On 2020-09-20 04:37:16, user nkeegel wrote:
I don't have any numbers, but my impression is that the season in Australia did not influence onset or severity.
On 2020-09-20 04:37:16, user nkeegel wrote:
I don't have any numbers, but my impression is that the season in Australia did not influence onset or severity.
On 2020-09-22 05:48:54, user Jack Zeller wrote:
No zinc? why? what was the outcome for those with covid? How does that compare with outcomes for age, sex, and risk matched.
On 2020-09-23 19:16:46, user Gaurav Pandey wrote:
This article has been published at https://www.thelancet.com/j....
On 2021-01-20 21:28:25, user Mirek wrote:
Slovak citizen here.
I quote "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived." – NOT TRUE. I've been to this testing and have given no written consent to be tested. They only wrote my name, address and phone number on a piece of paper.
"I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained." – NOT TRUE. If not tested, you couldn't go to work. Not even to take a walk outside, just go buy groceries, or go to the drug store and stuff like that.
On 2021-01-23 12:29:18, user Martin wrote:
And one more thing. No one in Slovakia knew that we are testing subjects. It was forced without any formal (or informal) consent of testing subjects.
On 2021-01-21 13:08:34, user John H Abeles wrote:
This analysis is largely of hospitalised patients with more severe Covid19– when viral replication has already peaked and patients are suffering largely from hyperinflammation/hyperimmune effects
Many studies of early, outpatient treatment ie within 5 days of onset of symptoms have shown benefit of combinations of hydroxychloroquine with zinc and either azithromycin or doxycycline
Likewise, most viral diseases respond to antiviral drugs only in early stages eg influenza to oseltamivir or herpes to valacyclovir
On 2021-01-23 20:40:07, user 1ProudPatriot wrote:
Thank you so much for engaging in this work. Desegregated data is so difficult to find in many sub-populations. It would be interesting (although there is likely limited or no funding resources for this) to see this data in a table alongside of other respiratory illnesses. My other wonder is in the evaluation of whether or not to have my daughter participate in the vaccine at this point. We have typically had her take the flu shot, but given the elevated adverse responses the vaccines have had when compared to the flu, it would be helpful to have a resource by which we can evaluate our decision. Thank you again. I just found out about his website and am grateful!
On 2021-01-28 18:24:01, user Joe wrote:
Looks to me that colchicine shows more benefit on men than on women. Probably this is because men are more likely to be smokers or have preconditions such as diabetes and hypertension. If excluding the data of the women patients, I assume the study would show a better stat significance among the male population with mild preconditions. This is worth further exploring. With this data, I may consider limited using colchicine only for those male Covid patients with mild preconditions. Overall, I think this study is constructive but a little bit disappointing, to be honest.
Good job, team, but you shouldn't have stopped at 75% recruitment in favour of quick results. A delayed but robust conclusion is much better than a hasty and uncertain one.
On 2021-01-29 21:08:41, user Jonas Ludvigsson wrote:
Very interesting study. We reported very similar data in late 2020 in Acta Paediatrica. https://onlinelibrary.wiley...
On 2021-01-30 11:55:17, user Doctor Avios wrote:
Why didn't you include a control group in your study? You have a database of 2.6 million members. You haven't "demonstrated an effectiveness of 51% of BNT162b2 vaccine against SARS-CoV-2 infection 13-24 days after immunization with the first dose." By only analysing data from vaccine recipients you have demonstrated that the relative risk of an RT-PCR positive case is 51% lower 13-24 days after the first dose compared to 1-12 days after the first dose. That is not the same as demonstrating effectiveness. If you want to demonstrate this you need to analyse the incidence of RT-PCR positive cases in the vaccinated group compared to an unvaccinated group.
On 2021-01-30 22:31:36, user Raghu SN wrote:
It is surprising that the effect of the difference in prevalence of the infection in the general population during the two periods being compared is not accounted for. For example, if the total cases per 100,000 is 40 in the first period and 80 in the second; if 4000 of the inoculated cohort were infected in the first period. It will be statistically expected that without the vaccine 8000 of the cohort would have been infected in the second period. And if actually only 2000 were infected, then the vaccine protected 6000 out of 8000 potential infections, that is 75% efficient. For these numbers, the methodology adopted in the study would calculate only 2000 out of 4000, that is 50%.<br /> Hope my drift is clear, though rustic.
On 2021-02-06 06:40:50, user David Epperly wrote:
Here's something that addresses Pfizer and Moderna and I agree that the 2nd dose is important. "While durability is improved with a 2 or more dose regimen, dose timing is subject to optimization."<br /> Evidence For COVID-19 Vaccine Deferred Dose 2 Boost Timing<br /> 1. Good efficacy of dose 1<br /> 2. Greater than 3 month durability of dose 1<br /> 3. Double vaccinated population<br /> 4. Dramatically reduce hospitalizations<br /> 5. Save ~ 90K US lives in 2021<br /> https://doi.org/10.2139/ssr...
On 2021-02-07 02:46:04, user Mark wrote:
When will updated results be posted?
On 2021-02-09 15:09:34, user Gemma Quinn wrote:
i would say Colchicine should be considered and to avoid steroids for chronic situations
On 2021-02-09 23:10:30, user Robert van Loo wrote:
Why do the authors talk about overdispersion as some infections seem to occur in clusters, and for me that would mean underdispersion.
On 2021-02-10 17:17:13, user bert jindal wrote:
could you provide me with more clarity on the parameters being measured to service the algorithm. As a clinician important diagnostic indicators include the history and presentation .does the system use patients symptoms age sex an ethnicity to derive its predictive value?
On 2021-02-10 18:47:50, user moshkreit wrote:
This study does not show anything until the authors release the details of the age distribution for the two groups. W/o that, UC groups could have 10 people above 75, mitigated by 10 younger people to keep the mean in check. Naturally, a group with people over 75 would have more subjects at risk at day 26 than a group where the oldest subject is only 71.
On 2021-02-13 09:00:43, user Guy André Pelouze wrote:
Hello,<br /> May we have any explanation and evidence for the choice of this strategy: "Success will be declared if there is a 90% probability that the intervention arm is better than usual care in<br /> reducing CRP. "? Is it based on preliminary data or on a choice of efficacy which is lower than usual in order to catch small effects?<br /> Thank you,<br /> Guy-André Pelouze MD MSc
On 2021-02-15 23:10:19, user Meredith Weiner wrote:
I beg you to change the bird Robin to a different bird. My daughter’s name is Robin as well as many other men and women. I appreciate the effort not to stigmatize people based on geography by naming variants after birds, but if the “Robin” variant takes off, you will be impacting my daughter and every other person named Robin.
On 2021-02-17 15:37:25, user Jules wrote:
Please review the pros and cons of using the names of birds (or any living animal) to differentiate between COVID variants. If a loved one dies from the bluebird variant, say, how might survivors feel when they see bluebirds? Might it not be a repetitive trigger for grief? And might not some people seek revenge on the birds? Furthermore, it is almostt inevitable that some will mistakenly think the birds carry or are responsible for COVID, putting robins and pelicans at risk the world over. And as Meredith rightly pointed out, it is damaging and most unfair to Robins everwhere.<br /> Why not use the names of colours? Or minerals? I am sure there are many alternatives that will serve the purpose.<br /> Having said all that, congratulations on your incredible work and contributions to public health. Thank you.
On 2021-02-19 20:02:18, user Miguel Blacutt wrote:
Note from authors: The title of this manuscript was previously, "I want to move my body - right now! The CRAVE Scale to measure state motivation for physical activity and sedentary behavior".
On 2021-02-22 02:12:28, user Sanjeev Mangrulkar wrote:
Was there a control group in this study where the neutralising antibodies developed after natural infection were tested for their efficacy against the newer mutants of the virus?
On 2021-02-25 14:32:20, user Haitham kussaibi wrote:
Dear valuable readers,
The current manuscript has been peer reviewed and published by<br /> the saudi medical journal<br /> DOI: <br /> https://doi.org/10.15537/sm...
Thank you for your interest.<br /> The Authors
On 2021-02-26 03:35:39, user Larisa Tereshchenko wrote:
Because this preprint was very large, we divided it and so we now have two separate (completely different) manuscripts published out of this preprint:<br /> (1) in European Heart Journal - Digital Health, ztab003, https://doi.org/10.1093/ehj... <br /> (2) in BMJ Open: BMJ Open. 2021 Jan 31;11(1):e042899. doi: 10.1136/bmjopen-2020-042899. PubMed PMID: 33518522
On 2021-03-02 18:20:29, user Martin Hepp wrote:
Ok, this is only a preprint. However, a wording like "provides a precise estimate of the true underlying SARS-CoV-2 transmission risk in schools and day-care centres." in the introduction sets all alarm bells of any scientist ringing. "precise" and "true" are bold words, rarely used in serious academic publications (where typically a prominent "threats to validity" section would highlight and discuss the limitations of the findings) - in particular, if the underlying method is relatively weak. Some limitations are discussed on pp.12 and 13, but in a rather superficial way.
Just a few major questions that challenge the overall contribution:
During the major part of the period of the analysis, the incidence was very low, in particular among young people. See https://corona-data.eu/medi... for a heatmap. Of the total duration of the study of ca. 17 weeks, only the last 5 - 6 weeks and thus less a mere 30 % had a significant incidence in the age-groups 0-4, 5-9, and 10-14, and it was lower than in the general population.
As children are less likely to be symptomatic and the testing regime has a strong bias towards symptomatic patients, it is a valid assumption that the share of undetected infections is higher among students and children than in the general population. As the authors' entire analysis and model for transmission is based on test-confirmed public health cases, the authors should have tested this hypothesis, e.g. by random PCR tests in areas and during periods with a sufficient community incidence. If you miss asymptomatic cases, you are not only invalidating your aggregate statistics, but of course also the entire graph of infections becomes incomplete and questionable.
On pp. 6 an 7, the authors cite the official definitions for cases and procedures; however, there is no information whether the theoretical guidelines for contact tracing, testing, non-pharmaceutical interventions like social distancing, masks, ventilation etc. were actually followed, and if the compliance remained stable over the course of the analysis and representative for the different groups. For instance, one could hypothesize that the effect of wearing mask in classrooms after November 20 is partially obscured by a reduction in ventilation due to cool weather and in general more time spent indoors. Taking the textbook definition of a characteristic of an observation and then assuming it to match the data is a significant threat to validity.
The same holds for the approach of instructing the DPHAs on how to use the questionnaire but not testing the quality of the results statistically or by cross-validation. How do you know that the DPHAs understood and applied your instructions properly? And even if they did, how do you know that the data they were using was correct? it is not a lot of effort to rule out or estimate the margin of error of a potential weakness.
The entire statistical analysis method is only a bit over half a page of largely spaced text (p. 8).
The claim that children are less likely to produce a sufficient viral load to infect others is highly disputed in the literature, see e.g. https://zoonosen.charite.de... these findings are not uniformly agreed (see e.g. https://www.sciencemediacen... "https://www.sciencemediacentre.org/expert-reaction-to-a-preprint-looking-at-the-amount-of-virus-from-those-with-covid-19-in-different-age-groups/)"), but it is not commonly accepted that children are unlikely to infect others. This challenges the assumption that asymptomatic individuals are unlikely to infect others even if they are themselves infected.
The authors state on p.12 that the rate of asymptomatic infections was relatively low with ca. 17%. Unfortunately, this population aggregate used by the authors obscures the influence of age on the likelihood of asymptomatic infections and hence on the number of undetected infections in school settings. A recent meta-study https://www.frontiersin.org... suggests that the rate is higher in children (p=0.5, CI 0.21 - 0.79) than in adults (p=0.3, CI 0.13 - 0.56). There is a lot of variance observed in the underlying studies, but the order of magnitude could explain a major share of the reported higher likelihood of infections originating from teachers than from students alone.
The focus on "hygiene practices" (p.13) as a recommendation conflicts with the widely accepted view that SARS-CoV-2 transmission is largely airborne and that sustained social contact in indoor environments is a high-risk setting, even with masks.
If the risk of students in school infecting teachers is so low, one should immediately stop the priority vaccination of teachers. I think the priority vaccination is justified.
For lay people: If children are less likely to show symptoms than adults, and testing and hence becoming an index case is more likely for symptomatic individuals, it will be no surprise that teachers, who are adults, are more often identified as index cases than children. If the data graph of humans interacting in the pandemic is incomplete, and there is a systematic bias that leads to more missing index patients being children, your findings can easily be a simple artifact resulting from the chosen approach.
Now, all science is tentative; we all know our papers could be improved, the evidence or data be more convincing, additional aspects be considered. The problem arises when this is combined with politics. The introduction (p. 5, 2nd paragraph) is heavily focussed on a positive view on re-opening school. The arguments raised are not wrong per se, but they are also not balanced - in a pandemic with a novel virus against which the majority of the human population seems to be immunologically naïve, other societal risks should be given the same space. If you motivate your research with the wish to reopen schools, readers have reason to assume that you are not neutral as to the outcome.
This is all common in the daily struggle of anybody in research and academia.
But when you combine such very preliminary work with substantial threats to validity with a bold claim in the intro and a conclusion in which you report with certainty that only 1 in 100 infected students will infect another person in school, knowing that there is a lot of heated debate in the society, then your "Ethical Statement" should be amended by "We knowingly accept that populist media like BILD, interest groups, and decision makers will use our fragile findings and our wording as solid evidence for a risk-prone opening strategy. Since we are so confident in our research, we take full responsibility for the societal consequences."
Doing preliminary research is unavoidable. Distributing it in a form that is the perfect bait for media and decision makers is unethical.
This
https://www.bild.de/ratgebe...
is the direct effect of your work.
More than ca. 3 million daily visitors on bild.de (likely largely from the German population) have seen their variant of your message.
On 2021-03-02 18:53:07, user Olivier Cazier wrote:
Contrary to MHS study of Pfizer in Israel , who took care of having vaccinated groups and unvaccinated groups with the same age, genre profile, comorbidities, etc, in this study, the two groups have very different profiles. They did a weighted correction, but give no details.<br /> As the results are quite different from MHS results, who gave a 57% efficiency for Pfizer first dose, one can be sceptical of the correftion method
On 2021-03-03 18:20:01, user LabMonkey wrote:
Eager to see the temporal distribution of some of these variants - any clue as to when they'll be visible in GISAID?
On 2021-03-04 14:34:51, user Paul McKeigue wrote:
Now published in BMC Medicine:
On 2021-03-10 11:48:29, user Erick wrote:
The percentage of participants who were female was Group 3 > Group 2 > Group 1, and women were shown to have more robust response than men to the infection and the vaccines. Based on this, what was the prior probability that the result obtained here would be due to the differing proportions of female/male in the three groups? Was the p-value adjusted for this or a test done to ascertain the Sex-effect?
What was the evidence presented to support conclusion (b) about the vaccine prioritization? Seems a lot of factors go into that decision than addressed here.
On 2021-03-10 13:52:58, user Jeffrey Brown wrote:
Seems as though an EHR system cannot answer the question posed no matter the inclusion\exclusion criteria. EHRs can only see care within their walls and we know that patients move across providers frequently even in short windows. This means that the look-back period for continuity of care is incomplete and introduces bias, that the look-back for prior conditions is also incomplete, and the outcome data are incompletely captured. Patients often moving across health systems in large cities (example in LA: https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052345/)"). It is critical to match data to the question, I don't think EHR data can answer the important question posed.
On 2021-03-10 15:41:21, user Theodore Petrou wrote:
Thank you very much for this study. I have a concern regarding your calendar adjusted calculation.
You report the overall IR of the unvaccinated LTCF to be 0.46. Looking at the VEca, I calculated the IRca for the unvaccinated to be 0.39, 0.23, 0.19, 0.05. Not one is above 0.46, the overall rate. How is this possible? Can you provide your code used to calculate VEca?
Also, I would have liked to see the all-cause death rate in unvaccinated vs vaccinated group.
Thank you,<br /> Ted Petrou
On 2021-03-11 05:04:28, user dick mazess wrote:
This is problematic as MRA using GWAS accounts for under 5% of the variance in calcifediol. It certainly does not account for UV exposure, dietary supplementation, sequestration of calcifediol in fat (which underlies the increased risk of COVID in the obese), factors affecting RAAS, seasonal variation, factors affecting FGF23 and 24-hydroxylase.
The authors state that the results do not apply to vitamin D deficiency yet 80% of hospitalizations are in the deficient. The selection of UK Biobank (401,460 of 443,734 cases) where the average calcifediol is 18ng/ml, well below the sufficiency level of 30ng/ml, may be problematic. Some other factor operative-Horizontal effect or collider bias.
The Castillo study (Andalucia) did not use a high dose of calcifediol but rather ????g266/week which is the equivalent of 34,000IU (ie 5000IU/day). The effectiveness of that dosing was confirmed by Nogues et al (Barcelona) in a much larger sample. The Murai study was a farce if only because bolus dosing induces FGF23 and 24-hydroxylation; also the followup was only 7 days.
The authors claim on line 417 that 10 MRA studies were of value but only #23 Trajanoska seems valid, #21 on D2 is wrong (see Dawson Hughes), as are #22 and #24) . MRA analyses of vitamin D have never been valid because of poor association with the phenotype. The authors should recognize this and note "GWAS, to date, have generally not focused on phenotypes that directly relate to the progression of disease and thus speak to disease treatment" Paternoster 2017 https://doi.org/10.1371/jou...
RB Mazess, Emeritus Professor
On 2021-03-14 00:26:00, user Nathan Weiss wrote:
Great analysis, two comments: The prevalence of suspected re-infections appears to be grouped closer to initial infections (days 100 to 149) rather than increasing over time as should be the case if deteriorating seroprevalence were the culprit. Also, while the cluster in cases in January is interesting, the authors suggest this is due to new strains while the number of suspected re-infections appears to increase from roughly 27 cases in December to 97 in January, matching the background increase in community new cases.
But I commend your work as one of the best accountings of the likelhood of secondary infections in a large (non-prison) population!
On 2021-03-14 07:19:10, user debernardis wrote:
Congratulations for this paper! I am excited that you could confirm the outcome of our observational study on disulfiram-treated patients in Northern Italy. Now waiting for the results of those two RCTs...
On 2021-03-17 11:01:56, user Olaf Storbeck wrote:
I hope this excellent paper receives the same attention like the rather poor work recently published ( https://www.medrxiv.org/con... ) which used a similar approach and failed to see the significant correlation between Vitamin D deficiency and Covid-19 due to severe errors in the data set and the methodology. <br /> However the message "Vitamin D is not correlated to Covid-19 outcome" was very fast amplified by the media worldwide as:<br /> - The Guardian (https://www.theguardian.com... )<br /> - The Business Insider (https://www.businessinsider... )<br /> - The Independent (https://www.independent.co.... )<br /> - Russia Today (https://www.rt.com/news/517... )<br /> - Politifact (https://www.politifact.com/... )<br /> - Hospital Health Care (https://hospitalhealthcare.... )<br /> - News Medical (https://www.news-medical.ne... )<br /> It is incomprehensible to me how this very obvious safe and efficient measure (sufficient Vitamin D supplementation for all) is neglected by nearly all authorities.<br /> I hope publications like this help to spread the meassage...
On 2021-03-19 23:07:51, user David Epperly wrote:
I am unable to access the supplementary data file. Please explain.
Also, I am unable to find the clinical definitions of moderate and mild from a symptom and test result perspectives. Please elucidate.
On 2021-03-20 15:21:28, user drmoienkhan wrote:
Published <br /> Khan MA, Menon P, Govender R, Samra A, Nauman J, Ostlundh L, Mustafa H, Allaham KK, Smith JEM, Al Kaabi JM. Systematic review of the effects of pandemic confinements on body weight and their determinants. Br J Nutr. 2021 Mar 12:1-74. doi: 10.1017/S0007114521000921. Epub ahead of print. PMID: 33706844.
On 2021-03-24 14:05:15, user Marvin K wrote:
I am a bit surprised that more CoV 2 RNA was found on the supply dampers downstream of the final filters. How do you explain this? Why would damper surfaces attract and hold virus elements with greater effectiveness than the final filters?
On 2021-03-25 12:37:30, user Bernhard Brodowicz wrote:
Protocol from one of the labs involved in vienna, the Vienna Covid-19 Detection Initiative (https://www.maxperutzlabs.a... "https://www.maxperutzlabs.ac.at/fileadmin/user_upload/VCDI/News/COVID19_Testing_VCDI_v1.1.pdf)") states that 'Ct values <40 are considered positive' (this is acc. to US CDC EUA protocol, when CDC-N1 and CDC-N2 target are used; for other targets a Ct reference was not reported). <br /> Sensitivity/specificity of the targets are described there as follows:<br /> CDC-N1: Very sensitive; SARS-CoV2-specific; low false-positive rate;<br /> IMP-ORF1b: SARS-CoV2-specific version of HKU-ORF1b-nsp14; very sensitive; low false-positive rate;<br /> CDC-N2: Very sensitive; SARS-CoV2-specific; false positives in presence of genomic DNA;<br /> E_Sarbeco: Sensitive; not SARS-CoV2 specific; reduced sensitivity in 384-well format;<br /> Especially as IMP-ORF1b and CDC-N2, which are described as 'very sensitive' but also false positives are mentioned, the interpretation of high Ct values > 40 as positives could raise questions when validation data (sensitivity, specificity, LOD) is not given and was not verified by individual labs (and different analytical setups) involved.
On 2021-03-29 23:21:53, user Javier wrote:
To estimate the age- and sex-adjusted <br /> proportions of cataract, diabetic retinopathy, glaucoma, and macular <br /> degeneration among the Arab American community, a notably understudied <br /> minority that is aggregated under whites.
On 2021-04-04 02:48:44, user SurgeonGate wrote:
Great Arab eye study! Arab Americans need more studies understanding their burden of disease compared to whites. Hopefully more soon!
On 2021-10-03 16:42:16, user Luke Yaldo wrote:
The peer reviewed, published version of this article can be found here: https://link.springer.com/a...
On 2021-03-30 15:12:27, user Derrick Lonsdale wrote:
When Japanese investigators found that Allithiamine was produced in garlic bulbs from thiamine by the action of an enzyme, they found that its biologic effect was better than that of the thiamine from which it was derived. Many different derivatives were synthesized and the one with the best biologic action was thiamine tetrahydrofurfuryl disulfide (TTFD).For example, pretreatment of mice with TTFD gave a significantly greater protection from cyanide poisoning than controls. It has little or no toxicity and should be used in a trial for Covid-19 patients.
On 2021-04-07 10:34:02, user Ariane Fillmer wrote:
The results you present appear to be really interesting. Thank you for sharing this. In order to allow the experienced reader to assess the data you show in a bit more detail, it would be great if you could add some more information on what you actually did: What scanner did you use (field strength does have a massive influence on the appearance of spectra)? What sequence did you use, and what methods did you use to calibrate for optimal data quality? How did you generate the basis sets that you used in LCModel? (Btw. in the data set of the COVID-A patient there is some signal contribution (at both echo times) that is clearly higher than noise but was not accounted for in your model, that might indeed be an interesting finding as well)
To help improve overall reporting standards in MRS and MRSI studies, a few colleagues of mine recently published a consensus paper on minimal reporting standards. This is also meant as a guide to help authors who are somewhat new to the field of MR spectroscopy, and help make the work better comparable to other studies and hence lead to overall improvement of impact of MRS papers: https://doi.org/10.1002/nbm...
On 2021-04-14 14:48:37, user David de Jong wrote:
The article has been published. <br /> Silveira, M., De Jong, D., Berretta, A. A., Galvão, E., Ribeiro, J. C., Cerqueira-Silva, T., Amorim, T. C., Conceição, L., Gomes, M., Teixeira, M. B., Souza, S., Santos, M., Martin, R., Silva, M., Lírio, M., Moreno, L., Sampaio, J., Mendonça, R., Ultchak, S. S., Amorim, F. S., … for the BeeCovid Team (2021). Efficacy of Brazilian Green Propolis (EPP-AF®) as an adjunct treatment for hospitalized COVID-19 patients: a randomized, controlled clinical trial. Biomedicine & Pharmacotherapy, 138:111526. https://doi.org/10.1016/j.b...
On 2021-04-15 10:01:38, user NA wrote:
What's going on with the publication status? It's been five months and we are in pandemic: why has not the review been completed more expeditiously? What journal was it submitted to?
On 2021-04-16 14:11:38, user Claudio Marabotti wrote:
I'd like to ask Authors why they did a retrospective study rather than a prospective one. The high number of cases in Italy in the so-called "second wave" would make easy to recruit two parallel matched groups, one "actively treated" and one serving as a control group, possibly in a couple of weeks. Moreover, even if some reason may explain the need of a restrospective analisys, I think that comparing patients in different epidemic phases seems to represent a source of bias. Actually, knowledge about the disease, and therefore clinical approach to it, was definitely different in the two phases.
On 2021-04-17 09:20:30, user Anna Kena wrote:
Politically biased?
It is surprising and appears bold to include a category "values" (Werte) in a study of drivers of the Corona-pandemic. And if so, to associate it with only two very restricted indicators: the election behaviour for just one political party (out of six) and the creed Catholic, neglecting the main other creeds Protestants, and Muslim.
You state:
"During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates."
The obvious question is, what was your motivation to select just one political party for your study?
There are these six parties in the German Parliarment (Bundestag), listed here with their results in the 2017 election: CDU/CSU (32.9%), SPD (20.5%), AfD (12.6%), FDP (10.7%), Linke (9.2%), Grüne (8.9%).
Hence the study seems politically biased which makes its scientific value questionable and spoiles your otherwise interesting work.
It is desirable that you mend this flaw during the peer-review process by considering now all parties and the relevant creeds. As a spin-off you might even explain the currently highest values of infection in Thuringia from the "values" of the gouverning party Die Linke.
On 2021-04-17 15:33:53, user Geng Wang wrote:
There are 11 cases (8+1+1+1) of B.1.351 in less than 800 samples, but the authors state "the B.1.351 strain was at an overall frequency of less than 1% in our sample". Did I miss something?
On 2021-04-26 04:10:49, user ????' ???? ??? wrote:
Tables S4-S7 show coefficients for both male and female. How come? One should be the reference.
On 2021-04-26 18:39:17, user William Alexander wrote:
Question: in Figure 4B, a vaccination rate of 5000 per day is purported to reduce daily deaths and total cumulative infections over rates of 8000 and 12000. Why is your model predicting this non-intuitive result?
On 2021-08-10 16:09:56, user Vilkus wrote:
On 2021-08-10 21:39:41, user Paul Gordon wrote:
Hi,
Thanks for posting. I am trying to reconcile the text and Figure 1, but am having trouble. The B.1 graphs appear to be identical to the B graphs, even though the stated fold-changes at the top of each NT graph are different between B and B.1. Secondly, the text highlights a very large changes in Kappa neutralization efficacy, but it is marked in the Figure 1a B.1 graph as not statistically significant. Could you please clarify?
Cheers,
Paul
On 2021-08-11 20:20:37, user amalio telenti wrote:
Please check the text: purifying selection = negative selection
On 2021-08-21 16:43:18, user Mark J Kropf wrote:
A good many issues are of question in regards to this work, after mulling it over a good time. Firstly, evolution is always going on. If one is defining mutations in the most general sense, no treatment alters that rate. However, if one means by mutation the generation of some particularly problematic change causing a variant, then perhaps the logic dealt with here is relevant. Evolution is not a process which can be terminated or quelled, though it may be channeled and controlled! Secondly, a period of about 5.5 months can give some possible resonance to the supposed finding, but the ability to alter progression needs to really have significant follow up. Is the process of some unfavorable change (i.e my latter use of 'mutation' above) really limited or is it only impeded and delayed? A true ability to confirm requires a longer period of analysis and the current argument conclusion may be somewhat presumptuous in its statement. Thirdly, I am concerned that the numbers may yet be a bit too small for the conclusion reached, though running a study with the proper enrolled numbers for such comparisons is probably too problematic to be practical.
I believe there is some evidence here, but perhaps not so complete as to be given the full impact that the conclusion provides. It is likely, but it is not confirmed to nearly the extent that I might desire for such a paper.
On 2021-08-12 01:05:28, user SkylarkV wrote:
CDC and FDA won't act on increasing calls for mRNA boosters for the J&J vaccinated unless the data support it, yet researchers appear to be simply ignoring J&J in their research, so those data can't be obtained. So much for for #HealthEquity!
On 2021-08-15 00:21:45, user Covid Hospitalist wrote:
This abstract of this pre-publication is highly irresponsible. There is no clear delineation between 'infection' and 'illness'. This is going to be taken out of context as 'vaccine failure' by multiple groups and news media sources. The drop in prevention of 'infection' ei detectable virus on PCR is important. AND without the data showing that it is still exceptionally effective at preventing hospitalization, is reckless. The authors need to fill in the rest of the blank... they quote the ability of the vaccine to decrease illness/hospitalization from the wild-type "wuhan" strain EUAs in the intro, but then completely leave it out of the results portion of the abstract??? How many antivaxxers/news media are actually scrolling down to table 7 to see that the rate of covid death for pfizer was 0/38,000(n rounded) and moderna 1/36000(n rounded). Seriously irresponsible headline grabbing abstract.
On 2021-08-20 12:18:37, user Jodi Schneider wrote:
Were there any differences in the underlying populations vaccinated with Moderna (mRNA-1273) and Pfizer/BioNTech (BNT162b2) in the Mayo Clinic Health System?
On 2021-08-13 16:42:49, user Dr. Jon wrote:
Isn't it pretty normal to assume those who have recovered from a disease are unlikely to get the same disease again?<br /> Why is this a controversy?
On 2021-10-17 22:54:41, user Rob Reck wrote:
If appears that there is no differentiation given to to the amount of time that passed since a subject contracted CoVid19. Waning immunity is an issue that has been studied. Certainly more study would be a good thing. But there is enough current data to know that it does happen. People who have had CoVid19 do get re-infected.
Given the existence of even a small number of reinfections, the claim that a person who previously was infected with CoVid19 need not be vaccinated is not supported by this study.
On 2021-08-13 17:27:45, user Chuck Crane wrote:
If you look at the questionnaire (the "supplementary materials" link) you find that the MD's and DVM's are "professional degree," and there is no "PhD" classification at all. It says "Doctorate," which includes Jill Biden's Ed.D. and so on. So the chart is deceptive.
D8 What is the highest degree or level of school you have completed?<br /> 1. Less than high school<br /> 2. High school graduate or equivalent (GED)<br /> 3. Some college<br /> 4. 2 year degree<br /> 5. 4 year degree<br /> 6. Master’s degree<br /> 7. Professional degree (e.g. MD, JD, DVM)<br /> 8. Doctorate
The paper is not in sync with the questionnaire, saying, e.g., "Those with professional degrees (e.g., JD, MBA) and PhDs were the only education groups without a decrease in hesitancy, and by May, those with PhDs had the highest hesitancy." I can't see how an MBA could look at the question and check "Professional degree" instead of "Master's Degree."
Think the paper needs a good proofreading.
Participation bias is a big issue. They asked a lot of people to participate, but only a small percentage did. The rather inane attempt to correct for this is to assume that if a particular class of respondents is under-represented, just assign responses from that class more weight, according to their proportion of the population ("post stratification adjustment").
On 2021-08-14 00:52:29, user Meredith Olson wrote:
Those with a doctorate who choose to spend time on facebook and are also willing to take the time to fill out the survey there are a particular subset of people with doctorates.
On 2021-08-15 02:02:46, user bcwbcwbcw wrote:
An online survey, where anyone can claim to have a PhD and no tests or controls for whether that's true? If you're anti-vax what better way to claim credibility than to lie and claim to have a PhD? In other past surveys , 6% of PhD's said they are Republican, yet the hesitancy results for PhD's are nearly the same as the strongest Trump supporters. (statistically possible but very unlikely.) (https://www.pewresearch.org... ) If I was a reviewer, I would ask see the breakdown of Trump support versus education level. If not consistent with other studies, the educational attainment data should be discounted.
I took this survey and it likely has some use as far as changes in totals over time but PhD's not really.
Let me give you a data point from a lab with about 1500 PhD's and tech staff. Everyone I've asked is vaccinated and I've asked everyone I'm in contact with.
On 2021-08-15 10:02:06, user Anna Z. wrote:
This paper is circulating among no-vax groups and used as a prof that educated people don't get the vaccine because they are not fooled by the government.<br /> How did you make sure that the survey was not circulated among no-wax groups that on purpose answered to obtain this result?
On 2021-10-05 10:08:23, user Samantha Hester wrote:
Members of the trans community are raising questions about your new exclusion criteria that eliminated people who self-identified as unicorns. Unicorns belong to the otherkin community and their responses could be in good faith.
Please review this post from a trans advocacy organization for more details:
On 2021-08-16 15:59:43, user A. Jamie Saris wrote:
There are some excellent comments below that I will not rehash, but I agree that this pre-print "as is" would not survive peer review without some serious revisions. Unfortunately, as this site is Open Source, this "study" is appearing in a lot of anti-vaxx rants on social media (it's been cited twice to me on Twitter so far today). It would be a great help if there were some printed caveats on sites like this (especially around topics where pseudoscience to outright quackery is rife) to dissuade people from taking VERY provisional results (from a flawed study with a modest number of participants) as "settled" science "proving the effectiveness" of Ivermectin.
On 2021-08-17 14:22:29, user Jon Hillman wrote:
'Taken together our results indicate that state implemented NPIs can lead to less COVID-19 cases and mortality.' How much less? CDC concluded it was no more than 1.8% reduction in cases: https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e3.htm Quantifying the reduction matters to anyone concerned with objectively measuring NPI cost vs outcomes.
On 2021-08-17 14:26:39, user Andrew Sefton wrote:
In the research, how were those previously infected by COVID-19 categorized? As unvaccinated? Excluded?
Specifically, I am interested in the viral loads of those previously infected by COVID-19 as it relates to:<br /> "Delta viral loads were similar for both groups for the first week of infection, but dropped quickly after day 7 in vaccinated people."
On 2021-10-05 22:04:39, user Brooke wrote:
It would be useful to know what sorts of samples were used for PCR testing — were they nasal swabs or saliva?
On 2021-08-18 18:37:59, user Sumit Rawat wrote:
On 2021-08-18 18:40:47, user Sumit Rawat wrote:
News article by ET health <br /> https://indianewsrepublic.c...
On 2021-08-20 23:58:21, user Chris Raberts wrote:
This model ignores the wave form observed repeatedly over the past year and a half. Covid infection is not a never-ending exponential function. Terrible.
On 2021-08-21 00:31:20, user Sonia Villapol wrote:
This article has been published in "Scientific Reports" on 09 August 2021, a Nature Publishing Group's open access research journal: <br /> https://www.nature.com/arti... <br /> Thanks! <br /> - Sonia Villapol
On 2021-08-21 19:03:01, user Jonathan C wrote:
Hello,
Thanks for an interesting analysis. CDC estimates a far higher infection rate (36.77/100k, <br /> https://www.cdc.gov/coronav... "https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/burden.html)"), <br /> at a similar rate for the 0-17 y group, although they do not seem to show data for the 12-17 y group).
Am I correct in interpreting your assumption that the infection rate for the <br /> investigated COVID-19-related period was at a far lower <10%? (and that 2.5% of all COVID-19 cases should represent males aged 12-17)
Or is there some information missing regarding your analysis?
On 2021-08-24 07:21:17, user Red wrote:
This paper is missing one very crucial piece of information: 6-month adverse event followup. Table S3 still reports only adverse event counts up to 1 month after the second dose, but nothing about longer followup periods. This is a violation of a commitment from the study's protocol where it was stated that 6-month safety data will be reported (section 9.5.1). And the only reason I can think of why such a data was not reported is because it suggests the treatment is not as safe as it is claimed.
On 2021-08-04 07:40:42, user Mike wrote:
I'm curious about the HIV infected patients. There were exactly 100 in both vaccine and placebo group. If you look at the co-morbidity tables, no other co-morbidity is balanced in that way. I suppose it's possible that this occurred by chance but it's a very small one if so. Also, why did they include HIV+ patients in the study at all, if they exclude them from all reporting of deaths and adverse events? The HIV+ can lead long lives these days, it's not quite clear to me why they are being treated separately here, especially as it should hopefully be clear if they died of AIDS.
On 2021-08-05 17:53:36, user pedro paulo castro wrote:
It doesn't seem right that a much lower number of subjects from the vaccinated group came down with COVID 19, but the same number died as in the placebo group, which seems to indicate therefore a higher proportion of deaths among those who contracted COVID 19 AND were vaccinated. There is a conspicuous lack of what would have been a very useful breakdown of the instances of death, in such a way that we could see, for both groups, what number of deaths was among those who had COVID or those who didn't have COVID. This prevents us from seeing whether a subject had COVID, but had his or her death reported as, say, cardiac arrest, for example, which might change the context a bit.
On 2021-10-03 02:28:34, user OBS wrote:
How come this preprint (and the very recent publication of this in NEJM) both say 15 deaths vaccine vs. 14 deaths placebo, but the FDA briefing document for the booster shot (which summarizes the safety of the primary 2-dose series, see page 7), says 21 deaths vaccine vs. 17 deaths placebo?
https://www.fda.gov/media/1...
21 vs. 17 doesn't seem to be an update of the 15 vs. 14 result, since the booster FDA briefing document specifies March 13, 2021 as the data cutoff date corresponding to the 21 vs. 17 result, and that is the exact same cutoff date mentioned in this preprint / NEJM article. So why the discrepancy- what is going on here?
On 2021-08-26 07:10:10, user William Brooks wrote:
To help readers clearly see the difference in infectiousness before, during, and after the various interventions (i.e., the states of emergency, school closures, and GoTo travel campaign),the authors should add the start and end points of the interventions in Figure 2.
On 2021-08-27 02:57:37, user Jason Eshleman wrote:
The author's model assumes that the generation time for the variants is the same. This seems to run counter to observations of a markedly shorter incubation period with delta. This analysis absolutely needs to be rerun without that assumption. Are we seeing greater transmission between generations or are we seeing a fitness advantage due to a shorter generation time?
On 2021-08-27 12:30:01, user Nikos Salingaros wrote:
Hello everyone. Alarming results indeed. Are there any data on the visual complexity of the indoor environment in which these babies were raised? Our group is trying to relate low intelligence to the lack of mathematical stimulation coming from visual patterns. This is especially relevant since exposure to natural complexity such as outdoor plants is severely limited during the lockdown. The preferred architectural style today is minimalist: very different from the visual complexity of past generations, and this factor might contribute. How do we get some data on this possibility?
On 2021-08-27 22:08:03, user evasmagacz wrote:
To look at the data from a different perspective:
In your first dataset:
Model 1: n = 16000 <br /> In patients who were previously infected: <br /> There were 5 symptomatic re-infections per 10000;<br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously vaccinated, <br /> There were 124 symptomatic re-infections per 10000;<br /> 5 hospitalisations per 10000 and no deaths.
In your second dataset:<br /> Model 2: n = 46000<br /> In patients who were previously infected: <br /> There were 15 symptomatic reinfections per 10000; <br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously vaccinated, <br /> There were 105 symptomatic reinfections per 10000 <br /> 5 hospitalisations per 10000 and no deaths.
In your third dataset:<br /> Model 3: 14000<br /> In patients who were previously infected: <br /> There were 16 symptomatic reinfections per 10000 <br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously infected and then vaccinated, <br /> There were 11 symptomatic reinfections per 10000 <br /> No hospitalisations per 10000 and no deaths.
On 2021-10-30 04:38:45, user Rn wrote:
The conclusions of this study stand in stark contrast to a report published today by the US CDC. https://www.cdc.gov/mmwr/vo...
Among COVID-19–like illness hospitalizations among adults aged >=18 years whose previous infection or vaccination occurred 90–179 days earlier, the adjusted odds of laboratory-confirmed COVID-19 among unvaccinated adults with previous SARS-CoV-2 infection were 5.49-fold higher than the odds among fully vaccinated recipients of an mRNA COVID-19 vaccine who had no previous documented infection (95% confidence interval = 2.75–10.99).
On 2021-08-29 20:54:09, user peter_wark wrote:
Thanks again Recovery trial.<br /> Participants admitted with COVID19; unable to maintain SpO2 <94% despite FiO2 0.4.<br /> Mean age 57yrs<br /> Primary outcome was intubation or mortality at d30.<br /> CPAP HR 0.72 (0.53-0.96) p=0.03<br /> HFO2 0.97 (0.73-1.23) p=0.85<br /> The number needed to treat for CPAP was 12 (95% CI, 7 to 105) and for HFNO was 151 (95% CI, number needed to treat 13 to number needed to harm 16).
On 2021-08-04 07:26:14, user oikoslibre wrote:
In the first chapter you talk about PCR.
I would like your opinion on the following document
https://www.fda.gov/media/1...
When I read this document , it becomes clear that this test is of no use at all
Positive results are indicative of active infection with SARS-CoV-2 but do not rule out bacterial infection or co-infection with other viruses. The agent detected may not be the definite cause of disease
In this document I also read: Since no quantified virus isolates of the 2019-nCoV were available for CDC use at the time the test was developed and this study conducted, assays designed for detection of the 2019-nCoV RNA were tested with characterized stocks of in vitro transcribed full length RNA.
Is it possible to write an article on this virus without the use of PCR data?
Do you have the isolated virus?
On 2021-08-05 18:41:36, user Ultrafiltered wrote:
With the probability of a PCR match of 1 with any sample comparison to a reference given 8 billion genotypes against strands of 30 to 50 mRNA, as DNA is expressed in any and all cells, the study only shows how many in the population are expressing a gene similar to a COVID phenotype, thus why the CDC has pulled its support of the PCR tests and going back to the process of isolation and identifying cells discovered through patient exam, similar to current Influenza like analoques. The basis of this paper goes to show that if you're sick with disease, you are sick with the disease and shed components, just like any other virus. The idea this effect is novel in this paper is superceeded by years of virology and research.
On 2021-09-16 07:33:29, user Chaos_14 wrote:
This study doesn't mention how many vaccinated vs unvaccinated people were tested.
"Notably, 68% of individuals infected despite vaccination tested positive with Ct <25, including at least 8 who were asymptomatic at the time of testing." (68% of what number?)
Since we know immune response, even with vaccines, decreases with age, it would be helpful to know the ages of the people in both the vaccinated and unvaccinated groups.
It would also be helpful to know the Ct in the samples of asymptomatic, <br /> unvaccinated people if there were any.
While it's beneficial to know that it's possible for infected vaccinated people to carry a viral load similar to infected unvaccinated people, this study left me with a lot of unanswered questions.
On 2021-08-06 23:22:56, user disqus_92pIDbtuHj wrote:
Hey, where's the full description of method and limitations? I get that this was published in medRxiv, a free distribution server for unpublished preprints that haven't been peer reviewed. It even states preprints "should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information".
This was a SMALL sample of 43 men... undergoing IVF and they served as their own self control. WHEN, was a sample after vaccination taken? WHEN was the baseline taken? HOW did they control for the effects of other variables... like the treatment recommendations these patients may have been following at the IVF clinic (especially since these were pulled Hospital IVF records)! They compared each man to his own baseline before and after vaccination, (14 men had male factor infertility, and 29 with normal spermogram results). Regardless all men were very likely receiving lifestyle, diet, or even medication recommendations! They also neglected to control season as a variable. Previous literature shows poorer sperm quality in Winter, and better quality in Spring. This design looked at two samples from each man somewhere between winter and spring. The same span of time for each man? No one knows!
On 2021-08-07 16:14:30, user Dmitry Pruss wrote:
Isn't it a time-of-testing confounding effect? In Israel, percent of positive tests increased from 0.1% in the beginning of the study period to 1.5% in its end, which would likely result in an artifactual increase of positive in those vaccinated (and tested) earlier...
On 2021-08-08 19:23:45, user Sam Wheeler wrote:
So against delta, 1 dose of The Moderna COVID-19 (mRNA-1273) vaccine seems much more efficient than 1 dose of Pfizer Biontech?<br /> And no data about how efficient is Moderna with 2 doses against delta?<br /> What do we know about Janssen = J&J? Janssen is very efficient if you take into account it is given as a single-dose, and one can boost it by taking a booster or primer with other covid vaccine.
On 2021-08-09 15:03:31, user Disha Agrawal wrote:
Figure 3b is surprising and difficult for me to understand. The Y-axis for all figure 3 results should be Geometric Mean of the ELISA tests, as per the text. Assuming that to be so, Figure 3b is Antibody to N protein, which should not be induced by Covishield. Yet most Covishield/Covishield samples seem positive, as shown, with no difference from Covaxin/Covaxin. A possibility I considered is that most people were already infected, but then the Covaxin/Covaxin group should have been strongly boosted. Clarification from authors or others who were able to figure it out is welcome.
On 2021-12-01 22:44:50, user Tom wrote:
The susceptibility of Chilrden was estimated by PCR-Testing alone and has a high variance in the 95-CI. I guess the numbers may be even lower.
On 2021-09-14 21:28:12, user Alberto wrote:
23 vaccinated individuals, samples collected 5.2 weeks (average) after the second dose of the vaccine. No information about age, health, etc... compared to 10 individuals infected one year prior to taking the blood samples and 7 infected less than 2 months prior to taking the blood samples. Again no information about age, health, etc...
Conclusion: "Hence, immune responses after vaccination are stronger compared to those<br /> after naturally occurring infection, pointing out the need of the vaccine to overcome the pandemic".
Isn't that conclusion going well over the possibilities of this study? When in real world studies with cohorts of > 25.000 individuals it has been proven that the immunity acquired from infection is vastly superior to that from vaccination, how should we take these results?
On 2021-12-15 06:52:55, user MD PhD wrote:
Although it's a small sample size still it would be worthwhile to know the antibody response to booster/third dose in 6 months vs 9 months group post-vaccination. Additionally whether these groups received first and second shots at 3-4 weeks or 7-8 weeks interval will offer pertinent information since this basic difference rendered more antibody response in the latter groups as per studies (the point being that boosters might turn out to an immediate requirement for the 3-4 weeks vaccination interval group while the 7-8 weeks interval group might potentially be able to put it off for a month or so in light of prior studies showing a robust antibody response with delayed vaccination)
On 2021-09-16 13:24:58, user Theo Sanderson wrote:
The apparent pattern of back mutations at position 142 is an experimental artefact due to errors in some Delta sequences. It emerges from the fact that Delta has SNPs in the primer binding site for ARTIC amplicon 72 (in a previous ARTIC scheme) which often result in the failure to amplify this amplicon, containing the G/D 142 locus, from Delta samples. Small amounts of contamination from other genotypes (e.g. B.1.1.7) that are amplified normally at this location can then lead to an amplicon here (typically with reduced depth). This results in a final sequence which appears to have a back-mutation at this position, and phylogenetic analyses can tend to group such samples together on trees.
T95I is in this same amplicon.
It is likely that the Ct correlations observed here reflect the fact that the correct G142D call is much more likely to be detected despite the low efficiency of amplification for samples with higher viral loads.
On 2021-09-16 13:35:24, user David Brown wrote:
There is evidence that abdominal obesity in both humans and chickens is determined by the fatty acid profile of the diet; specifically, the linoleic acid content. Read pages 7-9 of this 2019 Master's Thesis. https://trace.tennessee.edu...<br /> For further comment regarding linoleic acid intake and vulnerability to COVID-19 complications, read these articles:<br /> https://www.medpagetoday.co...<br /> https://www.science.org/doi...
On 2021-09-17 17:04:42, user kdrl nakle wrote:
This would all be OK if we could rely on COVID reporting but we cannot. For example a continent of 1 billion people, Africa, on Wednesday reported 12,000+ cases while we have seropositivity in Kenya of 50%! Meaning, their numbers as reported, are a joke. India that reported some 33 million cases had more likely some 900 million cases. And similar things are happening throughout Asia, Latin America, and Eastern Europe. In other words, your statistics are a joke.
On 2021-09-19 12:46:53, user daan joubert wrote:
I rhink you are referring to the article entitled "Africa Dailye deaths.100k etc" showing the difference between the high incidence in the upper and lower parts of the continent compared to the equatorial region where Ivermectin is used against tropical parasites and there are few deaths. It seems to have been removed for some guessable reason.
On 2021-09-22 01:46:06, user jhick059 wrote:
Dear authors,
I believe your denominators (15,997 Moderna doses and 16,382 Pfizer doses) are off by more than a factor of 10.
Ottawa Public Health has 342,656 doses of Moderna and 485,178 doses of Pfizer between 2021-06-01 and 2021-07-31. Link: https://open.ottawa.ca/data...
You also state (pg. 6/20) that your data suggest a tenfold higher incidence than other papers estimating an incidence of 1/100,000. A tenfold higher incidence than 1/100,000 is 1/10,000, which is closer to the value you would obtain with the adjusted denominator.
Sincerely,<br /> Joseph Hickey
On 2021-09-22 03:14:20, user Norsksoul wrote:
It is a preprint article but they basically identified all vaccine recipients in Ottawa during the June 1 through July 31 study period. <br /> This was the denominator of the study group. <br /> Anyone from this study group that was admitted with Acute Myocarditis or Pericarditis within 1 month of a Moderna or Pfizer vaccine became the numerator. <br /> So 32 cases occurred in 32,379 vaccine recipients which comes out to a 1/1000 incidence. This study should be done in the 12-18 year old age range and the incidence would likely be even worse.<br /> But wait,....it gets even worse. <br /> That 1/1000 incidence is in a group of 32,000 men AND women. <br /> But out of 32 cases of myocarditis, 29 occurred in men. <br /> That’s 90%! <br /> They unfortunately don’t give the data on male/ female percentages in the study group denominator but if we assume a 50/50 split, then the male incidence is actually 29/16,189 or 1 in 558 males vaccinated. <br /> 1/558<br /> 1/558<br /> 1/558<br /> Let that sink in for a minute. <br /> This is reckless medical malpractice at its worst.
On 2021-09-23 06:52:46, user White Rabbit wrote:
There are several issues about the meta-analysis by Martinoli et al. for example they wrote they did a meta-regression in order to explain the the huge between-study heterogeneity affecting the results, but no meta-regression results appears anywhere. They observed a statistically significant publicaton bias ("We found an indication for publication bias (P=0.03)" ,page 10) a serious but unaddressed issue. Ther are also inconsistencies between the results and the conclusions, e.g. though they found that "Children and adults showed comparable SARS-CoV-2 positivity <br /> rates in most studies" (page 9)" the abstract reads "children are 43% less susceptible than adults".Furthermore in some tables and forest plots, they used as denominator the total of students and staff altogether instead of students only, to estimate the students incidence.
On 2021-09-23 15:49:33, user kdrl nakle wrote:
What is needed more is the distance between the shot and data collection. We need longer duration period for VE evaluation. Your time period is too short.
On 2021-09-23 18:14:58, user kdrl nakle wrote:
n-28, n=29, n=106 and no significant difference between 2.4x10^5 and 3x10^4? That is because your samples are small. I think that 8 fold increase would be significant if you got bigger samples.
On 2021-09-24 06:20:12, user Ella Lively wrote:
Where can I get the questionnaire for thus study please?
On 2021-09-28 15:09:49, user Tomas Maximus wrote:
Looks like the proportion of breakthroughs climbed dramatically as time went on, with breakthrough accounting for 17% of total new cases in July. Wonder what the August and September numbers showed.
On 2021-09-29 04:15:27, user Nikki wrote:
I work as an account Escalation Specialist/call center supervisor who takes over Escalated calls. I've never had issues with missing small details which are required to do my job. I caught covid in mid July, had a horrible experience with two weeks worth of severe vertigo, nausea, fever spikes, tons of phlegm, panic attacks. <br /> One month and a half after recovery, I've had 4 major fails which may ultimately end up costing me my job. <br /> My pcp, therapist, and boss appear to disregard this when I try to explain to them about the fogginess. <br /> As a very detail oriented person, I just don't miss those things.... never in my 15 years in callcenter experience.
On 2021-09-29 11:30:07, user kdrl nakle wrote:
Good example of making a paper focused on useless graphics instead on self-explanatory data.
On 2021-10-02 14:59:26, user Alberto wrote:
Thanks for the detailed report. I'd only like to ask about the last sentence included in the abstract: "The beneficial and protective effects of the COVID-19 vaccines far <br /> outweigh the low potential risk of neurologic and psychiatric reactions. Going through the paper I haven't seen anything that attempts to estimate these rinks vs. benefits in any way (let alone a systematic way, by age, risk of severe disease in case of COVID-19, etc...). It seems like a statement that's been added there arbitrarily and does not belong to a scientific paper that not actually evaluating any risks associated with the disease itself or the vaccine efficacy to prevent them.
On 2021-10-03 07:19:18, user Ruth Berger wrote:
That age and male sex are major risk factors is well known; mortality associations with pandemic wave should not be reported without factoring in varying levels of underdiagnosis (to my knowledge, it was larger in the first wave than the second) and age-specific vaccination rates.
On 2021-10-04 06:54:30, user kdrl nakle wrote:
Simple yet important result, meaning we should definitely know Cp (Ct) value after getting tested. The next thing would be to investigate transmissibility but that is obviously much harder research.
On 2021-10-04 12:12:28, user LG27 wrote:
Why was there no stratification by prior infection? It's not even mentioned in the limitations.
On 2021-10-05 22:59:35, user MrMemeinator wrote:
So you're seeing the same thing we all saw in the UK and Israel months ago. Color me surprised...
On 2021-10-16 12:57:40, user gerryz wrote:
Can anyone tell me if there is evidence infection symptoms are milder in vaccinated? Articles?
On 2021-10-06 16:56:58, user zega wrote:
Baseline is 1/100000/365days https://www.myocarditisfoun... vast difference, they an be off by 8000times and still below...
On 2021-10-07 22:22:53, user Robyn Schofield wrote:
"As the devices do not meet medical device electrical safety standards (EN60601) they were operated at a distance of >=1.5metres from any patient." Can the authors please clarify - what wavelength the UV was operating at, and whether this device has been tested for ozone production / loss rates. I assume that the EN60601 requires ozone production to be tested for? If ozone is being produced (or destroyed to odd oxygen) that this would need testing before deployment in a medical setting. Ozone, a respiratory irritant gas, will easily travel more than 1.5m (so distance should not be seen as useful in setting safety protocols for electronic air cleaning devices in a medical setting).
Are hospital rooms with no ventilation in line with current infection prevention and control or hospital design / operational guidelines in the UK? In Australia this would be in breach of both our hospital design and operating guidelines which require a minimum of 6 ACH for all hospitals.
The effectiveness of UV with air high flow rates has to be questioned (because the exposure time for bio-aerosols is short) - are the authors able to separate the effectiveness of the filtration over the UV features? Most literature on this point shows that in real-world operation the HEPA provides 99.97% of the removal of bio-aerosols from air and the advantage of UV is untested / unproven (this is particularly true at 1000m3/h flow rates this device is operating at). I assume this device will be noisy >65dB - can this please be specified.
On 2021-10-11 18:40:32, user Andrew T Levin wrote:
Diamond Princess Cruise Ship. The manuscript makes no reference to any epidemiological analysis of this episode, which informed seminal assessments of the age-specific infection fatality rate (IFR) of COVID-19.[1-4] Nonetheless, that evidence is particularly relevant, because the cruise ship’s passengers included 1231 individuals ages 70+ who were not merely “community-dwelling” but healthy enough to embark on a multi-week grand tour of southeast Asia. Following extensive RT-PCR testing, 335 passengers ages 70+ were confirmed to have been infected with SARS-Cov-2, and 13 of those passengers died from COVID-19 – an IFR of about 4%. Moreover, the strong link to age is underscored by the even higher IFR of 8% for passengers ages 80+. Given the size of that sample (which meets the 1000+ threshold used here), this evidence should certainly be incorporated into this meta-analysis.
Comprehensive Tracing Programs. The manuscript makes no reference to countries that succeeded in containing the first wave of the pandemic in spring 2020 through systematic tracing and testing of all contacts of infected individuals.[5] Such evidence is particularly relevant here, because the virus was contained within the “community-dwelling” populations of those locations and never spread to any elderly care facilities. For example, in the case of New Zealand, there were 256 infections and 19 deaths among adults ages 70+ -- an IFR of about 7%.
Hospitalized Patients. The manuscript cites a single study (published in July 2020) that examined the association between comorbidities and mortality risk of COVID-19.[6] However, that study was not able to distinguish whether comorbidities were linked to greater prevalence (the probability of getting infected) or to a higher IFR (the risk of mortality conditional on infection). Unfortunately, the manuscript makes no reference to any subsequent studies on this issue. In particular, a large-scale study of U.K. BioBank participants found that measures of frailty were indeed associated with higher mortality rates in the overall panel but not linked to mortality within the subset of hospitalized COVID-19 patients.[7] In effect, the prevalence of COVID-19 was markedly higher among residents of U.K. nursing homes compared to individuals of similar age living in the community, but the IFR was not significantly different. Those findings directly contradict a key assertion made at the start of this manuscript.
Prior Meta-Analysis of Community-Dwelling Populations. The introduction of this manuscript neglects to mention that an existing meta-analysis study (published in Nature in November 2020) was specifically focused on assessing IFRs excluding deaths in nursing homes.[8] That study estimated the link between age and IFR using seroprevalence and fatality data for adults less than 65 years old, and then showed that the model predictiions were consistent with data on fatalities among community-dwelling adults ages 65+. Moreover, that study used seroprevalence data adjusted for assay characteristics, and the results were obtained using a rigorous Bayesian statistical model that incorporated random variations in the time lags between infection, seropositivity, and fatal outcomes – a striking contrast to this manuscript, which uses rudimentary assumptions to address those issues.
Other Meta-Analyses. The introduction of this manuscript briefly refers to two other meta-analysis studies of the link between age and IFR.[5, 9] However, the manuscript then asserts: “Importantly, the vast majority of seroprevalence studies include very few elderly people.” (p.5) That assertion is supported by a single citation to the SeroTracker database, which provides comprehensive coverage of all existing national, regional, and local seroprevalence studies across the globe.[10] However, this assertion is completely incorrect as a characterization of the preceding meta-analysis of age-specific IFRs. As indicated in Levin et al. (2020, figure 5), that meta-analysis study included seroprevalence data on older adults (including narrow brackets for ages 60-69, 65-74, 70-79, and 75-84 as well as open-ended brackets for ages 60+, 65+, 70+, 80+, and 85+) from nine national studies (Belgium, France, Hungary, Italy, Netherlands, Portugal, Spain, Sweden, and the U.K.) and eight regional locations (Ontario, Canada; Geneva, Switzerland; Connecticut, Indiana, Louisiana, Miami, Missouri, and San Francisco, USA).[5]
On 2021-10-14 17:12:39, user lbaustin wrote:
Has this been submitted to any of the MEDLINE indexed journals who publish on this topic?
On 2021-10-22 17:10:09, user Jeremy M Bartels wrote:
Can you clarify if Ct values decrease, does viral load go up or down?
On 2021-10-31 02:14:19, user Peter Jaji wrote:
So this shows vaccinated people spread the virus as much if not more then unvaccinated?<br /> Please enlighten
On 2021-12-01 22:19:03, user Kevin J. Black, M.D. wrote:
You'll want to cite and discuss this article:<br /> Snowden JS, Craufurd D, Griffiths HL, Neary D. Awareness of involuntary movements in Huntington disease. Archives of Neurology. 1998;55(6):801-805.
On 2021-05-27 10:15:21, user Qian Dong wrote:
Code is now available through: https://github.com/s0810110...
On 2020-12-03 21:43:53, user kdrl nakle wrote:
It could also be that association is purely coincidental. Meaning people that die more often are older people and they are also more likely to be vitamin D deficient. So you really have nothing here.
On 2020-04-05 22:12:33, user Soarintothesky wrote:
What delay was used for the time adjustment. A 10 day delay for cases>deaths in the Aneirin Bevan University Health Board in Gwent in South Wales shows a 22% CFR.
On 2020-06-04 00:48:58, user James Van Zandt wrote:
Vitamin C is a common supplement. I suggest you track whether patients had taken vitamin C (and how much) before or in the early stages of their illness. If it is helpful, then we would like to know when it is most helpful.
On 2020-04-10 00:26:48, user Brothers in arm wrote:
Curious to know why the BCG vaccination last only about 20 years. I have had mine as an infant, without any further boosters. Still my skin tuberculin test remains reactive after almost 50 years. The reaction subsides before follow up check on day 3. This is read as negative for active TB. I assume the slight reaction as due to having had BCG, and I still have immunity. People who never had it do not get any reaction or erythema. Maybe any immunization can confer some cross immunity?
On 2020-04-02 07:42:02, user japhetk wrote:
I don't know why, but medrxiv keeps deleting my warning comments.
So, I write brief comments again.
This study doesn't control important variables as kept suggested in comments and probably the findings are due to spurious correlations.
The one of uncontrolled important variable is "when the infection spread in the country". This study should have used the measure like "number of deaths or patients 10 days after 100th patients were detected in the country". Other analyses are doing that.<br /> The second uncontrolled important variable is "how long the country advanced BCG measure". UK, for example, advanced the BCG measure for more than 50 years. So, majority of nations are experienced with BCG.<br /> The third uncontrolled important variable is GDP. You can see the most of nations without BCG is Western rich countries which can do more tests, which are popular from tourists.<br /> I did analyses controlling these variables, and all the correlations between the length of BCG measure with coronavirus data (how fast the 100th patients were detected in the country, number of patients or deaths ten days after the 100th patients were detected in the country) are all insignificant. They did not even show the statistical tendencies.
Many people have wrong ideas how effective BCG is though this preprint. Somebody has to give warnings. Please do not delete this warning.
On 2020-06-30 08:50:43, user Simon Liebing wrote:
I have 2 questions to the study:<br /> What explanation have the authors that only 2 of 5 indicators are positive?<br /> Why the virus vanishes after March 2019 again?
On 2020-06-30 11:18:06, user Kevin McKernan wrote:
Interesting work. Great to see the qPCR replicated at another lab and spike in controls.<br /> It would be very helpful to sequence the Amplicons to see if any variation exists that can augment the phylogenetkcs of the disease.
On 2020-06-30 14:50:46, user Munir Hazbun wrote:
On 2020-06-30 21:40:24, user David Sbabo wrote:
"Anonymized raw data were extracted from the abovementioned databases on<br /> July 2, 2020."
July 2?
On 2020-07-01 22:29:58, user John wrote:
Loneliness is prevalent in COVID-19 crisis. Patients with Coronavirus are more lonely during the pandemic. Interesting findings for health psychology, psychological impact, public health, epidemiology and psychiatry.
On 2021-12-31 13:45:45, user Allan Lorentzen wrote:
What does negative effectiveness mean?
On 2020-07-07 14:29:11, user Anika Knuppel wrote:
This article has been accepted for publication in the International Journal <br /> of Epidemiology, published by Oxford University Press.
On 2020-07-07 18:11:59, user Tajemniczy don Pedro wrote:
Makes sense. https://pubmed.ncbi.nlm.nih...
On 2021-01-26 03:42:24, user Terran Melconian wrote:
Thanks for sharing this very interesting article.
On page 6, for the definition of the x and z transforms, they are both given as sin(2*pi*t/tau). One of them is presumably meant to be a cosine, right?
On 2020-06-21 11:55:23, user Dirk Monsieur wrote:
Rough estimate: 1% infected at 12th of March; chances that 84 random people are not infected: 0,99^84 = 43%<br /> I'm not a statistical expert, but I think a power analysis would be good.
On 2020-07-13 22:41:50, user Jim Coote wrote:
I share the concerns expressed in the previous 2 comments. Surely the decrease in antibody levels would be entirely expected after the primary response. The acid test would surely be whether there was a good secondary response to any Covid19 based antigen. Any analysis of that should examine the cell based response as well as the humoral.
Considering the concerns likely to be raised by their findings, I think it is a serious omission not to compare the data to antibody levels typically seen following primary responses to infections on which we have solid information on long term immunity, (both weak and strong). However this would be a completely academic consideration provided a good secondary response to Covid 19 antigen / virus was seen.
On 2020-04-17 15:25:24, user Dr. James R. Baker wrote:
Interesting approach and pretty convincing, but it does not take into account the number of asymptomatic infections associated with COVID. That is really substantial; some estimates of 30-50 percent. That would then double your number, wouldn't it?
On 2019-11-12 00:51:39, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 10, 2019
Monday, November 11, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,287, of which 3,169 confirmed and 118 probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> 411 suspected cases under investigation;<br /> No new cases confirmed;<br /> No new deaths of confirmed cases have been recorded;<br /> 3 people healed from the CTE in North Kivu in Mabalako;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;
NEWS
Awareness and vaccination day for Beni mototaxi drivers with the support of Unicef S / Coordination MVE Beni, Wednesday 06-11 - 2019 HIVUM room
• There were many, about three hundred, the drivers of Mototaxi Beni invited to a day of awareness and vaccination against Ebola Virus Disease this Wednesday, November 06, 2019 in the HIVUM room.
• This day is welcome for the city of Beni during this period of EVD epidemic which, unfortunately, displays a lethality of 86.3% among motorcyclists, as pointed out by Dr. Pierre ADIKEY, Coordinator of the response of Sub Coordination of Beni.
• Thus, in his presentation, he focused his message on the risk of transmission of EVD among motorotaxi drivers and the conduct to be held in the exercise of their craft to protect themselves and the community.
• He asked bikers more often to respect the measures of prevention, namely: washing hands regularly, stopping at checkpoints, not being bribed to divert checkpoints, not carrying suspicious parcels and reporting and / or direct any suspicions of illness to colleagues or the community.
• In order to circumscribe the day, Dr. P. ADIKEY traced the path of the last Motard who died of EVD before his death confirmed at the CTE. To close his presentation, he made a reminder of the various events that prevented the teams of the response from working: among other things the days of the dead city, the fire of the vehicles of the riposte, the destruction of the structures of the care, the cases of resistance and others whose bikers were part of it.
• Dr. Bibiche MATADY, as Epidemiologist and Chair of the Monitoring Commission, introduced to the participants the importance of accepting to be listened to if you are in contact with a case, to let yourself be followed for the entire period indicated and to orient in a management structure as soon as the first sign appears. She also emphasized the collaboration between the bikers and the teams of the response.
• To justify this day again, one of the 3 Hikers shared his testimony and urged his colleagues to collaborate and follow the recommendations of the response teams starting with vaccination.
• Vaccination is one of the preventive measures against EVD, said Dr Adonis TERANYA, the Chair of the Immunization Subcommission. In his presentation, he explained the evolution of the vaccination protocol, the current targets, the side effects and the action to take in the event of an adverse event. Before calling for the voluntary vaccination of participants, he spoke about vaccines currently used in the DRC.
• In his words, the President of Bikers reiterated to the Coordinator the commitment of his organization and all its members to support the interventions of the response, while affirming its availability to any solicitation for the fight against the disease to Ebola virus in the city of Beni and its surroundings.
• The day ended with the vaccination of 100 Bikers and some of their dependents.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-15 16:53:09, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT NOVEMBER 13, 2019
Thursday, November 14, 2019
• Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 527 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;
NEWS
Ebola Virus Disease Response Co-ordination Announces Three Road Traffic Accident in Bunia, Ituri
• The overall coordination of the response to the Ebola Virus Disease epidemic in North, South Kivu and Ituri was informed on Thursday 13 November 2019 of the tragic traffic accident between two motorcycles, one of which carried three agents of the riposte;<br /> • These three officers, who work for the Epidemiological Surveillance Commission at the Point of Entry and Control, were returning from Bunia to Mambasa, where they are respectively delivering;<br /> • This accident occurred around Marabo in Bunia on the evening of Wednesday 13 November 2019;<br /> • The balance sheet reports an officer who died at the scene and two others who were seriously injured, including one in a coma. The two wounded were taken to the Nyakunde Reference General Hospital in Ituri for appropriate care;<br /> • The overall coordination of the response sends its deepest condolences to the grieving family and expresses all its compassion and solidarity to the injured officers, while wishing them a quick recovery.
Effective start of Johnson & Johnson vaccination in two Goma health areas
• Ebola vaccination with the Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday, November 14, 2019 in two Karisimbi health areas in Goma City , North Kivu Province;<br /> • The Epidemic Response Coordinator for Ebola Virus Disease in North, South Kivu and Ituri. For this purpose, Prof. Steve Ahuka Mundeke visited the vaccination sites to inquire about the evolution of activities in the field. He was satisfied with the work of the teams;<br /> • He took the opportunity to invite the population of the targeted areas to be vaccinated in order to protect themselves from the resurgence of the Ebola virus;<br /> • Several people were present in Majengo and Kahembe health areas to get vaccinated. The first person to be vaccinated is a Kahembe community leader who has been protected against the Ebola virus today and also in case of a possible new Ebola outbreak. This community leader has appealed to all residents of his community and sites targeted to come take this second vaccine. "This is an opportunity not to be missed, because it is said that prevention is better than cure, " he said;<br /> • The logistics of this vaccination are provided by the international non-governmental organization Médecins Sans Frontières of France (MSF / France).<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, this second vaccine, called Ad26.ZEBOV / MVA-BN -Filo , is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine complements the first, the rVSV-ZEBOV, the vaccine used until then in this epidemic. Manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018, it was recently approved.
Closing of the training workshop for media professionals in Beni on the role and responsibility of journalists during public health crises
• The Deputy Mayor of the city of Beni, Muhindo Bakwanamaha Modeste, closed this Thursday, November 14, 2019 in Beni in the province of North Kivu the training of media professionals on the role and responsibility during public health crises;<br /> • The coordinator of the Beni Ebola Ebola response sub-coordination, Dr. Pierre Adikey, on behalf of the Coordinator-General of the Response, Prof. Steve Ahuka, wished to see these kinds of trainings be organized, not only in other sub-Coordination of the response, but also throughout the Democratic Republic of the Congo so that journalists from all over the country are ready to face any possible epidemic crisis;<br /> • This training, he said, is part of the zero-case Ebola strategy and strengthening the health system of tomorrow;<br /> • The focal point of Beni's journalists, Moustapha MULONDA, reaffirmed the commitment of journalists to combat Ebola Virus Disease through various programs and publications disseminated and published by their respective media thanks to the new tools acquired during this period. training;<br /> • This training was organized by the Ministry of Health in collaboration with the World Health Organization and benefited from the facilitation of the overall coordination of the response, UNICEF, CDC Africa and MSF.
VACCINATION
• Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 251,637 people have been vaccinated;
• Vaccination with the second Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday November 14, 2019 in Goma. This vaccine was approved on 22 October 2019 by the decisions of the Ethics Committee of the School of Public Health of the University of Kinshasa and 23 October 2019 of the National Ethics Committee;
• Until then, only one vaccine was used in this outbreak. This is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee in its decision of 20 May 2018 and which has recently been approved.
MONITORING AT ENTRY POINTS
• A 27-year-old woman from Butembo for Goma, an escaped suspect from Makasi Hospital in Butembo, North Kivu, was intercepted at the Kanyabayonga checkpoint in Kayna. When she was intercepted, she experienced signs such as fever at 38.4 ° C, severe asthenia, abdominal pain and vaginal bleeding. It was sent to the KAYNA Transit Center.
• Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 116,622,388 ;
• To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-30 17:00:40, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 27, 2019
Thursday, November 28, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,309, of which 3,191 are confirmed and 118 are probable. In total, there were 2,201 deaths (2,083 confirmed and 118 probable) and 1077 people healed.<br /> • 443 suspected cases under investigation;<br /> • 5 new confirmed cases, including:<br /> o 4 in Ituri in Mandima;<br /> o 1 in North Kivu in Mabalako;<br /> • 2 new deaths of confirmed cases, including:<br /> o 2 new community deaths in Ituri in Mandima;<br /> o No deaths among confirmed cases in CTEs;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Three members of the Ebola Virus Epidemic response killed during an attack in Biakato, Ituri
• Following the attack on the sub-coordination of the Biakato response in Ituri on the night of Wednesday 27th to Thursday 28 November 2019, three members of the Ebola response teams in this sector lost their lives ;<br /> • It is a provider and a driver of the vaccination committee and another driver;<br /> • In addition to these three deaths, there are 7 wounded and 6 others with psychological disorders and extensive material damage.<br /> • A good number of these teams from Biakato were evacuated in three waves to Goma. As soon as they arrived, they were greeted by a coordination team led by Prof. Steve Ahuka, general coordinator, who also visited the wounded before going to inquire about the security conditions and accommodation of evacuees. He did not fail to comfort them.
VACCINATION
• The vaccination commission is in mourning. A service provider and a driver of his team were killed on the night of Wednesday 27 November 2019 following attacks at the Biakato base in Ituri;<br /> • 2nd day without vaccination activity with the 2nd J & J vaccine following the disorders initiated by young people related to the security situation in Beni;<br /> • 724 people were vaccinated, until Tuesday, November 26, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,373 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.
MONITORING AT ENTRY POINTS
• Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 121,159,810 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2021-10-10 03:44:51, user kdrl nakle wrote:
The results on T-cells is quite murky here, without much explanation. You really need a bigger sample to be able to see this better, your sample of 46 is too small.
On 2020-01-25 23:16:22, user White_Runner wrote:
There is another study that puts the R0 value in 1.4 to 2.6, still very high.<br /> However not apocaliptic levels like the one described here.<br /> Also, this R0 value can change in time as long as the adequate restrictions are taken in place.<br /> So, yeah guys, expect the best, have some precautions and happy lunar new year.
On 2020-02-12 18:53:53, user S.T wrote:
Table 2 has a typo(I presume). It states the time window is from 25th December 2010**
On 2022-01-28 20:26:41, user Hussein Turfe wrote:
Was there any relation found between those who had THC in their urine and coming into the ED stating that they had a suicidal ideation?
On 2022-01-28 20:33:22, user Mohamad Kabbani wrote:
Fantastic article! Very informative and the ideas are easy to understand. This is a good baseline to get a better understanding on how different things have become during and after covid-19. We can learn what a pandemic can do to a population and compare it to this data as a reference point.
On 2020-03-22 15:56:46, user Sinai Immunol Review Project wrote:
Main findings<br /> The authors characterized the immune response in peripheral blood of a 47-year old COVID-19 patient. <br /> SARS-CoV2 was detected in nasopharyngeal swab, sputum and faeces samples, but not in urine, rectal swab, whole blood or throat swab. 7 days after symptom onset, the nasopharyngeal swab test turned negative, at day 10 the radiography infiltrates were cleared and at day 13 the patient became asymptomatic.
Immunofluorescence staining shows from day 7 the presence of COVID-19-binding IgG and IgM antibodies in plasma, that increase until day 20. <br /> Flow cytometry on whole blood reveals a plasmablast peak at day 8, a gradual increase in T follicular helper cells, stable HLA-DR+ NK frequencies and decreased monocyte frequencies compared to healthy counterparts. The expression of CD38 and HLA-DR peaked on T cells at D9 and was associated with higher production of cytotoxic mediators by CD8+ T cells.<br /> IL-6 and IL-8 were undetectable in plasma.<br /> The authors further highlight the presence of the IFITM3 SNP-rs12252-C/C variant in this patient, which is associated with higher susceptibility to influenza virus.
Limitations of the study<br /> These results need to be confirmed in additional patients.<br /> COVID-19 patients have increased infiltration of macrophages in their lungs{1}. Monitoring monocyte proportions in blood earlier in the disease might help to evaluate their eventual migration to the lungs.<br /> The stable concentration of HLA-DR+ NK cells in blood from day 7 is not sufficient to rule out NK cell activation upon SARS-CoV2 infection. In response to influenza A virus, NK cells express higher levels of activation markers CD69 and CD38, proliferate better and display higher cytotoxicity{2}. Assessing these parameters in COVID-19 patients is required to better understand NK cell role in clearing this infection. <br /> Neutralization potential of the COVID-19-binding IgG and IgM antibodies should be assessed in future studies.<br /> This patient was able to clear the virus, while presenting a SNP associated with severe outcome following influenza infection. The association between this SNP and outcome<br /> upon SARS-CoV2 infection should be further investigated.
Relevance<br /> This study is among the first to describe the appearance of COVID-19-binding IgG and IgM antibodies upon infection. The emergence of new serological assays might contribute to monitor more precisely the seroconversion kinetics of COVID-19 patients{3}. Further association studies between IFITM3 SNP-rs12252-C/C variant and clinical data might help to refine the COVID-19 outcome prediction tools.
References<br /> 1. Liao, M. et al. The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.02.23.20026690.<br /> 2. Scharenberg, M. et al. Influenza A Virus Infection Induces Hyperresponsiveness in Human Lung Tissue-Resident and Peripheral Blood NK Cells. Front. Immunol. 10, 1116 (2019).<br /> 3. Amanat, F. et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.03.17.20037713.
Review by Bérengère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2021-10-29 15:37:00, user Rogerblack wrote:
I find refreshing the repeated ''these associations did not survive correction for multiple comparisons'.<br /> An interesting paper.
On 2020-10-22 21:07:13, user Lee Jimmy wrote:
How does this relate to the nursing home outbreak in the La Crosse area ?<br /> James T. Lee, MD PhD FACS FIDSA FSHEA
On 2020-05-20 16:50:49, user Peter Ellis wrote:
Table 1 presents the data, showing 40 positive tests and 689 negative tests, i.e. an average prevalence of 5.49% across the course of the study. Elsewhere in the manuscript, the sensitivity is given as 100% (meaning none were missed) and the specificity as 98.3% (meaning there is a 1.7% false positive rate.
This being the case, can the authors please explain:
1) Why the caption for Table 1 reports 789 patients given that 40 + 689 = 729?
2) How they adjusted for false positives. 40 / 729 = 5.49%, which minus the 1.7% false positive rate leaves around 3.79% positive across the course of the whole study.<br /> [A Bayesian adjustment would be more accurate, this will suffice for now]
3) Given that the true positive rate in the samples they measured is around 3.79% across the whole study, how do they calculate a population prevalence of 4.6% at the start, rising to 7.1% at the end of the study. The methodology for this is entirely lacking.
On 2020-04-22 02:20:27, user Mike wrote:
This was certainly an interesting paper. It's done a lot of work and the findings are notable. IMHO it warrants as much attention as the pro-HCQ study via Dr. Raoult. While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required, but it is worth the read.
Observations/Questions:
1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease”<br /> 2. "we cannot rule out the possibility of selection bias or residual confounding”<br /> 3. demographic: 100% male, 66% black, median age ~70 (59 youngest)<br /> 4. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 5. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated? <br /> - What was the quantity/frequency of the treatments? <br /> - Were the treatments consistent between hospitals?<br /> 6. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that and what does that suggest?<br /> 7. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 8. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not accustomed to. The Conclusions portion (and page after) seeming to address topical issues of the times. Perhaps this introduces my own subjective bias, but I infer potential for analysis/deciphering bias when the study shows awareness of other controversial studies being conducted, rather than being a standalone independent study of its own; essentially, it leaves me to question motivations of the author, rather than that motivation being scientific discovery. I don't mind such commentary in the Discussion section, I'm just not as accustomed to seeing it in the Abstract.
On 2022-10-28 07:00:48, user Sujoy Ghosh wrote:
This manuscript has now been published as follows: <br /> Ghosh, S., Roy, S.S. Global-scale modeling of early factors and <br /> country-specific trajectories of COVID-19 incidence: a cross-sectional <br /> study of the first 6 months of the pandemic.<br /> BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12...
Kindly update the link in medrxiv. Regards, Sujoy Ghosh
On 2022-11-07 06:03:04, user Daniel Corcos wrote:
I don't see any adjustment for the date of infection. There is a high probability that nirmatrelvir treatment was used on average at a different time, against infections with a different ratio of viral variants.
On 2022-12-01 05:07:56, user Amir Hossein Nikzad wrote:
Hi medRxiv, this pre-print is published on Schizophrenia in July 2022. Here are the link: https://www.nature.com/arti... and the doi https://doi.org/10.1038/s41.... Regards, Amir.
On 2022-12-12 06:18:02, user Stephanie Byrne wrote:
This article has been accepted for publication in the International Journal of Epidemiology, published by Oxford University Press. A DOI and link to the published article will be available soon.
On 2022-12-29 19:11:31, user tshann wrote:
Given the stated benefits of these vaccines, why are we doing modeling studies rather than real RCT's. It's been over 2 years with these products, when will we see the science instead of more modeling studies?
On 2023-01-23 00:20:10, user Stephen Akar wrote:
I think this is a fantastic work by this great team. I am working on YF in Nigeria.
On 2023-01-23 11:55:19, user Yang Wen wrote:
This manuscript has been reviewed and published on Frontiers in Psychiatry.
Please cite the latest version of this manuscript via https://doi.org/10.3389/fps....
On 2023-03-10 18:13:37, user Kirsten Wiens wrote:
This manuscript has now been published online at Lancet Microbe: https://doi.org/10.1016/S26...
On 2023-03-29 14:07:37, user YUE BAI wrote:
Hi,
Could you please link the preprint to the published article (https://ieeexplore.ieee.org...? "https://ieeexplore.ieee.org/document/9670745)?") Thank you!
On 2023-04-16 07:02:33, user Basheer wrote:
The paper is published at https://www.eurekaselect.co...
On 2023-09-06 12:08:36, user Hema Chaplin wrote:
This article has been accepted for publication in PLOS One (Open Access) on August 9 2023 and can be found here https://doi.org/10.1371/jou...
On 2023-10-22 13:47:09, user Rahma Menshawey wrote:
This research has now been peer reviewed and published here: <br /> https://bnrc.springeropen.c...
On 2023-11-21 02:18:06, user Marco Confalonieri wrote:
The finding that high glucose levels can predict glucorticoids (GCs) benefit surprised most of us. All we who performed the included RCTs thincked to hyperglycemia as an adverse effect of GCs, not paying attention to glucose blood level at admission. Nevertheless, there are several reports pointing out hyperglycemia but not diabetes alone associated with increased in-hospital mortality in community-acquired pneumonia (BMJ Open Diab Res Care 2022;10:e002880). It should be noted that AI doesn't have the same prejudices than human researchers.
On 2023-12-05 02:49:44, user Author wrote:
This article is now published on Journal of Psychopharmacology: https://doi.org/10.1177/02698811231211219
On 2024-01-11 13:59:49, user Mahalul Azam wrote:
This work now published in https://doi.org/10.15294/ke...
On 2024-02-22 14:38:06, user Albee Messing wrote:
Please provide more clinical information about the proband.
On 2024-02-26 19:05:34, user Pia Ostergaard wrote:
Final peer reviewed, published version can be found here: https://pubmed.ncbi.nlm.nih...
On 2024-06-02 22:51:12, user Hellen Paskaleva wrote:
p. 3, line 10: Clostridium is gram-positive, not gram-negative.
On 2020-04-16 00:27:24, user Adam Danischewski wrote:
China has a BCG Vaccination policy and there may be other aspects that may cause Chinese results to differ from the United States.
On 2024-10-16 16:27:20, user CDSL JHSPH wrote:
I quite enjoyed this article. I found it very interesting as it proposed significant thoughts to how we can improve antibiotic treatment. I wanted to comment about some of the thoughts I had while reading this article. I first wanted to see if the results that were found in TB could be translated into other bacteria infections, such as staph. or strep. species. I also wanted to see if the results found for antibiotics in this article could be translated to other pathogenic treatments including antivirals or antifungals. Finally, in terms of future approaches could we see a systemic or ordered approach when it came to treatment duration whether bacterial, viral, or fungal in nature, or is it mostly going to be drug/ species specific?
On 2024-10-23 00:04:57, user Mohammad Shah wrote:
Hello!
Thank you for sharing this preprint. I really enjoyed reading it. Your application of techniques like MCP-Mod and FP for duration-ranging trials provides valuable insights into detecting duration-response relationships much more effectively than traditional approaches. I also appreciate how you highlight the risks of underestimating the MED in smaller sample sizes and suggest using conservative thresholds to mitigate those risks—this is such a critical point.
One thing that really stood out to me was how you clearly lay out the limitations of traditional duration-response methods, while proposing model-based techniques, like MCP-Mod, as a better alternative. Your comparison of different models and how they behave with varying sample sizes and regimen responses is especially insightful for optimizing TB treatment duration.
Like others have mentioned, it’d be fascinating to see how this approach could be applied to other chronic diseases, such as HIV or hepatitis. Is that something you’re considering or perhaps already working on? Additionally, applying these model-based techniques to real-world patient data, where comorbidities and adherence issues add more complexity, seems like a natural next step. It would be interesting to see how that plays out in practice.
I also found your discussion on model selection particularly thought-provoking. Your suggestion of using MCP-Mod alongside Fractional Polynomials under different assumptions opens up an exciting possibility for integrating multi-model approaches in early-phase trials. I wonder if combining these models, maybe in a hybrid MCP-Mod/FP approach, could improve adaptability, especially in trials with more heterogeneous patient populations—those with comorbidities or fluctuating adherence, for example.
Lastly, your use of simulations to predict treatment efficacy in the face of sample size imbalances touches on a key challenge in trial design. Have you thought about how this framework might be extended to adaptive trial designs? It seems like interim analyses could help adjust treatment durations dynamically based on early patient responses, which could make trials even more efficient.
Overall, this was a great article, very informative and forward-thinking!
On 2020-05-01 10:56:16, user Ivan Berlin wrote:
Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Comment of the results concerning smoking related issues. Corrected Version. Please ignore the previous one.<br /> Ivan Berlin, Paris, France<br /> The title is somewhat misleading. Only 3789 persons were tested for SARS-CoV-2, no data on the 2,022,438 are reported.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19 +.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> It seems that only individuals with symptoms were tested, however this is not clearly stated.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with positive SARS-CoV2 alcohol use disorder was reported by 48/585 (8.2%), versus 480/3204 (15%) among those with negative SARS-CoV-2 test. Among those with alcohol use disorder, 9.1 tested positive. <br /> Among SARS-CoV-2 positives there were 216/585 (36.9%) never smokers vs 826/3204 (25.8%) among SARS-CoV-2 negatives. 20.7% tested positive among never smokers. Among SARS-CoV-2 positive persons 179 (30.6%) were former smokers vs 704 (22%) among SARS-CoV-2 negatives. 20.3 % tested positive among former smokers. Among SARS-CoV-2 positive individuals 159 (27.7%) were current smokers vs 1444 (45.1%) among SARS-CoV-2 negative individuals. 9.9% tested positive among current smokers. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. Among individuals with SARS-CoV-2 positivity there were 338/585 (61%) persons with smoking history (former + current smokers=ever smokers) and among those with SARS-CoV-2 negativity 2149/3204 (72%) were ever smokers. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals.<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95% CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion
To the best of our knowledge, this is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, looking at smoking history (former + current smoking=ever smokers), less subject of classification bias, the difference seems to be less. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. There is no report of biochemical verification of smoking status. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.
The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-CoV-2 positive individuals raises the question of the specificity of the smoking finding and raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test directly or through associated health disorders e.g. hepatic disorders as a consequence of alcohol use. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. <br /> It seems that all individuals included were tested because they had symptoms suggestive of COVID-19. It is surprising that only 585/3789 (15.4%) tested positive. This should be discussed.<br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disorders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective, cross sectional analysis, are insufficient to support the hypothesis that current smoking protects against SARS-CoV-2 positivity.
On 2020-04-16 21:17:49, user Sinai Immunol Review Project wrote:
Key findings:
The authors wanted to better understand the dynamics of production SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia and the correlation of virus-specific antibody levels to disease outcome in a case-control study paired by age. The retrospective study included 116 hospitalized patients with COVID-19 pneumonia and with SAR-CoV-2 specific serum IgM and IgG detected. From the study cohort, 15 cases died. SARS-CoV-2 specific IgG levels increased over 8 weeks after onset of COVID-19 pneumonia, while SARS-CoV-2 specific IgM levels peaked at 4 weeks. SARS-CoV-2 specific IgM levels were higher in the deceased group, and correlated positively with the IgG levels and increased leucocyte count in this group, a indication of severe inflammation. IgM levels correlated negatively with clinical outcome and with albumin levels. The authors suggest that IgM levels could be assessed to predict clinical outcome.
Potential limitations:
There are limitations that should be taken into account. First, the sample: small size, patients from a single-center and already critically ill when they were admitted. Second, the authors compared serum IgM levels in deceased patients and mild-moderate patients and found that the levels were higher in deceased group, however even if the difference is statistically significant the number of patients in the two groups was very different. Moreover, receiving operating characteritics (ROC) curves were used to evaluate IgM and IgG as potential predictors for clinical outcome. Given the low number of cases, specially in the deceased group, it remains to be confirmed if IgM levels could be predictive of worst outcome in patients with COVID-19 pneumonia. The study did not explore the role of SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia.
Overall relevance for the field:
Some results of this study have been supported by subsequent studies that show that older age and patients who have comorbidities are more likely to develop a more severe clinical course with COVID-19, and severe SARS-CoV-2 may trigger an exaggerated immune response. The study seems to demonstrate that the increase of SARS-CoV-2-specific IgM could indicate poor outcome in patients with COVID-19 pneumonia, however given the very small sample size, the results are not yet conclusive.
Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-04-16 22:12:24, user Amy E. Herr wrote:
During the COVID-19 pandemic, we are grateful for the authors’ urgency in assessing N95 respirator decontamination methods. It is in this spirit of collegiality that we draw attention to an aspect that could (unintentionally) cause confusion: the PS19Q thermopile sensor mentioned in the Methods section does not appear to be suited to detect the virus-killing UV-C light emitted from the source. The authors are aware of the possible confusion and are working diligently to check into and, if needed, address the concern.
As background: from the manufacturer’s specifications, the PS19Q thermopile sensor mentioned in the preprint appears to only detect wavelengths as low as 300 nm, which is above the UV-C germicidal wavelength range (<280 nm). Low-pressure mercury UVGI bulbs emit a 253.7 nm peak [EPA]. 260 nm is the peak UV-C germicidal wavelength for inactivating virus via DNA and RNA damage [Kowalski et al., 2009, Ito and Ito, 1986]. The germicidal efficacy arises primarily from the UV-C dose, with the UV-B dose (280-320 nm) providing significantly lower germicidal efficacy. At 300 nm, UV light is ~10x less effective at killing pathogens than at 254 nm [Lytle and Sagripanti 2005]. UV-A dose (320-400 nm) is considered minimally germicidal [Kowalski et al., 2009; Lytle and Sagripanti 2005; EPA]. We are concerned about the potential adverse health outcomes that might stem from use of the PS19Q thermopile sensor not matched to the UVGI wavelengths for N95 FFR decontamination.
As best practices, all researchers working on UV-C methods are encouraged to use a calibrated, NIST-traceable, UV-C-specific radiometer to report not just UV-C irradiance, but also UV-C specific dose, as a minimally acceptable UV-C dose of 1.0 J/cm^2 is sought on all N95 FFR surfaces. For additional detail from the peer-reviewed literature, please see the 2020 scientific consensus summaries on N95 FFR decontamination at: n95decon.org
Again, we thank the authors for their timely research and quick action to confirm suitability of their experimental design, all of which aim to better inform decision makers working to protect the health of heroic front-line healthcare professionals during the COVID-19 pandemic.
References cited: <br /> • Manufacturer’s specifications, the PS19Q thermopile sensor: https://www.coherent.com/me...<br /> • EPA: ULTRAVIOLET DISINFECTION GUIDANCE MANUAL FOR THE FINAL LONG TERM 2 ENHANCED SURFACE WATER TREATMENT RULE: https://nepis.epa.gov/Exe/Z...<br /> • Kowalski et al., 2009: https://link.springer.com/c...<br /> • Ito and Ito, 1986: https://onlinelibrary.wiley...<br /> • Lytle and Sagripanti 2005: https://www.ncbi.nlm.nih.go...
On 2020-04-16 22:28:29, user Quandary wrote:
interested in states, my own and others. How to find them in this chart?
On 2025-05-21 23:31:22, user Yanning Liu wrote:
This article has recently been published on Radiology https://doi.org/10.1148/radiol.241597
On 2025-06-15 21:35:28, user CP wrote:
Great paper! The text makes reference to a "Supplementary Notes" section that doesn't seem to be in the PDF - is this part of the material that will be made available after peer reviewed publication? Sorry if this is a naive question; I'm new to preprints.
On 2022-01-26 22:15:44, user Siguna Mueller, PhD, PhD wrote:
Does the "fully vaccinated" group ALWAYS include those with (partial) natural immunity (i.e., those previously infected? This is at least what Table 1 says: these belong into the same group. Yet, throughout, this group is referred to as the "fully vaccinated." This does not seem to affect the conclusion that vaccination is in large part responsible for driving O's increased transmissibility (because the incr. OR is seen for the booster group as well). Apart from this, I am struggling to see how the other results are obtained. I seem to be missing how the factor of previously infection gets incorporated in the study. It would be helpful if this could be made explicit, please. Thanks!
On 2025-08-10 00:53:33, user Nihal wrote:
these guys have a great paper
On 2022-01-29 19:43:45, user A Thumb wrote:
Where is the VE data for 2 doses against severe outcomes with omicron in the abstract? Annoying omission.
On 2022-02-03 17:13:18, user Brian R Wood wrote:
Has the study accounted for the fact that if Omicron has less severe symptoms than Delta or COVID-19 Classic, the number of reported infections is likely to be significantly lower? Additionally, I would speculate that those who got vaccinated and boosted are also more likely to be tested than those who did not, but just speculation, no data to back it up.
On 2020-05-13 01:25:02, user Joe Exum wrote:
N.C. is projected to need more ICU units than it has capacity in August? Is this correct?
On 2022-02-08 21:10:34, user Sara wrote:
Thank you for your comment, unfortunately, I did not receive your comment once you replied. 1- we are in the era in the big data, more projects are aimed at generation of large cohort that we can depend upon to derive our clinical decision. <br /> The analysis used the data from US, the model will be deployed and can be used after that to predict the survival time of small cohorts. <br /> 2- We investigated the hazards assumption, we agree with you, we should add the results in the manuscript<br /> 3- SEER database identify the surgery as the surgical removal of the tumour.<br /> 4- I agree with you on the grade, it was on the old grading system for glioblastoma which is mentioned on SEER guidelines. Updated version will be posted and will update the analysis removing this one<br /> 5- we agree with you, we will change it in the updated comments<br /> 6- It is not insane! Developing models that consider these cases is a challenge. These models will be deployed for survival prediction of different cases of glioblastoma with different survival times.
7- we are developing a model that can be used for the routine data "we use", in this case US cancer data. We have a model that performed well so it can be deployed in the future for the clinical use for our routine data. the model is trained on large sample size that we believe it will achieve accurate prediction results for any routine data. The deployment of the model and its use in clinical practice is the goal. I hope you see the full picture.
Thank you for your comments.
On 2022-02-09 01:07:23, user Avi Bitterman wrote:
This paper dichotomizes a continuous variable to get a barely statistically significant result (P=0.044). But this is just dichotomania. Time to treatment is a continuous variable, not a binary variable. The appropriate test for this continuous variable is a regression along the continuous variable. Not a dichotomized sub-group analysis.
Using the same numbers this author uses from Table 1, we ran a regression which failed to show a significant effect of treatment delay on outcome P=0.13
Aside from being the appropriate test, another advantage of a regression here is it avoids the possibility selective dichotomization along the proposed moderator variable to get the desired result (a barely significant P value the authors just so happen to have found).
I would also be happy to have a discussion with the authors to elaborate on the above as well as discuss numerous other critical errors with this analysis as well.
On 2022-02-09 11:30:32, user Felix Schlichter wrote:
The authors explain that the data was gathered from community testing. They further note that mass testing has been available to "Dutch citizens experiencing COVID-19 like symptoms or who have been in contact with someone testing positive for SARS-CoV-2".
If one assumes that the inmune status affects the intensity and probability of exhibiting symptoms, wouldn't the sample be biased? Even if the real odds of being positive for individuals with primary vaccionation and booster were equal, the ones with booster would be underrepresented as they would not test as often if they tend to exhibit less symptoms. Is this not a limitation of the study?
Could the authors not show the results separated by the reason for testing (contact vs symptomatic) to account for this limitation? if the reason for testing was having been a contact, this limitation would not be there.
On 2022-02-09 18:08:12, user Syncope wrote:
Peter A. McCullough purposely forgot to disclose his involvment in Topelia Therapeutics https://web.archive.org/web...
On 2025-11-11 03:32:18, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
In the week after the Jan 7 ignitions, virtual (clinic) respiratory visits jumped 41% in highly exposed areas and 34% in moderately exposed areas, totaling 3,221 excess visits, a clear, short-term signal health systems can act on.
Virtual cardiovascular visits rose by ~35% across exposure groups in that first week (~2,424 excess visits), pointing directly to surge planning for virtual care during wildfire weeks.
On the day of ignition (Jan 7) in highly exposed areas, outpatient neuropsychiatric and injury visits were about 18% higher than expected, evidence that mental-health demand starts immediately, not just respiratory care.<br /> The exposure framing is reproducible: simple proximity bands (<20 km vs >=20 km within LA County) applied to a 3.7-million-member health system and a five-category visit dashboard (all-cause, cardiovascular, injury, neuropsychiatric, respiratory) that others can copy.
Scaled to all LA County residents, the estimates imply ~16,171 excess cardiovascular and ~21,541 excess respiratory virtual visits in the week after ignition, strong justification to expand virtual capacity during major fires.
On 2022-02-17 20:55:31, user RT1C wrote:
Table 3 (bottom) contains HR for boosted vs. non-boosted at various times (<6, 6-9, >=9 months). Aside from the minor labeling issue (hopefully not actual analysis issue!) that 6-9 months and >=9 months are not distinct subsets, overlapping at 9 months, I don't see how you could have made this analysis in the first place unless you have incorrectly defined POIC. You wrote, "we defined the proximate overt immunologic challenge (POIC) as the most recent exposure to SARS-CoV-2 by infection or vaccination." That means POIC for boosted subjects would be time since the booster dose as that is the most recent vaccination. Yet, considering how recently boosting began, how could you have boosted subjects with 6-9 or >=9 months POIC? (In your text you wrote, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days).")
On 2022-02-17 21:29:51, user RT1C wrote:
You state, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days)." Is that a typo or did you truly mean a positive 6? i.e., did you mean -38 to -6 days, or -38 to 6 days? If the latter, you actually included subjects who were vaccinated with boosters after the study period began? If that's the IQR, then I assume the full range extends much further into the study range. Those are VERY recently boosted. In your discussion, you should not say, "boosting with a vaccine designed for an<br /> earlier variant of COVID-19 still provides significant protection against infection with the Omicron variant." without also providing a time associated with that. For example, you might add to that sentence "for a period of at least 1 month" or whatever. It seems important to stress the limitation of the study in this manner, to avoid giving the impression that the booster provides long-lasting protection against infection when that is not shown by your study.
Finally, on a related matter, how did you treat individuals who tested positive before 7 days after their booster? If, as some research suggests, vaccination temporarily increases susceptibility to infection (for about 2 weeks), by including subjects who were vaccinated within the study period, you may have biased findings against those without boosters.
On 2025-11-26 01:10:16, user Alda wrote:
Congratulations for all the colaborators!! <br /> When will be published ? (perhaps 2026)
On 2022-02-24 11:08:09, user AgentMMK wrote:
This study really looks very dubious.
On 2022-03-03 12:42:31, user Dan Bolser wrote:
This paper seems to be published here:<br /> https://pubmed.ncbi.nlm.nih...
but the preprint doesn't link it.
On 2022-03-04 16:06:11, user Tracy Beth Høeg, MD, PhD wrote:
The peer reviewed version including numerous international datasets estimating rates of post vaccination myocarditis is now available. We have included risk-benefit calculations for children with a history of infection and used overall infection hospitalization risks (rather than just 120 days risks) both pre and during omicron. http://doi.org/10.1111/eci....
On 2022-03-28 18:14:47, user August Blond wrote:
Dear colleagues,<br /> I am having difficulty understanding figure 3, the two graphs that are plotted with GFP/EGFR.<br /> Zooming in on the four ovals - red, blue, black, green - I see that the scattered-plots are themselves contained in a smaller perfect ovoid.<br /> Can you explain how you manage the computer processing of your samples?<br /> In reference 13, the method for doing multiplex FACS, these close to perfect ovals do not appear. There are still points that are not perfectly integrated into the "virtual" geometrical structure.<br /> As is the case with all FACS using gating.<br /> Would it be possible to generate point clouds that have not been "artificially" modified after gating?<br /> Best regards,<br /> August Blond
On 2022-05-31 08:26:48, user Christoph Lippert wrote:
transferGWAS: A similar article from my group (accepted at OUP Bioinformatics): https://www.biorxiv.org/con...
ContIG: also related (accepted at IEEE CVPR): https://arxiv.org/abs/2111....
We would appreciate a reference.
On 2022-06-08 17:08:32, user Ted Gunderson wrote:
Should this be considered a scientific study or an advertisement?
What evidence is there that what the authors refer to as "(non-variola orthopoxvirus and monkeypoxvirus specific)" actually causes the disease that is currently being diagnosed all over the world as "monkeypox".
This is a paper funded by Roche that says "Our tests work!"
"ML and DN received speaker honoraria and related travel expenses from Roche Diagnostics."
Roche has gotten lots of press recently about their monkeypox tests.
On 2022-06-09 20:11:19, user John Doe wrote:
Interesting paper that confirms and complements prior molecular findings on this devastating malignancy. A strength of this study is the inclusion of a relatively large series of patients (n = 47) considering the rareness of the disease. The results suggesting a diverse origin of BPDCN are of special interest, and the figure on potential therapies against the disease is visually appealing. However, data analysis and data interpretation have certainly problems and inconsistencies. In particular, the results on CNV pathogenicity produced by X-CNV are highly questionable and dubious, and I would strongly advise against using those results to guide data interpretation. Among deleted regions (suppl. data) classified as non-pathogenic by X-CNV are: 1p36.11 (ARID1A), 5q33.1 (NR3C1), 7p12.2 (IKZF1) and 9p21.3 (CDKN2A–B). All these are well-known tumor suppressors with demonstrated pathogenicity in numerous human cancers. Besides, prior studies back up the recurrent deletion and pathogenicity of these cancer genes in BPDCN [refer to papers by Lucioni M et al. Blood. 2011;118(17), Emadali et al. Blood. 2016;127(24), Bastidas AN et al. Genes Chromosomes Cancer. 2020;59(5), Renosi F et al. Blood Adv. 2021 9;5(5)].
Puzzling enough, despite claiming the use of the X-CNV results to determine pathogenicity of CNVs, it appears that the authors chose to highlight anyway some deleted and gained regions classified as non-pathogenic by X-CNV (ARID1A, CDKN2A) as well as other regions not even formally called by GISTIC (e.g. TET2). This is even harder to comprehend considering that 7p12.2 (IKZF1) is clearly one of the most conspicuous peaks in the analysed cohort (Figure 3A); yet, completely ignored in the text and figure!? Quite baffling. In short, the paper would greatly benefit and improve from re-interpreting and discussing the data considering the existing literature on BPDCN genetics.
On 2020-04-21 21:10:27, user Bruno Vuan wrote:
Article says, page 7,
"This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."
In summary sample has
Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation
Conclusion: "overall effect of such biases is hard to ascertain"
Additionally
There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)
On 2022-06-24 22:03:50, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint. This certainly represents a large amount of work and careful consideration!
I have some questions / comments:
1) Is there a way for me to calculate enhanced scores for myself?
For example, I would like to learn more, but I was not very satisfied with the PRS that I listed for my own genomic sequence in this blog post:
https://cdwscience.blogspot...
2) In the blog post link above, there seemed to be a noticeable disadvantage to the PRS without taking the BMI into consideration for Type 2 Diabetes.
In this paper, age is an important factor in Figure 1 for the PRS.
If other non-genetic factors are known, do you have a comparison for non-PRS models? <br /> For example, I wonder how performance of age + BMI (+ other established factors) compares to the plot for Type 2 diabetes in Figure 1.
3a) I see that the percent variance explained is sometimes provided (such as Supplemental Figure 5), but sometimes it is not.
For example, in Figure 3, the effect per 1 SD of PRS is higher for LDL cholesterol than height. However, how does the ability to predict an individual's height from genetics alone compare to the ability to predict an individual's LDL from genetics alone?
After a certain age (as an adult), the exact value for my own LDL has varied more than my height. However, I was not sure how that variation by year compared to others and/or the variation over decades.
In general, I would like to have a better sense of how absolute predictability compares for height versus disease scores. I also understand that there are complications with binary versus continuous assignments, but it is something that I thought might be helpful.
3b) I see AUC statistics in Supplemental Figure 2, described as for AUROC. However, am I correct that some of the cases are not well balanced with controls?
If so, should something like AUPRC be provided (possibly as a complementary supplemental figure)? I believe the idea is described in Saito and Rehmsmeier 2015; the application is very different, but you can see the inflated AUROC values in Figure 1A of Xi and Yi 2021. I expect that there are other good ways to illustrate the differences with PRS in cases and controls of varying proportions, but that was one thought.
In the context of genomic risk, I might expect that high predictability in a small number of individuals may be preferable over a small difference in low predictability in a large number of individuals. There is emphasis on thresholds like top/bottom 3% (in many but not all figures), which I thought might be consistent with that opinion.
So, I think something like Figure 1 was helpful. In order to try and capture how false positives change when sensitivity increases, I am not sure if something similar for positive predictive value might help? I would consider that very important if the PRS might be used for screening purposes.
4) In the Supplemental Methods, I believe that you have a minor typo:
Current: 100,000 Genomes Project (100KGP). The 100,00 Genomes Project, run by Genomics England,<br /> Corrected: 100,000 Genomes Project (100KGP). The 100,000 Genomes Project, run by Genomics England,
Thank you very much!
Sincerely,<br /> Charles