On 2020-08-04 08:17:28, user OxImmuno Literature Initiative wrote:

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

On 2020-08-04 08:27:16, user OxImmuno Literature Initiative wrote:

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

On 2020-07-02 18:46:51, user Julio C. Spinelli wrote:

Having personally arquitected several clinical trials, the phase II results in young volunteers forces me to provide a word of caution to our collective desire to quickly develop a vaccine for COVID-19. <br /> The frequency and severity of many of the AE's described in this preprint for the young (18-55) and healthy population described here doesn't bode well for the results of a phase III clinical trial. Not until the phase II results of the older cohort are known. Furthermore, extrapolating these data to the Latin and Black populations would be pure hubris on our part. Further phase II data is required before we move into phase III trials<br /> Dr. Julio C. Spinelli

On 2020-07-02 20:46:36, user C'est la même wrote:

The claim of 99.3% specificity seems very high compared to other antibody tests when tested with large population samples.

But that aside, some readers seem to be inappropriately concluding that undersampling in specific regions during that period can be generalised to conclude that the true cumulative incidence is ten times the total number of confirmed cases.

This is unfounded for two reasons. The first is that regions with very high case numbers (Such as NYC) were temporarily overwhelmed in terms of testing capacity and correspondingly very high test-positivity rates. However over time, the testing caught up with demand and with expanded testing, the test-positivity rates dropped by the expected order of magnitude and likely "caught up" for at least some of the participants who were missed.<br /> The second reason is the sample in the study is not a true random population based sample, but a convenience sample which is also biased towards higher test-positivity rates.

Thus while I don't disagree with the conclusion of the authors, I urge strong caution among readers who are tempted to conclude that true case numbers are a magnitude of order higher than officially reported.

On 2020-07-03 20:21:43, user Marm Kilpatrick wrote:

Interesting paper. <br /> Could you clarify if the incidence values in Fig 1,2 and throughout are:<br /> Incidence = Cases in age group X/total population<br /> OR<br /> Incidence = Cases in age group X/population of age group X<br /> Since the age groups represent different fractions of the total populations this would change the intercept of the different incidence values/curves.<br /> Thanks!<br /> marm

On 2020-08-07 20:38:52, user Adam Garland wrote:

We are developing a test for SARS-CoV-2 in saliva. Is there any chance you have saliva samples leftover from this study that you'd be willing/able to share with us?

On 2020-08-10 08:17:44, user OxImmuno Literature Initiative wrote:

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

On 2020-07-06 16:17:27, user OxImmuno Literature Initiative wrote:

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

On 2020-07-06 16:20:42, user OxImmuno Literature Initiative wrote:

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

On 2020-07-06 18:41:52, user Fatnot wrote:

Unlikely that just zinc supplementation would work,,,a zinc ionophore is also required.. We also have the report from Dr Zelensky, in Rockland County, NY, who treated hundreds with<br /> a combo including zinc and HCQ, resulting that few required hospitalization. The report is anecdotal...but another term for a set of anecdotes is of course DATA And with data and analysis, one can draw conclusions and confidence intervals.

On 2020-08-12 08:06:03, user Marek Kochanczyk wrote:

The source code to analyze data and generate figures as well as updated figures are available at GitHub: https://github.com/kochancz...

On 2020-08-12 12:37:37, user Marc Imbert wrote:

It is worth to not that this study has more cormobity and symptomes for the group treated with HCQ and AZT. All patient not treated has a mild desease while about only 63% in the group treated. Further one should use a healthy scientific scepticims regarding hasting conlusions based on studies at the late stage of the desease. In particular with the description of the evolution of the disease which is now known,

.

On 2020-07-10 18:25:07, user Joanna Spencer-Segal wrote:

The authors speculate in the discussion that "It is also possible there is an effect via mineralocorticoid receptor binding in the context of SARS-CoV-2 induced dysregulation of the renin-angiotensin system." It is not clear what this means, but dexamethasone has minimal activity at the mineralocorticoid receptor, which distinguishes it from the other corticosteroids often used in critically ill patients (methylprednisolone, hydrocortisone). More clarification of what they mean regarding "mineralocorticoid effect" and rationale about why dexamethasone was chosen for this study would be welcome.

On 2020-07-13 11:08:47, user Andrew D'Silva wrote:

In any infection IgM responses converting to IgG responses fall over time and rise when there is a secondary antigen exposure. Why do these findings suggest that there is loss of immunity with declining neutralising antibody levels? Surely the questions are: what happens after secondary antigen exposure? Do the neutralising antibody levels rise again? Do they protect from developing the same clinical disease again? Do they affect severity of disease after second exposure?

On 2020-08-17 22:15:16, user Roy Verdery wrote:

Neutralizing activity assessed using Vero cells may not reflect clinical<br /> activity. Nature (2020). https://doi.org/10.1038/s41....

On 2020-08-24 04:45:43, user Bill Pilacinski wrote:

Now it will be important to identify those in the population who are immune so that the early limited supply of vaccine can be used for those susceptible individuals of high priority as we attempt to reach herd immunity.

On 2020-07-15 13:15:39, user E Y wrote:

Something is wrong here, the IHME projected 2020 total US death is about 250000, that's 0.08% of the US population, how can that cause 1% of reduction of life expectancy of US population?

On 2020-08-20 08:37:09, user Mitchell X Kriegman wrote:

does second hand smoke carry covid shedding?

On 2020-08-21 19:06:14, user Heather Gray wrote:

I'm a layman but I want to express my appreciation for exploring the issue from this angle

On 2020-08-25 09:01:02, user Tjabbe wrote:

Evidence for what, that it doesn't work for late stage covid in hospitalised patients? Is that even news? How come at this stage in the pandemic we are still publishing reports that claim medication be ineffective "for treating covid19" when in fact it was only tested for patients with severe covid19 already in the hospital. We all know patients will not be sent to a hospital in the Netherlands for covid unless they have progressed pretty far. <br /> The report describes hcq being used on patients when deteriorating in several of the hospitals, affecting mortality, and media outlets conveniently leave out this part of the puzzle.

If you want to curb covid, or if you want to write off medication as being useless "for covid" , start doing trials on early outpatient treatment.

On 2020-08-30 11:33:10, user Martijn Weterings wrote:

One problem with those S(E)IR compartmental models is that they always assume/pretend that a virus is spread homogeneously among a well mixed population. According to such models, the chance that someone in a small village in the South infects somebody else, is the same chance for anyone. The same for somebody in the North as somebody in their immediate family or other people in close neighborhood.

Such compartments are obviously not realistic for modeling an entire country. More suitable are networked S(E)IR' or spatial S(E)IR models. In such models, the virus spreads more like an ink blot.

Due to the local saturation, growth rates are already decreasing early on. Models that do not incorporate local saturation will 'compensate' (in order to get the same early deflection) by either reducing R0, or the (effective) population, or the reporting factor (upscaling the number of infected). If you try to fit a simple compartment SIR model to real data, then you will get unrealistic epidemiological parameters.

What they are doing in this article, dividing the population into layers with different rates of infection, is effectively shrinking the population that is 'reached' by the virus.

So this effectively makes the population smaller, but the question is whether it is the right way to shrink the population? Instead of a parameter in a mechanistic model, it might better be regarded as a parameter in an empirical model. It is an extra variable to ensure that the unsuitable simple SEIR model corresponds somewhat better with the measurements.

In reality, there are several effects that cause the observed epidemiological curves to deviate from the simple models (Besides heterogeneity, the use of local distribution in spatial or networked S(E)IR models, instead of global homogeneous compartments, is another important one).

By only including only a single effect in fitting, you get that all other effects are absorbed by that one effect. The result is an unrealistic estimate of the epidemiological parameters, which will not be suitable for extrapolation (for example calculating the 'herd immunity' percentage).

It is to be expected that this model, with only the heterogeneity incorporated, will likely underestimate the percentage to reach herd immunity. This is because it is overestimating the effect to compensate for the lack of other non-incorporated effects (and spatial models will be able to model the same deflection of the curves, but with less reduction of the herd immunity).

The above is a severe systematical problem, which will result in a bias towards smaller herd immunity percentages.

In addition: The fit with the curve is strongly determined by an interaction of the population size and the factor between the reported infections and actual infections (in a simple S(E)IR model, the two have the same effect). Such correlation between the two parameters will cause great inaccuracy.

And these are considerations that do not yet mention the problems with measurements of the epidemiological curve. For instance, the inaccuracies in reporting are not easily solved with a single (constant) reporting fraction. In order to estimate epidemiological parameters we need more direct experimental data (e.g. detailed information about contact tracing). From those we can deduce more directly the variations in infection rates and estimate the potential impact on herd immunity. Just fitting a model to the curve is a bad idea.

On 2020-09-02 15:42:59, user Abhaya Indrayan wrote:

We are waiting for further doubling of cases to 4 million in India so that we can test our model.

On 2020-09-23 07:52:50, user Subhajit Biswas wrote:

Pleased to see other scientists are supporting with further evidences, the trend we had observed and reported as early as April 2020.

Based on non-overlap of dengue and COVID-19 global severity maps and evidences of SARS-CoV-2 serological cross-reactions with dengue, we proposed that immunization of susceptible populations in Europe, North America and Asia (China, Iran) with available live-attenuated dengue vaccines, may cue the anti-viral immune response to thwart COVID-19.

https://www.preprints.org/m...

Our publications in this area to support our proposition:<br /> 1) COVID-19 Virus Infection and Transmission are Observably Less in Highly Dengue-Endemic Countries: Is Pre-Exposure to Dengue Virus Protective Against COVID-19 Severity and Mortality? Will the Reverse Scenario Be True?

Clinical and Experimental Investigations, Volume 1(2): 2-5.<br /> https://www.sciencereposito...

1. Nath, H., Mallick, A., Roy, S., Sukla, S., & Biswas, S. (2020, June 19). Computational modelling predicts that Dengue virus antibodies can bind to SARS-CoV-2 receptor binding sites: Is pre-exposure to dengue virus protective against COVID-19 severity?. https://doi.org/10.31219/os...

2. This one in medRxiv!

Now, other scientists are observing the same trend in Brazil! Exciting!

See recent publication below and news coverage

1.https://www.medrxiv.org/content/10....

1. Study suggests dengue may provide some immunity against COVID-19.<br /> https://timesofindia.indiat...

Amazing! Nature has its own ways of controlling parasite aggression! Antigenic correlation between a flavivirus and a coronavirus was unprecedented.

Existing and licensed dengue vaccines could be tested in SARS CoV2 animal models and tried in dengue non-endemic countries.

Use in dengue-endemic countries may be problematic as such vaccination can elicit antibody-dependent enhancement of subsequent dengue infections.

On 2020-11-26 14:59:36, user Raul_Y wrote:

This paper is of ridiculously low quality. They even forgot to mention what dose of dutasteride was used.

On 2021-06-09 23:47:14, user Gnash wrote:

Is there any data on any group who received only AZM or only received HCQ?

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

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

On 2020-08-05 08:11:07, user Rosemary TATE wrote:

I have performed a review of this paper on publons https://publons.com/review/...

On 2020-08-09 17:02:50, user David Leidner wrote:

Article says all data fully available, but no link to the data is provided.

On 2020-08-09 21:12:25, user Cynac wrote:

The results appear to show a significant relationship between menopause and diagnosis of Covid-19 by your algorithm. There is no significant association with positive Covid test ("proven" Covid) or severe disease.<br /> The significant symptom associations do include fever, but not cough or even the anosmia. Whereas "skipping meals" is a highly significant association.<br /> This brings the major possibility that it is your algorithm for diagnosing the disease that best relates to menopause, perhaps by some quirky inclusions.<br /> There must also be some difficulties in allowing for age etc. When the influences of these factors themselves are not precisely defined.<br /> This study is clearly worthwhile, and of interest. But the way the abstract will be viewed in the media might be an over-simplification.

On 2020-08-13 00:49:59, user Jesse Baker wrote:

Regarding a passage in this MedRxiv post (July 21, paragraph 3 with citation to reference #15), “Additionally, recent clusters of COVID-19 cases linked to a…restaurant in Wuhan are suggestive of airborne transmission.”

Although the index case having lunch on Jan. 24 was from Wuhan, the restaurant was in Guangzhou. Indeed, its location far from Wuhan so early in the spread of Covid increased Guangzhou CDC’s confidence that the other patrons were infected by the index case and no one else.

On 2020-08-13 07:57:05, user Zeit wrote:

Very interesting manuscript. I think it may be wise to remove isotopes from your data as it seems clear that you have associations of monoisotopic peaks and their isotopic peaks with phenotypes. If you correlate the retention times of ions most correlated with each other by area count/signal, it should reveal that they are non-independent ions.

On 2020-08-13 20:06:24, user Rhyothemis wrote:

Could the low number of deaths in Kenya be at least partly attributable to low per capita protein consumption? It seems as though many countries with low per capita protein consumption rates are reporting relatively low per capita COVID death rates. Mechanistically, such an association (if it exists) could be related to lower baseline mTOR activation.

On 2020-08-15 14:01:48, user Dom_Pedulla wrote:

Joao not only had Dude made some very good points, but in observational trials like this, everything depends on the nitty gritty data. I notice the huge qualifier "recent" in the results sentence, noting that carefully since in many studies these kinds of adjectives disclose or hint at certain erroneous tendencies or conclusions in even in "meticulous peer-reviewed studies". I am requesting the paper to analyze for myself, and suspect strongly that what it may show is a "benefit" for only the very recently vaccinated, and that either long after it either ends up being a net liability as regards COVID death risk, or that the timing isn't possible to discern because the investigators avoided studying all but the recently vaccinated.

We'll see.

On 2020-08-15 23:30:43, user Nan wrote:

To those who tweeted and regarded this as evidence that masks don't work,

This article does NOT imply masks don't work. If one wishes to draw such a conclusion, a direct comparison is required on the disease risk when wearing masks versus not. From both the fifth and sixth comparison in the figure and a related article (https://www.bmj.com/content... "https://www.bmj.com/content/369/bmj.m1442)"), masks are better than not wearing at all! This article only says physical distancing is very important for cloth and surgical masks. It means that besides wearing normal masks, I should be cautious about a strict physical distancing. This agrees with common sense that the more protections (e.g., masks, distancing, etc.) we have, the safer we are.

Also, is physical distancing always easy and tangible to follow? The answer is no. You cannot guarantee that you are always in safe distances with other people in the street. In contrast, masks are a lot more perceptible. They reduce exposure to the contaminated air. Masks are also a sign of caution. A sign that everyone should protect their community by reducing transmission.

On 2020-08-16 19:15:38, user Skadu SkaduWee wrote:

One of the fundamental assumptions of the paper is the use of a previously tested positive saliva sample to prepare the serial dilutions used for the limit of detection studies. However, the authors omit to declare how this initial copies/ul value was arrived at and by whom.

On 2020-08-17 09:34:02, user buddinggenetics wrote:

The principal author has stated in media that the cost per test is $10, however in the text the cost is listed as $1.29-4.37/sample. Pricing should be consistently stated to avoid misleading the public and/or scientific community. Also, the text states that the price per sample is low, which is a relative term, and gives no price estimates of other established tests for comparison.

Multiplexing the samples is a fundamental improvement of testing, however there is insufficient evidence to show eliminating the N2 primer set is justified. There needs to be an analysis of how many inconclusive test results (N1 positive and N2 negative/ N1 negative and N2 positive) would now become positive or negative tests as a result of eliminating the N2 primer set. Also, in Supp Fig 2, the data appear irregular with a bimodal distribution when a Gaussian distribution would be expected. The authors do not discuss the reason for this in the text. Furthermore, the failure of the N2, E, and ORF1 sets may be due to the HEX fluorophore. Would they have worked using a different fluorophore? Would the authors have eliminated N1 if they had by chance used HEX on N1?

The Source Data files are not posted.

On 2020-08-17 18:18:04, user OxImmuno Literature Initiative wrote:

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

On 2020-08-18 07:49:11, user Rawi Naddaf wrote:

This research has been published in Experimental Biology and Medicine. DOI: 10.1177/1535370220941819<br /> Please cite this publication when when you reference this work.

On 2020-08-18 12:42:45, user Saban Öztürk wrote:

The peer-reviewed and published version of this article can be accessed using the following DOI:<br /> https://doi.org/10.1002/ima...

Also, this link can be used for direct access to this article: <br /> https://onlinelibrary.wiley...

On 2020-08-18 18:07:44, user Eric Vallabh Minikel wrote:

Excellent, important study, with carefully considered conclusions from the authors. Some readers may assume that if plasma NfL can become elevated 2y before onset, then NfL could be used as a prevention trial entry criterion, a primary endpoint, or a basis for deciding which patients are eligible for drug access/reimbursement. Importantly, the authors of this paper do not assert that their data support those applications. I believe there are three key considerations here that should be factored into any clinical application of plasma NfL quantification in pre-symptomatic genetic prion disease: genotype (rapid vs. slow PRNP mutations), age (affects reference range for NfL), and cross-sectional (as opposed to longitudinal) number of people in a prodromal state at any given time. I have written a detailed blog post here: http://www.cureffi.org/2020...

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

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

On 2020-08-19 11:00:05, user AbsurdIdea wrote:

Have I understood this right: " Vitamin D dose was not significantly associated with testing positive for COVID-19."? So taking vitamin D does NOT reduce the probability of testing positive for CoViD-19...Then, why take it against CoViD-19? For the rest - correlation or causation? Healthier people are likely to have a higher probability of sufficient vitamin D, conversely, people in poor health for any reason are likelier to have low vitamin D. Also there is a difference between becoming infected i.e. the virus actually entering into a person and propagating and the degree of illness and complication once being infected. This study does not appear to address these factors. Finally the phrase "499 had a vitamin D level in the year before testing" does not make sense. All people have some level of vitamin D.

On 2020-08-19 17:59:56, user petsRawesome1 . wrote:

"Of the 43 patients randomized to ConvP 6 (14%) had died while 11 of the 43 (26%) <br /> control patients had died."

That sounds like the study showed promise on the key metric, mortality, it just did not have enough data when it was stopped. It would be good to be very clear about the reasons for discontinuing the study, as the New York Times of Aug 19, 2020 is quoting this paper as "Last month, one such trial in the Netherlands was stopped when researchers realized that patients given plasma showed no difference in mortality"

On 2020-08-20 01:45:02, user giorgio capitani wrote:

How it can be proved without any doubt that the virus present in the aerosol actually infects a person? it can present but be harmless. Where is the evidence of the actual trasmission of the infection? the presence in the aerosol is not evidence of the transmission of the virus it's another pair of shoes. Or somebody can be infected and others not. How can you tell one thing from the other? they are two different moments: the presence of the virus in the aerosol, the actual transmission of the virus.

On 2020-08-21 13:38:24, user Susan Levenstein wrote:

If I understand the paper correctly, its most striking result is the isolation of Patient 1's virus from the VIVAS air sampler located 4.8 m away. But according to the Figure, Patient 1 had to walk right past that air sampler, closer than 1 m, every time he went to the bathroom. Couldn't that be a simpler explanation for how it picked up his virus?

On 2020-08-23 16:11:32, user Ang wrote:

Hello there,<br /> below a question for someone with the right competence.

True the approach of this work is great, it might result that they are right or wrong we'll see, starting from the review outcomes. However a common person would ask: "Why can't we do a direct and conclusive experiment about transmissivity through aerosol?". A direct experiment is to put a never infected person in the same room with a SARS-CoV-2 ill person, without the physical possibility to exchange any particle between them except air/aerosol. 100 person would cover a good statistics in terms of age, gender, time of exposure and other characteristics of the volunteer. Is this possible? How can be that in the entire world we cannot find 100 voluntaries that are available for the following experiment. Why is this something not done yet?

On 2020-08-22 07:54:04, user Drifa Belhadi wrote:

Updated preprint available here : https://www.medrxiv.org/con.... Published in Clinical Microbiology and Infection, doi: 10.1016/j.cmi.2020.05.019

On 2020-08-24 06:26:34, user Stan Himes wrote:

For COVID-19 you should include co-morbidity data, without this key information (which may be contained in full article) the data presented is worthless.

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

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

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

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

Best Regards,<br /> Eva

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

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

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

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

On 2020-08-27 11:22:55, user pto wrote:

I thought the index cases of that conference were all local residents of the Boston area. If so, that certainly wouldn't rule out a previous introduction a few weeks earlier. Say when international university students returned to Boston 3 to 4 weeks earlier.

On 2020-08-27 13:31:15, user Joe Psotka wrote:

Using data from Florida creates misleading expectations because Florida's decrease in March and April was largely from Snowbirds' and part time residents' departure from the State. Some people estimate that one-third of Florida's winter population leaves in the Spring to avoid the summer heat.

On 2020-08-27 23:26:12, user Vinci P, MD wrote:

There might be other explanations for better prognosis in post-menopausal women taking oestradiol: they were probably healthier than women not taking oestradiol, because HRT improves health. <br /> In addition I cannot understand why all post-menopausal women have better prognosis than men, since their estrogens are similar to those of men. Maybe it is the absence of testosterone, and not the presence of oestradiol, which makes the difference.<br /> Could you comment this, please?

On 2021-06-30 12:12:11, user SinoBen wrote:

The language used here is seething with something I've not commonly found in the typical scientific paper.

On 2020-08-29 21:03:28, user Sam Well wrote:

I cannot believe your work is being "advertised" as a hail mary when you conclude..."Additional results from larger randomised controlled trials are needed"

Please refer to the "sale" of your source:<br /> https://www.eurekalert.org/...<br /> https://www.dailymail.co.uk...

On 2020-08-30 15:05:05, user Henry Johnson wrote:

Does anyone know whether similar experiments have been done with woodwind instruments. I'm particularly interested in clarinet. The instruments work differently. The sound comes out of a variety of places...

On 2020-09-04 19:44:32, user Art Framer wrote:

Excuse my ignorance but it seems that the tests for covid 19 are looking for the virus itself. Wouldn't the tests have a higher rate of success if they looked for signs of the body's reaction to the virus?

On 2020-09-07 16:03:04, user Joe B wrote:

We don't know how long ago the vitamin D levels were obtained in these patients. This is especially true in the COVID patients, because we have no idea if they truly were "deficient" at the time of their infection. Additionally, you never tell us in the methods that you were going to examine supplementation, and how you were going to do that (and assure adherence). Can vitamin supplements not be purchased over the counter in the countries involved in this study? Finally, I assume you categorized people by "sex" and not "gender" as sex if the term used for male/female DNA based differences.

On 2020-09-07 16:34:55, user A. Baluja wrote:

Hi! The last version for the reference [Baluja et al, 2020] has been finally peer-reviewed and published in:<br /> https://www.tandfonline.com...

The updated version of the preprint:<br /> https://www.medrxiv.org/con...<br /> Thank you for continuing research in such important topic!

On 2020-09-09 19:12:11, user Michael Bishop wrote:

I don't believe the authors' data, which would imply that SARSCOV2 was circulating with little increase or decrease in Dec 2019 - Feb 2020 until suddenly taking off in late Feb early March.

On 2020-09-10 16:51:51, user Thomas Waterfield wrote:

Thanks Sunil. It was great to chat the other day.

We have produced a protocol that is currently with BMJ Open. The data presented here relates to the first clinic appointments (16th April to 3rd of July) for all participants. The symptom data was reported using RedCap data capture with retrospective reporting of illness episodes prior to the attendance from the beginning of the pandemic in February. In all instances the symptoms were reported without the participant knowing their antibody status. Data were entered by trained members of the research team.

On 2020-09-13 01:19:25, user mzbaz wrote:

There is an unfortunate typo in the horizontal axis unit label of Fig 3b, which should be "minutes" not "hours", consistent with the "15 Min Rule" vertical line, as well as the discussion in the text.

On 2020-09-16 19:31:28, user Ibrahim Hussin wrote:

I believe the precipitation in the weather type of Drizzle has effect on the outbreak

On 2020-09-18 16:27:11, user kdrl nakle wrote:

These types of papers that are masquerading as science are nothing more than speculations. Even IMHE forecasts from this Spring are laughable now. This is in the same venue.

On 2020-09-22 03:41:51, user Direk Limmathurotsakul wrote:

This article has been accepted for publication at the Emerging Infectious Diseases (EID). https://doi.org/10.3201/eid...

On 2020-09-24 10:16:48, user Camila Hobi wrote:

I would like to congratulate the authors for this paper! Wonderful idea! The hypothesis that children can be protective rather than harmful is very plausible! Unfortunately since the beggining of pandemic people are saying the opposite based in misbeliefs and not in science. It’s very important to test this hypothesis in other countries. Reading this paper, I asked myself “why keep schools closed?”

On 2020-09-27 03:43:34, user LB wrote:

It is well known that magnesium absorption is an issue with elevated gastric pH from PPIs. <br /> Please evaluate the possibility that the individuals who had a history of taking PPIs might have had magnesium deficiency, which altered their immune response to SARS-CoV-2.<br /> - Linda Benskin, PhD, RN

On 2020-09-27 05:53:27, user Vincenzo Cerullo wrote:

So obvious that some viral infection can trigger autoimmune diseases.... so why not use this to trigger anti-tumor immune response!!

On 2020-09-27 22:00:20, user nan wrote:

It would be be helpful to know the dosage.

On 2020-10-04 20:57:37, user William James wrote:

Do you think you should refer to the paper by Kojaku at al [Submitted on 5 May 2020 (v1), last revised 14 Sep 2020 (v3)] at https://arxiv.org/abs/2005...., which largely represented this analysis formally some months earlier? [I have no conflict of interest.]

On 2020-10-06 19:20:10, user LB wrote:

Great article, and a very elegant study design! Has this been submitted for publication?

Linda Benskin, PhD, RN

On 2020-10-15 22:40:24, user Marm Kilpatrick wrote:

Dear Dr. van Beek and co-authors,<br /> Thank for your this important work!<br /> In your Table 1 you appear to be grouping results for multiple assays together:<br /> Panbio™ COVID-19 Ag rapid test (Abbott), and Standard Q COVID-19 Ag (SD Biosensor);<br /> and COVID-19 Ag Respi-Strip (Coris BioConcept), and GenBody COVID-19 Ag (GenBody Inc)<br /> I *think* you did this because they had similar LODs but it'd be more informative if you could show results for each assay independently. <br /> It would also help to know the sample sizes for each of the assays in each group of patients.<br /> Finally, specificity is a potential issue with these rapid antigen assays. Did you test samples that were negative by PCR to determine this (acknowledging that PCR could miss viral RNA, especially if not done at the same time)?<br /> thank you,<br /> marm

On 2020-10-22 11:33:50, user Paul Peerbooms wrote:

It would be interesting to see the protective effect of the flu-vaccination when only staff with contacts with patients is considered.

On 2022-10-24 17:42:56, user CDSL JHSPH wrote:

Dear Mekkes et. al.,

Thank you for sharing your work with us! Creating models to predict neuropathological diagnosis based on temporal signs and symptoms is very significant research, and I’m looking forward to seeing where this heads in the future! I enjoyed reading this paper, especially since it introduced me to techniques and concepts I was previously unfamiliar with. That being said, while reading I did notice some parts that I think could be given further clarity in order to make this paper more accessible to those not within the immediate field. There are a lot of abbreviated disorders mentioned, and I noticed that some were explained in the introduction however I could not find the proper matching terms for disorders abbreviated in later sections. I think it would be a great benefit if there were a word key with the disorders and their corresponding abbreviations. Especially since different disorders may be represented by the same acronyms, so googling it may not provide the reader with the correct one. Also, I was wondering if you plan to do another study focusing on optimizing these models to diagnose mental illnesses and psychiatric conditions in a separate paper? I understand the main focus on brain disorders and neurodegenerative diseases since those can be linked to prominent neuropathological changes, but when reading the abstract I was given the impression that mental illnesses would be focused on to a larger degree than I noticed in the paper. I would love to see any future steps you take with this, especially since the alterations in cognition and behavior associated with mental illnesses can be observed from live patients, and don't necessarily have to be inspected retroactively like from brain donors.

On 2022-10-24 18:20:28, user Jordan Ross wrote:

This study was aimed to understand the complexity of diagnosing brain disorders in society today. The author utilizes data in statistical analyses to identify this crossover and give rise to further research in the field. This journal article emphasizes the use of a computational pipeline to establish a series of clinical synopses regarding a series of brain disorders from donors that were previously diagnosed. Donors from the NBB underwent a series of cross-disorder research to identify signs and symptoms associated with psychiatric diagnoses. Overall the article had great analyses and synthesis of ideas pertaining to the mechanisms of symptomatology and how it differentiates across brain disorder profiles. One thing to note is organization in this article. Maybe incorporate these main figures within the results section with titles and keys for each to limit confusion to the reader. Additionally, in the methods section (2.7.3 Observational profiles of the signs and symptoms), the author referred to a figure but did not implicitly state which one. It is rather stated as: (Figure number?). This should be better examined. Lastly, the addition of implicitly stating the need for further research is a great way to highlight the need for experimental study amongst the reader. With this, it would be helpful to highlight your targeted audience within this article (i.e. professional, student, etc.). If you have a targeted audience with a scientific background you will not need to go into further depth to define terminology and specific neurological pathways in this study. Overall, job well done and very interesting study!

On 2022-11-08 15:26:46, user W_R_1 wrote:

Hi,

Thanks for the very interesting and useful paper!

I wondered if the stated z-value calculation (p26, next-to-last line) has a typo of a "-" sign rather than a "+" in the denominator?

I was also curious as to the properties of this test statistic in general, given that the two beta estimates aren't necessarily independent (i.e. when calculating the variance of the difference between the two beta estimates, their covariance can't necessarily be assumed to be 0?)?

On 2022-11-26 06:41:30, user Soichiro Obara wrote:

Dear Prof. Schindler,

First I (Soichiro Obara, the PI of this project) apologize for being replying to your great comments.

Second, our project has modelled the European APRICOT as you commented. We would sincerely appreciate your suggestions from the viewpoint of the author of the APRICOT.

On this matter, before launching this project, we got in contact with the principal investigator of the APRICOT, Prof. Walid Habre, who has been giving many suggestions and comments to us.

As you might have concerns regarding data acquisition, he also gave comments as to how tough the data acquisition and cleaning in the APRICOT.<br /> Hence, this time, we assume that we may need to change the case report form (actually modelling the CRF of the APRICOT) after a pilot study which hopefully will be conducted February or March in 2023.<br /> (I have heard from Prof. Habre that a pilot study was conducted in three centers to examine the feasibility of the protocol and the CRF in the APRICOT.)

If possible, I would appreciate your "specific and direct" suggestions as to which data items in the APRICOT might be difficult or be unnecessary to collect.

Again we would sincerely appreciate your great suggestions and comments, if you kindly gave, in the future again.

I am very pleased if you kindly get touch with me on behalf of the Asian Society of Paediatric Anaesthesiologist research committee.

Soichiro Obara (Japan)<br /> e-mail address: soichoba1975@gmail.com

On 2022-12-09 13:17:21, user Maja wrote:

I applaud the authors for their meaningful work on reporting COVID-19 trials. The results are fascinating. Only 19% of COVID-19 clinical trials were published within three months of completion, which shows how much work has to be done regarding research transparency, publishing trial results, and responsibility while conducting clinical trials. Timely publication of results and avoiding research waste in clinical trials should be a priority, especially during global public health emergencies, such as the COVID-19 pandemic.

On 2022-12-27 15:27:14, user nobiggie wrote:

The most interesting piece of information here seems to be that the more shots you get the more likely you are to get sick but somehow it goes unmentioned

On 2023-01-06 12:54:34, user Mike Verosole wrote:

A more recent, peer reviewed publication involving a much larger population set showes that indeed vaccinated people were more likely to be infected vs previously infected individuals, however, the vaccinated group was about 25-35% less likely to be hospitalized or experience death. Maybe this paper will be reviewed in light of this new publication

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 2023-01-13 22:09:06, user WILLY CESAR RAMOS MUÑOZ wrote:

This paper has been published in Healthcare journal: https://www.mdpi.com/2227-9...

On 2023-01-16 01:47:52, user Brian Piper wrote:

This is a timely review on an important topic!

In evaluating the safety of the relaxation of take-homes, it might be helpful to consider that “The number of clients receiving methadone increased from 306,440 in 2011 to 408,550 in 2019 and then decreased to 311,531 in 2020” [1]. This reduction from 2019 to 2020 of 97,019 patients is a 23.7% decline!<br /> The increase in the number of overdoses involving methadone may only appear equivalent to the increase in all opioid overdoses when one does not take this factor (i.e. using the population size and not the number of methadone patients as the more appropriate denominator) into account.

The authors also are encouraged revisit the important policy research in England and Scotland which identified sizable declines in methadone overdoses following implementation of supervised administration [2].

Citations

1. National Survey of Substance Abuse Treatment Services (N-SSATS): 2020 Data on Substance Abuse Treatment Facilities. Table 3.2.

2. Strang et al. Impact of supervision of methadone consumption on deaths related to methadone overdose (1993-2008): analyses using OD4 index in England and Scotland. BMJ 2010;341:c4851.

On 2023-02-09 16:44:55, user lepromatous wrote:

Missing some citations here... Example: https://pubmed.ncbi.nlm.nih...

On 2023-02-09 23:16:58, user Constantinos C wrote:

The published version can be accessed via the following link: https://www.nature.com/arti...

On 2023-02-20 07:41:20, user Martin Schecklmann wrote:

I miss in the discussion the meaning of relevant dose of TMS with respect to e-field. We could demonstrate that F3-based method is similar to neuronavigation-based method with respect to clinical efficacy even if induced e-field was lower for F3-method (doi: 10.1016/j.brs.2021.01.013). In addition your sample size is very low and correlation between clinical efficacy and e-field may be biased by outliers (figure 3).

On 2023-02-22 16:16:10, user Robert Clark wrote:

Major flaw in your analysis. <br /> You noted the report by Peterson et al included cardiac arrest cases where they survived:<br /> “This study identified 173 confirmed SCD cases (and 158 SCA cases with survival), so on average 43 SCDs per year.”

Since there has been a great increase in awareness of cardiac arrests and arrhythmia in athletes there have been a great increase in AED’s (automatic defibrillators) available and those trained in CPR.

Then many of those cases even in just the last couple of years survived who would have died previously. So you should have also counted the number of cases who had cardiac issues who also survived in the current, pandemic era.

Additionally Maron et al described other causes other than just cardiac arrest as the cause of the sudden deaths. Since the Goodsciencing report considers several kinds of serious life threatening illnesses arising in athletes you should also have done a separate count of those cases in the Goodsciencing report, again both of those who died and those who survived.

Robert Clark

On 2023-03-02 18:33:37, user Daniel Park wrote:

Fascinating. Lines up well with other evidence including the Lewnard et al. study showing interactions b/w pneumococcal carriage and SARS-CoV-2. We also saw similar patterns with viral load (+ severity) and pneumococcal carriage with human endemic CoVs: https://journals.lww.com/pi...

On 2023-03-17 20:15:27, user Guilherme Mattos Jardim Costa wrote:

Peer-reviewed version available at: https://doi.org/10.1186/s12...

On 2023-04-04 06:16:50, user Kevin J. Black wrote:

I think you'll want to examine the spatial delayed response task, which Tamara Hershey and other have studied in Parkinson disease, e.g. https://pubmed.ncbi.nlm.nih...

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 2023-05-16 21:45:14, user Maria Log wrote:

This is really very strange and disappointing that you consider the use of private e-mail as an indicator of fake articles. Since my years as a PhD student I always use, and I use now and will use my personal email in all my articles. There are many reasons for that, but the most crucial one is that I (as well as any other researcher)can change my affiliation and then my institutional e-mail will be inactivated and ones who are interested in my publication will not be able to get an answer to their questions. So the use of an email linked to the organization is unfair and irresponsible. Here is a link to my google scholar profile: https://scholar.google.com/... - good luck with finding any fake articles!

On 2023-05-24 14:24:40, user Shaffi Fazaludeen Koya wrote:

The article is now published in Lancet SEA journal,<br /> https://www.thelancet.com/j...%20in%202016%E2%80%932020 "https://www.thelancet.com/journals/lansea/article/PIIS2772-3682(22)00130-5/fulltext#:~:text=The%20pooled%20hypertension%20control%20rate,28.0%25)%20in%202016%E2%80%932020").

On 2023-06-07 16:45:42, user Nathan Pearson wrote:

In this study (of American patients to whom bivalents were available only as booster), all bivalent recipients by definition got 3+ total mRNA vaccine doses, while the current preprint text's control group got 2+.

As such, to compare equal dose counts (if not timing relative to prior waves etc.), can the authors please add a sub-analysis of peer bivalent (original or BA.4/5) vs. 3+ dose (not 2+) monovalent recipients?

Not doing so inherently confounds any additional benefit of bivalent vs. monovalent formulation with a group difference in total doses per participant.

Thanks

On 2023-06-07 17:10:10, user Nathan Pearson wrote:

Also: in adding such needed apples-to-apples comparison of bivalent vs. monovalent peer boostees (i.e., with 3+ doses in each otherwise background-similar group), can the authors take care to control for time since last dose?

I.e., if bivalent boostees got boosted at most k months before data freeze (so were tracked for severe COVID for <=k months after last dose), likewise tally cases of severe COVID in monovalent boostees <=k months (but not longer) after their last dose.

Thanks.

On 2023-06-22 16:56:53, user Benjamin Shuman wrote:

This is interesting work. The descriptions of methodological variation (task, muscles measured, amplitude normalization method) certainly impact how the studies compare to one another. However, there is no discussion of filter parameters and its impact on the interpretation of synergy metrics. With more aggressive filtering the resultant processed EMG signal is less variable which also has a direct impact on the number of synergies extracted or tVAF1/DMC. The Collimore article is noted for having the least number of synergies identified but also has the most aggressive LP filtering (4hz). There may be additional trends in the article findings when looked at through a filtering lens and I would encourage the authors to consider this. Finally, please note that DMC is a linear transformation of tVAF1 (Steele 2015). As such the trends in DMC are directly comparable to tVAF1.<br /> Thanks,

On 2023-07-10 13:53:43, user Carlos Menck wrote:

This work was published in Carcinogenesis and the correct citation is: <br /> Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, SarasinA, Menck CFM. Mutational signatures and increased retrotransposon <br /> insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print. <br /> PMID: 37195263.

Corradi C, Vilar JB, Buzatto VC, de Souza TA, Castro LP, Munford V, De Vecchi R, Galante PAF, Orpinelli F, Miller TLA, Buzzo JL, Sotto MN, Saldiva P, de Oliveira JW, Chaibub SCW, Sarasin A, Menck CFM. Mutational signatures and increased retrotransposon

insertions in xeroderma pigmentosum variant skin tumors. Carcinogenesis. 2023 May 17:bgad030. doi: 10.1093/carcin/bgad030. Epub ahead of print.

PMID: 37195263.

On 2023-10-06 12:35:59, user Ashok Palaniappan wrote:

An expanded, significantly revised and advanced version post peer-review is now available [open-access]:<br /> Muthamilselvan S, Raghavendran A, Palaniappan A. Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression. PLoS One. 2022 Feb 24;17(2):e0249151. doi: 10.1371/journal.pone.0249151. PMID: 35202405; PMCID: PMC8870460.

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

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

On 2021-12-21 15:35:35, user Greg Mock wrote:

Has any association of PANS and the NOD2 gene been studied?

On 2023-10-24 02:18:23, user CDSL JHSPH wrote:

Dear Dr. Bi et al,

Thanks to your work on influenza, which has provided a new proof that the residual repeat vaccination effect might be explained by different rates of subclinical infection between repeat and non-repeat vaccinees via two proposed mechanisms, the infection block hypothesis and enhanced vaccine immunogenicity and protection post-infection.

As a reader who doesn't know much about the field ,I can give you some reading feedback for your reference.<br /> First I think your article provides three important pieces of evidence. <br /> 1,Repeat vaccinees were vaccinated earlier in a season by one week.<br /> 2,Clinical infection influences individuals’ decision to vaccinate in the following season while protecting against clinical infection of the same (sub)type.<br /> 3,Adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination.<br /> 4,Adjusting for subclinical infection could theoretically attenuate this effect.

On the basis of your good work, I would like to offer a bit of advice for readers who are not experts in this area. First is the article structure section. I hope this provides some perspective to help you publish. First of all the name of the title may be too long for non-specialized readers. It may lose some of the attention. Furthermore the explanations of within-season waning, recent clinical infection, and subclinical infection could have come in the INTRODUCTION instead of the METHOD before being mentioned. Another thing is that I think you could put in the conclusion that has some summarizing words underneath the fig so that readers might find them easier. Also the part you put in the appendix about theoretical modeling some of it is necessary for understanding the model, if you could summarize it necessary and put it in the body methodology would help understanding.

The next aspect is about research, first of all your work is very relevant and on this basis you are perfectly placed to capitalize on this aspect. First of all you can try to find some way (like sampling) to compare your theoretical model with the results of the data response you got, so that you can speculate about the effect of the vaccine in reality and the possible number of Subclinical infections.

After that comparing your model with real data would be an interesting aspect. And this can emphasize the correctness of your model and increase the credibility of your article. Although overall this article has been highly relevant with enough realistic data.

However, on this basis, one can consider whether the data from different regions (the five regions sampled) are very different? For example, are the probabilities of tuning into a vaccination strategy similar in different regions, and do repeat vaccinees in each region tend to get vaccinated earlier in the flu season than non-repeat vaccinees? These comparisons of data from different regions can be added to the article as they relate to the reproducibility and generalizability of your model's and conclusions.

Finally, thank you for this article, which provides very good evidence for the causes of Reduced effectiveness of repeat influenza vaccination, and this article has the advantage of incorporating a lot of details that were not considered in previous studies and provides a good interpretation of the errors, providing new ideas and theoretical models for the field. And I personally learned a lot of research ideas from you through this article, thank you for your work!

On 2023-11-01 18:49:19, user Samuel Packard wrote:

Using aOR to compare each state to Hawaii as a reference group doesn't make much sense conceptually. Would be better to use a statistical test to assess whether each state was significantly above or below national average.

On 2023-11-03 16:41:46, user Dr. Hans-Joachim Kremer wrote:

It is not well described, what exatly is control. The estimates in Figure 1 are not comprehensible given the few details provided. <br /> Fig 1: It is written that Pfizer bivalent vs. Moderna bivalent is shown, however, the text suggests that Pfizer bivalent was compared with its own contol (before vaccination). What is true? If in fact you compared them to their own control: Where are respective details?<br /> It appears that in that Figure the "all" in the first row has a different meaning from the "all" in the third row. Note that the "16" of the first row, denoted as "all", is repeated in the fifth row, but denoted as "new".<br /> Anyhow, the "new" is not explained in the text and the "all" might have a meaning different from common understanding, as it appears that it stands for "after matching", so not really "all".

On 2023-11-20 15:39:28, user Deepak Gupta wrote:

Now published in Open Health doi: 10.1515/ohe-2023-0008 which is a condensed letter to editor of my this medRxiv preprint doi: 10.1101/2022.10.02.22280605 along with my other medRxiv preprint doi: 10.1101/2023.08.15.23294123

On 2023-11-23 22:56:05, user Elizabeth Korevaar wrote:

This paper has now been peer reviewed and published at Research Synthesis Methods:

Korevaar, E, Turner, SL, Forbes, AB, Karahalios, A, Taljaard, M, McKenzie, JE. Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study. Res Syn Meth. 2023; 14(6): 882-902. doi:10.1002/jrsm.1669

On 2023-12-07 15:20:52, user Alex Liber wrote:

This article has now been published: Liber AC, Faraji M, Ranganathan R, Friedman AS. How Complete Are Tobacco Sales Data? Assessing The Comprehensiveness Of US Tobacco Product Retail Sales Data Through Comparisons To Excise Tax Collections. Nicotine Tob Res. 2023 Nov 2:ntad214. doi: 10.1093/ntr/ntad214. Epub ahead of print. PMID: 37933997.

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

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

On 2023-12-14 17:58:32, user Michelle Carras wrote:

Very curious about this. It's worth considering, but the authors had some big challenges with their sample, which is a relatively well-off, very White population, a bit older than the US average. They have a very high rate of premorbid asthma (about 2-4x the usual), but the thing I find most curious is the use of naltrexone (20% of the sample) and ivermectin (18% of the sample) to treat these new symptoms. Since these uses are very off-label, it makes me wonder about the types of patients who get them. Same with intermittent fasting as a non-pharmacological treatment. I'd be interested in seeing other responses to this and whether it ultimately gets published.

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

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

My questions:

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

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

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

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

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

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

Back to my questions...

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

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

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

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

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

On 2024-01-16 18:40:48, user Weina Jin wrote:

This is the initial paper preprint. The published paper is at: http://dx.doi.org/10.1016/j.artmed.2023.102751

The updated preprint and supplementary materials S1-S3 can be found at:<br /> https://www2.cs.sfu.ca/~hamarneh/ecopy/aiim2023.pdf<br /> or <br /> https://github.com/weinajin/multimodal_explanation/tree/main/neurosurgeon_clinical_study_data_analysis/paper_preprint

Video paper presentation:<br /> https://www.youtube.com/watch?v=rhLQQsc8Z8Y&t=29s

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 2024-03-18 13:01:53, user Varsha Gouthamchand wrote:

Follow the link https://doi.org/10.1093/bjr... for an extended and published version of this article.

On 2024-04-27 22:04:26, user Linette Roungchun wrote:

I am a TSW sufferer and this work is important. I have suffered with TSW on and off since the young age of 3, so to see the science backing up the fact that TSW is in fact, a separate entity from Eczema is so validating and healing. Huge thank you to Kelly, Kathy, and Dr. Ian Myles!

On 2024-04-28 00:05:10, user pfwindsor wrote:

Thank you for the opportunity to comment on this excellent TSW study. I am not a scientist, but have more than 10 years of personal experience with TSW and have studied and published material through the American Academy of Dermatology. I also moderated ITSAN’s online forum for several years where I reviewed thousands of posts and responded to thousands of questions about TSW. I am currently involved with ITSAN research advocacy and would be happy to offer additional input.

Line 33 and Line 66 refer to “prolonged exposure” and “longterm use” of TCS. While my own experience with TSW involves longterm use of TCS, the anecdotal data suggests that some TSW cases follow a short term course of TCS treatment, such as for poison oak. Others report TSW symptoms emerging after decades of TCS use. Our theories are centered around multiple variables for susceptibility to TSW including, but not limited to length of use. We also considered potency, quantity and length of exposure and areas of application. Your study doesn’t seem to address these other potentially significant variables.

Line 67 further states that TSW is related to “abrupt cessation” of TCS use. This may be the case for your small study cohort, but it has not been studied or established within the TSW community.

Line 73 -74 states that “improvement is seen through avoidance of TCS therapy”. This is misleading. Overwhelming anecdotal evidence suggests that TSW symptoms that emerge after cessation of TCS become more acute before they resolve.

Line 306-307 suggests a threshold of 4 months of TCS exposure. Is this an arbitrary threshold identified for purposes of your study or is there a source that proposes or establishes this threshold? I am not familiar with any particular exposure timeframe associated with a risk of developing TSW. In fact, given the anecdotal evidence of TSW with short term exposure, this may not be a valid metric.

On 2024-04-28 21:04:46, user Marly Peel wrote:

PLEASE! PLEASE! PLEASE do more research & acknowledgement of this TERRIBLE problem! I just recently discovered info on this & it all matches my symptoms exactly. Imagine a horrific itch that you cannot scratch enough to make it STOP! I don't think I've ever been this close to panic as I have during those times. There's no way to sleep or rest AT ALL when this is happening. That doesn't even include the flaking, shedding, red & raw areas, irritated & antsy feelings one can have. WE NEED HELP! Plus, there needs to be a HUGE WARNING included with every top.steroid...that doctors know about & take to heart. Top.steroids might be a quick "fix"...but they DO NOTHING to address the underlying reason for the problem in the first place. It's time for common sense to COME HOME!

On 2024-04-27 16:09:01, user Alicia F. wrote:

As someone who is 19 months into TSW, this research is SO important. It helps us get one step closer to finding a treatment and understanding how our bodies are affected by topical steroids.

On 2024-04-27 18:39:09, user Haley DelPlato wrote:

As a young adult whose life has been put on hold for the past 3 years due to Topical Steroid Withdrawal, I can't thank you enough for this work!

Seeing studies about TSW not only helps validate my pain that so many medical professionals have dismissed, but also contributes greatly to advancements in dermatopathology looking forward. The current stigma that makes TSW such a controversial concept NEEDS to be eradiated, a complex task that ultimately relies on substantiated clinical proof to combat misinformation. Unfortunately, the current scope of dermatopathology has kept so many folks unaware of TSW and trapped in harmful cycles of topical corticosteroid addiction. I hope this will be the first of many legitimate works seeking to uncover the truths about this tragic condition so future generations will be believed, treated, and cared for with dignity, in ways the current dermatological standards simply haven't allowed for.

Appreciate the strides this study has taken toward a more compassionate reality for TSW sufferers!

On 2024-04-28 12:21:49, user Henry wrote:

How much Berberine is required for the improvements to begin? Really interested in this study, thank you!

On 2024-04-28 19:43:09, user Laura Mihalidesz wrote:

I am a TSW sufferer and made a signifacant change to the worse in my condition when TS were introduced to my life 30 years ago, stopping the usage a age 26 and going into TSW the symptoms are uncoparable to eczema. This study is important to understand what harm can TS cause in the long run and after 9 years in TSW I still suffer from symptoms. The importnce of this research is not a question but answer to many patient's questions and finding and developing treatment methods.

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

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

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

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

On 2024-05-10 04:50:19, user Kaitlin Lee wrote:

Thank you for this long overdue research. It gives me hope that in the near future TSW will be universally diagnosable, treatable, and most importantly, prevented. I have had eczema for ten years, just a few patches here and there, which never interfered with my livelihood. Now 5 months into TSW, so many things have been put on hold for me. People suffering from TSW need treatment and support and medical professionals need to start acknowledging this condition.

On 2024-04-30 16:10:19, user Cameron Daniels wrote:

Hello! My name is Cameron Daniels and I am a Undergraduate Student at Sam Houston State University. As part of a class project we were told to pick a preprint and provide the authors with relevant comments that could help their study. I really enjoyed reading your study and learning about new data collection techniques. Here are my comments on your paper that could help you improve the paper:

* List the number of cycles for the first round of PCR in the patient samples. You did it for the in vitro experiment, but you must've forgotten about it in the patient one. The number of PCR cycles helps to be able to replicate your experiment and also see if too many cycles were run because the more cycles that occur leads to the more mistakes, chimeras, and faulty sample reads. Consider looking at this article to validate my response: Sze MA, Schloss PD.2019.The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. mSphere4:10.1128/msphere.00163-19.https://doi.org/10.1128/msphere.001...

* Specify filtering for chimeras and PCR errors, which is a must to ensure accurate data.

* List the number of sample reads for the ASV units. The sample reads will help understand your data better.

* Make sure to include coverage measures and do it before filtering of singletons. Calculating Good's Coverage or Chao1 helps to ensure that representative sampling occurred.

* Great introductory paragraphs but seems to have many long run-on sentences. Consider breaking up those sentences for readability.

* A preserving agent is mentioned numerous times as important to capsule function. What is this agent and what concentration?

* Fig4A visualizes beta diversity. Should also use an appropriate statistical test, such as ANOVA, too determine if the community compositions are significantly different with probiotic addition.

* The probiotic contains two specific strains, while the microbiome data is from V3-V4 sequencing, and so cannot identify at the species or strain level.

* An increase in Lactobacillus or Bifidobacterium is expected for the probiotic-treated patients. What would be more interesting is to analyze if certain other taxa are significantly increased or decreased in abundance by the probiotic strains, as the question of how those two strains affect the rest of the community is scientifically important and would enhance the literature.

* Fig5 and 6 show that stool sampling vs capsule are different. Authors also need to include analysis to show that capsule and endoscopy are not different, since capsules are proposed as a non-invasive alternative.

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

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

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

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

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

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

Detailed comments are below:

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

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

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

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

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

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

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

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

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

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

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

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

On 2024-07-05 14:17:45, user Benjamin Glemain wrote:

Published article for this pre-print: https://www.nature.com/arti...

On 2024-08-02 13:43:41, user Paul Levay wrote:

This preprint has been superseded by:

Levay P, Finnegan A (2024) The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid). Journal of the Medical Library Association, 112(3): 316-328. https://jmla.pitt.edu/ojs/jmla/article/view/1806

On 2024-10-03 03:31:24, user Max Lamparth wrote:

This paper is nowpublished through the CoLM conference proceedings with new results: https://openreview.net/forum?id=1pgfvZj0Rx

On 2024-10-19 20:32:11, user CDSL JHSPH wrote:

I thoroughly enjoyed reading your paper and found it to be a significant contribution to the field of tuberculosis treatment. The exploration of model-based methods, particularly MCP-Mod, to enhance traditional qualitative approaches is both timely and necessary. Your findings highlight the effectiveness of these methods in detecting duration-response relationships, especially in small sample trials, which is a crucial aspect of optimizing treatment strategies.

I am particularly intrigued by the potential for these model-based approaches to be applied beyond TB. It would be fascinating to investigate whether the insights gained from your research could be translated to other bacterial infections, such as Staphylococcus or Streptococcus species, as well as to the treatment of viral and fungal infections. This could open new avenues for improving antibiotic and antiviral therapies.

Additionally, your suggestion to incorporate patient characteristics and risk factors into the analysis is commendable. I believe that further exploration of how different design parameters—such as sample size and time intervals—affect the accuracy of the model could provide valuable insights. Understanding how to integrate more patient-centric data into real-world clinical settings would enhance the applicability and scalability of your findings.

Thank you for your valuable contribution to this important area of research. I look forward to seeing how your work evolves and the potential implications it may have for personalized treatment strategies in the future.

On 2024-10-18 00:43:20, user CDSL JHSPH wrote:

Thank you for sharing your research. I have read your paper and deeply appreciate the necessity and advantages of using model-based methods, such as MCP-Mod, to improve traditional qualitative approaches. The model-based methods you proposed have demonstrated excellent performance in detecting the duration-response relationship, especially in the context of small sample trials, and their potential application is very promising. Additionally, your suggestions for incorporating patient characteristics and risk factors pave the way for more personalized treatment strategies in the future. However, I wonder if it would be possible to further explore how different design parameters, such as sample size and time intervals, might affect the accuracy of the model, and how more patient-centric data could be integrated into the analysis in real-world clinical settings. I believe this could help enhance the understanding of the model’s applicability and scalability. Thank you once again for your valuable contribution to this field, and I look forward to seeing more of your research in the future.

On 2024-12-05 12:13:20, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The study "Pre-existing anti-polyethylene glycol antibodies in pregnant women and newborns" investigates the prevalence and levels of pre-existing anti-PEG antibodies in pregnant women and their newborns, revealing significant safety concerns over the use of PEGylated drugs in these populations. The study highlights maternal age and certain lifestyle factors, such as cosmetic use and consumption of take-out food, as influencing the prevalence and levels of these antibodies. The implications for public health lie in the potential reduced efficacy and increased adverse reactions to PEGylated drugs.

Potential Major Revisions<br /> 1. Study Design and Population Detail Improvement: The current study design section provides a basic overview of the population criteria (pg. 7) but lacks deeper context about the broader representativeness of the sample size and demographics. Additional detail on potential regional and healthcare-specific biases can help contextualize the findings better for international readers.

1. Methodological Clarification: Some methodological aspects, such as the exact ELISA techniques used and their validation, are mentioned cursorily (pg. 11). A more comprehensive separate methodological section could provide greater clarity and benefit reproducibility.

2. Detailed Analysis of Influencing Factors: The discussion of influencing factors like maternal age and cosmetic use (pg. 11) needs expansion to delve into how these were statistically analyzed and how robust these findings are. The differences in antibody prevalence and levels based on lifestyle factors should be discussed with more supporting data.

3. Potential Confounders: Addressing potential confounding variables not examined in the study could enhance its robustness. Consider expanding the discussion around potential other environmental and genetic factors influencing anti-PEG antibodies not addressed in this study.

4. Discussion on Clinical Implications: While the study raises concerns about the safety of PEGylated drugs, it stops short of providing specific clinical recommendations or guidelines. This section could be expanded to address more direct implications for clinical practice and public health policies (pg. 10).

Recommendations<br /> 1. Expand and Detail the Methodological Section: Expand where necessary, especially focusing on the validation and comparison of ELISA techniques used between this study and previous studies.<br /> 2. In-depth Statistical Analysis: Include more detailed statistical tables and charts to back the discussions around influencing factors and antibody levels.<br /> 3. Address Confounders: Identify and address other potential influencing factors and confounders that were not examined and discuss their potential impact on the findings.<br /> 4. Clinical Guidelines Discussion: Provide a more detailed discussion with possible clinical guidelines or recommendations addressing the raised safety concerns about the use of PEGylated drugs in seropositive populations.

Potential Minor Revisions<br /> 1. Typographical Errors: Correct minor typographical errors, such as any found in the description and presentation of data in figures and tables (pg. 7).<br /> 2. Formatting Consistency: Ensure that formatting is consistent throughout the document, particularly around headings and subheadings for better readability.<br /> 3. AI-Generated Content Analysis: No significant AI-generated content was detected in the document. The content is likely produced by human authors, given the nuanced arguments and specific scientific context presented.

By addressing these points, the study could provide a more thorough and accessible analysis of its findings, enhancing its contribution to the understanding of pre-existing anti-PEG antibodies in pregnant women and newborns.

On 2024-12-06 02:09:34, user xPeer wrote:

Courtesy review from xPeerd.com

Summary

The preprint titled "RGnet: Recessive Genotype Network in a Large Mendelian Disease Cohort" introduces RGnet, a novel tool for analyzing recessive genotypes in large cohorts, focusing on compound heterozygotes and homozygotes. The study applied RGnet to the SLC26A4 gene within a cohort of individuals with hearing loss, identifying significant pathogenic variants and demonstrating the tool's potential for advancing the understanding of recessive genetic disorders. The paper highlights the novelty of RGnet, the methodology involving variant preprocessing, phasing, network construction, and permutation-based enrichment analysis, and presents the results from its application to the CDGC cohort.

Potential Major Revisions

1. Reproducibility and Data Availability:
2. Ensure that the datasets and tools used in this study are accessible. Although the paper mentions the availability of RGnet on GitHub, details about accessing specific datasets (e.g., CDGC data) were not explicit.

3. Example: "RGnet is available from GitHub at https://github.com/jiayiiiZeng/RGnet " (page 1) but does not provide direct links or instructions for data access.

4. Robustness of the Methodological Framework:

5. Explain the justification for the chosen phasing methods (trio-based, read-based, expectation-maximization algorithms) and their combination.

6. Example: "This study employs a combination of trio-based phasing, read-based phasing, and an expectation-maximization phasing algorithm" (page 3). However, specific reasons for selecting these methods are not provided.

7. Statistical Analysis:

8. Provide a more detailed description of the permutation tests used for RG enrichment analysis and why 100,000 permutations were specifically chosen.

9. Example: The paper states that "100,000 permutations were performed" without detailing the basis for this choice (page 5).

10. Ethical Considerations:

11. Include a section discussing ethical considerations, particularly concerning patient data privacy and consent given the sensitive nature of genetic data.
12. There is no mention of ethical reviews or consent processes, which is crucial for studies involving human genetic information.

Potential Minor Revisions

1. Typos and Grammar:
2. Correct minor typos and ensure grammatical consistency. For example:
3. Line 18, page 1: "To address this 18 gap" should be "To address this gap".

4. Line 58, page 2: "research3,4" should be "research" followed by proper citations.

5. Formatting Issues:

6. Ensure consistent citation formatting throughout the text.

7. In the reference section, ensure that all references, such as URL links, are formatted and hyperlinked correctly. For example, repeat the formatting used for URL links like " https://doi.org/10.1101/2024.12.02.24318353 " for other references as well.

8. AI Content Analysis:

9. The paper does not provide any indications of AI-generated content. It appears authentically authored by humans, considering its depth and technical specialization.

Recommendations

1. Increase Transparency in Methodological Choices:

2. Provide a more granular explanation of the methodological decisions, particularly around the choice of phasing methods and permutation tests.

3. Enhance Data Accessibility:

4. Ensure that all datasets and supporting materials are accessible, with clear instructions for researchers wishing to replicate the study or apply the RGnet tool.

5. Incorporate an Ethical Review Section:

6. Add an ethics section discussing how patient data was handled, the consent process, and any relevant ethical approvals obtained for this study.

By addressing these major and minor revisions, the paper can be significantly strengthened, ensuring clarity, reproducibility, and ethical adherence, which are vital for advancing research in genetic studies.

On 2025-02-24 23:45:15, user Stephen Goldstein wrote:

I am now pasting in the review done by Gemini 2.0 Flash Thinking Experimental. I have not edited it.

Prompt: Please conduct an extremely rigorous, journal-agnostic peer review of this manuscript.

Response:<br /> I have now thoroughly read the manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination" by Bhattacharjee et al., available at https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1 .

Here is an extremely rigorous, journal-agnostic peer review of this manuscript:<br /> Extremely Rigorous, Journal-Agnostic Peer Review of Manuscript: "Immunological and Antigenic Signatures Associated with Chronic Illnesses after COVID-19 Vaccination"<br /> Manuscript DOI: https://doi.org/10.1101/2025.02.18.25322379 <br /> Manuscript URL: https://www.medrxiv.org/content/10.1101/2025.02.18.25322379v1.full-text

Overall Assessment

This manuscript by Bhattacharjee et al. investigates the complex and highly sensitive question of immunological and antigenic signatures associated with chronic illnesses reported following COVID-19 vaccination. The authors employ a multi-faceted approach, combining clinical data from a prospective cohort study with deep immunophenotyping and autoantigen profiling. The study's aim is to identify potential immunological correlates of these post-vaccination chronic conditions, which is a crucial area of investigation given ongoing public health discussions and concerns.<br /> While the study addresses a highly relevant and important question, and utilizes advanced methodologies, this review finds that the manuscript in its current form presents significant limitations in its methodology, analysis, interpretation, and overall conclusions. From a rigorous, journal-agnostic perspective, the manuscript requires substantial revisions and further validation before it could be considered to provide robust and reliable scientific evidence. The current findings are preliminary and require cautious interpretation, and the manuscript overreaches in its conclusions and implications.

Major Strengths (Limited in Impact by Weaknesses)<br /> Addresses a Highly Relevant Question: Investigating potential immunological links to chronic illnesses post-COVID-19 vaccination is undeniably important for public health and vaccine confidence.

Prospective Cohort Design: The prospective cohort study design is a strength, allowing for systematic data collection and comparison between vaccinated individuals with and without reported chronic illnesses.

Multi-Omics Approach: The use of multi-parameter flow cytometry for immunophenotyping and autoantigen microarray profiling are advanced techniques that offer the potential to identify complex immunological signatures.

Clinical Data Integration: The study attempts to integrate clinical data (symptomology, diagnoses) with immunological data, which is essential for translational relevance.

Major Weaknesses and Critical Concerns (Requiring Substantial Revision)

Case Definition of "Chronic Illnesses": Critically Vague and Subjective. The definition of "chronic illnesses" post-vaccination is alarmingly vague and relies heavily on self-reported symptoms and broad diagnostic categories (e.g., "dysautonomia," "POTS," "fibromyalgia"). There is no clear, objective, and validated case definition used. This introduces significant subjectivity and potential for bias in patient selection and outcome assessment. The lack of specific inclusion/exclusion criteria for these "chronic illnesses" undermines the scientific rigor of the entire study. It is unclear if these are new-onset conditions post-vaccination, or exacerbations of pre-existing conditions, or simply background rates of these conditions temporally associated with vaccination.

Lack of Appropriate Control Groups: The study compares "Vaccinated individuals with chronic illness" to "Healthy vaccinated controls." This control group is insufficient. Crucially, there is no unvaccinated control group with similar chronic illnesses. Without this, it is impossible to determine if the identified immunological signatures are specific to post-vaccination illnesses, or are simply signatures associated with these illnesses in general, regardless of vaccination status. Furthermore, a "healthy unvaccinated" control group would be essential to assess baseline immunological differences related to vaccination itself.

Causality vs. Association: Fails to Establish Causality. The study is fundamentally correlational. It fails to provide any robust evidence for a causal link between vaccination and the reported chronic illnesses. The temporal association alone is insufficient to establish causality. The manuscript over-interprets correlational findings as suggestive of causal relationships, which is scientifically unsound. Confounding factors (e.g., pre-existing conditions, other exposures, reporting bias) are not adequately addressed or controlled for.<br /> Small Sample Size and Statistical Power: The sample sizes for the "chronic illness" groups are relatively small (e.g., n=15-20 per group in some analyses). Given the complexity of immunological data and the heterogeneity of "chronic illnesses," the study likely lacks sufficient statistical power to detect subtle but potentially meaningful differences, and is highly susceptible to false positives. The statistical analyses presented are descriptive and lack rigorous correction for multiple comparisons, further increasing the risk of spurious findings.

Over-Interpretation and Over-Generalization of Findings: The manuscript overstates the significance and generalizability of its findings. The conclusions are presented in a manner that suggests a stronger link between vaccination and chronic illnesses than is warranted by the limited and preliminary data. The discussion section speculates broadly on potential mechanisms without strong empirical support from the study itself. The manuscript risks contributing to vaccine hesitancy by presenting preliminary and correlational data as stronger evidence than it is.

Limited Validation and Reproducibility: The findings are not validated in an independent cohort. Without external validation, the reproducibility and generalizability of the identified immunological signatures are highly questionable. Given the small sample sizes and potential for batch effects in omics data, validation is absolutely essential.

Lack of Clinical Characterization and Phenotyping: Beyond broad symptom categories, the clinical characterization of the "chronic illness" groups is superficial. Detailed clinical phenotyping, including objective measures and validated diagnostic criteria, is lacking. This makes it difficult to interpret the clinical relevance of the identified immunological signatures. The manuscript does not adequately address the heterogeneity within these broad "chronic illness" categories.<br /> Potential for Reporting Bias and Selection Bias: Individuals who believe they have experienced adverse events post-vaccination may be more likely to participate in studies investigating vaccine-related illnesses. This introduces potential reporting bias and selection bias, which are not adequately addressed in the manuscript.

Ethical Considerations and Responsible Communication: Given the sensitive nature of the topic and the potential for misinterpretation, the manuscript needs to demonstrate a heightened awareness of ethical considerations and responsible scientific communication. The current manuscript, in its over-interpretation and lack of caveats, falls short in this regard. The potential for public misinterpretation and the fueling of vaccine hesitancy needs to be carefully considered and mitigated.

Recommendations for Substantial Revision (Minimum Requirements for Consideration in a Rigorous Journal)<br /> Develop and Implement Rigorous, Objective Case Definitions:

This is the most critical revision. The authors must develop and apply clear, objective, and validated case definitions for each "chronic illness" category. This requires moving beyond self-reported symptoms and broad diagnoses. Consult with relevant clinical specialists to establish specific inclusion and exclusion criteria based on objective clinical findings, validated diagnostic tools, and established disease classifications. Vague terms like "dysautonomia" and "POTS" are insufficient without precise operationalization.

Include Essential Control Groups: Absolutely essential revision. The study must include:<br /> Unvaccinated individuals with similar chronic illnesses: To determine if the immunological signatures are specific to post-vaccination illnesses or are general features of these conditions.

Healthy unvaccinated controls: To assess baseline immunological differences related to vaccination itself.<br /> Focus on Establishing Causality (or Acknowledge Purely Correlational Nature): The authors must either:<br /> Significantly temper their interpretations and conclusions to reflect the purely correlational nature of the study. Avoid any language that implies or suggests causality. Clearly state the limitations of correlational findings. <br /> OR<br /> Implement methodological approaches to strengthen causal inference. This would be extremely challenging in this observational study design, but might involve more sophisticated statistical methods to address confounding, mediation analysis, or focus on subgroups with stronger temporal associations. However, even with these, establishing robust causality will be very difficult.

Increase Sample Size and Statistical Power: Larger sample sizes are needed, particularly for the "chronic illness" groups, to improve statistical power and reduce the risk of false positives. This may require multi-center collaboration and a significant expansion of the study.

Rigorous Statistical Analysis and Correction for Multiple Comparisons: Implement more rigorous statistical analyses, including appropriate correction for multiple comparisons in omics data. Focus on robust statistical methods and validation of findings.

External Validation in Independent Cohort: Validation of the identified immunological signatures in an independent cohort is essential to assess reproducibility and generalizability. Without validation, the current findings are highly preliminary and should be interpreted with extreme caution.

Enhance Clinical Characterization and Phenotyping: Provide much more detailed and objective clinical characterization of the "chronic illness" groups. Include validated clinical assessments, objective measures, and explore heterogeneity within these groups.

Address Potential Biases: Acknowledge and discuss the potential for reporting bias and selection bias. Implement strategies to mitigate these biases where possible, or at least transparently discuss their potential influence on the findings.<br /> Responsible and Cautious Communication: The manuscript must be rewritten with a much more cautious and balanced tone. Avoid over-claiming, over-generalization, and any language that could be misconstrued as definitive proof of vaccine-induced chronic illnesses. Emphasize the preliminary and correlational nature of the findings, and the need for further rigorous research. Clearly discuss the limitations of the study and the potential for misinterpretation.

Recommendation for Publication<br /> In its current form, this manuscript is not suitable for publication in any rigorous peer-reviewed journal. It suffers from critical methodological flaws, significant limitations in its analysis and interpretation, and overreaches in its conclusions. Substantial revisions are absolutely necessary to address the major weaknesses outlined above.<br /> Without these extensive revisions, the manuscript risks contributing to misinformation and vaccine hesitancy without providing robust and reliable scientific evidence.<br /> If the authors are able to undertake the very significant revisions recommended, particularly regarding case definitions, control groups, causality, and validation, the manuscript might then be reconsidered for publication in a specialized immunology or clinical immunology journal, but even then, would require extremely careful and balanced framing of the findings. Publication in a very high-impact journal like Science or Nature remains highly unlikely given the fundamental limitations in the study design and the sensitive nature of the topic.

In conclusion, while the research question is important, the current manuscript requires a fundamental re-evaluation of its methodology, analysis, and interpretation to achieve scientific rigor and responsible communication of findings.

On 2025-02-21 05:12:28, user Evan Stanbury wrote:

Re "PVS participants also had lower anti-spike antibody titers, primarily due to fewer vaccine doses", ie the people with more vaccine doses had less PVS. This contradicts the hypothesis that vaccines cause Post-Vaccine Syndrome, since the dose-response relationship contradicts the hypothesis.

On 2025-02-21 05:14:49, user Evan Stanbury wrote:

A Machine-Learning (ML) model was able to distinguish fairly well between the "sick" and "not sick" cohort. But most ML models are incapable of explaining why a decision was made.<br /> In particular, ML cannot distinguish between the following two hypotheses: (A) "PVS is a Post-Vaccination Syndrome" (ie Iatrogenic) vs (B) "PVS is a Post-Viral Syndrome" (ie a symptom of disease). This is an important distinction since the the reported training data does not include patients with Long COVID.

On 2025-02-24 06:06:47, user Daniel Corcos wrote:

This work is a good start to appreciate the existence and organic nature of a post-vaccination syndrome. What worries me is the low number of control subjects. A much larger number of control subjects would allow adjustments to be made, particularly regarding the number of vaccine doses received.

On 2025-03-26 03:22:48, user Pavel Montes de Oca Balderas wrote:

It seems risky to claim so vehemently that vaccines saved lifes when the studies cited to backup this claim did not consider the syndrome described here that could also lead to death. <br /> Moreover, one of the references used here to demonstrate that vaccines saved 14 millions lifes through a math model has been called a "fiasco" because simple maths with world data records DO NOT fit their model. Also the authors of that paper have conflicts of interest as some are sponsored by the WHO and others.

Longdom Publishing SL<br /> https://www.longdom.org <br /> The discrepancy between the number of saved lives with COVID-19 vaccination and statistics of Our World Data.

On 2025-03-10 17:26:08, user Ruhollah Dorostkar wrote:

I am Dr. Ruhollah Darskar, a specialist in medical virology with 20 years of research experience in the field of virology and vaccines.<br /> The article... has been published without considering the facts of the covid-19 virus and the complications of this virus and the corona pandemic, and unfortunately without mentioning the round-the-clock efforts of Iranian scientists in the field of identifying and controlling the corona disease.<br /> For example, due to the lack of diagnostic kits in the world, at the beginning of the spread of the disease, more than 20 of my colleagues and I worked around the clock in the field of rapid virus detection and identification and the launch of the virus detection kit.

In order for our activities to be accurate and completely scientific, my colleagues and I have been away from home for a long time and were stationed at the hospital and many researchers and colleagues have also been infected with the corona disease.

Considering the publication of thousands of articles about the scientific activities of the Corona pandemic in Iran, it is unethical to create ambiguity about the scientific activities of Iranian researchers.

On 2025-03-14 14:22:15, user Amir wrote:

The paper’s statistical approach is troublingly opaque; without a detailed account of the one-way ANOVA assumptions or the Prophet model’s parameter choices, the reported findings on vaccine delays and excess mortality remain unconvincing and methodologically fragile.

While the manuscript highlights a correlation between delayed vaccination and excess deaths, it fails to adequately control for crucial confounding variables such as healthcare infrastructure, socioeconomic status, and demographic differences, making its causal claims appear overly simplistic.

Claiming that 50,000 excess deaths resulted directly from delayed vaccination campaigns is a bold assertion that lacks the necessary evidentiary support; the absence of comprehensive sensitivity analyses and diagnostic metrics renders this figure speculative at best.

On 2025-03-15 20:37:50, user Ali Molaverdi wrote:

The authors appear to conflate correlation with causation when attributing excess mortality to political populism, an interpretative leap that undermines the scientific validity of their conclusions and calls for a more cautious analysis.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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