 13:58:22, user Rosemary TATE wrote:

Excellent and interesting paper. However, although you say you adhered to the relevant EQUATOR (TRIPOD) guidelines I note that you have not uploaded the checklist. Very few people seem to do this although they tick the box that they have. I'm wondering why? Can you enlighten me?

On 2020-12-30 22:01:49, user Henry wrote:

" translated to a rise of 21.1 nmol/L of 25OHD in the UK Biobank population, a rise that is comparable to what can be achieved with vitamin D supplementation, especially in short courses[38]."

A 21nmol/liter serum raise is not much. That is what you get when you supplement too little vitamin D (400 iu / day for short courses).

Did you compare 150 nmol / L and higer to 30 nmol / L? I would say someone has optimal vitamin d at 150 nmol / L.

On 2020-08-08 00:52:19, user Cyraxote wrote:

The covidestim site shows 12 rows of 4 states. That's 48 states. Maryland is one of the missing ones, but I don't know the other.

On 2021-01-20 15:30:41, user Zuzana Kollarova wrote:

This statement is NOT TRUE and the citizens of Slovakia have no idea they are a part of some medical research:<br /> "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived - Yes"

We have been all forced to this by a number of restrictions and consequences presented by the prime minister and government prior the testing and they let the "choice" to us. If we wouldn´t take part on that testing we couldn´t go to work, to any store, bank, post office etc.. Only basic needs could by fulfilled like grocery shopping, pharmacy etc. Healthy people who refused to take part on this had to stay at home in quarantine like they were infected and could go outside without the risk of getting a fine, if a police would control them randomly on the streets. This lasted 14 days.

They used army, our president found out just from the papers and not officially. She has been called a traitor by the prime minister just one day before the mass operation should start, when she asked for a really voluntary participation for the citizens.

The testing has been done by anonymous, also not always professional medical staff, without knowing their names and place of work.

Those blue papers (test result confirmation) do not contain the necessary legal requirements to be called a "certificate" officially by the law.

And now, we are in the middle of 2nd mass "screening" now, since Jan 18 2021 during the winter, even though the scientist didn´t recommend it at all in current situation.

And again- no one is collecting our written and signed consent. From Jan 27 2021 there will be again 2 groups of people - the "blue" ones and the rest of us. The country will be then split into two half by the results and the worse half of the country has to undergo this procedure 1-2 times again until the Feb 07 2021 and until our prime minister will be satisfied with the results...

On 2021-02-07 17:39:47, user Martin Gažák wrote:

Dear all, regarding the statement "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived." is a lie. I as a citizen of Slovak Republic participated already 4 times on AG testing, every time under the threat of ban of movement, ban of work, yesterday because without my negative test my child would not be accpeted at school. I was never informed that I am participating on any sort of research. I did not sign anything. I consider the above statement as misleading and unethical, especially because the authors are government officials. Best regards, Martin Gazak

On 2021-08-21 22:44:12, user Ands Hofs wrote:

Can we please introduce more calibrated PCR that measures the mucosa DNA count and gives out<br /> Viral Load = viral units / mucosa DNA units?<br /> Only then the force and technique of swabbing is not changing the resulting viral load wildly.

Even RTLAMP.org is able to do this, open science test, might be liked by some in the comments here, done in a parallel test, and Boston Children's Hospital did a very good job showing children sometimes have 10x higher calibrated viral loads, which has to be treated early with determination in nasopharynx and mouth, like with xylitol + Grape Seed Extract nasal spray, puff and breathe in a bit to protect vocal chords, upper trachea, whole nasopharynx. Vulnerables: add azelastine as pre-spray dito. Report on CARVIN (11.8.2021) shows excellent efficacy.

If you want "life" reproduction rate, you have to train a dog, see scent dog identification of samples .. covid. Work of TiHo, small animals university Hannover. Built a training device they call scent learning box, like a game for the dog. In one week it is on 95% congruency of PCR, but better: 4 days before infectiousness, and quasi live. It doesn't over-diagnose and does diagnose viral replication. <br /> There is a group that built a speech interface for a dog. This would enable to train the dog on many illnesses, differentiate flu from covid, and even predict severe case, as it can smell susceptibility to autoantibodies (or in another picture: prevalence of MCAS, see Prof. Afrin on Covid. Having deep implications on therapy and prevention. The tricky part is to diagnose MCAS with its 200 mostly congruent symptoms to Post-Covid. So obviously related except scarred tissue of course.)

Even better: build an electronic nose as sensitive as a dog's nose on some marker molecules for covid and a tensor flow neural network in a mobile phone to read it out. The do progress with mice conk cell detecting proteins they use as film on a chip based electronic nose.

On 2021-08-24 01:52:14, user Raihan Farhad wrote:

Please answer the following, in the interest of academic integrity: <br /> 1. What is the effectiveness of masks used in your model? What number did you use? What type of mask? Worn in what way. I can only assume, given the absence of any experimental data regarding un-regulated masks stopping Covid aerosols, you either assumed the effectiveness of a mask or used someone else's assumption. Please divulge.

1. What is the assumption you made about % of kids having been exposed already? Covid has been around for a while now. If you assumed 0 previous exposure, that is unrealistic, but please state so clearly. If you assumed any other number, please explain how you came to that number and state that number.

2. What is the duration of infectiousness assumed in your model. According to science, someone infected is infectious for about 5 days. After that, even if he dies, he is not infectious. Please explain the temporal nature of infectiousness assumed in your model.

3. Please make your entire model / simulation software (all code) and all parameters, assumptions public.

On 2020-08-18 17:33:47, user Rodolfo Rothlin MD wrote:

Dear Dr. Turgeon,<br /> Thank you for your commentary and your interest in our manuscript. Please, find my answers below.

1. Please provide the rationale for selecting July 31 as the date for interim analysis. Please also provide details regarding this interim analysis, including pre-specified stopping rules, who had access to the data. Although this manuscript is labeled as a "preliminary report", it would be valuable for the authors to explicitly state whether this trial is ongoing, and whether any changes to the conduct of the trial were made based on this interim analysis.

The rationale for selecting July 31st was made for several reasons: First, we assumed that by that date we would have between 50 and 100 patients. Although our estimated sample size was 390 (which we rounded to 400), it is worth noticing that this number accounts for a scale factor on both the mean and the variability estimations; without these factors, the estimated sample size was 52 patients total, and with only a variability factor of 2 the total estimated number was 100. Therefore, we evaluated that July 31st was an appropriate time to make the first interim analysis. Second, as you may know, Argentina was expected to be peaking around that time. And it actually seems that it may be doing it right now. So, a second reason for that date was our prediction that if the results were valuable it could be a useful information for our health authorities. The trial is still ongoing and a second interim analysis will be carried out at 140 patients.<br /> 2. In version 1 of the article on this site, the Methods section had a sentence that stated "No concealment mechanism was implemented". This was subsequently removed in version 2 yesterday. Please clarify what is meant by this. Did the authors mean to imply that allocation concealment was not performed, or was this an erroneous statement intended to describe the unblinded nature of the study? Please also describe the process for treatment allocation and how allocation concealment was maintained.

The sentence you refer to was removed because it was inaccurate. The problem emanated from the fact that our protocol did not foresaw a concealment mechanism. However, during the conduct of the trial, although no mechanism like closed envelopes with randomization was used, on site enrollment was made by an investigator and randomization was made by a second investigator who was unaware of the clinical characteristics of the participant. We are confident that no bias towards the control group was present as reflected by data on table 1 of our manuscript.<br /> 3. The authors describe a change in the primary outcome in terms of timing of CRP measurements. However, I note that the clinicaltrials.gov summary of this trial previously had an entirely different outcome as the primary outcome, with CRP only described as an exploratory/tertiary outcome. The authors should describe the timing and rationale for switching the outcome from a clinical one (need for supplemental oxygen in the first 15 days post-randomization) to the inflammatory biomarker CRP.

As you might have read in the methods section of our manuscript, data from our trial was uploaded by a third party. Unfortunately, endpoints from a working version of the protocol were submitted. This was corrected as soon as we noticed it.<br /> 4. Despite changing the timing of CRP measurements, data on this modified primary outcome of CRP was missing in a large proportion of patients at day 5, and in the majority of patients at day 8. Further details should be provided regarding the reason for missing data, how this was handled in their analyses, and how this should temper conclusions.

Data missing from days 5 and 8 were related to several factors. Some patients were discharged before day 5 and before day 8. Others were lost at day 5 for logistical reasons. <br /> No imputations were done to account for these values.<br /> 5. Finally, performing an interim analysis and disseminating their results in the midst of an open-label trial with subjective endpoints can pose challenges to maintaining impartiality. The authors should describe how they will mitigate potential allocation, performance, and detection and attrition bias during the remainder of the trial.

We disagree with CPR measurements being subjective<br /> Again, thank you for helping us clarify these points.<br /> All the best,<br /> Rodolfo Rothlin

On 2021-02-03 00:57:08, user kdrl nakle wrote:

Internationa Univerlsity?<br /> C'mon, nobody proofreading the paper?

On 2020-08-24 11:06:25, user Atif Habib wrote:

An excellent paper which provided the importance of short birth intervals and the associated factors in the context of Pakistan. The results indicate that lack of contraception and illiteracy significantly contribute to the problem however it is pretty evident that priority should be given to modern contraception which is comparatively a low hanging fruit in comparison to averting illiteracy.

On 2021-06-25 00:23:11, user Otheus wrote:

While I would like to believe the results of this study, the details and the presentation of their numbers leaves much to be desired for the numerically astute/obsessed. A CI of "0 to infinity" means someone has not really done the statistics right at all. In the online preprint of the article, which may be an older version, the article cites -- somewhat deceptively -- provides the percentages in terms of the number of infections in one group with respect to the number of infections in another group. So, 99.3% of the infections were from people not previously infected and not vaccinated and 0.7% of infections were from vaccinated group. The problem with doing it that way is that you have a much larger population of people not infected and not vaccinated than the other sub-populations.

Imagine you have two drawers of socks, and in each drawer, there is the same ratio of white socks to red socks -- let's say 1 red sock for every 9 white socks, ie, 10% red. You then pull out 10 socks from each drawer. From the first drawer, you pull 5 red socks and 5 white socks, and from the second drawer, you pull 1 red sock. It would seem the first drawer had a greater percentage of red socks. In fact, the first drawer had 500 socks and therefore 50 red socks, while the second drawer had exactly 10 socks and only 1 red sock. The probability of this happening in each case is about the same. The number of red socks to socks drawn from the first drawer is 50%, but for the second drawer, it is 10%. But if you look carefully, the first drawer has 5x the number of red socks as the second. The ratio of red socks drawn to total *red* socks in that drawer is 10% in both cases.

At any rate, from the math given, I calculate that the rate of those got sick among those who were not vaccinated and did not have previous infection was about 10%, while the rate of those who got sick among those who either had been vaccinated or had a previous infection was about 1.2%. Since 0 people with a previous infection reported getting sick, we get 0%. What's significant here is that the population of those unvaccinated and not infected was 10x higher than that last group.

An important sentence from the paper sticks out: "Not one of the 2579 previously infected subjects had a SARS-CoV-2 infection, including 1359 who remained unvaccinated throughout the duration of the study." Those numbers appear high enough with respect to the lower bound of the infection rate (1.2%) to have enough statistical power. You'd expect to find at least 31 cases for the null hypothesis. It seems quite improbable to get 0 results unless previous infections provide very strong protection.

On 2020-08-26 13:53:31, user Melimelo wrote:

This is amazing. Well done! Can you share pictures of the equipment you made?

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

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

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

Keywords

• SARS-CoV-2 specific T cells

• Adoptive T cell transfer

• COVID-19

Main findings

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

Limitations

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

Significance

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

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

On 2020-05-05 03:43:04, user Sinai Immunol Review Project wrote:

A possible role of immunopathogenesis in COVID-19 progression

Anft M., Paniskaki K, Blazquez-Navarro A t al.; medRxiv 2020.04.28.20083089; https://doi.org/10.1101/202...

Keywords

• SARS-CoV-2 spike protein-specific T cells

• COVID-19

• adaptive immunity

Main findings

In this preprint, 53 hospitalized COVID-19 patients, enrolled in a prospective study at a tertiary care center in Germany, were assigned to moderate (n=21; light pneumonia), severe (n=18; fever or respiratory tract infection with respiratory rate >30/min, severe dyspnea, or resting SpO2 <90%), and critical subgroups (n=14; ARDS, sepsis, or septic shock) according to clinical disease. Moderately and severely ill patients with a PCR-confirmed diagnosis were recruited within four days of clinical onset, whereas critically ill patients were enrolled on average within 14 days of diagnosis on admission to ICU. To account for the overall longer hospital stay in ICU cases prior to inclusion, repeated blood samples were obtained from moderately and severely ill donors within eight days post recruitment. For 10 out of 14 ICU patients, no follow up blood samples were collected. At recruitment as well as on follow-up, circulating lymphocyte counts were below reference range in the majority of enrolled COVID-19 patients. Relative frequencies were significantly reduced in critically vs. moderately, but not vs. severely ill individuals, with substantially lower NK as well as CD8 T cells counts, and a concomitant increase of the CD4:CD8 T cell ratio in ICU patients. Basic phenotypic and immune cell subset analysis by flow cytometry detected lower frequencies of central memory CD4 T cells as well as reduced terminally differentiated CD8 Temra cells in critical COVID-19. Moreover, a decrease in activated HLA-DR+ CD4 and CD8 T cells as well as in cytolytic CD57+ CD8 T cells was observed in critical vs. severe/moderate disease. Similarly, frequencies of CD11a+ CD4 and CD8 T cells as well as CD28+ CD4 T cells were lower in critically ill donors, indicating a general loss of activated bulk T cells in this subgroup. In addition, a reduction of both marginal and transitional CD19+ B cells was seen in patients with severe and critical symptoms. Of note, on follow-up, recovering severe COVID-19 patients showed an increase in bulk T cell numbers with an activated phenotype. Importantly, SARS-CoV-2 spike (S)-protein-specific CD4 and CD8 T cells, identified following stimulation of PBMCs with 15-mer overlapping S protein peptide pools by flow-cytometric detection of intracellular CD154 and CD137, respectively, were found in the majority of patients in all COVID-19 subgroups at the time of recruitment and further increased in most subjects by the time of follow-up (antiviral CD4 >> CD8 T cells). Most notably, frequencies of both antiviral CD4 and CD8 T cells were substantially higher in critically ill patients, and virus specific CD4 and CD8 T cells in both critically and severely ill subgroups were shown to produce more pro-inflammatory Th1 cytokines (TNFa, IFNg, IL-2) and the effector molecule GzmB, respectively, suggesting an overall increased magnitude of virus-specific T cell inflammation in the context of more severe disease courses. Furthermore, frequencies of antiviral CD4 T cells correlated moderately with anti-S-protein IgG levels across all patient groups.

Limitations

In general, this is a well executed study and most of the observations reported here pertaining to overall reduced bulk T cell frequencies (along with lower NK and other immune cell counts) as well as diminished numbers of T cells with an activated phenotype in ICU vs. non ICU COVID-19 corroborate findings in several previous publications and preprints (cf. https://www.jci.org/article... https://academic.oup.com/ji... https://www.nature.com/arti... https://www.medrxiv.org/con... https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.17.20061440v1.full.pdf)"). Notably, in contrast to many previous reports, the prospective study by Anft et al. enrolled a relatively larger number of COVID-19 patients of variable clinical disease (with the exception of mild cases). However, there are a few weaknesses that should be addressed. Most importantly, the choice of statistical tests applied should be carefully revised: e.g. comparison of more than two groups, as seems to be the case for most of the figures, requires ANOVA testing, which should ideally be followed by post-hoc testing (despite the somewhat confusing statement that this was conceived as an exploratory study). Given the overall limited case numbers per clinical subgroup, trends even though they might not reach statistical significance are equally important. Similarly, some statements are overgeneralized and should be adjusted based on the actual data shown (e.g. the authors continue to refer to gradual reductions of activated T cell subset numbers in moderately vs. severely vs. critically ill patients, but for the majority of data shown substantial differences are apparent only in ICU vs. non-ICU patients). Moreover, it would be helpful to include representative FACS plots in addition to explanatory gating strategies provided in the supplemental document. There are also several inconsistencies regarding the order of data presented here (e.g. in the main manuscript, Fig S5 is chronological referred to before Fig S4) as well as pertaining to relevant technical details (according to both the main manuscript and the gating strategy in Figure S5, virus-specific CD4 T cells were identified by CD154 expression; however, in figure legend S5 virus-specific CD4 T cells are defined as CD4+ CD154+ CD137+). Additionally, from a technical point of view, it is somewhat intriguing that the percentages of virus-specific T cells identified by expression of CD154 and CD137, respectively, following peptide simulation seem to differ substantially from frequencies of CD154+ or CD137+ INFg+ virus-specific T cells. Assuming a somewhat lower extent of cellular exhaustion in the moderate COVID-19 group, one would expect these cell subsets to mostly overlap/match in frequencies, therefore suggesting slight overestimation of actual virus-specific T cell numbers. In this context, inclusion of positive controls, such as CMV pp65 peptide stimulation of PBMCs from CMV seropositive donors, in addition to the already included negative controls would also be helpful. Moreover, in view of the observation that virus-specific T cells were found to be increased in critically ill ICU over non-ICU patients, a more stringent characterization of these patients as well as assessment of potential associations with clinical characteristics such as mechanical ventilation or death would add further impact to the findings described here. Finally, this study is limited to anti-S protein specific T cells. However, evaluation of N and also M-protein specific T cell responses are likely of great interest as well based on current knowledge about persistent M-protein specific memory CD8 T cells following SARS-CoV-1 infection (cf. https://www.microbiologyres... "https://www.microbiologyresearch.org/content/journal/jgv/10.1099/vir.0.82839-0)").

Significance

In addition to reduced frequencies of activated bulk T cell numbers, the authors report an enhanced virus-specific T cell response against S protein epitopes in critically ill COVID-19 patients compared to severely and moderately ill individuals, which correlated with anti-S protein antibody titers (also cf. Ni et al.: https://doi.org/10.1016/j.i... "https://doi.org/10.1016/j.immuni.2020.04.023)"). This is an important observation that mirrors previous data about SARS-CoV-1 (cf. Ka-fai Li C et al.: https://www.jimmunol.org/co... "https://www.jimmunol.org/content/jimmunol/181/8/5490.full.pdf)"). Furthermore, in accordance with a recent preprint by Weiskopf et al. (https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.11.20062349v1.full.pdf)"), virus-specific CD4 T cells were found to increase in most patients over time regardless of clinical disease, whereas antiviral CD8 T cell kinetics seemed slightly less pronounced. Moreover, in the majority of moderately and severely ill cases, virus-specific T cells against the S protein could be detected early on - on average within 4 days of symptom onset. Longitudinal studies including larger numbers of COVID-19 patients across all clinical subgroups are therefore needed to further evaluate the potential impact of this observation, in particular in the context of previously described pre-existing memory T cells cross-reactive against human endemic coronaviruses (cf. https://www.medrxiv.org/con... https://journals.sagepub.co... "https://journals.sagepub.com/doi/pdf/10.1177/039463200501800312)").

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

On 2021-02-19 21:51:45, user Michael Verstraeten wrote:

Did you consider to imply Flaxman e.a., Estimating the effects of non-pharmaceutical interventions on Covid-19 in Europe, Nature, 584, 257 - 261 in your study, or didn't it meet the inclusion criteria?

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 2020-09-10 21:35:51, user Nadav Rakocz wrote:

This paper was presented as part of the KDD'20 AI for COVID-19 workshop.<br /> A short talk can be found here: https://insights.kensci.com...

Or here:

https://www.youtube.com/wat...

On 2021-07-11 12:09:52, user Radical Rooster wrote:

The article fails the first test of objectivity: SELF COLLECTED SAMPLES. Scientific research must be rigorous, procedures cannot vary from one person to another. Sampling must be done by only a select group of samplers. In this paper, there were 3,975 samplers.

On 2021-07-17 14:17:15, user killshot wrote:

Unless "efficacy" and "positivity" are more clearly defined, this is meaningless. Eg, using PCR amplification of 35 or greater for diagnosis then using an amplification of 24 for "transmission" would greatly favor vaccine-related prevention of transmission but would be faux data. Also, unless there is randomization for vitamin D levels -- shown to impair virulence if > 36 ng/mL -- the data is also meaningless.

On 2020-09-29 04:18:22, user Melvin Joe wrote:

Its good sign ! ! By wearing the masks we could flatten the curve of affected persons. I am strictly using HY Supplies Inc masks to reduce the infection !!

On 2022-05-02 23:00:41, user Brian Mowrey wrote:

The authors evince no apparent regard for the importance of the interval between PCR+ and serum sample (PDV), especially given the small number of presumed infections among the mRNA-1273-vaccinated in the main analysis, simply remarking

Anti-N seropositivity at the PDV was similar when stratified by median days from illness

Not for the vaccinated, it wasn't (50% vs 32% when stratified, Table 1). Did the authors merely lump both groups together to get around investigating why N-seropositivity was 18% lower in the -5-53 day vaccinated set?

In fact, the same stratification should have been expected to produce different N-positivity rates in the placebo group, had infections been evenly spaced in the -5-53 day interval. Since the authors find only 74% Day 29 N-positivity for placebo participants who are PCR+ on Day 1, and 60% on Day 57 for placebo who are PCR+ on Day 29, it's clear the placebo group isn't defying standard expectations about seroconversion not being instantaneous (bearing in mind a higher false positive rate on Day 1/29 due to screening, obviously) - until the main analysis, when suddenly there is no apparent penalty for near-PDV infections. So maybe there were almost no near-PDV infections in the placebo group (as in, infections skewed toward February due to seasonal patterns) while in the vaccine group the opposite was true (infections skewed to March due to the waning of infection efficacy)?

Thus, both the values for the placebo group and Covid-vaccine group suggest uneven time between PCR+ and PDV. The authors make no comment on this problem and *do not present* a plot of to-PDV-intervals for either group, even though they obviously had full access to that data. This is a glaring oversight at best.

It's not the only one. Have the authors never heard of false positives / base rate fallacy? I doubt it. So why isn't this taken into account, when comparing a group with a frequent outcome to a rare outcome? Among 14.5k participants in both arms, a mere 25 false PCR+ in both groups would be enough to render the main results way off the mark (Placebo: (100% x (1-((.066x648 - 25)/(648-25)) = 97.1% mRNA-1273: 100% x (1-((.593x52 - 25)/(52-25)) = 77.8%). Yet no consideration of the problem is made. The word "false" is not even in the text.

On 2021-04-13 23:43:54, user greenorange041 wrote:

I think it is quite important to differentiate between simple cloth masks and medical / FFP2 masks that were adopted relatively recently and were shown to be more effective in preventing possible contagion. Accounting for this could potentially make a big difference and lead to smaller estimated effects for some measures given that visitors wear more efficient masks.

Another aspect to consider is that some activities banned as a result of the NPIs can be performed both indoors and outdoors. You seem to be aware of this difference, but apparently you are unable to consistently account for it in your analysis. Hence you cannot differentiate between the effect of closing indoor gastronomy and closing only outdoor gastronomy (and hence reopening it) with all necessary hygienic measures in place, which will presumably be far less significant.

The NPIs on your list are also defined quite broadly. In particular you don't consider closing sport facilities and banning even outdoor sports to be a separate NPI. Banning hotel stays for touristic purposes is also not on your list.

Finally, you don't seem to account for season or weather in your analysis. In winter when people tend to have weaker immunity, some measures can indeed make a difference. But in summer the situation can be quite different and some measures could add little no value.

A very sad and disappointing consequence of this study is that it motivates governments to keep in place the same already known plain and undifferentiated measures even though 1) their effect may be largely overestimated in the current situation (by that I first of all mean wide adoption of masks and FFP2 masks in particular as well as limits on the number of visitors / clients that are already in place) and 2) their effect can be different under different conditions (indoor / outdoor activities), however this difference is not analysed.

Another disappointing consequence is that not listing and not analysing certain more detailed measures (closing sport facilities and banning tourist stays in hotels) will most certainly lead to keeping them in place even though the study doesn't provide any explicit evidence in favour of such measures.

To put in in another way: can someone get an answer to the following questions from your study: what is the possible negative (if any) effect of opening retail given that everyone wears an FFP2 mask and there is a limit on the number of clients, what is the possible effect of allowing outdoor sports, of allowing outdoor gastronomy with sufficient hygienic precautions in place (though without express testing), what is the possible effect of opening hotels? And (what is more important), what is the effect of all this given warm temperatures and more daylight? As far as I understand, it is not possible to answer these questions based on your study, but governments may still be tempted to interpret the conclusions as a justification not to relax any measure in a meaningful way.

Of course, correct me if I am wrong.

On 2021-04-29 12:24:41, user Ric wrote:

I think that this estimation strategy is seriously flawed.

The underlying assumption is that Rt is on averge constant over time, unless some measures are taken by the government. This is obviously false, since all epidemic sooner or later ends even without any intervention. Moreover, government actions are obviously taken when the number of cases is already high and Rt could have started to decline on its onw, so you are basically confusing a correlation with causation.

This is seriously concerning. I have already seen articles by general press pushing for more interventions based on this completly unreliable estimations. Please revised your methodology completly or be clear that this is a correlation that does not estimate any effect

On 2021-11-12 10:44:25, user Ken wrote:

The next step could be linking the TeKWP to the hospitalization rate in the ICU so to have a real time indicator on the stress that the structure can withstand in relation to the new cases

On 2021-09-08 22:00:39, user Cheeseman wrote:

The authors' statements regarding the effectiveness of universal masking must be in concordance with a systematic review from December 2020 titled "Physical interventions to interrupt or reduce the spread of respiratory viruses" (https://dx.doi.org/10.1002/... "https://dx.doi.org/10.1002/14651858.CD006207.pub5)").

The key discussion element is: "The pooled estimates of effect from RCTs and cluster-RCTs for wearing medical/surgical masks compared to no masks suggests little or no difference in interrupting the spread of ILI (RR 0.99, 95% CI 0.82 to 1.18; low-certainty evidence) or laboratory-confirmed influenza (RR 0.91, 95% CI 0.66 to 1.26; moderate-certainty evidence) in the combined analysis of all populations from the included trials."

This review stands in stark contrast to the authors' position that face masks are effective at mitigating viral transmission. To maintain scientific legitimacy, the authors must decrease the strength of their claims on mask effectiveness in light of this review article.

On 2021-08-05 18:32:18, user Anette Stahel wrote:

Dear moderator,

I've now reviewed, edited and updated my earlier comment to the present study [1]. I hope this will allow for it to be posted.

I'm sorry, but this study is not correct. That is, the pool of people used as denominator when calculating the percentage of COVID-19 infected people who developed CVT and PVT is greatly inadequate. I'll explain what I mean.

In the abstract of the study, it's stated:

"COVID-19 increases the risk of CVT and PVT compared to patients diagnosed with influenza, and to people who have received a COVID-19 mRNA vaccine."

However, when comparing the risk of developing condition X from disease Y with the risk of developing condition X from something else, eg vaccine Z, you first and foremost need to make a correct assessment of how how large the pool of people with disease X is. And to do that, you need to make an estimate. Merely counting the number of people who've sought out primary or secondary care for their symptoms won't do. Not even if you include all the people who were asymptomatic but sought out the care center anyway in order to take a test to see if they were infected (simply because they wanted to know) and then tested positive.

No, you need to include all infected persons in the total pool of people belonging to the health care facility/facilities in question, including the ones who don't go test themselves because of being asymptomatic, or of not having the energy to do it due to their symptoms, or of being into alternative medicine, or of lacking interest/knowledge about the infection et c. There may be many of different reasons. This means you need do make an estimate, otherwise the denominator in the calculation of the percentage who develop condition X from infection Y becomes incorrect.

A study measuring the risk of developing condition X from infection Y using a smaller denominator than one including everyone infected may be useful at times, but it can not be used for comparison with a correctly calculated vaccine risk.

I will use the study Estimation of the Lethality for COVID-19 in Stockholm County published by the Swedish Public Health Agency [2] as an example of a correctly calculated risk, based on an adequately defined denominator. The fact that this is a calculation of the lethality percentage from COVID-19 and not the CVT and PVT percentage is irrelevant, the point is that the same mathematics used in this study should've been applied in the present Oxford University study. From page 13 in the Swedish study, in translation:

"Recruitment was based on a stratified random sample of the population 0-85 years. In the survey we use, the survey for Stockholm County was supplemented with a self-sampling kit to measure ongoing SARS-CoV-2-infection by PCR test. The sampling took place from March 26 until April 2 and 18 of a total of 707 samples were positive. The proportion of the population in Stockholm County which would test positive was thus estimated at 2.5%, with 95% confidence range 1.4-4.2%."

For a complex reason, which I won't go into but is described in detail in the study text, one needs to use a slightly higher percentage when multiplying it with the total number of people in the pool, but that's of minor importance. Anyway, in this study they had to use the figure 3,1169% and when they multiplied it with the number of people in Stockholm County, 2 377 000, they got 74 089. This estimate was then the correct denominator to use when calculating the percentage of people who died from COVID-19 in Stockholm County during this time period.

The numerator was the number of people who died in Stockholm County with a strong suspicion of COVID-19 as a cause, which was 432, no incorrectness there either - as long as a suspected cause number, not a diagnosed cause number, is also used as the numerator when calculating the lethality from the COVID-19 vaccine when the infection lethality and vaccine lethality rates are compared.

So, what they found was that the lethality from COVID-19 in Stockholm County was 0,58%. This is a correct figure, as long as we keep in mind the fact that some of the suspected COVID-19 deaths may later become diagnosed as unrelated to the infection.

The above is thus how the authors of the present Oxford study should've carried out their calculations but they didn't. From their text:

"Design: Retrospective cohort study based on an electronic health records network. Setting: Linked records between primary and secondary care centres within 59 healthcare organisations, primarily in the USA. Participants: All patients with a confirmed diagnosis of COVID-19 between January 20, 2020 and March 25, 2021 were included."

This excludes a considerable amount of infected persons in the total pool of people belonging to all of these primary and secondary care centers, who didn't go test themselves because of a number of reasons (being asymptomatic, being alternative medical, not having the energy or interest for it, et c).

If they'd used the adequate figure in the denominator, the percentage of people established to've developed CVT and PVT from COVID-19 would've gotten vastly lower. However, the percentage of people determined to've developed CVT and PVT from the mRNA COVID-19 vaccines was fully correctly carried out since there are no unregistered vaccinated cases and therefore the registered figure is to be used.

Via the Oxford study's Figure 2 and Table S2 [3], I calculated the following figures: First time CVT cases diagnosed after administration of the mRNA COVID vaccines amounted to 6.6 per million and first time PVT cases after same vaccines amounted to 12.5 per million.

Now, there's a study titled Estimation of US SARS-CoV-2 Infections, Symptomatic Infections, Hospitalizations and Deaths Using Seroprevalence Surveys published by the American Medical Association [4], which has estimated the percentage of infected people in the US looking at roughly the same time period as the Oxford study. From the paper:

"An estimated 14.3% (IQR, 11.6%-18.5%) of the US population were infected by SARS-CoV-2 as of mid-November 2020."

With an infection rate around 14.3%, the estimated number of infected people of the 81 million patients in the healthcare database referred to in the study would've amounted to 11 583 000. This number gives us a hint as for the size of the denominator which should've been used in the calculation instead of the figure of 537 913 confirmed diagnoses.

However, since the Oxford study not only looked at CVT and PVT arising from people having the infection around mid-November 2020 but looked at a much longer time period, from January 20, 2020 to to March 25, 2021, a number far greater than 11 583 000 should be applied. What we need is to estimate how many of the 81 million patients which were infected at least once during these 14 months in question. For the calculation to be really accurate, we need the total, accumulated number of infected people. But since that number isn't found without a very comprehensive and time consuming investigation, we instead have to use the signs ">" ("greater than") and "<" ("less than") here. So, the correct denominator, which should've been used instead of the 537 913 figure, is >11 583 000.

Further, the study says that first time CVT was found in 19 of the patients following COVID-19 diagnosis and first time PVT in 94. This actually means that the rates of CVT and PVT elicited by COVID-19 were much lower than the rates of CVT and PVT elicited by the vaccines. COVID-19 elicited PVT cases, correctly calculated, amounted to <8.1 per million - only about two thirds of the 12.5 per million for the vaccines - and the CVT cases amounted to <1.6 per million - a mere fourth of the vaccines' 6.6.

Interestingly, with their work including this method error, these authors have provided scientific validation of the growing suspicion that the COVID-19 vaccines give rise to thrombocytic complications to a much greater extent than does COVID-19 (which is the opposite of what's stated in the study), because even if the 537 913 figure is inadequate, the other figures in the study are most likely not.

It should also be said that the disclaimer inserted towards the end of the Oxford study by no means can be referred to in order to justify this method error. From the disclaimer:

"However, the study also has several limitations and results should be interpreted with caution. (--) Third, some cases of COVID-19, especially those early in the pandemic, are undiagnosed, and we cannot generalise our risk estimates to this population."

The reason why this passage cannot be referred to, is that 11 000 000 or so omitted cases impossibly can be defined as "some", when the number of denominator cases determined in the study merely constitutes a small fraction (5%) of that figure.

Finally, I'd like to suggest a reading through of the English translation of the Swedish COVID-19 lethality study that I took up in the beginning of my text as a correct, comparative example [5]. This is the main paper that the Swedish equivalent to CDC, the Public Health Agency (Folkhälsomyndigheten), refers to when talking about the COVID-19 lethality here and it's put up on one of the major information pages of their website. I really recommend reading all of it, because it explains so well and in such detail how come this model of denominator calculation without exception must be used in studies like these, which aim to investigate the rate of injuries/complications arising from an infectious illness.

Anette Stahel <br /> MSc in Biomedicine <br /> Sweden

References

1. Taquet, M, Husain, M, Geddes, J R, Luciano, S & Harrison, P J (2021) Cerebral Venous Thrombosis and Portal Vein Thrombosis: A Retrospective Cohort Study of 537,913 COVID-19 Cases medRxiv https://doi.org/10.1101/202...
2. Svenska Folkhälsomyndigheten (2020) Skattning av Letaliteten för Covid-19 i Stockholms Län https://www.folkhalsomyndig...
3. Taquet, M, Husain, M, Geddes, J R, Luciano, S & Harrison, P J (2021) Cerebral Venous Thrombosis and Portal Vein Thrombosis: A Retrospective Cohort Study of 537,913 COVID-19 Cases OSFHome https://osf.io/a9jdq/
4. Angulo FJ, Finelli L, Swerdlow DL. Estimation of US SARS-CoV-2 (2021) Infections, Symptomatic Infections, Hospitalizations and Deaths Using Seroprevalence Surveys (2021) JAMA Netw Open https://jamanetwork.com/jou...
5. The Swedish Public Health Agency (2020) Estimation of the Lethality for COVID-19 in Stockholm County Online translation of [2] https://translate.google.co...

On 2020-04-02 20:13:43, user Sinai Immunol Review Project wrote:

Main findings: The authors analyzed 4000 test results from 28 COVID-19 patients of which 8 were confirmed severe COVID-19 cases and 20 were confirmed cases of mild COVID-19 infection. They found that the overall level of serum CRP increased in all cases irrespective of the disease severity. They observed that serum cystatin C (CysC), creatinine (CREA), and urea, biochemical markers of renal function, were significantly elevated in severe COVID-19 patients compared to mild patients.

Critical Analyses: <br /> 1. Figure duplication in panels G and H of Figure 2 <br /> 2. Survey area is limited to one center.<br /> 3. Small number of participants in the survey.<br /> 4. Elderly people in severe groups and relatively younger people in the milder group. The baseline parameters may differ in both groups, considering the age difference.<br /> 5. Although not clearly stated, this is a cross sectional study and interpretation of results is difficult. The markers that were found to be significantly different between groups are very non-specific. Renal failure and high LDH are not surprising findings in critically ill patients. <br /> 6. There is a very minimal description of the patient's baseline characteristics. It would be important to know for example what were the symptoms at presentation, how long patients had symptoms for before inclusion in the study, duration of hospitalization before inclusion. This would help interpret whether results reflect difference in severity of disease or simply a longer course of disease/hospitalization. <br /> 7. It is unclear what the authors mean in the discussion when they mention “which may be the result of prophylactic use of drug by doctor” (Discussion section, line 6). Type of the drug used is not specified.

Relevance: This study offers insights on some laboratory markers of mild vs severe cases of COVID-19 infection. Glomerular cells highly express ACE2 which is the cellular receptor for SARS-CoV-2, and impaired kidney function might represent a marker of virus-induced end organ damage.

Reviewed by Divya Jha/Francesca Cossarini as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-26 02:33:57, user Elisabeth Bik wrote:

Figures 2 and 3 would work better if the letters A, B, C, etc, were replaced by the actual serum marker name. Also, panels G and H in Figure 2 appear to be duplicated (same Y axis label and same graphs). Could the authors check, please?

On 2021-08-09 17:23:34, user vaxpro wrote:

author indicated "The sample size of our trial design meets the minimum safety requirement of 3,000 study participants for the vaccine group, as recommended by the FDA, and WHO guidance". Neither FDA nor WHO recommends sample size for an individual study. sample sizes of the individual studies are driven by the objectives. sample size for approval is a different matter. In fact, what FDA says in the referenced document is that "FDA does not expect to be able to make a favorable benefit-risk determination that would support an EUA without Phase 3 data that include the following, which will help the Agency to assess the safety of the vaccine:...ii. All safety data collected up to the point at which the database is locked to prepare the submission of the EUA request, including a high proportion of enrolled subjects (numbering well over 3,000 vaccine recipients) followed for serious adverse events (SAEs) and adverse events of special interest for at least one month after completion of the full vaccination regimen". also "FDA does not consider availability of a COVID-19 vaccine under EUA, in and of itself, as grounds for stopping blinded follow-up in an ongoing clinical trial". WHO guidance is for general vaccine development with major caveats highlighted in the guidance document. the selective and mis- interpretation of the FDA and WHO guidance is regrettable.<br /> immunogenicity is the primary objective of the study. it's curious why author chose to report full set of IgG data at all visits but NT data only at selected visits.post injection pain was reported in ~20% placebo (saline) subjects, which far exceeded any historic rates in this type of population. although no impact on the interpretation of safety for the active arm within this double blinded trial, the observation should be investigated for potential trial conduct issues and discussed.<br /> author stated "MVC-COV1901 has recently been granted EUA in Taiwan". however this is not a scientific issue for this trial, should not be included in trial report in a peer reviewed journal.

On 2020-04-04 10:53:48, user Statistics wrote:

almost 80% at the cross test(!)... so what about Iris diagnosis? Tuberculosis was over 80 % back in the 50s... so if anyone has time to evalute (complete time table please) I will appreciate; also check the completeness of the waldeyer throat ring of the infected; just for the interest...have thanks and praises

On 2020-04-04 19:45:40, user Ibraheem Alghamdi wrote:

That is the thing with ecological studies, they are good at generating hypotheses and interesting, but, they prove nothing.

On 2020-04-04 22:05:26, user PhilipandHeidi Kapitaniuk wrote:

Here in France the BCG has been widely used, and we still are losing many people to the Coronavirus. They need to be looking at more than just a few countries. This does not sound serious to me.

On 2020-04-07 16:26:34, user Richard wrote:

they stopped the BCG vaccination in australia in 1982, interesting that the death rates in australia amongst the older people are lower than seen in other countries,

On 2020-03-30 15:52:29, user Rosemary TATE wrote:

Hi, I have just performed a review of this preprint. I hope it is useful. I'm a medical statistician. I'd certainly like to see the next version, and it would be good if you could take my comments on board. I'd be happy to help with the stats if you need it.

On 2020-04-02 17:08:35, user Yaira Ca Ce wrote:

Interesting correlation BUT... Is it strong enough the correlation of data from countries in different stages? Being from Mexico makes me think of the lack of detection in my country. The tests haven't been applied massively, and there are MANY cases of atypical pneumonia and flu. Interesting isn't it? It might be that a low morbility and mortality is due to under developed countries in their first stages or there's not a massive testing. Still interesting to consider it. At the end it will be more realistic to make a comparison.

On 2020-04-05 19:18:00, user Sinai Immunol Review Project wrote:

Main Findings: <br /> The study compares IgM and IgG antibody testing to RT-PCR detection of SARS-CoV-2 infection. 133 patients diagnosed with SARS-CoV-2 in Renmin Hospital (Wuhan University, China) were analyzed. The positive ratio was 78.95% (105/133) in IgM antibody test (SARS-CoV-2 antibody detection kit from YHLO Biotech) and 68.42% (91/133) in RT-PCR (SARS-CoV-2 ORF1ab/N qPCR detection kit). There were no differences in the sensitivity of SARS-CO-V2 diagnosis in patients grouped according to disease severity. For example, IgG responses were detected in 93.18% of moderate cases, 100% of severe cases and 97.3% of critical cases. In sum, positive ratios were higher in antibody testing compared to RT-PCR detection, demonstrating a higher detection sensitivity of IgM-IgG testing for patients hospitalized with COVID-19 symptoms.<br /> Limitations of the study:<br /> This analysis only included one-time point of 133 hospitalized patients, and the time from symptom onset was not described. There was no discussion about specificity of the tests and no healthy controls were included. It would be important to perform similar studies with more patients, including younger age groups and patients with mild symptoms as well as asymptomatic individuals. It is critical to determine how early after infection/symptom onset antibodies can be detected and the duration of this immune response.<br /> Relevance:<br /> The IgM-IgG combined testing is important to improve clinical sensitivity and diagnose COVID-19 patients. The combined antibody test shows higher sensitivity than individual IgM and IgG tests or nucleic acid-based methods, at least in patients hospitalized with symptoms. <br /> Review by Erica Dalla as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-05 22:01:17, user Kirsten McEwen wrote:

Can the authors provide power analysis?

On 2020-04-06 11:27:18, user Eleanor Johns wrote:

"Conclusion: The SARS-CoV-2 prevention needs to focus on the screening of asymptomatic patients in the community with a history of contact with the imported population, especially for children and the elderly population." 28% of infected COVID19 individuals are asymptomatic, as PCR and Antibody testing covered a wide population in a Chinese province. We must test EVERYONE.

On 2020-04-06 14:39:20, user Jason Kidde wrote:

It would be interesting to see how blood type applies along age groups with regard to disease severity and death. If the finding is preserved across age groups, this would add muster. Additionally, I'm curious about looking into death rates and severity across geographic regions. For instance how much does blood type explain the death rates in Eastern Europe being that the type A allele is more common in this region, while the type A allele virtually does not exist in South America. Will this result in lower death rates in South America? So far Brazil has a fairly average death rate (4%) compared to other nations whereas Chile's is quite good at 0.7%. I realize that many other factors effect this, principally the testing vs true total disease as well as healthcare infrastructure.

On 2020-03-25 12:29:20, user jenniepoole wrote:

Hi were any of the patients with <br /> type A also checked for RH negative or were they all A + ?? This is important to me.

On 2020-03-25 15:31:18, user Sinai Immunol Review Project wrote:

These authors compared the ABO blood group of 2,173 patients with RT-PCR-confirmed COVID-19 from hospitals in Wuhan and Shenzhen with the ABO blood group distribution in unaffected people in the same cities from previous studies (2015 and 2010 for Wuhan and Shenzhen, respectively). They found that people with blood group A are statistically over-represented in the number of those infected and who succumb to death while those with blood group O are statistically underrepresented with no influence of age or sex.

This study compares patients with COVID-19 to the general population but relies on data published 5 and 10 years ago for the control. The mechanisms that the authors propose may underlie the differences they observed require further study.

Risk stratification based on blood group may be beneficial for patients and also healthcare workers in infection control. Additionally, investigating the mechanism behind these findings could lead to better developing prophylactic and therapeutic targets for COVID-19.

On 2020-03-27 00:50:17, user Simon Brazendale wrote:

I read this paper quickly and feel that people need to keep an open mind either way, the question is 'can this finding be reproduced?'. Certainly meta analysis seems to have been achieved, but the I squared values are greater than 25% for blood groups A and AB, which suggests some heterogenity. Lets see if out this global tragedy other studies can reproduce this as well as the original finding needing further scrutiny. It may be part of a key to better understanding or just a red herring

On 2020-04-03 03:05:56, user Philomena Okeke wrote:

I like more studies and research to be done on this.If group A+ are vulnerable then they should be protected from this Coronavirus.The B and O should really help especially the 0 group. I am sure that more researchers need to provide more evidence on this critical issues. <br /> Thanks

On 2020-04-06 18:50:06, user Theodore Koukouvitis wrote:

Insightful and readily quantifiable. The author is confident enough to make specific, short-term predictions and warn against the danger of a full removal of social distancing measures.

This paper should be peer reviewed and evaluated ASAP.

On 2020-04-06 18:54:14, user Sinai Immunol Review Project wrote:

This study examined antibody responses in the blood of COVID-19 patients during the early SARS CoV2 outbreak in China. Total 535 plasma samples were collected from 173 patients (51.4% female) and were tested for seroconversion rate using ELISA. Authors also compared the sensitivity of RNA and antibody tests over the course of the disease . The key findings are:

• Among 173 patients, the seroconversion rates for total antibody (Ab), IgM and IgG were 93.1% (161/173), 82.7% (143/173) and 64.7% (112/173), respectively.

• The seroconversion sequentially appeared for Ab, IgM and then IgG, with a median time of 11, 12 and 14 days, respectively. Overall, the seroconversion of Ab was significantly quicker than that of IgM (p = 0.012) and IgG (p < 0.001). Comparisons of seroconversion rates between critical and non-critical patients did not reveal any significant differences.

• RNA tests had higher sensitivity in early phase and within 7 days of disease onset than antibody assays (66.7% Vs 38.3% respectively).

• The sensitivity of the Ab assays was higher 8 days after disease onset, reached 90% at day 13 and 100% at later time points (15-39 days). In contrast, RNA was only detectable in 45.5% of samples at days 15-39.

• In patients with undetectable RNA in nasal samples collected during day 1-3, day 4-7, day 8-14 and day 15-39 since disease onset, 28.6% (2/7), 53.6% (15/28), 98.2% (56/57) and 100% (30/30) had detectable total Ab titers respectively Combining RNA and antibody tests significantly raised the sensitivity for detecting COVID-19 patients in different stages of the disease (p < 0.001).

• There was a strong positive correlation between clinical severity and antibody titer 2-weeks after illness onset.

• Dynamic profiling of viral RNA and antibodies in representative COVID-19 patients (n=9) since onset of disease revealed that antibodies may not be sufficient to clear the virus. It should be noted that increases in of antibody titers were not always accompanied by RNA clearance.

Limitations: Because different types of ELISA assays were used for determining antibody concentrations at different time points after disease onset, sequential seroconversion of total Ab, IgM and IgG may not represent actual temporal differences but rather differences in the affinities of the assays used. Also, due to the lack of blood samples collected from patients in the later stage of illness, how long the antibodies could last remain unknown. For investigative dynamics of antibodies, more samples were required.

Relevance: Total and IgG antibody titers could be used to understand the epidemiology of SARS CoV-2 infection and to assist in determining the level of humoral immune response in patients.

The findings provide strong clinical evidence for routine serological and RNA testing in the diagnosis and clinical management of COVID-19 patients. The understanding of antibody responses and their half-life during and after SARS CoV2 infection is important and warrants further investigation

On 2020-04-07 23:26:41, user Sam Raredon wrote:

Here is a video briefly showing and describing the technique:

PReVentS Circuit Demonstration - COVID-19 / Yale / Niklason Lab<br /> https://www.youtube.com/wat...

On 2020-04-07 23:34:20, user Karl Riley wrote:

It's worth looking at the benefits of a strategic infection variant, whereby those known to be at least risk of death are exposed to the virus in a controlled environment and then 'released' back into the general population thereby facilitating herd immunity. You could even have those, then known to be immune, as a form of shield in areas looking imminently vulnerable (hospital admission figures could be part of the data used for this) to the spread of the infection.

On 2020-04-08 10:19:35, user Rosemary TATE wrote:

Hello, thank your for this interesting article.<br /> Could you please upload the relevant checklist. I believe this is PRISMA? I cant see this anywhere. You will need this if you are intending to publish.

On 2020-04-11 08:07:44, user Jeff Aronson wrote:

This looks like an interesting study, with an important warning about the combination of hydroxychloroquine + azithromycin, which people are beginning to use. But why misleadingly title the paper "Safety of hydroxychloroquine …" when what is being reported is serious adverse events, i.e. unsafety? I hope that when the authors prepare their paper for peer review, they will use a more accurate description.

On 2020-04-13 12:22:24, user Dr. Phillips wrote:

1. In the methods for the ELISA it says that "Serial dilutions of serum and antibody samples were prepared in ..." What was the antibody that was serially diluted?
2. In the figures of AUC, what do the horizontal bars represent? For some it looks like a mean but others, e.g. IgM in Fig 3A, it doesn't.

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 2020-03-14 19:08:41, user Marcia Walker wrote:

This is so interesting, thank you for this! My question is - why did you start with January and not December? The first known case was traced to 1 December, there may have even been cases before then, surely it is possible that it already started spreading internationally before the end of December?

On 2020-03-29 21:18:16, user Randy k wrote:

This study is guessing at how many people were infected. The whole formula is based on a guess.

On 2020-03-20 22:45:31, user Sinai Immunol Review Project wrote:

The aim of this study was to identify diagnostic or prognostic criteria which could identify patients with COVID-19 and predict patients who would go on to develop severe respiratory disease. The authors use EMR data from individuals taking a COVID-19 test at Zhejiang hospital, China in late January/Early February. A large number of clinical parameters were different between individuals with COVID-19 and also between ‘severe’ and ‘non-severe’ infections and the authors combine these into a multivariate linear model to derive a weighted score, presumably intended for clinical use.

The paper is lacking some crucial information, making it impossible to determine the importance or relevance of the findings. Most importantly, the timings of the clinical measurements are not described relative to the disease course, so it is unclear if the differences between ‘severe’ and ‘non-severe’ infections are occurring before progression to severe disease (which would make them useful prognostic markers), or after (which would not).

This paper among many retrospective studies coming from hospitals around the world treating individuals with COVID-19. In this case, largely because of the sparse description of the study design, this paper offers little new information. However, studies like this could be very valuable and we would strongly encourage the authors to revise this manuscript to include more information about the timeline of clinical measurements in relation to disease onset and more details of patient outcomes.

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