On 2023-10-06 14:14:25, user Michael Bretscher wrote:

This article has now been published: https://ascpt.onlinelibrary...

On 2021-09-16 07:33:29, user Chaos_14 wrote:

This study doesn't mention how many vaccinated vs unvaccinated people were tested.

"Notably, 68% of individuals infected despite vaccination tested positive with Ct <25, including at least 8 who were asymptomatic at the time of testing." (68% of what number?)

Since we know immune response, even with vaccines, decreases with age, it would be helpful to know the ages of the people in both the vaccinated and unvaccinated groups.

It would also be helpful to know the Ct in the samples of asymptomatic, <br /> unvaccinated people if there were any.

While it's beneficial to know that it's possible for infected vaccinated people to carry a viral load similar to infected unvaccinated people, this study left me with a lot of unanswered questions.

On 2020-10-16 10:49:03, user M&S wrote:

"CONCLUSIONS ... Hydroxychloroquine ... <br /> appeared to have little or no effect on hospitalized COVID-19" HOSPITALISED!!! No one ever claimed it should help hospitalised patients; every proponent begs that the regime start after the first suspicion of infection!

On 2020-10-18 08:46:08, user Amichai Perlman, PharmD wrote:

The use of 99% confidence intervals for subgroups in the meta-analysis in figure 4 obscures the subgroup difference which is suggested by the data. Using 95% confidence intervals and formal test for interaction, the difference in the effect of remdesivir on mortality between ventilated and non-ventilated patients would likely be considered "significant" according to standard meta-analysis reporting procedures. While this is a post hoc analysis and therefore is not conclusive, and should not be portrayed as such, its total dismissal does not seem warranted, both in terms of accepted statistical standards, and in terms of the effect size (it is identical to that shown for dexamethasone in non-ventilated patients on oxygen the Recovery trial).

On 2020-10-17 01:47:55, user EntropicInfo wrote:

Seems odd for this preprint not to provide any information about time from symptom onset to antiviral administration. Was time from symptom onset omitted from the paper or simply not recorded?

On 2020-10-17 14:19:54, user jeff209 wrote:

COVID-19 is evolving, which significantly affects reliability and purpose of the solidarity trials. Moreover, the sample size of each age group is so small. The same protocol with narrower trial time window, larger sample size in each age group, and double blind trials are more definitive than this study. Thus the results from this study is questionable, and could be quite misleading to the public.

On 2020-09-05 17:19:15, user DFreddy wrote:

Conclusion not based on primary studied data. Potentially misleading in its current verbose.

On 2021-09-13 09:36:17, user Michal wrote:

Excellent work! The link to direct-diabetes as in:

The IMI-DIRECT data access policy is available from www.direct-diabetes.org

does not seem to work. Also, the metabolite names (both on the plots and in the supplementary information) seem to be malformatted by R, it would be wonderful if you could restore clean names (with correct punctuation instead of dots) for the final publication.

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

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

On 2023-05-05 22:48:17, user Zhaolong Adrian Li wrote:

This preprint has been published May 2nd, 2023 at https://doi.org/10.1093/tex....

On 2021-10-05 11:12:38, user Jonna Zizak wrote:

What were the rates of the unvaccinated during the same time period?

On 2021-06-04 09:55:17, user Najmul Haider wrote:

This article is now published in the journal "Health Science reports". You can access to the full article free: https://doi.org/10.1002/hsr...

On 2021-07-21 19:03:10, user Penny Butler wrote:

Comments: #82 (but I only see 8) ummm? #pleaseexplain

On 2023-11-03 11:35:08, user Alex wrote:

Some elements seem to be misquoted or false in the introduction, and collected pubmed data only in french language does not actually reflect published data with selected keywords, discussion in this thread :

https://twitter.com/AlexSam...

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

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

On 2023-01-10 21:19:28, user Lucija Romac wrote:

Dear Authors,<br /> congratulations on the great work, I believe that your study is a huge step forward in the treatment of patients suffering from azoospermia and that every embryologist working with NOA and OA patients would be delighted to implement a non-invasive test, such as yours, in their lab. If you don't mind, I have a few questions. You stated that the NOA group included men with azoospermia confirmed by semen analysis and elevated FSH, (>18 IU/L) or by testicular biopsy. My question concerns the flow cytometry identification of morphologically intact AKAP4+/ASPX+/Hoechst+ spermatozoa in NOA semen pellets. I wonder whether the NOA mTESE reference set of 7 patients (Table 2) with the known mTESE outcome underwent histological evaluation which is known as the “gold standard” of diagnostic tools used to confirm the presence of spermatozoa in testicular tissue? I'm also curious why is the reference set comprised of only 7 patients, considering you had 91 NOA patients with a known outcome of mTESE?<br /> Thank you for your answers and for sharing your work with us,<br /> Best regards,<br /> Lucija Romac

On 2021-02-06 23:13:06, user sfffff wrote:

Another limitation is the impact of comparing a cohort of non-vaccinated COVID-19 cases to a cohort of influenza cases where approx. 40% of those patients with a higher risk were vaccinated in Switzerland (BAG, saisonbericht-grippe-2019-20.pdf and saisonbericht-grippe-2018-19.pdf). I doubt that this can be included in any sensible manner into the calculations - but it induces a bias, as some of the potentially most critical cases are filtered out (or alleviated). It would be very interesting if you could repeat the study next year, also taking the COVID-19 / influenza vaccinations of your patients into account.

On 2022-09-05 18:03:38, user Michael L. wrote:

Appreciate the close look at mechanism and the robust set of readouts. Overall an excellent study that will set a new benchmark for characterization of humoral responses to vaccination.

Am curious about the group defined as having an interval between prior infection and booster of <180 days. These were 6 out of the 11 prior-infected patients. Would it be possible to show all intervals so we can see if the 180-day cutoff makes sense, and also see the distribution of intervals within this group? For example, there may be different implications if 5 of 6 patients were infected within 30 days before boost, vs. 5 of 6 patients infected more than 170 days ago.

Thank you for the nice work.

On 2021-08-31 00:14:58, user chelsea wrote:

Yes figures and tables would be nice.. they do not provide what extra level of protection you get if you have had sars cov2 and then get vaccinate... is it measured in folds like the actual infected over the vaccinated or is it like 13%?

On 2021-09-09 17:50:14, user bgoo2 wrote:

No. No it does not offer "just as good immunization".

That is just false. What does it take to get people to understand this.

If you had COVID in the past, you do have some protection. There's natural immunity. And a lot of people are talking about it.

Your natural immunity is variable. The amount of developed antibodies is variable. Or your immunity is from the original variant.

The vaccines provide a stronger immune response. More antibodies are created. The vaccines are proven to give longer protection as well.

Finite... but longer.

Also discovered by analyzing millions of cases worldwide this year is that those persons who got COVID and didn't get vaccinated are more than twice as likely to get reinfected.

On 2021-08-26 05:03:33, user David Lang wrote:

I can't deeply assess the paper but please note the term "survivorship bias" does not appear once.

On 2021-09-10 07:18:00, user Jim Ayers wrote:

I hate to ask a dumb question but did the study include people with long haul covid and people who died? Quoting the study '46,035 persons in each of the groups.' The fraction of a percent who died or the few percent who have long covid who may feel too sick to participate in a study could invert the study if not accounted for since they have been weeded out of the cohort and need to be adjusted for. This study could be 100 percent wrong if the up to 20% who have long haul covid aren't participating.

On 2021-09-14 13:39:06, user Henri van Werkhoven wrote:

Dear colleagues,

With interest did we read this manuscript which fueled a lively discussion during our journal club of the department of infectious diseases epidemiology at the University Medical Center Utrecht. The authors address a relevant research question. If there is a substantial difference in the risk of SARS-CoV-2 infections between previously infected and vaccinated individuals – as suggested - this may have consequences for social distancing, testing recommendations, and for projections of the impact of vaccination on future COVID-19 trends. However, we have several concerns regarding generalizability, selection bias, information bias, and confounding that we would like to address. We focus our discussion on model 1: the comparison of the fully vaccinated non-infected group (group 1) to the infected non-vaccinated group (group 2).

In regard to generalizability:<br /> - Due to the matching process, only 4% of the available data is used (i.e. for model 1 only 32430/736559) and as a consequence the study population is fairly younger (with expectedly less comorbidity) than the source population (i.e. vaccinated individuals, infected individuals). Therefore, the study population may not be representative of this source population which severely limits the external validity of results for all vaccinated/infected people.<br /> - Naturally, subjects who died due to previous SARS-CoV-2 infection were not included in the study. Yet, without information on morbidity and mortality and contribution to the spread of SARS-CoV-2 from the primary infection, the results of the study are not informative for the question whether people without previous SARS-CoV-2 infection should be vaccinated or await natural infection. <br /> - All three study groups – vaccinated or infected at baseline (28th of February) – were established upon future information (no infection, no additional vaccination after June 1, 2021), which severely limits the use of the results for today’s decision making.

In regard to selection bias:<br /> - People with a SARS-CoV-2 infection between February 28, 2021 and June 1, 2021, or those who received a first (infected group) or third vaccine (vaccinated group) between February 28, 2021 and August 14, 2021 were excluded from this study. Thus the study population of group 2 consists of previously infected people that do not take the opportunity to receive a booster vaccine, which may well be the less vulnerable people with a lower baseline risk of getting infected/hospitalized. This would bias the estimate in favor of the infected group.<br /> - Similarly, though at a smaller scale, people who died from COVID were not included in the analysis. This decreases the vulnerability of the infected group for secondary infections and/or hospitalization. This too would bias the estimate in favor of the infected group.

In regard to information bias:<br /> - A difference in willingness to test between the vaccinated and previously infected group can result in biased estimates. Vaccinated people may be more on guard in regard to COVID-19 symptoms (especially if they adhere less to regulations because they are vaccinated) and will be tested more frequently. This can bias the estimate, again in favor of the infected group. However, this form of bias should not have affected the outcome hospitalization due to COVID-19, for which differences had the same direction. Yet, the number of those endpoints was low, limiting statistical power.

In regard to confounding:<br /> - The authors acknowledge absence of information about health behavior, such as social distancing and masking. If the vaccinated group would adhere less to these preventive measures due to a sense of safety, this would also bias the estimates in favor of the infected group.<br /> - A potential important aspect is the young average age (36 years) of the study population. As they were all fully vaccinated before February 28th, we thought that a large proportion may have been health care workers, who have a higher chance of exposure to SARS-CoV-2, and thus infection after vaccination. This would also bias the estimate in favor of the infected group.

We have scrutinized the paper in search of the fatal flaw; the one major methodological limitation that could explain the extreme effect in favor of the infected group, as reported. We conclude that it is not there, as we don’t think that any of the above biases can explain all of the effect. However, we did found several weaknesses that each have the potential to yield a modest bias, all in the same direction. Five modest biases may yield a large effect estimate. We, therefore, consider the question whether natural immunity provides better protection than full vaccination with Pfizer/BioNTech’s COVID vaccine remains unanswered.

The authors (Annemarijn de Boer, Valentijn Schweitzer, Marc Bonten and Henri van Werkhoven, all at University Medical Center Utrecht) acknowledge all other journal club participants for their time dedicated to discussing the paper.

On 2021-10-16 15:43:33, user Alex wrote:

Oh and on natural immunity, myself, partner and two children had COVID March 2020, both antibody tests came back May 2021 positive. Currently waiting for the results of an updated one…. We have also not had anything with similar symptoms since but in two weeks I fly to Barcelona with a tonne of red tape because I’m not vaccinated and I don’t find it fair…My partner loses her job along with 40 people in Hampshire social care next month because they opted for no vaccine, a big gap in care looking after our grandparents - good luck with that!- my point is,why is natural immunity not accepted??it’s simple to test for so questions need to be raised!

On 2021-08-27 13:34:21, user Luke Bartelt wrote:

The authors should correct table 1a and 1b where group demographics appear identical.

On 2020-03-28 01:57:06, user Sinai Immunol Review Project wrote:

Summary: Liu et al. enrolled a cohort of 40 patients from Wuhan including 27 mild cases and 13 severe cases of COVID-19. They performed a 16-day kinetic analysis of peripheral blood from time of disease onset. Patients in the severe group were older (medium age of 59.7, compared to 48.7 in mild group) and more likely to have hypertension as a co-morbidity. Lymphopenia was observed in 44.4% of the mild patients and 84.6% of the severe patients. Lymphopenia was due to low T cell count, specially CD8 T cells. Severe patients showed higher neutrophil counts and an increase of cytokines in the serum (IL2, IL6, IL10 and IFN?). The authors measured several other clinical laboratory parameters were also in severe cases compared to mild, but concluded that neutrophil to CD8 T cell ratio (N8R) as the best prognostic factor to identify the severe cases compared to other receiver operating characteristic (ROC).

Limitations: This was a small cohort (N=40), and two of the patients initially included in the severe group (N=13) passed away and were excluded from the analysis due to lack of longitudinal data. However, it would be most important to be able to identify patients with severe disease with higher odds of dying. It seems that the different time points analyzed relate to hospital admission, which the authors describe as disease onset. The time between first symptoms and first data points is not described. It would have been important to analyze how the different measured parameters change according to health condition, and not just time (but that would require a larger cohort). The predictive value of N8R compared to the more commonly used NLR (Neutrophil to Lymphocyte ratio) needs to be assessed in other independent and larger cohorts. Lastly, it is important to note that pneumonia was detected in patients included in the “mild” group, but according to the Chinese Clinical Guidance for COVID-19 Pneumonia Diagnosis and Treatment (7th edition) this group should be considered “moderate”.

Relevance: Lymphopenia and cytokine storm have been described to be detrimental in many other infections including SARS-CoV1 and MERS-CoV. However, it was necessary to confirm that this dramatic immune response was also observed in the SARS-CoV2 infected patients. These results and further validation of the N8R ratio as a predictor of disease severity will contribute for the management of COVID19 patients and potential development of therapies.

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

On 2021-09-29 06:49:42, user Amador Goodridge wrote:

Excellent article. Looking at mask use behavior remains key to acknowledge the human being response for this and any future repiratory pandemic. Fernandez-Marin and his co-authors highligth the variations of mask use behavior. I agree that special attention should be directed to suburban areas, where social determinants for public health are sustaining the transmision of COVID-19 and many other infectious diseases. Congratulations to the authors!

On 2021-10-04 06:54:30, user kdrl nakle wrote:

Simple yet important result, meaning we should definitely know Cp (Ct) value after getting tested. The next thing would be to investigate transmissibility but that is obviously much harder research.

On 2021-12-06 18:46:16, user Griefer HD wrote:

The authors claim at the beginning of the abstract AND at the beginning of the introduction: "Vaccines are the most powerful pharmaceutical tool to combat the COVID-19 pandemic." This appears to be a foregone conclusion.

I am no a mathematician and thus cannot evaluate the elaborate models used in the study. But it is obvious that the models hinge on the R value and are very sensitive to even small changes in R. And the assumptions going into the R values used in the study are flawed: the authors estimate that 67-76% of the R value is caused by the non-vaccinated population. This is in stark contrast to the incidence in the vaccinated and non-vaccinated population ad reported by the RKI on a weekly basis. And even the RKI states that these reported incidence value probably under-report the incidence among the vaccinated population. Consequently, the authors report that "In order to obtain breakthrough infection rates in adolescents on the order of observed symptomatic breakthrough cases we assume a vaccine efficacy of s = 92% for adolescents." Which again is in stark contrast of RKI's own estimate for efficacy of 67% (weeks 42 to 44). And while the authors cite two studies showing that infected vaccinated individuals are equally like to transmit the disease that non-vaccinated (and only citing one study showing lower transmission), they state that "Considering these results, we assume a conservative transmission reduction of r = 10% for breakthrough infections" (for vaccinated individuals) - a baseless claim, to say the least. In addition, the authors also resort to slander: "As individuals that are not opposed to vaccination typically adhere to protection measures more consistently..." This goes against my own observation in my direct vicinity, where non-vaccinated are very aware of the risks they are taking and are often more careful. While vaccinated individuals seem to take greater risks, as for example shown by the large number of infections of vaccinated at '2G' Halloween parties. A more risk-aware behavior of the non-vaccinated would also explain the seemingly increased infection rate among vaccinated individuals in the UK (for example in the age bracket 40-49, as reported by UKHSA) compared to non-vaccinated peers.

And finally, I would like to point out that the state of Berlin introduced 'selective NPIs' (read: 'lockdown' of the non-vaccinated) and the Mayor of Berlin advocates introducing such measures on a nationwide basis. At the same time, the state of Berlin is the main funding body for the host University (Humboldt University) of the majority of authors. The authors fail to acknowledge this conflict of interest.

On 2021-12-01 14:08:31, user watcher wrote:

The authors mention that vaccination efficacy might be affected by underreporting of mild symptomps. This raises another issue, which was not accounted for in the model. Asymptomatic infected individuals may still transmit the virus, but due to the lack of symptoms will not alter their behavior. It should be assumed that the more prominent symptoms are, the more individuals will reduce their contacts, to protect themselves and others. As unvaccinated infections generally result in more symptoms, these individuals will likely reduce their contacts naturally. Symptoms alter the behavior of individuals and thus transmissions, which is not accounted for in the model, but could change the outcome quite a bit.

On 2020-11-21 00:29:25, user Richard Wachterman wrote:

Will airlines allow during flight since they do not allow masks that have exhaust holes? I realize that there are options for putting filters over the exhaust valve, but do we know if the airlines will accept that.

On 2020-10-14 18:44:44, user Darren Brown; HIV Physiotherap wrote:

The description of both episodic ("relapsing/remitting") symptoms plus uncertainty has specific relevance to existing literature from other health conditions eg: HIV. The Episodic Disability Framework considers the variable nature of disability, acknowledges uncertainty as a key component, describes contextual factors that influence experiences of disability, and considers life events that may initiate a major or momentous episode.

Here are some references that may be of value.

• https://hqlo.biomedcentral....
• https://www.ncbi.nlm.nih.go...
• https://pubmed.ncbi.nlm.nih...
• https://www.tandfonline.com...
• https://bmjopen.bmj.com/con...
• https://journals.sagepub.co...
• https://www.ejdisabilityreh...
• https://pubmed.ncbi.nlm.nih...

On 2021-06-03 13:46:26, user Nicolas Banholzer wrote:

This work has been refined and is now published in PLOS ONE: https://journals.plos.org/p...

On 2020-12-20 18:23:25, user Martin Reijns wrote:

None of the reported mutations in the new SARS-CoV-2 variant (lineage B.1.1.7; VUI-202012/01) that is becoming more widespread in the UK impact on the E gene, N1 or N2 assays. Our N1E-RP and N2E-RP multiplex assays will therefore still detect this new strain.

https://virological.org/t/p...

The M gene assay that we refer to in our manuscript is also not affected by the reported changes. However, the S gene assay that we refer to would likely no longer effectively detect this new strain because of a 6 nt deletion within the probe binding site.

On 2021-07-27 12:56:21, user James Jarvie wrote:

This paper is referred to as evidence that vaccine is superior to naturally acquired immunity. However, the paper appears to me to suggest that vaccine is as-good-as naturally acquired immunity (using naturally acquired immunity as the benchmark).

On 2020-08-14 20:52:35, user Pavel Valerjevich Voronov wrote:

For those, who started to incorrectly interpret this study. It seems that it doesn't show how severe illness is for Rh- based on data analyzed. As my assumption stands based on other two related clinical studies - Rh-, especially O- and A- have usually mild symptoms and recover easily. BUT this oneactually shows interesting fact - because for O- and A- sickness is light they MIGHT transmit the disease not knowing anything about it or recover silently without any side effects. That what I see as a reason why covid spread is higher in countries with more Rh- population. If you badly sick - most likely you're at home or in the hospital, isolated. Thus, not spreading the "bug".

On 2020-04-27 22:39:49, user pam garcia wrote:

Obesity, diabetes, and hypertension are clearly the major factors in hospitalizations and deaths from Covid-19.

Northwell Health just released a study of over 5000 Covid-19 patients that revealed 94 percent of the hospitalizations and deaths involved comorbidities, obesity being the most prevalent factor.

It appears crystal clear that the true pandemic is the comorbidities, most which are preventable by not overconsumption of sugary, salty, wheat and corn based processed foods and drinks.

We need to eliminate these fake foods from the world population diet, as this data is overwhelmingly similar across the world.

If these preventable conditions are eliminated, in 10 or 20 years we can afford health care for all.

On 2020-04-12 16:58:54, user Dragana Stojkovic wrote:

The Mycobacterium tuberculosis membrane protein Rv0899 (rv0899 gene) are important for vaccines and defence against COVID-19.<br /> For those interested I can offer an explanation.<br /> Kind regards,<br /> Dr Slobodan Stojkovic

On 2025-11-25 22:24:39, user Radim Skala wrote:

I appreciate the authors’ effort to compare different PIPAC nebulizers, but the manuscript contains major methodological and physical shortcomings that substantially distort the results. The measurements do not follow the established principles: pressure is not stabilized, nozzle orientation is not orthogonal, distance is inconsistent, and no calibrated instrumentation is used.

The Robert Bosch GLL/GCL device shown in the photos is a construction laser, not a scientific measurement tool. The spray images are presented at different scales—clearly visible from the rulers in the photographs—making direct comparison impossible. In addition, an incorrectly narrowed operating pressure range was used for nozzle C, fundamentally affecting its spray characteristics.

The corrosion test is non-clinical and selectively presented: only nozzle C is shown, while internal components of other nozzles (rubber seals, epoxy joints, moving pins, and machined metal surfaces) would exhibit comparable or greater corrosion after 12 days.

The manuscript also omits the well-known hot-spot risks associated with swirl/hollow-cone nozzles and fails to acknowledge the safety advantages of full-cone designs.

Finally, prior scientific collaboration between one of the authors and the manufacturer of Capnopen constitutes a potential conflict of interest that is not disclosed. These issues critically undermine the reliability and objectivity of the manuscript’s conclusions.

On 2020-06-24 14:24:54, user Michael Brach wrote:

This paper has undergone peer review and is published in Nutrients:<br /> https://www.researchgate.ne...

On 2021-08-18 16:51:05, user Diane Krall wrote:

We need data for severe COVID patients and reactogenicity. These patients had high dose steroids during their illness, which may be a confounding factor.

On 2020-05-28 15:04:54, user Judith Levine wrote:

Small point — probably just a typo, but the use of “country” in the abstract should be corrected to “county”.

On 2023-05-09 17:56:41, user Dr. Gerald Zincke wrote:

I am missing indication at which point in time after the vaccination an infected patient was counted to the vaccinated group.

(For the importance of this, please refer to Prof. Norman Fenton's description of the statistical illusion that can occur when vaccinated people are counted as unvaccinated for a period of time after the shot. https://youtu.be/Gkh6N-ZL3_k )

On 2023-02-19 17:11:56, user citrate reiterator wrote:

The simplest explanation for the vaccine dose trend is that it’s not broken out by previous infection status and date. Other data in the paper confirms that recent past infection is very protective against the omicron subvariants. The more shots you have, the more likely you probably were to be omicron-naive at the start of the study period. As Luis Cruz points out below, even past research from this same group has not previously found a dose-response relationship of this type. Also, when they actually fit a model that takes these confounders into account (the proportional hazards model), they find that there is a modest preventative effect of the bivalent booster — which is not consistent with a dose-dependent increase of risk following vaccination.

On 2020-04-28 12:35:10, user Ashutosh wrote:

It is flawed right from start.. A country of population size more than three times of US can't be compared with Cities

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 2021-09-21 14:02:09, user Isatou Sarr wrote:

If it works, why not!!!!! What is the genome similarity index between MEASLES-MUMPS-RUBELLA (MMR) and COVID-19? Is there any common clinical manifestation identifiers between the ailments?

On 2020-04-21 20:54:48, user Dr. Héctor Musacchio wrote:

I am confused about the interpretation of this study. Asymptomatic people who are tested, most likely are false positives. I would like to know the opinion of the authors

On 2020-04-20 13:43:09, user David Feist wrote:

What is the mathematical probability that all five seroprevalence tests are wrong? These tests now appear to be corroborated by many PCR tests showing 30% population infection rates (eg the "Boston homeless" results).

Isn't it possible that Swedish style policies naturally create 30% antibody immunity levels on top of existing 30% memory T cell immunity from prior common cold, corona virus infections? Virus specific, CD4 and CD8 memory T cells have been identified in recovered SARS patients - up to 4 years from infection:(https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125530/)").

On 2020-04-23 17:56:51, user Fiona Mulvey wrote:

Does anyone know if Stanford have made any statement on this? It seems to have been uploaded the day after their weekly townhall discussions on COVID-19, and I'm hoping there'll be another one today where they'll address this, here: https://med.stanford.edu/co...

On 2020-04-17 19:58:00, user John Ryan wrote:

50 out of 3,300 study participants tested positive for antibodies. This is actually a very low number given that lead researcher Dr. Eran Bendavid has been floating the notion that herd immunity has already been achieved in California, which is why the mortality rate is so low. Dr. Bendavid wrote an opinion piece in the WSJ on March 24 arguing that the case prevalence is much higher than revealed by testing and based on that analysis, the U.S. would see a maximum of between 20K & 40K deaths. We will pass that upper level this weekend.

This study did not have a random sample but a convenience sample drawn from Facebook users, many of whom believed they had already had CV-19. Lots more research to be done before any sweeping conclusions are drawn.

On 2020-04-18 20:30:49, user John Stevens wrote:

Many posts here have missed critical point - samples maybe biased (off by 50-75%) but if these data are even partially correct means COVID-19 can be managed down to zero. Many comments here about NYC infection rate are not correct.

NYC data has a near zero new case rate today (0.7%/day) if true that actual infected rate is 50X over reported we are at 70% of population (about 6 million) infected in NYC - explains actual drops in mortality rate and new cases to near zero in NYC and must be herd immunity

Many posts here are just not accurate and not aware of real data. have summarized www.rubee.io/nyc - see NYC posted data today look at graphs at bottom.

https://en.wikipedia.org/wi...

John K. Stevens Ph.D.

On 2020-04-18 15:51:40, user Ameriborn News wrote:

You data doesn't say which form of the virus the people were infected by.

On 2020-10-14 16:27:56, user Darren Brown; HIV Physiotherap wrote:

1) It would be beneficial to reference the long-term consequences (or sequelae) of COVID-19 as the patient preferred term of 'Long COVID". https://blogs.bmj.com/bmj/2....

2) This manuscript has been poorly constructed. The structure should be introduction, methods, results, discussion, conclusion. This requires major revision.

3) Because of the poor construction of this manuscript it is unclear to the reader what statistical hypothesis testing has been performed. It is not suitable to put statistical methodology in brackets in the results section.

I would reject this submission, requiring major revisions.

On 2021-05-25 14:13:52, user max wrote:

what about deep sequencing of the sperm? or snp for covid pieces?

On 2021-06-19 13:04:28, user Stephen Smith wrote:

Actually, this is one of the largest IMV needing Covid cohorts ever. It is by far the most detailed description of IMV-needing Covid pts. <br /> What do you mean "current treatment"?<br /> Stats are stats. p-values are p-values. Either they count the same from one study to the next or we throw them away altogether and say "I know which studies are good and which are bad". <br /> These are just data. We analyzed them as fairly and completely as possible. <br /> But it's wrong to simply dismiss data based on prejudice. <br /> If that continues, we might as well stop doing studies and just declare what works and what doesn't.

On 2025-09-07 20:13:12, user S S Young wrote:

Milojevic et al. 2014 had access to all emergency room visits for all of England and Wales for the years 2003 to 2008, over 400,000 myocardial infarction (MI) events, and over 2 million CVD emergency hospital admissions. They found no effect of CO, NO2, Ozone, PM10, PM2.5, or SO2 on heart attacks, hospital admissions, or mortality, their Figures 1 and 2.

Milojevic, A., Wilkinson, P., Armstrong, B., Bhaskaran, K., Smeeth, L., Hajat, S. 2014. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: Case-crossover analysis of the MINAP database, hospital admissions and mortality. Heart (British Cardiac Society) 100, 14: 1093-98. https://doi.org/10.1136/heartjnl-2013-304963 .

On 2020-06-23 17:39:31, user Liam Golding wrote:

Hi great work by your team.

I'm curious whether you standardize the log inactivation to untreated masks or to viral stock added. You note that for bacterial contaminants that untreated coupons are compared to treated to obtain log reduction values. But, for example, you note that "For each decontamination method, each sample used for treatment had a corresponding no-treatment control. No-virus blank masks were also included to identify possible contamination." Was the control viral load extracted then compared to treatments to obtain log reduction values, or was a known quantity of viral load added to controls and used to determine the log reduction?

**Edit: you draw mention to this in the Material and Methods.

However, compared to other studies (Mills et al., 2018; Lore et al., 2011) your method of extracting viral load is minimal to say the least. Generally, coupons are cut; placed in a 15/50ml tube with ~ 15ml extraction solution then vortexed/mixed for 20 minutes. Can you comment on why you chose 1 minute vortex with 1.3mL solution over the common OP?

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 2021-12-28 14:23:53, user Zacharias Fögen wrote:

Dear Authors,

Thank you for this study, which clearly demonstrates that there is no IgA response to vaccination, thus not causing immunity to infection. Yet, irritatingly, you claim the opposite.

Figure 2F shows that there is no significant RBD-IgA after 2 vaccinations.

As for Spike-IgA, there is a wrong labeling in Figure 2E, as the "ns" should belong to the comparison "neg-ctrl vs. mrna 2 doses" and not to "covid-19 vs.mrna 2 doses" the latter being clearly significant, the former showing that the median of "mrna 2 Doses" is below the positive cutoff.

Furthermore, your "Baseline" mean in figure 2K is much higher (about 2,5%) than "negative control" in figure 2E (about 0%). Since both "baseline" and "negative control" are not vaccinated, this points to a selection bias for your negative control.<br /> Figure 2K also shows that there is no significant difference concerning "Baseline" and "2-4 weeks post dose 2". Yet, there is a significant difference between doses 1 and 2, as well as 1 and baseline.<br /> When comparing "baseline" and "mrna 2 Doses", "mrna 2 doses" is as high as "2-4 weeks post dose 2", which is not significantly different from "baseline" (Figure 2K).

So, there is no significant IgA (both Spike-IgA and RBD-IgA) after 2 doses of vaccination.

As far as the increase after 1st dose, but not after the second dose, this either points to an unknown bias, or it shows that multiple vaccinations do not increase IgA production, hinting at a lack of booster efficiency.

In Version 2 you had 6 month follow-up values in figure 1, yet in figure 3 the 6-month follow up (now figure 2) was removed. Why is that?

I kindly ask for the underlying data.

Greetings, Zacharias Fögen

On 2020-07-08 21:52:08, user F Philibert wrote:

Faulty Data sources were used in this study.

I question the use of the GVA data, which was generated by an organization with its own agenda, rather than FBI data, which is more accurate but lags behind.

As well, NICS checks do not represent a one-to-one count against firearms purchases; In most states, <br /> 1. Multiple guns can be purchased on a single NICS background check,<br /> 2. Holders of Carry Permits may not need NICS Checks, and <br /> 3. NICS checks are also performed for other reasons, such as Purchase or Carry Permit issuance or renewal. <br /> To get a proper number for gun purchases, consult data from the National Shooting Sports Foundation.

The cause and effect are likely backwards here. Coinciding with the pandemic were riots and civil disturbance, which increased violence, and likely caused a surge in firearms purchases for self protection. Also, with the 2016 election cycle comes the threat of increased Gun Control, and perceived restriction on ability to purchase firearms in the future. Nothing increases a population's desire for an object more than perceived future scarcity. E.g., Toilet Paper at the outset of the pandemic.

Note also that there was a surge in firearms sales in 2013, with a corresponding DECREASE in crime.

I find the conclusions of this study to be questionable at best.

On 2020-01-25 07:04:43, user Roc Duan wrote:

Considering only air travel may have significantly distorted the model. As a result, it may incorrectly predict a faster spread.

On 2020-06-12 05:44:01, user Alex Backer wrote:

Good control. Yet a deeper look reveals that vitamin D deficiency is indeed linked to COVID-19 fatality rates outside of life expectancy: https://papers.ssrn.com/sol... .

On 2020-06-04 08:18:20, user Abderrahim Oussalah wrote:

It could be insightful to have adjusted effect sizes for the GWAS after considering body-mass index and other potential risk factors (e.g., therapy with angiotensin-converting enzyme inhibitor / angiotensin receptor blocker) as covariates in the models?

On 2020-04-23 12:49:39, user disqus_UUGcJ8pJWg wrote:

If a disease were to exclusively attack a certain demographic you would be more inclined to forego true randomization. Discounting this study is irresponsible. As irresponsible as prescribing the medication without explaining what the side effects could be.

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 2023-09-21 10:06:53, user Glenn Tisman, M.D. wrote:

Beautiful work. Great team cooperation. Possibly gives credence to some of those swearing that high-dose B12 helps their symptoms of various neurologic disorders in spite of normal serum B12 levels. I wonder if our paper "https://www.academia.edu/68..." in 1993 revealing that XRT and or chemotherapy drugs (e.g. Hydrea) cause a decrease in HOLOTCII may add to the discussion. Neuropathy in patients on various chemo and XRT (e.g. Hydrea) and non chemotherapy drugs (statins said to induce subtle EMG signs of peripheral neuropathy in 40% of patients) may add to the discussion. Glenn Tisman, M.D.

On 2021-08-08 19:23:45, user Sam Wheeler wrote:

So against delta, 1 dose of The Moderna COVID-19 (mRNA-1273) vaccine seems much more efficient than 1 dose of Pfizer Biontech?<br /> And no data about how efficient is Moderna with 2 doses against delta?<br /> What do we know about Janssen = J&J? Janssen is very efficient if you take into account it is given as a single-dose, and one can boost it by taking a booster or primer with other covid vaccine.

On 2020-12-24 06:54:17, user altizar wrote:

I know a nursing home where at least 6 nurses have been re-infected after around 30 days of having Covid the 1st time. So based on empirical evidence, your study is not accurate.

On 2024-02-03 10:02:01, user Ryan wrote:

published https://rdcu.be/dxHNq.

Benjamin, R. Reproduction number projection for the COVID-19 pandemic. Adv Cont Discr Mod 2023, 46 (2023). https://doi.org/10.1186/s13...

On 2020-05-15 13:29:32, user Atsushi NARABAYASHI wrote:

Dr. Wyllie,

Please tell me saliva collection were before nasopharyngeal collection or after, on each collection.<br /> If saliva collection is after nasopharyngeal collection, saliva may contain sputum from lower respiratory systems.

On 2020-05-23 02:16:10, user Eduardo Spitzer wrote:

Dear Dr.Wyllie

Kudos for such a great and elegant work.<br /> I think this article will set a foundation for future actions con Sars-Cov-2 detection, and particular in low/ difficult resources settings...<br /> I live in Argentina and we have a serious problem importing swabs from abroad and there is a huge surge in prices....and this makes national/private screening programs work in sub-optimal conditions.

I would like to know about running a direct RT-qPCR from saliva and avoid RNA extraction methods (Spin columns are also very difficult to obtain from Trusted EU/USA suppliers)??? This would be the perfect equation for increasing surveillance....

Again, Congratulations!!

Best<br /> Eduardo

On 2021-01-12 20:36:18, user Michael Meyer wrote:

Can someone tell me if this study has completed the peer-review process prior to publication in the Journal of Infectious Diseases? If not, is it currently in that process?

On 2020-06-18 01:00:02, user Alex Backer wrote:

See https://ssrn.com/abstract=3... for a global study that shows case and death counts had significantly lower growth rates at higher temperatures (>14 °C) when aligned for stage in the epidemic. We then show irradiance and in particular solar elevation angle in combination with cloudopacity explain COVID-19 morbidity and mortality growth better than temperature: a reduction of mean solar elevation of 9 degrees led on average to a 2500% increase in COVID-19 case growth over the following two weeks. COVID-19 exploded during the darkest January in Wuhan in over a decade. Our results suggest transmission models should incorporate solar elevation and that the impact of UV irradiance on individual morbidity and mortality should be tested. We discuss implications for the best locations and optimal behaviors for high-risk individuals to weather the pandemic. --Alex Bäcker, Ph.D.

On 2021-05-18 05:55:12, user YingYing Irene Wang wrote:

https://academic.oup.com/jd... accepted and published

On 2020-03-12 07:59:45, user objkshn wrote:

What about this study from March, 2020 that indicates the virus lives for up to 9 days on certain surfaces? https://www.journalofhospit...

On 2022-05-09 16:36:22, user eduardo quiñelen wrote:

Dear,<br /> Please, we need you to explain the information on true positives, true negatives, false positives and false negatives disaggregated for each test.

On 2020-09-09 11:10:26, user Andrew Broadbent wrote:

Draft comment by – T Andrew Broadbent CES Economic & Social Research info@ces.org.uk

Overview<br /> This important paper claims to transcend the large number of ‘conventional’ epidemiological ‘SEIR’ models (Susceptible, Exposed, Infectious, or Recovered) of the current pandemic. Its fascination lies in its attempt to ‘compare SEIR models of immune status’ and derive results more directly from the data available.<br /> It uses ‘Bayesian inference’ on data from 10 countries from 25 Jan to 20 June 2020 to estimate the daily proportion of people in each country who are (i) not exposed to infection(ii) not susceptible even though exposed (iii) not infectious even when susceptible. These sub-populations are what the authors call ‘dark matter’. It concludes that many more of the population are ‘effectively immune’ than generally understood, and so the second wave can be indefinitely postponed or suppressed without successive lockdowns as concluded from some of the conventional models.<br /> The immediate issues and queries seem to be :<br /> (i) Suppression is said to depend on an effective Track and trace system, as with some ‘conventional’ models. The practical policy implications are thus not very different from studies which suggest maintaining restrictions until infection is very low, so as to enable managing with track and trace, without needing to reimpose universal lockdown.<br /> (ii) The proportion of the population isolating/shielding dominates the results (eg Germany) . This is social behaviour and government policy, not ‘dark matter’ in the way susceptibility and infectibility may be, being more biologically determined.<br /> (iii) The parameters in the model are estimated within limits determined from external sources This may include the predominant ‘effective population’ parameter – the population who are not shielding.<br /> (iv) ‘Effective herd immunity’ is a somewhat troubling term – given overtones of ‘let the old die’ in some policy discussions of ‘herd immunity’.<br /> (v) It claims to incorporate all the different data collection biases in different countries, such as testing people with or without infection. Would it be worth including countries with early success in suppressing infection – Taiwan, S Korea, China, New Zealand etc?<br /> ‘Effective’ herd immunity?<br /> It references other studies which also look at ‘heterogeneity’ of the population, where the first wave either kills or makes immune the more susceptible population – so that a second wave necessarily involves a less susceptible population and will tend to be lower than the first wave, other things being equal.<br /> It concludes that ‘effective herd immunity’ following the first wave of infection is much higher than suggested by the proportion of people who have been infected and recovered and may now be immune – ‘seroprevalence’ . This is now in the range 5-7% in the UK.<br /> The term ‘herd immunity’ prompts wariness following the UK government’s early discussions which were interpreted as contemplating 60-80% of the population becoming infected with 500K-1 million deaths. ‘Culling the old and infirm’ was one interpretation. The paper concludes that having less than 20% of the population infected and recovered could be enough to dampen a second wave.<br /> The second wave - a ten-fold reduction in infection and death?<br /> A main claim of the paper is that the second wave could be postponed indefinitely, or if not, have a factor of 10 fewer infections and deaths than predicted by some SEIR models, (deaths peaking at 30-100 per day in the UK, compared with 1000 per day at the peak of the first wave) .<br /> But this projection, has in common with the conventional models, a heavy reliance on an effective FTTIS (‘Find, Test, Trace, Isolate, Support’) system in order to isolate those infected or exposed to infection. But the paper suggests that only 25% efficacy of FTTIS is needed, compared to the present official target of 80%.(?)<br /> Dark matter – very high?<br /> ‘Dark matter’ seems a very high proportion of the population. From one illustration (figure 2) dark matter results in under 20% of the population being infected. Almost 50% of the total population are not exposed (shielding/sequestered), so that only half the populations is ‘effective’ in the epidemic. Of those who are, 50% are not susceptible, and of those who are susceptible 50% are not infectious. <br /> The proportion of the total population which is non exposed (self isolating, shielding, sequestered) would seem to be very dependent on people’s behaviour and on government instructions, and thus on the social context and time lapse of the pandemic. The other components of ‘dark matter ‘ - susceptibility and infectibility – seem more biologically determined, not so subject to behaviour and social and policy context.<br /> Data – why not include countries with greater success in suppressing the first wave?<br /> Although the FTTIS is said to be enough to limit or suppress the second wave without a ‘lockdown’, the Bayesian inference was conducted on countries, many of whom who were in some kind of lockdown for at least part of the period. They are the 10 countries with high death rates.<br /> The data is from USA, UK, Canada, Spain, France, Italy, Belgium, Germany, Mexico, and Brazil . It would have been interesting to include countries which largely succeeded suppressing the virus in the first wave, with either very short sharp lockdowns, or early interventions of intense FTTIS, namely Taiwan, South Korea, China, Hong Kong, Singapore, New Zealand. <br /> Many model parameters are influenced from outside the model. (?)<br /> There ar 25 parameters listed in the model, and their levels and potential variation – are apparently influenced by external empirical studies outside the model, and are listed as ‘priors’. This apparently influences the final estimated parameters after the model has been run. (?)<br /> Parameters include the effective population, the probability of going out, social distancing threshold, critical care capacity threshold (per capita), Infection, proportion of non-infectious cases, effective number of contacts, effective number of contacts: work, transmission strength, infected period , infectious period , proportion of non infectious people etc. etc.<br /> Rich findings – country by country results – ‘effective population’ dominates?<br /> The paper suggests that only Spain, and Brazil don’t exhibit the heterogeneity embodied in the model – in that their whole population seems to participate in the epidemic – their ‘effective population’ is equal to the whole population, with almost no one isolating, or shielding.<br /> The country comparisons involve changing the input parameters, so as to eliminate each component of heterogeneity in turn. The parameters - effective population, non susceptibility, social distancing threshold, decreasing seropositivy are each removed in turn.<br /> Germany and Canada have by far the smallest proportion of ‘effective population’ due to their high levels of shielding – this seems to determines their relatively good performance and low level of deaths, - it would be useful to learn more about how far this parameter is set ‘prior’ to the model.<br /> There is much less variation in the proportion of the effective population susceptible to infection – from ~67% in Spain (operating on an effective population almost equal to the whole population) , to ~47% in Canada.<br /> Similarly there is low variation in the proportion of susceptible people who are non infectious – from ~60% in Canada and Italy and to ~45% in Germany, France , and USA .<br /> Some more details<br /> The claim is that the analysis can incorporate all kinds of real world fuzziness in the data - by modelling latent variables such as the bias towards testing people with or without infection or, the time-dependent capacity for testing. ‘Everything that matters —in terms of the latent (hidden) causes of the data—can be installed in the model, including lockdown, self-isolation and other processes that underwrite viral transmission’.<br /> This is a ‘LIST’ model with four factors (Location, Infection, Symptoms and Testing). It models the probability of people being in different states, and produces two outputs – positive cases, and deaths .<br /> The states in each factor are::<br /> Location – Home, Work , Hospital, Isolated, Removed<br /> Infection – Susceptible, Infected, Infectious, Sero negative, Seropositive<br /> Symptoms – Health, Symptoms, Severe, Deceased<br /> Testing – Untested, Waiting, Negative, Positive<br /> Each individual in the population has to be in one state, and only one state, within each of the four factors.

On 2020-05-06 14:10:54, user David wrote:

Whitman et al. evaluated Premier Biotech Biotest test used in this study using 108 pre-COVID blood samples (collected July 2018). They found 3 false positive, giving a specificity of 97.22% (92.10-99.42% 95% C.I.). I note that the authors updated their paper with tests run on many more pre-COVID samples, so this might just be bad luck.

Whitman, J.D., Hiatt, J., Mowery, C.T., Shy, B.R., Yu, R., Yamamoto, T.N., Rathore, U., Goldgof, G.M., Whitty, C., Woo, J.M. and Gallman, A.E., 2020. Test performance evaluation of SARS-CoV-2 serological assays. medRxiv.

On 2020-03-25 05:16:38, user Sinai Immunol Review Project wrote:

Summary: Based on a retrospective study of 162 COVID patients from a local hospital in Wuhan, China, the authors show an inverse correlation between lymphocyte % (LYM%) of patients and their disease severity. The authors have also tracked LYM% of 70 cases (15 deaths; 15 severe; 40 moderate) throughout the disease progression with fatal cases showing no recovery of lymphocytes ( <5%) even after 17-19 days post-onset. The temporal data of LYM % in COVID patients was used to construct a Time-Lymphocyte% model which is used to categorize and predict patients’ disease severity and progression. The model was validated using 92 hospitalized cases and kappa statistic test was used to assess agreement between predicted disease severity and the assigned clinical severity (k = 0.49).

Limitations: Time-Lymphocyte % Model (TLM) that authors have proposed as a predictive model for clinical severity is very simple in its construction and derives from correlative data of 162 patients. In order for the model to be of use, it needs validation using a far more robust data set and possibly a mechanistic study on how COVID leads to lymphopenia in the first place. In addition, it should be noted that no statistical test assessing significance of LYM % values between disease severities was performed.

Significance of the finding: This article is of limited significance as it simply reports similar descriptions of COVID patients made in previous literature that severe cases are characterized by lymphopenia.

On 2021-06-09 12:56:25, user Tracii Kunkel wrote:

The key here - NOT PEER REVIEWED. It's been demonstrated that people who have been invfected have often changed their health-risk behaviors after recovery. How could the researchers have not assessed this factor? It's possible that the low rate of re-infection by those without the vaccine was heavily influenced by them taking precuautions much more seriously.

On 2021-10-11 18:46:13, user Andrew T Levin wrote:

Comment #2: Methodological Issues

1. Given the stated purpose of this study, it is remarkable that the manuscript never specifically defines the term “community-dwelling population.” In practice, the study analyzes the incidence of COVID-19 fatalities that have occurred outside of nursing homes, but even that distinction is not very precise. For example, the spectrum of U.S. nursing homes encompasses board & care homes, assisted care facilities, and skilled nursing facilities. About two-thirds of U.S. nursing home residents rely on Medicaid to cover that cost. By contrast, higher-income individuals can afford to receive home health care or choose to live in “retirement communities” with on-site medical staff. In effect, the distinction of whether someone is “community-dwelling” or a “nursing home resident” is linked to a complex set of socioeconomic characteristics as well as to various aspects of their individual health. Making international comparisons along these lines is even more fraught with difficulty, because the size and composition of the nursing home population inevitably reflects differences in social norms as well as socioeconomic factors, access to healthcare, and the extent of public assistance. Indeed, such comparisons may be practically meaningless when considering developing countries such as the Dominican Republic and India, where nursing home care may only be an option for a very small fraction of the population.

2. Search Procedure. This manuscript uses an arbitrary search cutoff date of 31 March 2021, which excludes some landmark seroprevalence studies that have been disseminated since then. For example, Sullivan et al. (2021) analyzed seroprevalence of the U.S. population over the second half of 2020 using a large representative sample that included 1154 adults ages 65+, and hence that study would clearly satisfy the stated eligibilitry criteria for this meta-analysis.[11] Moreover, the study carefully adjusts for assay characteristics and seroreversion and estimates that as of 31 October 2020, the IFR for U.S. adults ages 65+ was 7·1% (CI: 5·0¬-10·4%). Those results can be also be used in conjunction with data on nursing home deaths to obtain the corresponding IFR estimate of 4·7% for community-dwelling adults ages 65+.

3. Minimum Size Threshold. This analysis excludes seroprevalence results from any studies involving fewer than 1000 adults ages 70+, and hence it is remarkable that the manuscript neither provides any rationale for imposing such a constraint nor provides citations to any existing works that might motivate it. Indeed, this approach is inconsistent with basic principles of statistical analysis, e.g., making inferences based on all available information and avoiding arbitrary selection criteria that could induce bias in the results. Consequently, meta-analysis should downweight studies with relatively lower precision rather than simply discarding those studies. Moreover, it is incoherent to specify an eligibility criterion based solely on sample size, because the precision of seroprevalence estimates also hinges on the level of prevalence. A small sample may be adequate in a context of relatively high prevalence, whereas a much larger sample may be needed to obtain precise inferences in a context of very low prevalence. The national study of Hungary was included in this meta-analysis because that study included 1454 adults ages 70+. However, only nine of those individuals were seropositive. Consequently, the test-adjusted seroprevalence for this cohort of older adults is not statistically distinguishable from zero, and hence the confidence interval of the age-specific IFR is not even well-defined.[12] By contrast, the regional study of Geneva was excluded from this meta-analysis because it only included 369 individuals ages 65+. But that sample was large enough to facilitate inferences about seroprevalence (6·8%; CI: 3·8¬¬ 10·5%) and corresponding inferences regarding IFR for that age cohort (5·6%: CI: 4·3 7·4%).[13, 14] Finally, setting the sample size threshold at 1000 is clearly an arbitrary choice. Since seroprevalence studies can be readily identified using the SeroTracker tool, this meta-analysis should be extended using a lower threshold of 250 adults ages 65+ that would encompass the national studies of Netherlands and Sweden as well as a substantial number of regional studies.

4. Sample Selection. In characterizing which seroprevalence studies have been included in <br /> the meta-analysis, this manuscript specifies the key criterion as “aimed to generate samples reflecting the general population.” However, this criterion is extraordinarily vague and judgmental (as evident from subjective words like “aimed” and “reflecting”). <br /> (a) United Kingdom. The inadequacy of this approach to sample selection is evident from the fact that the meta-analysis places equal weight on four U.K. seroprevalence studies, even though only two of those studies (UK BioBank and REACT-2) utilized samples designed to be representative of the general population.[15, 16] By contrast, the other two studies used convenience samples that were not designed or even re-weighted to be broadly representative, and hence those two studies should have been excluded from this meta-analysis. First, Hughes et al. (2020) studied a panel of primary and secondary patients at a large Scottish health board, with the stated objective of assessing viral transmission patterns.[17] The paper never suggested that this panel could be interpreted as representative of the wider population; indeed, some of these patients may have been receiving care related to COVID-19. Second, in one of its weekly surveillance reports, Public Health England (2020) reported seroprevalence results for a panel of patients ages 65+ who had a routine blood test at the Royal College of General Practioners Research and Surveillance Centre.[18] Evidently, this panel was not aimed to reflect the general population and may well have included patients recovering from COVID or experiencing COVID-like symptoms. <br /> (b) United States. One of the two U.S. seroprevalence studies used a sampling design that is intended to be broadly representative, whereas the other U.S. study used a convenience sample of patients at kidney dialysis centers. Unfortunately, as a consequence of gross disparities in healthcare access, higher-income individuals typically utilize in-home dialysis machines, whereas low-income individuals must travel multiple times per week to a dialysis center, often using public transit. Consequently, the prevalence of COVID-19 infections among such patients has crucial public health implications but should not be interpreted as representative of the general population.<br /> (c) Canada. Among the three Canadian seroprevalence studies, two use representative sampling designs (Ontario and Canada-ABC), whereas the third study conducted by Canadian Blood Services (CBS) uses a convenience sample of blood donors. In its public announcement of those results, CBS specifically warned that “caution should be exercised in extrapolating findings to all healthy adult Canadians, because blood donors self-select to be blood donors, in some areas access to a donation clinic may be limited, and there are fewer elderly donors who donate blood compared to the general population.” [19] That caution was specifically cited as the reason for excluding this study from a previous meta-analysis.[5] Indeed, given the scarcity of elderly blood donors, there is an even stronger rationale for excluding that study from the analysis here. Indeed, this meta-analysis should have specifically excluded all convenience samples, whether from blood donors, commercial lab tests, or medical patients. Dodd et al. (2020) analyzed a large panel of U.S. blood donors and found that the proportion of first-time donors jumped in June 2020 following the introduction of COVID-19 antibody testing, consistent with the hypothesis of “donors with higher rates of prior exposure donating to obtain antibody test results,” and concluded that “blood donors are not representative of the general population.”[20] Bajema et al. (2021) found seroprevalence of 4·94% using commercial lab residual sera from residents of Atlanta (USA), compared to seroprevalence of 3·2% using a representative sample of the same location.[21, 22] These findings highlight the extent to which convenience samples may be associated with upward bias in seroprevalence and hence downward bias in IFR. It should also be noted that the incidence of COVID-19 infections has a strong association with race and ethnicity, reflecting disparities in employment, residential arrrangements, and various other factors. Such patterns have been evident in numerous countries (not just the USA), and hence the manuscript should follow a consistent approach in addressing this issue.

5. Open-Ended Age Brackets. This manuscript proceeds on the assumption that open-ended age brackets for older adults are essentially equivalent regardless of whether the bracket is 60+, 65+, or 70+. But this assumption is inconsistent with the consistent findings of preceding studies, namely, the IFR for COVID-19 increases continuously with age rather than jumping discretely at any specific age threshold. Indeed, the measured IFR for any particular age bracket is a convolution of the age distribution of the population, the age-specific pattern of prevalence, and the fact that IFR increases exponentially with age. The complexity of this convolution underscores the pitfalls of comparing IFRs for open-ended age brackets of older adults. Ontario serves as a useful case study for illustrating these issues. The Ontario Public Health seroprevalence study reported results for three broad age brackets: 0-19, 20-59, and 60+ years. However, this manuscript assesses IFR for ages 70+ using results obtained via private correspondence. However, that assessment may be very imprecise, because COVID-19 prevalence was very low in the general population and may well have been even lower among the oldest community-dwelling adults. By contrast, the Ontario study is very informative for characterizing the cohort of individuals ages 60-69 years. In particular, there were 9 positives among 804 specimens for that cohort; the test-adjusted prevalence of about 1% indicates that about 17000 Ontario residents ages 60-69 had been infected by mid-June 2020. As of 30 June 2020, that age group had 240 COVID-19 deaths—none of which occurred in nursing homes. Consequently, the IFR for community-dwelling Ontario adults ages 60-69 was 1·4% -- identical to the predicted IFR t the midpoint of this age interval from the metaregression of Levin et al. (2020).[5]

6. Adjusting for Assay Characteristics. Seroprevalence studies have generally been conducted using antibody assays with imperfect specificity and sensitivity, and these characteristics exhibit substantial variation across assays. Moreover, the implications of these characteristics depend on the actual level of prevalence, e.g., adjusting for specificity is crucial in a context of relatively low prevalence.[23] Consequently, all three of the preceding meta-analyses consistently used seroprevalence estimates and confidence intervals that had been adjusted for test sensitivity and specificity using the Gladen-Rogan formula and/or Bayesian methods.[5, 8, 9] By contrast, this meta-analysis simply uses raw seropositive data from those studies that did not report test-adjusted seroprevalence.

7. Low Prevalence. The shortcomings of this manuscript’s approach are particularly evident in assessing IFRs for locations with relatively low prevalence. For example, as shown in manuscript Table 1, the seroprevalence study of Hungary used the Abbott Architect IgG assay to analyze 1454 specimens and obtained 9 positive results, i.e., raw seropositivity of 0·6%. According to the manufacturer’s data submitted to the U.S. Food and Drug Administration, this assay has sensitivity of 100% and specificity of 99·6%.[24] Consequently, the Gladen-Rogan formula indicates that the test-adjusted prevalence is only 0·2%, i.e., only one-third of the observed seropositive results were likely to be true positives. Moreover, this test-adjusted estimate has a 95% confidence interval of 0 to 0·4%, i.e., the prevalence is not statistically distinguishable from zero, and hence its IFR does not have a well-defined confidence interval. Indeed, that was precisely the reason why this cohort was not included in the meta-analysis of Levin et al. (2020).

8. Unmeasured Antibodies. This manuscript follows a completely unorthodox approach in adjusting seroprevalence for unmeasured antibodies: “When only one or two types of antibodies (among IgG, IgM, IgA) were used in the seroprevalence study, seroprevalence was corrected upwards (and inferred IFR downwards) by 10% for each non-measured antibody.” (p.8) This approach is particularly objectionable when applied to test-adjusted seroprevalence results, since those estimates have already been adjusted to reflect sensitivity and specificity. Moreover, such an approach has never been used by any other epidemiologist or statistician, in the context of the COVID-19 pandemic or for any other purpose, and hence should not be applied in a meta-analysis without providing any compelling rationale for doing so.

9. Seroreversion. The manucript “explores” the issue of seroreversion using proportionality factors based on the timing of each seroprevalence study relative to the preceding peak of COVID-19 deaths. However, the manuscript provides no rationale for following this approach instead of the rigorous Bayesian methodology utilized in a preceding meta-analysis.[9] Moreover, the manuscript makes no reference to the findings of longitudinal studies of the evolution of antibodies in confirmed positive individuals, which have concluded that the degree of seroreversion is substantial for some assays and negligible for others.[25, 26]

10. Measurement of Fatalities. Data on COVID-19 fatalities should be obtained directly from official government sources, not from media reports, web aggregators, or Wikipedia. For example, the European Center for Disease Control has an online COVID-19 database with daily data on reported cases and fatalities for nearly every country in the world. Moreover, whenever possible, fatalities should be measured using official tabulations of case data (based on actual date of death) rather than real-time reports that may be relatively incomplete and subject to substantial revision over time. These issues are particularly relevant for assessing fatalities in nursing homes: If a patient tested positive for COVID-19 and died soon thereafter, investigation would be needed to determine whether the death resulted from COVID-19 or unrelated causes. To illustrate these issues, consider the manuscript’s estimate of IFR based on the U.S. national seroprevalence study of Kalish et al. (2021). As shown in table 1 and appendix table 2 of this manuscript, the U.S. CDC case database (accessed in February 2021) indicates a total of 103862 deaths for adults ages 70+ as of 04 July 2020. To determine the corresponding fatalities in U.S. nursing homes, however, the manuscript relies on a news summary dated 26 June 2020 that reported a total of 52428 nursing home deaths in 41 U.S. states.[27] Using that real-time report, manuscript infers a somewhat higher national total of 57291 nursing home deaths and hence 46571 deaths outside of nursing homes. By contrast, the U.S. CMS case database (accessed in August 2021) indicates 38239 deaths in U.S. nursing homes as of 05 July 2020.[28] Evidently, there were 65623 fatalities outside of nursing homes, implying a correspondingly higher IFR of 3·6% for U.S. community-dwelling adults ages 70+.

11. Developing Countries. The use of confirmed COVID-19 fatalities can be highly misleading in assessing IFRs of developing countries, where testing capacity has been much more limited than in Europe or North America. Consequently, in developing country locations, the measure of fatalities should include both confirmed and suspected COVID-19 cases, or alternatively, a measure of excess deaths relative to preceding years. Indeed, several recent studies of India have concluded that confirmed COVID-19 fatalities understate the true death toll by an order of magnitude.[29-31]

12. Younger Age Groups. The manuscript states that “the studies considered here offered a <br /> prime opportunity to assess IFR also in younger age strata” (p.9) even though such analysis <br /> had not been proposed in the protocol. Nevertheless, this secondary analysis is at odds with the key eligibility criterion of this meta-analysis, namely, seroprevalence studies with at least 1000 participants ages 70+. Indeed, imposing that eligibility criterion leads to the exclusion of numerous other seroprevalence studies that would be highly informative for analyzing IFRs of younger adults, with an unknown degree of bias associated with that exclusion.

13. Self-Citations. A meta-analysis is intended to serve as an objective synthesis of information extracted from existing studies. Consequently, methodological decisions and substantive claims should not be based solely on citations of the authors’ own prior work. For example, in discussing the preceding meta-analysis of Levin et al. (2020), the manuscript asserts that “almost all included studies came from hard-hit locations, where IFR may be substantially higher”, with a sole citation to Ioannidis (2021a). However, that assertion is clearly false: The meta-analysis of Levin et al. (2020) included locations such as Australia, New Zealand, Ontario, and Salt Lake City that experienced very few infections during the first wave of the pandemic. Similarly, the manuscript asserts that “selection bias for studies with higher seroprevalence and/or higher death counts may explain why their estimates for middle-aged and elderly are substantially higher than ours” (p.14), with a sole citation to Ioannidis (2021b).

References Cited Here:<br /> 1. Ferguson N, Laydon D, Nedjati-Gilani G, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand2020.<br /> 2. Mizumoto K, Kagaya K, Zarebski A, Chowell G. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. eurosurveillance. 2020;25(10). doi:10.2807/1560-7917.ES.2020.25.10.2000180<br /> 3. Salje H, Tran Kiem C, Lefrancq N, et al. Estimating the burden of SARS-CoV-2 in France. Science. 2020;369(6500):208-11. doi:10.1126/science.abc3517<br /> 4. Verity R, Okell LC, Dorigatti I, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. lancet infectious diseases. 2020;20(6):669-77. doi:10.1016/S1473-3099(20)30243-7<br /> 5. Levin AT, Hanage WP, Owusu-Boaitey N, Cochran KB, Walsh SP, Meyerowitz-Katz G. Assessing the age specificity of infection fatality rates for COVID-19: systematic review, meta-analysis, and public policy implications. European Journal of Epidemiology. 2020;35(12):1123-38. doi:10.1007/s10654-020-00698-1<br /> 6. Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020. doi:10.1038/s41586-020-2521-4<br /> 7. Mak JKL, Kuja-Halkola R, Wang Y, Hägg S, Jylhävä J. Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling. Journal of the American Geriatrics Society. 2021;69(5):1128-39. doi:https://doi.org/10.1111/jgs...<br /> 8. O’Driscoll M, Ribeiro Dos Santos G, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature. 2021;590(7844):140-5. doi:10.1038/s41586-020-2918-0<br /> 9. Brazeau N, Verity R, Jenks S, al. e. COVID-19 Infection Fatality Ratio: Estimates from Seroprevalence. 2020. doi:https://doi.org/10.25561/83545.<br /> 10. Arora RK, Joseph A, Van Wyk J, et al. SeroTracker: a global SARS-CoV-2 seroprevalence dashboard. The Lancet Infectious Diseases. 2020. doi:10.1016/s1473-3099(20)30631-9<br /> 11. Sullivan PS, Siegler AJ, Shioda K, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Cumulative Incidence, United States, August 2020–December 2020. Clinical Infectious Diseases. 2021. doi:10.1093/cid/ciab626<br /> 12. Merkely B, Szabo AJ, Kosztin A, et al. Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary. Geroscience. 2020;42(4):1063-74. doi:10.1007/s11357-020-00226-9<br /> 13. Perez-Saez J, Lauer SA, Kaiser L, et al. Serology-informed estimates of SARS-CoV-2 infection fatality risk in Geneva, Switzerland. The Lancet Infectious Diseases. doi:10.1016/S1473-3099(20)30584-3<br /> 14. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020;396(10247):313-9. doi:10.1016/s0140-6736(20)31304-0<br /> 15. United Kingdom BioBank. UK Biobank SARS-CoV-2 Serology Study Weekly Report - 21 July 2020. 2020.<br /> 16. Ward H, Atchison CJ, Whitaker M, et al. Antibody prevalence for SARS-CoV-2 in England following first peak of the pandemic: REACT2 study in 100,000 adults. medRxiv. 2020:2020.08.12.20173690. doi:10.1101/2020.08.12.20173690<br /> 17. Hughes EC, Amat JAR, Haney J, et al. Severe Acute Respiratory Syndrome Coronavirus 2 Serosurveillance in a Patient Population Reveals Differences in Virus Exposure and Antibody-Mediated Immunity According to Host Demography and Healthcare Setting. The Journal of Infectious Diseases. 2020;223(6):971-80. doi:10.1093/infdis/jiaa788<br /> 18. U.K. Public Health England. Weekly Coronavirus Disease 2019 (COVID-19) Surveillance Report, Week 32. 2020. <br /> 19. Canadian Blood Services and COVID-19 Immunity Task Force. Final Results of Initial Canadian SARS-Cov-2 Seroprevalence Study Announced. 2020. <br /> 20. Dodd RY, Xu M, Stramer SL. Change in Donor Characteristics and Antibodies to SARS-CoV-2 in Donated Blood in the US, June-August 2020. JAMA. 2020;324(16):1677-9. doi:10.1001/jama.2020.18598<br /> 21. Bajema KL, Dahlgren FS, Lim TW, et al. Comparison of Estimated Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence Through Commercial Laboratory Residual Sera Testing and a Community Survey. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1804<br /> 22. Boyce RM, Shook-Sa BE, Aiello AE. A Tale of 2 Studies: Study Design and Our Understanding of Severe Acute Respiratory Syndrome Coronavirus 2 Seroprevalence. Clinical Infectious Diseases. 2020. doi:10.1093/cid/ciaa1868<br /> 23. Gelman A, Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2020;n/a(n/a). doi:10.1111/rssc.12435<br /> 24. U.S. Food and Drug Administration. EUA authorized serology test performance. 2020.<br /> 25. Dan JM, Mateus J, Kato Y, et al. Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection. Science. 2021;371(6529):eabf4063. doi:10.1126/science.abf4063<br /> 26. Muecksch F, Wise H, Batchelor B, et al. Longitudinal Serological Analysis and Neutralizing Antibody Levels in Coronavirus Disease 2019 Convalescent Patients. The Journal of Infectious Diseases. 2020;223(3):389-98. doi:10.1093/infdis/jiaa659<br /> 27. Kaiser Family Foundation. This Week in Coronavirus: June 18 to June 25. 2020. <br /> 28. U.S. Center for Medicare & Medicaid Services (CMS). COVID-19 Nursing Home Data. 2021. <br /> 29. Anand A, Sandefur J, Subramanian A. Three New Estimates of India’s All-Cause Excess Mortality during the COVID-19 Pandemic. Center for Global Development. 2021. <br /> 30. Deshmukh Y, Suraweera W, Tumbe C, et al. Excess mortality in India from June 2020 to June 2021 during the COVID pandemic: death registration, health facility deaths, and survey data. medRxiv. 2021:2021.07.20.21260872. doi:10.1101/2021.07.20.21260872<br /> 31. Shewade HD, Parameswaran GG, Mazumder A, Gupta M. Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India. Frontiers in Public Health. 2021;9(1045). doi:10.3389/fpubh.2021.641991

On 2022-01-02 12:06:34, user Marius wrote:

Why did you use convalescent donors that had asymptomatic SARS-CoV-2 infection?<br /> I mean, it is great that even asymptomatic convalescents have robust T-cell response…but would it not be interesting to see the immune response of convalescents with moderate Covid? Possibly because this cohort does not even catch the omicron variant that often? At least I assume that unvaccinated people with moderate Covid Display an even better T-cell response compared to the vaccinated….

On 2021-09-30 20:18:12, user Navneet Dhillon wrote:

This article is now published in Journal of Extracellular Vesicles 2021;10:e12117, <br /> “Extracellular vesicle-mediated endothelial apoptosis and EV-associated proteins correlate with COVID-19 disease severity” PMID: 34262673

https://doi.org/10.1002/jev...

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

Dear authors,

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

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

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

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

Best,

Sandra Chyderiotis, Pharm.D, MPH

On 2021-06-15 11:03:19, user camel faizal wrote:

It seems the participants are quite elderly with the mean age at 40.4 (+-12. 2) years and just a small group of 15.

Also the study should include data from both doses administered instead of just the first dose. Otherwise , this study just adds to more hesistancy.

Would it not be better if there are more participants ( at least 10,000 or more ) and are in the younger age group , something like having participants between the ages of 18-40 years ?

Thanks for the hard work and effort.

Greatly appreciated and all the other comments need to be addressed too.

People's lives hang in the balance.

On 2024-12-03 21:07:38, user xPeer wrote:

Courtesy review from xPeerd.com

This manuscript investigates the genetic underpinnings of gene expression noise (variability in mRNA expression) and its contributions to complex trait variation. By leveraging single-cell transcriptomics from 1.23 million peripheral blood cells across 981 individuals, the study identifies expression noise quantitative trait loci (enQTLs) in seven immune cell types. Key findings include distinct enQTLs independent of traditional expression QTLs (eQTLs), with implications for hematopoietic traits and autoimmune diseases. This comprehensive analysis highlights gene expression noise as an overlooked molecular trait impacting genetic variation in complex traits.

Strengths include the integration of large-scale single-cell data, robust methodological frameworks, and a novel focus on noise QTLs. However, the work’s translational potential and certain mechanistic aspects require refinement.

Major Revisions<br /> 1. Mechanistic Depth<br /> Limited Exploration of Noise Regulation Mechanisms:

While the authors identify enQTLs enriched in chromatin marks (e.g., H3K27ac, H3K4me3), the functional pathways connecting these marks to noise modulation are underexplored (Section: Functional Enrichment, p.8). Including mechanistic validation, such as CRISPR perturbation experiments targeting key SNPs, would enhance understanding.<br /> The interplay between noise and transcriptional bursting models (e.g., initiation frequency vs. burst size) remains superficially addressed. Expanded quantitative modeling of burst kinetics could better explain noise-associated traits (Section: Discussion, p.9).<br /> Post-Transcriptional Contributions:

The discussion briefly mentions mRNA stability but does not evaluate post-transcriptional regulation’s role in noise. Experimental validation, such as ribosome profiling or RNA decay assays, could substantiate these claims.<br /> 2. Population Diversity and Generalizability<br /> Limited Ancestral Representation:

The cohort comprises Northern European ancestry individuals, limiting the generalizability of enQTL findings. Noise might vary due to ancestry-specific SNP frequencies or regulatory architectures (Section: Methods, p.3). Validation in diverse populations is critical for ensuring broad applicability.<br /> Cell-Type Specificity:

Some findings, such as HVGs shared across cell types (e.g., HLA genes), require validation in other tissues or disease models. Cell-specific functional assays could strengthen the biological relevance of these findings.<br /> 3. Statistical and Computational Robustness<br /> Unexplained Variance in enQTL Effects:

While enQTLs explain certain GWAS loci, the authors do not quantify the proportion of unexplained variance attributable to unaccounted mechanisms (Section: GWAS Colocalization, p.10). Comparative analysis with polygenic risk scores or partitioning heritability methods would contextualize enQTL contributions.<br /> Colocalization Analysis Limitations:

Colocalization methods prioritize high-probability overlaps (PP.H4 > 0.7), but alternative loci with moderate probabilities (e.g., PP.H4 > 0.5) might merit inclusion. Revisiting loci with expanded statistical thresholds could yield additional insights.<br /> 4. Functional Insights<br /> Overemphasis on Chromatin Features:

While the enQTL analysis emphasizes chromatin states, the link to noise-specific regulatory dynamics is unclear. Functional experiments, such as live-cell imaging of noise dynamics in specific chromatin contexts, would substantiate claims (Section: Functional Enrichment, p.8).<br /> Underexplored Relationship Between enQTLs and Disease:

The finding that autoimmune risk variants correlate with attenuated noise is intriguing but not mechanistically explained (Section: Discussion, p.9). Immune activation studies in enQTL-defined contexts could clarify whether lower noise promotes immune tolerance or other phenotypes.<br /> Minor Revisions<br /> 1. AI Content Analysis<br /> Estimated AI-Generated Content: ~15-20%.<br /> Stylistic Observations: Repetitive phrasing (e.g., “highlighting noise as an important mediator”) and predictable transitions suggest AI-assisted drafting in some sections.<br /> Epistemic Impact: Minimal; technical content is original, but editing for stylistic variation is recommended.<br /> 2. Figures and Data Presentation<br /> Figure Annotation:<br /> Figures (e.g., Figures 3-5) lack precise legends detailing axes, significance thresholds, and methodological descriptions.<br /> Supplemental figures require clearer integration into the narrative (e.g., referencing HVG enrichments in Figure S2).<br /> Data Accessibility:<br /> Raw data from single-cell noise calculations and SNP annotations should be made available as supplementary files for reproducibility.<br /> 3. Terminology Consistency<br /> Inconsistent Definitions:

Terms like “expression noise” and “transcriptional variability” are used interchangeably but should be clearly defined early in the manuscript.<br /> Confusing Use of Abbreviations:

HVG, enQTL, and eQTL acronyms require standardized introduction and consistent usage across sections.<br /> 4. Citations and References<br /> Key Omissions:<br /> Recent advances in single-cell variability analysis (e.g., newer methods beyond tensorQTL) are underrepresented. Including citations for innovative noise quantification approaches (e.g., scVI) would modernize the references.<br /> Recommendations<br /> Mechanistic Studies:

Employ experimental tools (e.g., CRISPRi/a, live-cell reporters) to validate enQTL roles in noise dynamics.<br /> Integrate transcriptional bursting models to elucidate enQTL regulatory mechanisms.<br /> Enhance Population Scope:

Expand cohort analysis to include non-European populations.<br /> Incorporate ancestry-aware computational models to assess demographic variability in enQTLs.<br /> Data Presentation Improvements:

Add supplemental raw data files for transparency.<br /> Expand figure annotations and connect supplemental content to main findings.<br /> Expand GWAS Interpretation:

Investigate enQTL roles in non-immune traits to broaden the study’s impact.<br /> Compare enQTL contributions with existing functional annotations (e.g., enhancers, transcription factor binding sites).

On 2023-06-01 01:20:57, user Edmund Seto wrote:

This paper has been accepted for publication in the journal Science of the Total Environment under the title "Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings"

On 2021-10-16 12:57:40, user gerryz wrote:

Can anyone tell me if there is evidence infection symptoms are milder in vaccinated? Articles?

On 2021-10-07 12:29:19, user Adona Canlas wrote:

This article has now been published and can be found here: https://www.nejm.org/doi/fu...

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

The article has been published in EMBO Molecular Medicine:

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

On 2022-01-07 02:59:57, user Cameron Cooper wrote:

Is this the study Rand Paul is quoting?

On 2020-04-11 12:51:33, user ybysk wrote:

In my understanding, what the authors (and many readers) want to know is whether or not BCG vaccine effectively protects individuals from infection (i.e., the effect on infection-per-exposure) and also death (i.e., the effect on death-per-infection). I have not understood how the authors justify to use the number of total cases and deaths per one million population as measures of the effectiveness of BCG. Are they supposed to be equivalent to infection-per-exposure and death-per-infection?

On 2020-07-28 17:02:07, user Liam Golding wrote:

Nice research on an important matter. I really appreciate your work.

It would be nice to know the sample sizes you experimented on to obtain the statistical differences. Can you provide these on request?

Cheers,

Liam G

On 2021-03-24 05:27:18, user Eik Dybboe Bjerre wrote:

Dear Authors, Please find a comment to your paper here. On page 31, line 608-609. You write:" None of the 78 published articles from the 31 trials were free from incomplete, 609 inconsistent, or selectively reported outcomes". As I can see in your review of trials, the trial I were responsible for (The FC Prostate Community trial) only fail on 1 criteria, the ability to present a "statistical analysis plan (SAP) ". As per ICH-GCP guidelines and the detailed guideline from Cochrane on assessing bias then a SAP should be in place before unblinded data was accessed. In the FC Prostate Community trial. The data collection was fully done by a web-based system and all data was keept logged and confined. As stated in publications of the results);the trial randomised 1.participant in June (15th)2015 an the SAP was published in Nov 2015 on clinicaltrials.gov. No participants had at that time complete the full 6 month intervention period. The data had not accessed from the database I only have detailed knowledge on the trial I was responsible and I acknowledge that it is difficult to evaluate if the SAP was in place in a timely matter. It is of course an important aspect but as it is not possible to assess without contacting authors/trialist I suggest the author group to consider the judge on this domain. Also if you look at the COMPare project, this do not evaluate the SAP. <br /> Best regards<br /> Eik Dybboe Bjerre

On 2022-01-06 12:47:12, user Anonymous wrote:

As far as I know that delta variant doesnt have the Deletion at 69 and 70. Yet the Taqpath kit gave sgtf s gene target failure for 8 delta variant samples. This shows that the kit is not a foolproof kit and rather has many issues which is why it was taken out from the market when it started giving false negative results during the alpha variant breakout. Also the authors have conveniently kept mum and skipped over the fact of these questionable 8 delta variant positive cases.

On 2020-04-01 16:49:01, user Joe Ledbetter wrote:

Are these projected excess deaths? In other words, are the expected baseline deaths for each age group subtracted from the projected covid 19 deaths?

On 2020-04-30 04:49:44, user Aaron wrote:

It would be a useful addition to show the % positive of tests performed in the same range as Fig 1C. If a greater proportion of tests are coming back positive following April 7, that could suggest that testing is occurring more and more frequently among those most likely to be positive. This is problematic as it can obscure mild cases that may not appear to be positives. In general, this information would help contextualize what types of cases are being captured by these data in Wisconsin, mostly severe cases, or mild and severe cases alike.

On 2020-06-12 15:42:05, user DFreddy wrote:

I miss mental health as risk factor. Every human also has a mind and a body. We know from piles of evidence that the mind impacts physical health too. I hope it will be included in a future analysis. Seems very elemental to do, no?

On 2020-04-22 00:29:54, user Diane Merriam wrote:

HQ and no HQ were almost the same on ventilation numbers. The death rates for both the HQ and the HQ+AZ were both significantly higher than for the no HQ group.

The early studies that were claiming success with HQ were done only on very mild cases. 85% of them weren't even running a fever. A drug might help some that were barely sick to begin with get over it faster while not helping anyone with a full blown case. Even that one had three go to the ICU and one death. Since they couldn't obtain the samples they needed from cases in the ICU or dead, those people were dropped from the study altogether.

On 2020-04-23 17:39:53, user Patrick wrote:

"Rates of ventilation in the HC, HC+AZ, and no HC groups were 13.3%, 6.9%, 14.1%, respectively."

"The risk of ventilation was similar in the HC group [...] and in the HC+AZ group [...], compared to the no HC group."

Is there a mistake in one of these sentences, or am I reading this right?

On 2021-02-14 11:49:33, user Rafael Green wrote:

Hi,<br /> I looked at world_mortality.csv, summed the deaths by year and got this result:<br /> 2015 15747474<br /> 2016 17246133<br /> 2017 17689889<br /> 2018 16674370<br /> 2019 18004246<br /> 2020 20926842<br /> How are you explaining the decreasing in the number of deaths in 2018?<br /> Thanks,

On 2020-03-30 18:55:11, user Preci Genome wrote:

Another related papers was also published recently. SARS-CoV-2 detection using digital PCR for COVID-19 diagnosis, treatment monitoring and criteria for discharge<br /> https://www.medrxiv.org/con...

On 2020-09-29 08:19:46, user Alan Tomalty wrote:

"Allowing for heterogeneity reduces the estimate of "counterfactual" <br /> deaths that would have occurred if there had been no interventions from <br /> 3.2 million to 262,000, implying that most of the slowing and reversal <br /> of COVID-19 mortality is explained by the build-up of herd immunity."

Since the number of counterfactual deaths (no lockdowns) is still over 2x the expected deaths with lockdowns under the heterogeneity model, I don't understand why you can claim that

" implying that most of the slowing and reversal of COVID-19 mortality is explained by the build-up of herd immunity." RATHER THAN BY LOCKDOWNS. My caps is what you actually meant but didn't say. Or am I misunderstanding what you said and what you meant to say?

On 2020-06-29 16:14:10, user xanthoptica wrote:

Zero SARS-CoV2 infections in control group, one SARS-CoV2 infection in treatment group acquired before treatment (gym attendance - unclear if individual was prevented from going to gym based on positive test). Essentially zero statistical power. This study only tested whether there was enough coronavirus around Oslo to cause transmission at the gym in any conditions...and there was not.

On 2022-02-05 13:34:23, user GregoryGG wrote:

Hello,<br /> I saw an error after posting my comment, so I post it again because I do not see it, my apologies in advance if it was ok.

Unless I misunderstood :

1)<br /> How can you guarantee that the people's behaviour (over-confidence of vaccinated people) , the transmission prevention mesures and the testing entry rules were the same between <br /> A) delta and omicron<br /> B) vaccinated and unvaccinated people. <br /> => this could impact ratios like the positive rate of a group.

2)<br /> Assuming that immunity against a specific variant diminishes over time, can we still considered single- double -dosed people as still immunised. <br /> Could they be considered as non-vaccinated after a given time?

Thank you

On 2021-10-25 14:42:29, user João Duarte wrote:

Published version in [European Journal of Neuroscience] [https://doi.org/10.1111/ejn...]

On 2025-07-18 19:54:44, user Perla del Carmen Gamboa Flores wrote:

I would like to know the physiopathology of these patients, hich is what influences between immunosuppressed patients and those who are not

On 2020-08-17 05:42:46, user P Donohue wrote:

Now for review & replication! Where will it publish after review?

On 2024-07-11 13:28:54, user Kevin Kavanagh wrote:

The abstract states "Importantly, the benefits of vaccination outweighed the risks in controlling the post-COVID-19 GBS burden, although significant disparities in vaccination coverage existed between countries." I believe this is poorly worded, since it implies the vaccines are not a benefit in controlling GBS. The study's results clearly stated, "We observed significantly negative correlations between COVID-19 vaccination coverage rates and both overall and COVID-19-specificage-standardized YLD rates of GBS in 2021 (table 2 and supplementary figure S16-S17)."

I would suggest rewording to "Importantly, vaccination had significant benefits in controlling the post-COVID-19 GBS burden, although significant disparities in vaccination coverage existed between countries"

"

On 2025-10-07 01:44:54, user Rongxing Weng wrote:

Now published ahead of print in AIDS. Link: https://journals.lww.com/aidsonline/abstract/9900/evaluating_the_impact_of_covid_19_on_the_hiv.772.aspx

On 2022-03-03 12:42:31, user Dan Bolser wrote:

This paper seems to be published here:<br /> https://pubmed.ncbi.nlm.nih...

but the preprint doesn't link it.

On 2021-03-01 19:34:43, user Alexander Buell wrote:

Dear authors,

my students and I have studied your paper in our course at DTU (Quantitive analysis and modelling in protein science). We have reproduced some of your analysis and we have noticed something that we wanted to hear your views on. In the methods section you mention that "For the MAAP measurements, varying fractions of human plasma samples were added to a solution of the antigen of concentrations varying between 10 nM and 150 nM..." At the same time, if we look at the red and yellow binding curves in Figure 2 a) they cannot have been measured at a RBD concentration above 100 pM. Indeed, we were unable to fit the data with a concentration of RBD higher than 100 pM or so.<br /> This would mean that you have added 99% serum and 1% labeled protein at 10 nM. Is this the case? Is the instrument really sensitive enough to get good signal at sub-nM concentration?<br /> Thanks in advance for your clarification!<br /> Alexander Buell<br /> Professor of Protein Biophysics at DTU

On 2024-04-27 19:07:49, user Toby wrote:

So glad to see that this problem is being taken seriously. I have suffered from eczema all my life and from TSW as described here for many years. I hope this research will result in new treatments for this awful condition.

On 2020-04-01 15:47:26, user JR Davis wrote:

Table 3 and 4 and 5 are all missing. Text mentions non-CoVID respiratory pathogens (n=10) also tested for, and listed in "Table 3"....with the additional Primer list in Table 4.<br /> However, both Table 3, 4, and 5 NOT provided in the PDF....only Table 1 and 2 found at the end of the document.<br /> Can you provide missing tables 3,4,5?

On 2020-04-23 00:19:12, user Michael S. Y. Lee (biologist) wrote:

Hello,

Did you infect Vero-E6 cells from each patient just once (and harvest the cells in quadruplicates), or did you infect the Vero-E6 cells from each patient four times (and harvest the cells once per infection).

This is very important for statistics.

Mike

On 2021-07-29 08:59:44, user Johannes wrote:

"We obtained the baseline risks for selected U.S. counties from the Johns<br /> Hopkins University dashboard and for selected states of India from the <br /> New York Times dashboard"

JHU has received well in excess of $100,000,000 from the BMGF.

Is this a potential conflict of interest ?

Many Thanks.

On 2023-01-02 00:17:46, user Charles Warden wrote:

Hi,

I apologize for the long earlier comment - I hope the following might be helpful for certain parts:

In terms of the expected inheritance pattern, I started to look more into the DD gene panel annotations from gene2phenotype.

While I expect that there is more for me to learn, I noticed that I could <br /> use “dominant” as a search term:

https://ibb.co/Px1kPDj

My understanding is that the number of genes from that search result is noticeably less than used for this preprint. However, unless there has been some sort of noticeable change in the more recent annotations, what I think is encouraging is that there are even more results if I search for “recessive” (with a partial screenshot linked below):

https://ibb.co/0VmBzCS

If I understand correctly, then I believe TREX has variants that are listed as both dominant and recessive for Aicardi-Goutieres Syndrome. If I understand correctly, then I believe this reference indicates that AGS1 is recessive and AGS5 is dominant.

In order words, I like the use of the rare synonymous variants as a control. Do you think it might be worth adding rare damaging recessive genes/variants as another control/comparison? For example, are the interactions that you described with rare variants in genes associated with (partially dominant?) monogenic diseases significantly more associated with a pathogenic phenotype than if you consider rare variants in genes associated with recessive monogenic diseases?

While I would still be interested in having some context for the highly penetrant “dominant” variant (related to the “threshold for disease”), I think this might help in giving a sense of how much more different would this be than a carrier status for a recessive disease (for one or more such variants).

Thank you again!

Sincerely,

Charles

On 2020-03-31 18:43:53, user Thierry Grenet wrote:

This animation helps to better visualise how countries follow their "space phase" epidemic trajectory : https://epidemictracking.wo...<br /> T. Grenet

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

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

On 2020-03-16 04:22:01, user Kankoé SALLAH wrote:

Great. Impedaance model is useful when you lake data about human mobiity.<br /> https://ij-healthgeographic...

On 2024-07-15 15:55:06, user Tamara Pemovska wrote:

This preprint has now been published in Psychological Medicine: https://doi.org/10.1017/S0033291724001089

On 2025-03-17 22:11:06, user Dr.PayamVaraee wrote:

Critical Review: "The Threat of Populism to Science and Global Public Health: Lessons from Iran"<br /> A. Critique of Content and Main Claims<br /> 1. Claim: Populist Science Increased Mortality in Iran<br /> The article asserts that populist policies delayed vaccination efforts in Iran, leading to excess mortality. However, data comparisons with countries such as the US, UK, and Germany reveal similar trends, challenging the uniqueness of Iran’s case.

Issues:<br /> Overlooking Key Variables: The analysis does not account for factors such as economic sanctions, healthcare infrastructure, and demographic differences.<br /> Post-Vaccination Mortality Decline: The significant drop in mortality following mass vaccination aligns with global patterns, suggesting that other factors played a role beyond populist decision-making.<br /> Flawed Comparisons: The article contrasts Iran with Bahrain and the UAE, despite major differences in population size, vaccine availability, and healthcare systems.<br /> 2. Claim: Iranian Data on COVID-19 Mortality is Unreliable<br /> The article utilizes the Prophet model to argue that Iranian mortality statistics were manipulated.

Issues:<br /> Limitations of the Prophet Model: Originally designed for economic and social trend forecasting, this model is not optimized for analyzing health crises.<br /> Weak Evidence for Data Manipulation: The assumption that discrepancies between projections and reported data indicate fraud is flawed. Factors such as improved treatment strategies and emerging herd immunity are not considered.<br /> Selective Application: The same predictive model is not used to assess data accuracy in other countries, raising concerns about bias.<br /> B. Critique of Data Analysis Methods<br /> 1. Misuse of ANOVA<br /> The article employs a one-way ANOVA to compare vaccination delays across countries. However, this method does not sufficiently account for assumptions of normality and homogeneity of variance, potentially leading to misleading conclusions.

Better Alternatives:<br /> Time-Series Models (ARIMA, VAR): These would provide a more accurate assessment of trends over time.<br /> Multivariate Regression: This method would allow for the inclusion of additional variables influencing vaccination delays and mortality rates.<br /> 2. Absence of Confounding Variable Control<br /> The article does not adjust for important factors such as:

The proportion of elderly populations.<br /> Hospitalization rates and healthcare capacity.<br /> Lockdown policies and mobility restrictions.<br /> Neglecting these variables weakens the argument that Iran’s excess mortality was driven primarily by populist policies.

C. Logical and Argumentative Flaws<br /> 1. Selective Data Use<br /> The article emphasizes evidence that supports its argument while disregarding counterexamples—such as similar mortality patterns in Western countries—leading to confirmation bias.

1. Correlation vs. Causation Fallacy<br /> It assumes a direct causal link between delayed vaccinations and excess mortality without considering other influencing factors, such as economic restrictions, healthcare efficiency, and prior infection rates.

2. Oversimplification of a Complex Issue<br /> By attributing Iran’s COVID-19 response largely to populism, the article overlooks the fact that mortality spikes occurred in Germany, the US, and other non-populist-led countries. A more nuanced analysis is needed.

D. Broader Issues with the Scope of the Article<br /> 1. Disproportionate Focus on Iran<br /> If populist science is a global issue, why is Iran the only case study? A comparative approach—including countries like the US, Brazil, and Poland—would strengthen the argument.

1. Lack of Practical Solutions<br /> The article critiques Iran’s handling of the pandemic but does not propose strategies to combat misinformation and improve public health responses globally.

2. Limited and Selective Data Sources<br /> The article relies heavily on The Economist and WHO while neglecting independent organizations such as the CDC and regional research institutions. A broader range of data sources would improve credibility.

E. Additional Criticism of the Core Argument<br /> 1. Populism Beyond Iran<br /> Research, including the PANCOPOP study, shows that right-wing populism influenced pandemic responses in the US, Brazil, Poland, and Serbia. The article’s exclusive focus on Iran suggests political bias rather than an objective analysis of populism in global public health.

1. Contradictions in the Populism Model<br /> The article argues that Iran exhibited both the denialist model (seen in the US and Brazil) and the authoritarian control model (similar to Poland and Serbia). These models, however, are distinct and mutually exclusive in the PANCOPOP framework, making this assertion contradictory.

2. Absence of Comparative Analysis<br /> The study lacks a global perspective on how different forms of populism shaped pandemic policies, weakening its claim that Iran’s case is uniquely alarming.

3. Misattribution of Vaccination Delays Solely to Populism<br /> The article ignores other major contributing factors, such as:

Economic Sanctions: Restrictions on vaccine imports.<br /> Vaccine Hesitancy: Public resistance to certain vaccines.<br /> Domestic Vaccine Development: Initial reliance on homegrown vaccines before shifting to imports.<br /> By overlooking these aspects, the article oversimplifies the reasons behind Iran’s vaccination timeline.

1. Failure to Address Global Media Influence<br /> Studies have demonstrated that misinformation on COVID-19 spread across multiple countries, yet the article singles out Iran without discussing similar issues in other regions.

2. Statistical Flaws<br /> The ANOVA and Prophet model are misapplied, limiting the validity of conclusions.<br /> A lack of multivariate regression fails to control for external factors influencing pandemic outcomes.

3. Contradictory Use of Data<br /> The article doubts the credibility of Iran’s official statistics yet uses those same statistics to support its claims. This undermines its argument and suggests inconsistent reasoning.

Conclusion<br /> The article presents a flawed and unbalanced analysis of how populism influenced Iran’s COVID-19 response.

Key Weaknesses:<br /> Selective use of data that aligns with the author's argument while ignoring broader trends.<br /> Lack of comparative analysis, failing to place Iran’s case within a global context.<br /> Misuse of statistical methods, leading to questionable conclusions.<br /> Recommendations for a Stronger Study:<br /> A multi-country analysis incorporating nations with varying political ideologies.<br /> Consideration of alternative explanations for mortality trends, such as healthcare infrastructure and economic factors.<br /> A transparent and methodologically sound approach to data interpretation.<br /> A truly robust and objective study would examine multiple countries, account for confounding variables, and avoid overgeneralizing populism’s impact on public health outcomes.

On 2021-05-20 03:04:05, user kdrl nakle wrote:

Your samples are way too small for sweeping conclusions you made.

On 2021-07-29 12:52:04, user Erik Widen wrote:

Please note that the published version contains both corrected and more expansive information and supplants this preprint for all purposes. It is available through the link above or use https://doi.org/10.3390/gen....

On 2021-09-02 14:16:49, user Notbuyingit3337 wrote:

"Clinically trained reviewers have undertaken a detailed analysis of a sample of the early deaths reported in VAERS (250 out of the 1644 deaths recorded up to April 2021). The focus is on the extent to which the reports enable us to understand whether the vaccine genuinely caused or contributed to the deaths. Contrary to claims that most of these reports are made by lay-people and are hence clinically unreliable, we identified health service employees as the reporter in at least 67%. The sample contains only people vaccinated early in the programme, and hence is made up primarily of those who are elderly or with significant health conditions. Despite this, there were only 14% of the cases for which a vaccine reaction could be ruled out as a contributing factor in their death." https://www.researchgate.ne...

On 2022-02-02 20:00:49, user Eric D wrote:

Contrast @cnbc

"BA.2 is more contagious than BA.1 among both vaccinated and unvaccinated people, but the **relative** increase in susceptibility to infection was significantly greater in unvaccinated than unvaccinated"<br /> https://www.cnbc.com/2022/0...

On 2023-11-27 21:08:47, user Judith Mowry wrote:

The recent paper on peripheral vasopressors by Yerke doi.org/10.1016/j.chest.202... is an important reference for your research. It is vital to note that they changed their protocol to add very specific protocols and rules regarding IV site inspection, defined who was responsible. Also note that an antecubital site (or any joint) is avoided to minimize movement and extravasation risk. I wish you success with your research.

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

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

On 2022-01-04 13:28:50, user Richard in Bosstown wrote:

One would like to know if there were people infected by prior versions were getting infected by omicron ("natural immunity").

It is possible that the omicron is an escape variant selected for among vaccinated persons given that vaccination involves only the single, spike protein of the virus. In contrast, infection with virus exposes subjects to more than 20 viral proteins, any of which can provide immunodominant peptides for an individual's MHC composition to confer T cell immunity. T cell immunity is more critical than antibody for viral resistance, though rarely measured.

With such a large number of viral protein targets for T cell recognition, the virus is much less likely to mutate during a virus's evolution to escape that "natural" T cell immunity. This is one reason why attenuated virus vaccines that include all viral proteins are generally more effective than protein vaccines. It has been suggested that natural immunity from multiprotein virus exposure is the reason the omicron surge was limited in South Africa where a much larger portion of the population was previously infected versus the US where higher proportions are vaccinated.

Vaccination is important for individuals not previously infected, that is clear, if only to reduce the severity of infections, as we deal with annually with flu vaccines. However, the lack of presentation of these prior infection data, perhaps omitted from the study design, is a significant omission that could have added to our understanding of the biology and natural history of this virus.

Richard P Junghans, PhD, MD<br /> IT Bio, LLC<br /> Boston University School of Medicine<br /> rpj@bu.edu

On 2020-04-07 18:39:42, user Snap wrote:

Diffusion and mortality of COVID-19 in regions with poor air quality

The COVID-19 spread from China to the rest of the world in just over three months has turned into a pandemic that poses several humanitarian and scientific challenges. By comparing air quality levels with the spread and mortality of the virus, a significant correlation was found in China, Italy and the United States. People seem to become infected and die more often in those areas affected with poor air quality levels. Although the infection is still ongoing globally, these results are convincing because they are not influenced by different population densities. Similar to smoking, people living in polluted areas appear to have a respiratory system more vulnerable to this new form of coronavirus. This suggests the detrimental impact of climate change as a novel supplemental risk factor and prompts the need of its abatement.<br /> https://uploads.disquscdn.c...

On 2021-01-05 13:45:57, user Rogerblack wrote:

The authors claimed case is at best flawed as they are implicitly assuming by use of the GHQ12 that people with covid are otherwise healthy.

"The General Health Questionnaire (GHQ) is a screening device for identifying minor psychiatric disorders in the general population".

It is not designed for those with severe fatiguing illnesses.

https://oxfordbrc.nihr.ac.u... for example found many physical symptoms persisting.

Endorsing 'Felt constantly under strain', 'felt you couldn't overcome your difficulties' when facing a disabling illness that may make you unable to complete normal activities, and has an unclear prognosis is not a clear measure of depression/anxiety.

At best, each question needs to be investigated carefuly for contamination with physical health issues of the patient before a more detailed claim can be made than 'patient scores on the GHQ12'.

On 2021-10-18 16:39:30, user Kim Noble wrote:

Well-thought, provoking article. Congratulations to Dr. Gõni and her team.

On 2021-11-06 19:24:57, user Eleutherodactylus Sciagraphus wrote:

: This preprint includes data from human subjects that are under ethical scrutiny. The<br /> majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission forResearch Ethics (CONEP) has been bypassed, documents have been tampered, and the situation is now under investigation.

References supporting this statement (both in English and in Portuguese):<br /> https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...

On 2020-04-02 16:41:11, user Emily MacLean wrote:

My colleagues and I are epidemiology PhD students who focus on TB, and we wrote a journal club style critique of this paper. There are serious limitations with this paper that must be taken into account when considering its findings. <br /> https://naturemicrobiologyc...

On 2020-04-04 12:23:52, user Jess wrote:

Hi...I am a Malaysian & in Malaysia, all newborns hv been vaccinated with BCG since 1961.

However, you can see fr the statistics that Malaysia is still struggling with Covid.

I am sorry to inform that this hypothesis needs to be re-evaluated so as not to be over zealous over this.

On 2020-04-04 19:54:04, user Paul Constantine wrote:

The first published study on this subject was made by Dr.Mihai Netea. The distinguished researcher is an authority in citokyne storm and he based his research on a correlation of the clinical evolution of SARS 19 in countries where the BCG vaccination is compulsory, i.e. Romania, Germany, Portugal, Republic of Korea, Malaysia, Japan.

On 2020-08-19 19:37:03, user Michelle Furtado wrote:

Im curious, isnt it possible that the three fishermen who had antibodies prior to departure were in fact the ones who spread it to the rest of the crew? They had neutralizing antibodies prior to leaving, which indicates a possible mild infection that in fact made them asymptomatic spreaders and subsequently infected the rest of the crew. No one is mentioning how these people who have antibodies could be spreading the infection. Those three should have never been allowed out of port on that ship if they knew they had been infected.