On 2020-08-13 09:48:47, user Steven Eisenstadt wrote:
Thieve extremely important findings confirm our worst fears regarding aerosol transmission of SARS Covid-19. It is a warning to every parent, healthcare worker and politician that this pathogen will be relentless in it’s transmission until an effective vaccine becomes available. SDE, md facs
On 2021-10-03 09:39:18, user kdrl nakle wrote:
Associated is the key word. It really means nothing. It is like claiming that people not washing their teeth are more susceptible to COVID, which is probably true as they are likely the ones to be with poorer overall health.
On 2020-04-20 18:14:27, user Wladimir J. Alonso wrote:
Not being temperature-dependent does not imply that it might not be seasonal. For instance, in equatorial regions seasonality of influenza is driven by the rainy and dry seasons, not temperature (see references below).
Tamerius J, Nelson M, Zhou S, Viboud C, Miller M, Alonso WJ. Global Influenza Seasonality: Reconciling Patterns Across Temperate and Tropical Regions. Environ Health Perspect. 2010
Alonso WJ, Viboud C, Simonsen L, Hirano EW, Daufenbach LZ, Miller MA. Seasonality of influenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol. 2007;165(12):1434–42.
Alonso WJ, Yu C, Viboud C, Richard SA, Schuck-Paim C, Simonsen L, et al. A global map of hemispheric influenza vaccine recommendations based on local patterns of viral circulation. Sci Rep. 2015;5:17214.
On 2022-03-20 18:28:48, user SB wrote:
First, in Table I there is an error for the RKI report of elderly. RKI printed 60.9 %!
On 2021-12-07 17:04:15, user LizzyJ wrote:
''Pandemic modeling'' is quickly becoming the astrology of mathematics & physics.
Want to get a lot of media attention and citations? Simply code up a little model and write up a paper about your unscientific predictions. As in all models, the chosen values for the parameters in this simulation are naive assumptions. There is no sufficiently high-quality or complete data on vaccine status and mode of infection (e.g. infected by a vaccinated or unvaccinated person) currently being reported by hospitals or health departments.
Pandemic modeling is an abstract and theoretical mathematical exercise with very high bias and uncertainty based on the underlying assumptions, factors included or excluded, incomplete input data, very poor data quality with big non-random gaps in the data, etc. It should not guide public policy. Only clinical studies and real-world medical data should guide policy.
On 2020-05-19 11:46:53, user James Eridon wrote:
Very practical, reasonable approach to the issue. Seems to indicate about a 50% reduction in poor outcomes. Not a silver bullet, but certainly a big deal, especially considering the low cost and ease of treatment. Tired of complaints about how it’s not double blind and randomized - that doesn’t make it invalid. It’s the sort of argument made by someone trying to advance an agenda, rather than knowledge. <br /> One minor point. I believe the OR and p-value on Intubation in Table 3 are off. The actual values are somewhat better than those shown.
On 2020-05-15 06:16:52, user brucewhain wrote:
I'm wondering about the 1st statement: "Nearly all comorbidities..." Where did you get that? The rest I agree with, insofar as it concerns COVID-19 - not being familiar with the other instances. The targeting of vulnerable people with zinc and some zinc ionophore looks like the obvious, instinctive and reasonable solution. Hoping we get to that, and quickly, but this study just proves what we otherwise uneducated internet sleuths have more-that-suspected for months now. The "authorities" move at a glacial pace regardless of life or deaths. It seems unlikely they will turn over a new leaf now.
On 2020-12-02 20:32:21, user Roberto Etchenique wrote:
Great work !! We had done the same procedure, but a bit cruder, for City of Buenos Aires, Argentina. Our results for our country (not published) are coincident with these ones. https://www.medrxiv.org/con...
On 2022-01-05 16:22:39, user Ryan Anderson wrote:
How long until the booster protection equals zero?
On 2021-01-21 22:42:13, user Hein De Waele wrote:
How about prophylactic use? What if it was taken by outpatients soon after first symptoms and no other treatments? Do you think it would reduce viral load?
On 2022-01-25 02:53:22, user chancet wrote:
I had thought the re-infection rates are higher because opportunity of natural immunity is taken away once you have been made immune through the vaccine. And we are seeing, what, 27 times the lasting protection through natural immunity? In that case I can see the phrase "damage" as possibly fitting in regards to what the vaccine has done to the immune system. Some will call that charged rhetoric, but I think that is a result of people retreating so far into the camp of "vaccine good" with immense religiosity.
On 2020-04-18 13:20:14, user mendel wrote:
The upwards adjustment suggests that (a) a few positive samples in high-population areas outweighed (b) a smaller proportion of positive samples in lower-population areas. I'm not really well versed with the demographics of Santa Clara, but if (a) is urban minority housing and (b) is the suburbs, it tallies with your observation.
Regarding your "less ability to participate", lower-income people may not have had access to a car, couldn't access the drive-through tests, and therefore contributed to the 693 no-shows (>20% of N).
On 2020-04-24 14:40:59, user Tomas Hull wrote:
"New York antibody study estimates 13.9% of residents have had the coronavirus, Gov. Cuomo says"<br /> When false negatives were to be included - those who have undetectable levels of antibodies, mainly young population - it could mean that 30%, or more people in NY already have the antibodies...
The study as well as Dr. John Ioannidis, Dr. Jay Bhattacharya, who have gone public with these findings, stand vindicated.
https://www.cnbc.com/2020/0...
Will herd immunity be achieved by the end of summer, or earlier, as predicted by another brilliant scientist, Dr, Wittkowski? It remains to be seen...
On 2020-04-20 15:25:45, user Michael A. Kohn, MD, MPP wrote:
We will have this posted soon at COVID-19-assay.net
On 2020-04-18 05:05:32, user Andy wrote:
Besides the self-selection, non-random bias, what's also troubling is that two of the authors argued that "fatality rate may be too high by orders of magnitude" in a WSJ commentary on March 24. Were they simply shooting for what they wrote in the WSJ commentary?
https://www.wsj.com/article...
In the WSJ commentary, they did not bother to cite data already available from the Diamond Princesses, which showed at most 2x cases (46.5% were asymptomatic at the time of testing, 17.9% of infected persons never developed symptoms).
https://www.cdc.gov/mmwr/vo...
In the WSJ commentary, they also discussed information from Vò. But they argued for 130x by applying cluster data to the whole province, when Dr. Crisanti who did the study said 50% to 70% were asymptomatic. That's maybe 2x to 3x, but nowhere near 130x.
https://www.wsj.com/article...
"Dr. Crisanti oversaw the testing of 95% of the residents of Vo’ in the days after the first infection was reported. He found 3% of the population had been infected and that just under half of those who tested positive were asymptomatic."
https://www.theguardian.com...
"Nonetheless, asymptomatic or quasi-symptomatic subjects represent a good 70% of all virus-infected people and, still worse, an unknown, yet impossible to ignore portion of them can transmit the virus to others."
When one incorrectly applies cluster data (3,300 people in Vò) to the entire province (population 955,000), one gets 130x, when you know for sure the factor should in fact be 2x to 3x. It seems the self-selection, non-random bias can be just as strong, and the 50x to 85x cannot be right.
On 2020-04-23 18:28:07, user Nicholas Hewett wrote:
Your same concern would be applicable to that of flu-related deaths in people that are untested.
On 2020-04-18 18:39:18, user jj wrote:
Where is the discussion of selection bias? You invite folks to get tested by advertising on Facebook... I think there will be an over-representation of folks who fear they have COVID-19 based on their recent interactions in places with or around COVID-19 cases.
Without randomization to eliminate self-selection bias, the authors should not be making any far-reaching conclusions that are now being picked up and reported by the media without providing proper interpretation.
I think this publication should be rejected for not doing this study properly.. and then seeking publicity!
On 2020-04-17 20:04:27, user jeffrey spence wrote:
I have some concerns about the confidence intervals presented in this preprint and hence some of the conclusions. It is stated that there were 2 positive tests out of 371 + 30 tests of known negative samples. This suggests a point estimate of the false positive rate of 0.5%, but a confidence interval of [.06%, 1.79%]. This includes the point estimate of the proportion of positives in the sampled individuals from Santa Clara County (50/3349 = 1.5% ). This means that the data are consistent with there being 0 positive individuals in the sample. As such, claims of a 50-85 fold excess of cases over confirmed cases, are much too precise to the point of being potentially misleading.
I'm not certain why the confidence intervals in Table 2 that account for the uncertainty in the false positive rate (1 - specificity) are so small, but I suspect that it may be due to using the delta method which is inappropriate for small sample sizes.
On 2020-04-25 21:08:15, user outdoorgirl0814 wrote:
My primary question on this study is why the IgM and IgG specific results were not presented, but rather pooled together. This seems like important information. From what I can tell, the test identifies them separately.
On 2020-04-22 02:59:42, user Eric Hadley-Ives wrote:
Your main concern is the false positives instead of the sampling bias? The researchers tried their test on 30+88=118 known negatives and didn't get a single false positive; and the manufacturer of their test reports getting 2 false positives on tests of 371 true negatives, so that's 2 false positives in 489 known true negatives. The same manufacturer had a different test that had 2 false positives in 15 known negatives in a study by Ria Lassauniere and associates, but that was a different test (same manufacturer, however, so you wonder about the consistency of the batches of their tests, I guess). But the sampling! The sampling is a huge issue. The sample was not representative. The researchers essentially put out the word in a community that was desperate for COVID-19 testing that they wanted people to come in and get tested for whether they had been exposed to SARS-CoV-2. You think people who had symptoms and were worried about whether they had been infected might have been a little more likely to respond to that call for research subjects? How many of the 50 positives were motivated subjects seeking a test they couldn't otherwise get?
On 2020-04-23 12:38:51, user Tomas Hull wrote:
There is a significant number of populations tested of which there is a large number, mainly younger population, who have cleared the virus out, and yet, no detectable levels of antibodies where found by the antibody test in their blood plasma.
By what mechanism those groups of people, mainly younger population, were able to overcome SARS-CoV-2 infection, if their immune systems didn't produce the detectable levels of antibodies?
Also, what does this phenomenon imply how widespread really the virus is, if many more people, who have been infected with SARS-CoV-2, are among the many of false-negatives for antibodies?
On 2020-04-22 00:40:03, user Unko J wrote:
It's nice to read below what essentially IS the 'peer-review' for this pre-print online paper! I wish I had read these comments last night before having a heated debate with my fellow quarantinees. My point was how could these possibly be 2%-4% of the population that is positive and yet Santa Clara has only 83 deaths? These divergent sets of data can't really exist in one universe, unless either we're wildly wrong about either a) the mortality rate or b) how many people can be asymptomatic and test positive with an Ab test. So yeah, between cross-reactivity against non-Covid antibodies and other false positives, I think we've decided to reject this paper. And aren't some of the authors the same on both papers?
On 2021-08-01 16:33:46, user RationalSkeptic wrote:
"Most participants who initially received placebo have now been immunized with BNT162b2, ending the placebo-controlled part of the study. "
Um, doesn't the defeat the purpose here? Isn't this trial suppose to be ongoing until Feb 2023?
On 2021-08-01 17:13:02, user Catriona wrote:
No number needed to vaccinate for any outcome (death, severe COVID, hospital admission, ICU admission)? Of course, NNV to prevent death is infinite as no deaths were prevented. But what about the other outcomes?
No number needed to harm?
What were the severe adverse reactions?
30 people in the placebo group were diagnosed with severe COVID and 1 in treatment group, so that’s a difference of 29.
150 severe reactions in placebo group vs 262 in vaccine group is a difference of 112. I feel we need to know a lot more details on these adverse events to be able to make informed decisions. As it looks like the number needed to harm is greater than the number needed to prevent harm.
I’m assuming that reducing PCR positivity if you have a mild illness isn’t what most people are really interesting. People are most worried about getting sick enough to be admitted to hospital, admitted to intensive care, dying or getting chronic fatigue syndrome and “long COVID” or some other autoimmune complication afterwards. Particularly if it affects their quality of life, employment, financial security, etc.
This study wasn’t designed to detect how the vaccine affects contagiousness or herd immunity. So can’t assume that reduced mild COVID will improve those things as it could as easily cause atypical symptoms which didn’t warrant a PCR test and allowed participants to interact with others while infectious.
On 2021-08-26 16:37:49, user Larry Melniker wrote:
The issue with Dr Hoffe conjecture is connecting D Dimer results, which are nonspecific, with serious ischemic events, which require specific testing results. He may be speculating on a True-True, but unrelated phenomena; otherwise D Dimer would be a routine part of ACS rule out work-up.
On 2021-10-26 20:40:41, user Gorra Lopez wrote:
I'm intereressed on how can you explain the high number of cases and deaths of covid in Israel and UK for example, despite high percentage of vaccination.<br /> (sorry for my bad english)
On 2021-10-23 05:44:10, user Syed wrote:
How do you see a lessening of infection by over 90% and say that there is no evidence that this vaccine saves lives. Just by the decreasing of infection, the viral prevalence would go down and lead to less infections overall. With lower infection, we would have lower deaths overall, as well. Not to mention, this study’s demographic isn’t well suited for a mortality rate study. I had said in a previous comment that it was exclusively young teens; I realized that I misread >16 years of age for <16 years of age. Still, the median age of 51 and 79% of the participants having no comorbidities does not allow for good data on mortality rate reduction. In order to test for that, we would need to compare mortality rates exclusively among those with comorbidity or those at high risk of severe infection. There is, then, an ethical dilemma here as those at high risk of mortality need preventative treatment, and withholding such treatment with placebo groups would be unethical. So, we usually analyze vaccinated versus unvaccinated individuals and their mortality after hospitalization, and the data is pretty clear that those who are vaccinated and hospitalized have a much better outlook than those unvaccinated.
On 2021-08-06 21:05:59, user BiotechObserver wrote:
Again, peanut oral immunotherapy is another example of something that was dosed repeatedly and chronically over that period of time. It's not the same thing as a vaccine administered once or twice. But you're also mistaken. The pivotal (pivotal meaning phase 3 trial used for FDA approval) Aimmune trial readout for peanut oral immunotherapy was at the 1 year mark. At that point a double blinded food challenge was done, and then the patients were *unblinded.* There was additional followup and safety analyses beyond this point, but the blinded portion of the trial ended at 1 year. (Btw, their positive results were announced in February 2018, and they filed for FDA approval by December 2018. Not with 2 or 3 years of followup as you claimed). This is yet another example, which supports my point, of a trial where primary efficacy readout was at one point in time, and safety followup continued after that point in an open label fashion. It's very common.
And no one in their right mind believes that the peanut sensitization that took place in the trial will cause mysterious adverse effects years later that are currently hidden in the body somewhere. The concern about safety with peanut oral immunotherapy was whether the "dose" of peanut will cause allergic reaction, and later on, how much peanut they can now safely tolerate and how much peanut will still induce an allergic reaction. What long term side effects did you imagine would occur?????
Before that food challenge was done and before the trial was unblinded, there was no proof of efficacy. Afterwards, there was. In the case of this vaccine trial, efficacy was demonstrated so the participants could not be forced to remain in a placebo group when vaccines became widely available.
Expecting longterm side effects (that don't emerge in the first say, 6 weeks after the shots) from a vaccination is unreasonable and bizarre. You present this case as if it's a highly likely outcome when in fact it's the polar opposite. There are placebo controlled trials ongoing in children and those will be used to determine whether these are approved for children, not longer followup in the adults. But even so, the longer followup continues, just as it is supposed to.
On 2021-07-27 14:06:37, user VailShredBetty wrote:
The conclusion of less diversity in the vaccinated leading to less infection? How do we draw this conclusion based upon less diversity? I think it takes time for a virus to work around a vaccine, no? Seems a little presumptuous. Logically speaking, this type of virus, like flu, will not be 'eradicated' by a vaccine....like all the others. (smallpox and polio were not eradicated by a vaccine....those are literally the only two examples people often give) Life will find a way and when we let viruses run their course, proper herd immunity is reached. lessons fro the past (compulsory smallpox vaccination) created more issues and actually spread the infection with those vaccinated 3 and 4 times dying at higher rates. Seems like this study was stopped way too early to make the conclusions given.
On 2023-06-18 16:46:29, user Matt Kmiecik wrote:
This paper was published online in PAIN in April 2023: 10.1097/j.pain.0000000000002909
On 2021-08-11 01:45:23, user Sli wrote:
Would be useful if you add a table listing the time from full vaccination for all those green dots beyond 10 days.
On 2021-08-01 15:04:13, user VirusWar wrote:
Hello, I've got serious doubts about this study :<br /> * performing RT-PCR tests up to 50 Cycle Threshold is not reliable. For exemple virus was found in Caravage (Michelangelo Merisi) body dead in 1610 with 45 CT !<br /> * vaccinated people were on one site and different testing methods were used on each site, so there is a systematic bias<br /> * for plenty of patients, included vaccinated ones, the study reports viral load up to 4 weeks. This is not emphasis in the text but it is quite extraordinary according to what we know currently. Details of viral load data and screening methods should be shared to check this
On 2020-04-07 23:34:47, user Snap wrote:
We are also publishing similar results for China, Italy and USA<br /> https://www.medrxiv.org/con...
On 2020-06-26 08:49:57, user Christian Schultze-Florey wrote:
This article has been accepted for publication in EBioMedicine: https://info.thelancet.com/...
On 2020-06-03 00:41:56, user Delemir Delev wrote:
you should also have concluded that there is very good agreement between Wantai Euroimmun and DiaSorin (ELISA vs.AA).
On 2020-07-23 05:37:54, user Greg Dingle wrote:
There is a bad typo in the abstract here:
103-105 RNA copies/min
vs
10^3-10^5 RNA copies/min
in the paper.
On 2020-04-20 15:36:38, user Philip Davies wrote:
Interesting study, thank you.
This is another study that attempts to ascertain if oral HCQ tablets can be of clinical use in patients more than one week into symptomatic disease, hospitalized with bilateral pneumonia and with evidence of established inflammatory reaction (cytokine storm). That's a big ask for any oral medication.
The study is again small (both arms have less than 100 patients). The most significant outcome measured (death) is realized in very small numbers (3 and 4). The confidence levels are extremely wide.
The are several problems with this study. There are marked differences in the two populations. The study honestly attempts to accommodate these confounding factors using a propensity score method (IPTW). Normally this method is valuable but here I can’t see that it has been well applied.
It pays to look at the raw data. There is a significant difference (between the two arms) in the initial intensity of disease.
At baseline (admission), HCQ arm comprises 78.3% men (>20% more of these higher risk patients than control arm with 64.9%); HCQ arm has 21.9% patients with more severe disease in the form of CT showing >50% lung affected). This is >80% more than in control arm (12.1%). HCQ arm has 90.5% patients with CRP > 40mg/l (CRP is a good indicator of impending/current severity). This is 10% higher than control arm (81.9%). HCQ arm had median O2 flow on admission = 3 litres/minute (50% higher than control arm at 2 litres / minute).
So, at baseline, the HCQ arm had significantly more patients with severe disease than control arm. The O2 flow is actually more significant than first sight would suggest. 2 l/m is always the first step in O2 therapy. The data shows us that most patients in the control arm could hold their sats on this first step therapy. This also means they may have been OK on just 1 l/m. We don't know. But we do know that most patients in the HQN could not hold their sats at that first step and needed an increase (3 l/m ... so that's 50-300% more O2 than control arm).
Admittedly there were other confounding factors which compromised the control arm more than HCQ arm (some chronic disease elements). But it's clear to me that disease severity was markedly more established in the HCQ arm.
Another factor to note: the HCQ treatment was not initiated at the moment those baseline values were obtained (on admission). The HCQ was initiated within 48 hours. So let’s look again at the timelines. The median duration of symptoms at admission shows that the HCQ arm comprised patients who were further into worsening illness: they were admitted on D8 compared to control, D7. They may not have had HCQ initiated until D10.
Then we look at outcomes: the raw data shows that the disadvantaged HCQ arm actually does better in the two most important outcomes, death and ICU admission. The HCQ delivers 12% less death and ICU admissions than the control arm. Admittedly the numbers are small so the confidence levels are very wide.
So what does that tell us? The answer is not much. But even accepting the poorly aligned baseline for disease severity, the outcomes with their wide 95% confidence levels do deliver a mildly promising indication on the 'swingometer'. They point more towards benefit than harm when using HCQ in this advanced disease state.
As a final comment on significant side effects (increased QT interval) from the use of HCQ. Once again, this trial used a particularly high dose of HCQ (600mg/day...right at ceiling dose for rheumatological use and much higher than the total antimalarial treatment dose). They also added azithromycin (another QT lengthening drug) to 20% of the HCQ patients. It’s not surprising at all to find such QT lengthening in a sick, more elderly population taking these medications in particularly high doses).
Further trials should utilize conservative doses of CQ/HCQ which have been proven safe in many millions of patients.
We don't yet know how this will pan out. We urgently need proper evidence. Statistically robust studies into prophylaxis and early intervention are likely to deliver the most interesting results.
Dr Philip Davies<br /> Aldershot Centre For Health<br /> http://thevirus.uk
On 2021-09-29 13:34:08, user Bigly conspicuously··· wrote:
Your comment is completely wrong (completely backwards).<br /> Asymptomatic only means:
a person who's known to be infected or disease-bound does not exhibit observable symptoms.
On 2022-11-08 00:00:25, user japhetk wrote:
This preprint makes the wrong comparison.<br /> It is possible for there to be more than one cause of death in the Ministry of Health, Labour and Welfare's report of suspected post-vaccination deaths. Myocarditis and myocardial infarction can be the cause of death even if myocarditis is listed.<br /> Demographic causes of death, on the other hand, list only one underlying cause of death: there can only be one cause of death per death. Therefore, it is not possible to compare the frequency of occurrence of the two.<br /> Also in terms of demographics, in 2021 there are 155 deaths from acute myocarditis I40; in 2020 there are 168; in 2019, 162; in 2018, 177; and in 2017, 167. Why are there the fewest deaths from acute myocarditis I40 in 2021 in the last five years if deaths from myocarditis caused by vaccines are identical to deaths from demographic I40 as the underlying cause?<br /> So from this you can see that the authors have clearly made the wrong comparisons and drawn the wrong conclusions.
Translated with www.DeepL.com/Translator (free version)
On 2020-04-07 16:32:46, user Roberta Caruso wrote:
Using the Diamond Princess (DP) as a case study, the authors estimate an IFR 'slightly less than 1%, although statistically affected by a rather large uncertainty due to the small number of deceased'. It should be noted that when analyzing data on such a reduced 'statistical' sample, it is not appropriate to refer to statistical uncertainties - the sample is too small to actually compute statistical errors that have any sense for the analysis. One should instead focus on the analysis of the systematic errors that affect the estimation of IFR in order to obtain the actual relevance of the estimation obtained by simply dividing the number of died passengers for the total number of infected people on board. In other words, since these errors cannot be computed for IFR on the DP, this number should not be used as a benchmark for further analyses. <br /> The final estimation of the total number of infected cases is so vague (line 332-333 and line 355-356: between 660 000 and 3 300 000 - a difference of 500%!) that there is no practical use for it. The lower boundary of the estimation is questionable in itself, given the criticalities of the estimations performed using the DP case study, thus implying a possibly larger error bar on the estimation of the total infected. <br /> Such a large uncertainty poses serious questions on the scientific soundness of the study.
On 2020-04-02 10:15:46, user Francois Alexandre wrote:
This study is interesting, but the reasoning is incomplete. Indeed, it takes about 1 month to die from the time when people get infect (about 10 days of incubation + 20 days between symptoms onset and decease). Therefore, the real number of patients infected is between 670 000 and 3.3 millions 1 month before the time where the decease number was collected, i.e. near the end of February. For an estimation of the number of cases at the end of March, we should wait for the number of deceased patients at the end of April.
On 2020-08-18 17:33:47, user Rodolfo Rothlin MD wrote:
Dear Dr. Turgeon,<br /> Thank you for your commentary and your interest in our manuscript. Please, find my answers below.
The rationale for selecting July 31st was made for several reasons: First, we assumed that by that date we would have between 50 and 100 patients. Although our estimated sample size was 390 (which we rounded to 400), it is worth noticing that this number accounts for a scale factor on both the mean and the variability estimations; without these factors, the estimated sample size was 52 patients total, and with only a variability factor of 2 the total estimated number was 100. Therefore, we evaluated that July 31st was an appropriate time to make the first interim analysis. Second, as you may know, Argentina was expected to be peaking around that time. And it actually seems that it may be doing it right now. So, a second reason for that date was our prediction that if the results were valuable it could be a useful information for our health authorities. The trial is still ongoing and a second interim analysis will be carried out at 140 patients.<br /> 2. In version 1 of the article on this site, the Methods section had a sentence that stated "No concealment mechanism was implemented". This was subsequently removed in version 2 yesterday. Please clarify what is meant by this. Did the authors mean to imply that allocation concealment was not performed, or was this an erroneous statement intended to describe the unblinded nature of the study? Please also describe the process for treatment allocation and how allocation concealment was maintained.
The sentence you refer to was removed because it was inaccurate. The problem emanated from the fact that our protocol did not foresaw a concealment mechanism. However, during the conduct of the trial, although no mechanism like closed envelopes with randomization was used, on site enrollment was made by an investigator and randomization was made by a second investigator who was unaware of the clinical characteristics of the participant. We are confident that no bias towards the control group was present as reflected by data on table 1 of our manuscript.<br /> 3. The authors describe a change in the primary outcome in terms of timing of CRP measurements. However, I note that the clinicaltrials.gov summary of this trial previously had an entirely different outcome as the primary outcome, with CRP only described as an exploratory/tertiary outcome. The authors should describe the timing and rationale for switching the outcome from a clinical one (need for supplemental oxygen in the first 15 days post-randomization) to the inflammatory biomarker CRP.
As you might have read in the methods section of our manuscript, data from our trial was uploaded by a third party. Unfortunately, endpoints from a working version of the protocol were submitted. This was corrected as soon as we noticed it.<br /> 4. Despite changing the timing of CRP measurements, data on this modified primary outcome of CRP was missing in a large proportion of patients at day 5, and in the majority of patients at day 8. Further details should be provided regarding the reason for missing data, how this was handled in their analyses, and how this should temper conclusions.
Data missing from days 5 and 8 were related to several factors. Some patients were discharged before day 5 and before day 8. Others were lost at day 5 for logistical reasons. <br /> No imputations were done to account for these values.<br /> 5. Finally, performing an interim analysis and disseminating their results in the midst of an open-label trial with subjective endpoints can pose challenges to maintaining impartiality. The authors should describe how they will mitigate potential allocation, performance, and detection and attrition bias during the remainder of the trial.
We disagree with CPR measurements being subjective<br /> Again, thank you for helping us clarify these points.<br /> All the best,<br /> Rodolfo Rothlin
On 2020-02-26 19:26:13, user ricwerme wrote:
Did I miss the exported case counts the paper used to determine the internal # of cases, or is it just "three" with assumed missed cases?
The abstract says "suggesting a underlying burden of disease in that country than is indicated by reported cases." Should that be "a greater underlying..."?
On 2025-11-12 10:19:44, user Martin Dyrba wrote:
The final journal article of this preprint just has been published:<br /> https://www.nature.com/articles/s41598-025-26227-2
On 2020-05-11 17:35:00, user DickRuble wrote:
s-CRP has been shown to correlate with BMI and waist circumference. We know that Covid patients with obesity fare much worse than other patients. It's been shown by others that low vitamin D also correlates (though causality is not established) with high s-CRP. I fail to see the novelty of the finding to justify publishing this paper. A majority of elderly patients show "vitamin D deficiency". They also fare poorly when infected with Covid-19. They also have loss of hair. Should we make them wear wigs, because loss of hair correlates with severity of symptoms?
On 2021-06-28 14:02:58, user Jomar Fajardo Rabajante wrote:
The updated version of this manuscript is published in https://link.springer.com/a...
On 2020-06-07 17:05:34, user Dr Shyamapada Mandal wrote:
Dear authors,<br /> Nice presentation; but before lockdown I, India adopted some containment measures that are required to be reflected in your work. Plus the current situation is different. <br /> Thanks,<br /> Dr. Shyamapada Mandal, University of Gour Banga, Malda-732103, West Bengal.
On 2021-07-06 02:38:59, user Nonyo Business wrote:
This is consistent with the 2019 WHO mask guidelines, saying there is little evidence they work but that in an epidemic maybe give them a try.
On 2021-05-29 03:44:48, user You Know you thought the same. wrote:
But the CDC has known this for years.<br /> https://wwwnc.cdc.gov/eid/a...
On 2021-10-20 09:17:00, user DanT wrote:
They wouldn't! They would be in the "previously-infected-and-once vaccinated individuals" (see Model 3). <br /> Again, this is a observational study, not a RCT.
RE: "Imagine someone in your unvaccinated cohort. Soon after the initial study date they develop an infection. 5 weeks later they have recovered and decided they should have had the vaccine, so they get one. Because you have insisted this group remain vaccine free you throw them out of the group and you lose their data. You have just thrown out an infection. Do this just a few times and it is guaranteed that your ‘vaccinated’ group is not reporting as many infections as it actually experienced. This easily accounts for the effect you report."<br /> This is nonsense. It assumes they would have been also infected (which as the study found is very rare) and that this simply accounts for the major difference!
On 2021-09-16 03:49:51, user Truther wrote:
They only used the PCR test to determine previous infection and it seems the re-infection rate is lower than the false positive rate on the PCR test so the question is has that been addressed in the quoted error?
On 2021-09-06 00:40:45, user Chentally Mallenged wrote:
Massachusetts 7 million people 66.23% fully vaccinated 76% 1 shot 609 people in the hospital. Same vaccination % Half the number of people and almost double the hospitalizations as Ontario. <br /> As of August 28, 2021 there were 4,483,344 fully vaccinated people and there were 19,443 cases in vaccinated people 651 of those 19,433 cases resulted in hospitalization and 144 cases resulted in death based on information reported to date Now what point were you trying to make?<br /> https://www.mass.gov/doc/we...
On 2020-06-21 14:24:56, user Robin H wrote:
Hi. I would like to ask for a clarification. The observations seem favorable to the treatments, notably before statistical adjustment.
Based on Table 1, some parameters needed obvious adjustments, such as male sex, obesity (26% in HCQ+AZI vs 12% in CTRL), current smokers, hepatic Failure (11% HCQ+AZI vs 4% CTRL), diabetes, asthma or COPD (12% HCQ+AZI vs 7% CTRL), overrepresented in the treated groups.
Figure S4 shows covariates before and after the IPTW.<br /> But PaCO2 and PaO2 are shown to be disbalanced before IPTW, and so were finally reweighted. However, in Table 1, there is no apparent disbalance for those parameters!
Am I wrong? Did I miss something?<br /> Since the adjustment "erase" the decrease in mortality for HCQ treatment, we need to be sure that reweighting was correctly done. There are a lot of transformation of the raw datas in this article… And also by a pretty "interventional" causal adjustment. <br /> So it is strange to have this final result after causal adjusment+IPT, when we expect that adjusting for obesity, hepatic Failure, male sex, asthma, etc, overepresented in the HCQ(+/-AZI) patients, would have favored the results for the treatment groups… Right?
EDIT: In complement, there are other mismatches between Table 1 and Figure S4, peculiarly for HCQ+AZI treatment.<br /> In Table 1, asthma, COPD and obesity are largely overrepresented in HCQ+AZI.<br /> But in Figure S4, those parameters are displayed as pretty balanced before adjustment...
Asthma is depicted as the most balanced parameter before adjustment in Figure S4, despite an important difference in Table 1 (13.2% HCQ+AZI vs 7.4% CTRL)...<br /> Something seems wrong. Or please, do not hesitate to indicate what I am missing. In this state, I would only consider raw and not adjusted datas.
On 2020-07-07 20:43:11, user Dr. David Ludwig wrote:
The final peer-reviewed manuscript is now published, with free full text available at Journal of Nutrition: https://academic.oup.com/jn...
On 2025-11-14 12:17:48, user Kaur Alasoo wrote:
This prepring has now been published at GENETICS:<br /> https://academic.oup.com/genetics/advance-article/doi/10.1093/genetics/iyaf210/8266564
On 2020-04-04 11:58:15, user Mick James wrote:
Doesn't this mean that everyone has to provide 6x as much sample material? Is collecting that amount an issue?
On 2025-04-10 21:33:23, user Will wrote:
line 165: It should be Figure 3 and not Figure 1.
On 2021-06-09 22:00:05, user Dr Chad wrote:
This would coincide with virtually all other RNA respiratory viruses studied. I am surprised that the initial instinct seems to be resistant to recognizing that natural immunity would be inferior to induced immunity when we have no precedent to suggest that would be the case.
On 2021-09-05 04:54:49, user dramour wrote:
The data used to make the CDC recommendations about mask wearing in children is also a Pre-Print. If you're critical of this pre-print, I would assume you're also critical of the push for use of experimental vaccines prior to their approval, and of the use of mandated vaccinations before we can measure long-term effects of a treatment.
On 2021-08-30 16:11:49, user Eduardo Amorim ????????? wrote:
Can you please explain how mf is calculated? You ms says "Mf was calculated as described previously [3]." But ref. #3 doesn't explain how mf is calculated -- at least I can't see it.
On 2020-04-14 05:35:46, user MAGA GENIUS wrote:
It is not the job of the professor to dumb down his findings because people may be misled. And why would you give the absolute risk of dying from Covington-19 last year? What matters is this year and the future. And based on the evidence we have so far it is very low. You're implying that the professor is ignoring future risks. But there is no evidence of future risks. Why would the absolute risk being very low mean we should stop vaccine developments? No where in his article does he say that there is no future risk of recurrence. As for quarantine that is a different question. The evidence in this article supports stopping it. You have no basis to say that the mortality data may increase by a factor of 10,000 in the future.
On 2020-04-13 12:12:59, user Mark Upton wrote:
Dear Prof Ioannidis and colleagues
In other work, you have cautioned about possible risks to public health when responding to exaggerated claims about SARS-Cov-2. A flip-side is that there may be risks to occupational health from accurate manuscripts that, never-the-less, may be taken out of context by policy makers and employers keen to reboot economies and businesses after perceived peaks in the local epidemic curves of Covid-19.
I worry that one of the main messages conveyed in the abstract of your manuscript, i.e., that the absolute risk of death from Covid-19 among individuals aged <65 years is low, and consistent with risks encountered on the roads, may be mis-applied in occupational settings as the “lockdown” is relaxed. Workers aged 40-64 years, an age-group in which your manuscript recognises that most of the deaths under age 65 occur, may be exposed to higher risks of Covid-19 related death in occupational settings with increased social mixing, compared to the rather theoretical diluted risks estimated across the entire population.
I entirely understand the reason why your manuscript uses a binary age classification at 65, and note that the first paragraph of the discussion section of the manuscript mentions the concentration of deaths at ages 40-64. However, this is omitted from the abstract, no doubt for reasons of word count, and so may be overlooked by time-pressed readers keen to learn lessons for policy and employment practice. Also, it would be helpful to make the rather obvious point that numbers dying equals fatality rate x numbers infected, and that whilst numbers infected may be low across when averaged across entire populations, infections cluster, and this includes occupational settings.
So, good paper, but please consider some caution in how your message is conveyed. I speak as someone with relatives, and patients among the key workers soon to be re-mobilised.
Kind regards<br /> Mark Upton
On 2020-04-17 06:28:45, user Paul Maxwell wrote:
But by taking a snapshot in time as the basis of the "absolute risk" is useless in that endeavor. The 'risk' by that method will continue to rise until the event is over. <br /> However the real risk is actually dropping due to their being less cases therefore less people to transmit.
On 2020-06-30 16:50:34, user Rhyothemis wrote:
Was lysophosphatidylinositol (LPI) measured in this study? I don't see it in the text or figures. A study of SARS patients found elevated LPI:<br /> https://pubmed.ncbi.nlm.nih...
If anyone has information on LPI in covid, please post a reply.
On 2021-10-05 01:50:50, user C.K. Franz, M.D., Ph.D. wrote:
For final version of this preprint: DOI: 10.1002/acn3.51416. <br /> Alternatively, see: https://pubmed.ncbi.nlm.nih...
On 2021-02-03 07:20:39, user Bildung Aber Sicher CH wrote:
This study failed to mention school autumn holidays. The data they have is, in reality, from a period of low community transmission (not high as they mention) because the 2 weeks holidays coincide with the start of the second wave in the canton. <br /> The sampling starts a week after school resumed, therefore too early to for cluster build up in schools/classes, especially when looking at antibodies which will only appear some weeks after infection.
It also didn't consider any new studies as references, when plenty was available that contradicts their assumptions and conclusions at the time of publication.
On 2020-08-14 22:06:28, user Sarfraz Saleemi wrote:
Thanks for the comments:<br /> The HCQ group with mild disease was also compared with non-HCQ which showed the same result. In particular, multivariate regression analysis concluded that only two factors were important in delaying time to PCR, HCQ and age
On 2024-11-15 06:07:16, user Nina T ang wrote:
May I have access to the FFA dataset please?
On 2022-06-07 20:44:20, user Iver Juster wrote:
Suggestion: Table 4 shows state of subjective recovery at various time points according to various forms of treatment. Suggest making it clear that the top 3 rows together sum up the 23 subjects, and the 4th row (IVIG) is a subset of the 23, who had not recovered by months 5-9, and were given IVIG. PS: Really good to see post-vaccination sequelae investigated; much to learn about how immune responses go awry here.
On 2020-04-11 00:50:14, user Kirsten McEwen wrote:
There's clearly a need to monitor COVID-19 sequence mutations over primer sites. Is there an initiative to share this info among testing facilities?
On 2021-04-12 15:40:57, user Philip Machanick wrote:
I wonder how the authors define mild to moderate when 4 patients died.
On 2024-06-21 10:46:13, user Mamadu Baldeh wrote:
This preprint has undergone peer review and is now published in BMC Infectious Diseases. The published article can be accessed via DOI 10.1186/s12879-024-09524-5 or directly at https://bmcinfectdis.biomed...
Please link this preprint to the published version
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 2021-08-09 10:52:29, user old farmer wrote:
It's my understanding that this study was conducted in the summer of 2020 and that ivermectin has been widely used in several countries like Brazil & India with very serious Coronavirus outbreaks for some extended period. If ivermectin was so effective why hasn't it been widely acknowledged. I can not believe that tens of thousands of doctors would ignore a really effective treatment if it existed in the face of this pandemic.
On 2023-10-23 04:42:47, user CDSL JHSPH wrote:
Dear Dr. Bi et al,
This is a valuable paper that examines the potential influence of prior-season vaccination on the risk of clinical influenza infection. You recognized that past research has shown that prior-season influenza vaccination is associated with an increased risk of clinical influenza infection among vaccine recipients. A key limitation of these previous studies is their reliance on a test-negative design, which fails to consider the intra-season timing of vaccination and the individual's history of clinical infection in the preceding season.
A noteworthy finding in this paper is that individuals who receive repeat vaccinations tend to get their vaccines earlier in the season compared to non-repeat vaccinees. Remarkably, even when after adjusting for this discrepancy in timing, it does not significantly alter the observed higher probability of clinical infection in repeat vaccinees.
Clinical infection seems to play a dual role in influencing vaccination behavior. First, it serves as a motivator, prompting individuals to get vaccinated in the following season. Second, it also provides some degree of protection against clinical infection of the same subtype. However, even after accounting for recent clinical infections, the effect of prior-season vaccination on the current season's clinical infection risk remains not significantly different.
A potential mitigating factor, subclinical infection, is theoretically posited to attenuate the effect of prior-season vaccination. However, you were clear in the paper that this aspect is still largely theoretical and necessitates further investigation to determine its actual impact on vaccine efficacy.
The primary contribution of this pre-print lies in its careful consideration of confounding factors, specifically the intra-season timing of vaccination and the history of clinical infection in the previous season. By addressing these variables, it challenges the established findings of prior research, which suggest an elevated risk of clinical influenza infection associated with prior-season vaccination. These insights carry significant public health implications, particularly in the realm of vaccine policy and compliance.
The paper is methodologically robust, particularly in the sections that explore the impact of timing and previous clinical infection. However, the discussion of subclinical infection is less conclusive, as it relies on a theoretical model and a pseudo-population. The exact details are not in the main body of the paper and was referred to the supplemental section. As the explanation for the main findings of the paper is hinged on subclinical infection, it may be helpful to develop this idea further in the main text.
In terms of its presentation, the paper is well-structured with clear delineation of sections, and the text is appropriately complemented by the figures. The inclusion of the "infection block hypothesis" in the discussion aids in facilitating a deeper understanding of the research.
Overall, this paper marks a significant breakthrough by challenging the conventional approach to assessing vaccine efficacy, incorporating the roles of vaccination timing and previous clinical infection. It also highlights the potential importance of subclinical infections, opening important conversations and may lead to enhanced strategies for data collection in this context.
We truly appreciate you sharing your pre-print with us.
On 2021-05-22 00:45:46, user Michael Plank wrote:
The model and results in this pre-print are now part of an article published in the Journal of the Royal Society of New Zealand, available open access at: https://doi.org/10.1080/030...
On 2020-05-22 02:17:36, user Mark Pundurs wrote:
The authors apparently assume that falling R-effective after March 15 was due to social distancing and stay-at-home - whereas R-eff in fact drops as an epidemic progresses even without increased preventive measures (cf https://www.cebm.net/covid-... "https://www.cebm.net/covid-19/when-will-it-be-over-an-introduction-to-viral-reproduction-numbers-r0-and-re/)").
On 2021-07-11 16:46:18, user A Clarke wrote:
Also, what effectiveness are you measuring? Symptomatic desease it seems however hospitalization and death is the real problem and this report out of the UK seems to say those rates are higher among the vaccinated; see page 13; https://assets.publishing.s...
On 2021-03-20 03:55:01, user Jean Tyan wrote:
Very inspiring and socially relevant work! Regarding your DAG, social determinants of health appear to be confounders on both the pathways between biological aging and healthspan and between biological aging and biological aging* due to the directionality of the arrows. I’m not sure I quite understand the correct analysis approach in this situation when evaluating the potential relationship between biological aging and healthspan—should regression models adjust for social determinants, even though they are conceptualized as an upstream cause? In addition, do you have any thoughts on how social determinants and weathering may be linked differently to aging, depending on the type of health outcome measured? A recent systematic review (Forde et al., 2019) reviewed studies on weathering examining a range of different health outcomes (e.g., allostatic load, mortality, telomere length, etc.) and found conflicting results—I would be interested to hear what you think of how these outcomes may relate to biological aging. Many of these variables are also available in the HRS data and could definitely be interesting to explore using the mediation analysis methods you describe here!
On 2021-10-21 23:30:23, user hiennessey wrote:
Dr. Adragao et al,
It was with great interest that I reviewed your article, and I thank you for taking the effort to report on an important subject. Atypical atrial flutter is more difficult to treat as compared to typical atrial flutter, and precise mechanism generating atypical ECG flutter patterns can only be determined by mapping and pacing EP studies. Your finding of low-density and prolonged LAT-Valley in a heterogeneous low-voltage area being an important triad to determine areas for successful ablation seems very relevant. I also appreciated the technical description of the mapping techniques as well as the diagrams which showed conduction through normal and scarred cardiac tissue, which would be helpful to other researchers who would like to duplicate your work. <br /> I share your opinions regarding the limitations of your study, the main ones being the reduced sample size, the "survival bias" and the unicentric retrospective aspect. As a clinician and a public health student, I would have liked to see more clinical information regarding the 9 patients chosen for the study: were they on any medications that may have affected their success rates, did they have prior cardiac surgeries. This information, if available, would have been helpful for me to determine whether my patients are similar to your study population. Furthermore, you also indicated that 2 of them had a previous typical AFL ablation and 1 patient had a previous mitral AFL ablation, and 3 patients had never performed a catheter ablation procedure. Therefore, it seems that your study is of a mixed patient population consisting of those who once had typical and now atypical atrial flutter, those who have had prior ablation which failed, and those who are undergoing ablation for the first time. It is interesting to see that all types of patients were successfully converted using your triad technique. However as you noted, there is no information regarding whether these patients successfully remained in sinus rhythm, or whether they converted back to atrial flutter. Furthermore, having such a heterogenous population may make stratification of results more complicated.<br /> Thank you again for a wonderful paper. I look forward to seeing future studies on this important subject.
On 2022-01-22 13:19:19, user Torsten Selle wrote:
Is it possible to extend the measurement series to smaller aerosols (5-2µm). I am asking in regards to aerosols that cannot be stopped by ffp2 or n95 masks.
On 2020-08-21 09:47:26, user David Simons wrote:
Hi,
It looks like this may already be formatted for submission but you may want to revisit the inclusion of Qi (reference 16) in particular.
The relevant pre-print has only dichotomised smoking into current and non-current smokers. The authors do not make clear that non-current smokers are never smokers. I've found this difficult to mannage in our own work so we do not include them in meta-analysis if there is no explicit never smoker category. It is unclear from your manuscript how you manage this.
Kind regards,
David
On 2021-08-24 15:12:55, user Maria Kozlova wrote:
Thank you for the research!<br /> But perhaps the descriptions for Figures 5 A and B in the text and in the picture are confused?
On 2020-10-23 02:17:54, user Pham Quang Tuan wrote:
Your data (Figure 1) does not support the conclusion that "As a linear trend from the first week of April, adjusted mortality risk decreased by 11.2% per week in HDU, and 9.0% in ICU". It may have been so at the beginning but has since slowed down, and would be nearer to 5% per week.
On 2025-08-30 04:18:58, user Cosmin Sandulescu wrote:
This paper is fascinating since it finds NfL changes after 24 weeks on lithium supplements. I have a few questions for the authors: In Table One, since you report the median, can you also report the IQR? Is it possible to add the mean? Please add the data at 24 weeks for NfL and GFAP. Can you explain how you calculated the values for the median % difference? Does Figure 1 show boxplots for the median % difference for each person, or is the difference for each individual? Can you explain the method by which you determined the lithium levels? Did you also measure lithium levels at the beginning of the study?
On 2020-04-27 10:23:45, user Gareth Gerrard wrote:
Hello - can I ask a question? For the data in Fig 1a, you performed a Mann-Whitney U test to show significance between the two methods. However, do these data include multiple paired samples? If so, since the data sets are not independent, would a Wilcoxon test have been more appropriate?
On 2020-05-07 02:49:41, user Tiruneh Hailemariam wrote:
Nice work! why is your observed limit of detection in copy number very high, i.e, ~5000/ml? CDC package insert says ~1000 copies/ml.
On 2022-08-01 17:42:19, user Nils Yang wrote:
Published at Lancet Child & Adolescent Health https://doi.org/10.1016/S23...
On 2025-03-15 20:46:54, user Ahmad ahadi wrote:
The dependence on sources like media articles and open letters—rather than a robust base of peer-reviewed literature—calls into question the credibility of the paper’s key assertions and diminishes its overall scientific impact.
In my opinion, the article's title is inaccurate. If populism is intended to refer to demagoguery, the people of Iran have demonstrated a more appropriate approach to medical knowledge and the truth about vaccines compared to those in many other countries.
On 2020-08-25 10:26:04, user Richard Harrison wrote:
Interesting paper. Perhaps unintentionally, illustrates the danger of relying on a T&T system which is worryingly slow, also bearing in mind that incubation periods may be shorter in some cases. Really need to encourage people with symptoms (suspected cases) to self-isolate immediately and notify known contacts directly to suggest they do the same unless/until reliable negative confirmation is received, to contain outbreaks more effectively and help move towards Zero COVID.
On 2023-06-29 09:40:33, user Nensi wrote:
The idea behind this study is truly interesting and highlights the critical importance of addressing the issues surrounding poor reporting and the quality of systematic reviews.<br /> However, some things could be changed to improve the quality of this study. Below you can find some of my comments regarding your manuscript.<br /> 1) The manuscript could use extensive language editing, as there are many grammatical and spelling errors. Many language editing programmes can be very useful for these purposes (Grammarly, Instatext etc.).<br /> 2) You begin the Methods section with the aim of your study, but you state that the aim was to do a study. You can see why that does not make sense. The aim of your study was to do a study. You should report here the specific purpose or what you wanted to assess (for example, the aim was to assess the reporting quality of systematic reviews published by authors from India from 2015 to 2020).<br /> 3) In Results, you decided to report the data in text and with two figures. However, when you have so much descriptive data, it could be presented more clearly with just a table. The table allows the authors to store large amounts of data in a small place, making it easier for the readers to go through the data and understand the results. <br /> 4) Another detail about presenting the results is that they should be written in the past tense instead of the present tense you used.<br /> 5) Also, when presenting descriptive data, it is recommended to report both absolute and relative numbers (for example, „Only 20 (15%) of the reviews have been registered in PROSPERO registry“).<br /> 6) You could benefit from using STROBE reporting guidelines for observational studies (https://www.equator-network... "https://www.equator-network.org/reporting-guidelines/strobe/)"). Reporting guidelines are handy in ensuring you have reported everything that needs to be written in an article.<br /> 7) There is also much room for improvement regarding the referencing. A small detail would be the in-text referencing where you put [1] after the full stop. So the general rule would be that if you use brackets, it should be placed inside the sentence, and if you want to put the number in superscript, it should be after the sentence. That could use a bit of tidying up. <br /> 8) Additionally, regarding the referencing, you have used different styles of referencing in the reference list and some of the references are not referenced correctly or at all. There are many programmes that can help you organize and write the references (EndNote, Zotero, Mendeley etc.).<br /> 9) And one more thing for future referencing, even though a study is methodological, it should be preregistered. All studies should be preregistered to promote open science and transparency in conducting scientific research.<br /> I hope these suggestions will help. Good luck with your work!
On 2020-08-27 23:02:34, user drklausner wrote:
This is an important and innovative report demonstrating the value of hospital based surveillance and how that informs our understanding of the Covid-19 epidemic. It demonstrates that there was an early and rapid introduction of cases resulting in hospitalizations in February and March. <br /> The continued monitoring of hospilazation data showed the severity of the "second wave" in the end of June and July was less severe than some thought.<br /> The report also describes the heterogeneity of the epidemic in the United States and both the time-dependent and geographical variation. Understanding that heterogeneity is critical such that the United States as a whole is not considered monolithically or with a one-size-fits-all approach. <br /> In terms of the measure of epidemic growith, the rate of change in incidence over time, that is similar to acceleration or deceleration in velocity and a useful parameter for epidemic monitoring. Epidemics may decelerate prior to declines in incidence.<br /> Congratulations to Dr. Bhatia
On 2022-01-21 05:16:30, user Victor Schoenbach wrote:
I found this article both valuable and important, but I do not understand the last two sentences ("Despite the small numbers of individuals included in this study, the findings are uniquely valuable because of the early detection of Omicron infection in frequent workplace Covid-19 testing to prevent spread. In real-world antigen testing, the limit of detection was substantially lower than manufacturers have reported to the FDA based on laboratory validation.")
The first of these sentences is confusingly worded; copy editing would help.<br /> The second sentence refers to a lower limit of detection. Perhaps I do not understand the technical meaning of that term, but I would have thought that meant that the real-world sensitivity of the antigen test was higher (virus detected at a lower level), whereas the article suggests the opposite.
On 2020-07-25 23:24:04, user BannedbyN4stickingup4Marjolein wrote:
I'm not a bio-mathematician but I've had a similar idea in my head for some time. I'm not comfortable with all of the maths so to an extent I have to take some of this on trust.
But the basics of it, as I understand it, is that transmission takes place when some yet to be defined criteria are satisfied (through air, via a surface, without a mask, indoors, whilst singing, who knows?) through a temporal network. It would certainly help to understand this mechanism better, but that's not the focus of the paper.
Early infection removes the easiest nodes from this network - those people most easily susceptible overlapping with those peole with the most contacts. The mechanism of node removal is death in a few cases and post infection immunity in the majority.
Just a couple of notes of caution then:
One obvious one is how long does immunity last? Suppose some kind of herd immunity is achieved at 20% infection of the population, but that a typical population (not a densely populated city like New York) is not infected to this level until infection acquired immunity starts to wane?
The second - and I am disappointed not to see more mention of this in the paper - what if a significant element of node removal is down not to post infection immunity but to changes in social behaviour in response to the epidemic?
R is a function not just of the pathogen but of the population it infects - its density is relevant, but so is its behaviour. This applies whether one models the population as a simple homogenous mass (SIR type models) or as a set of discrete interconnected agents.
Then no sooner does everyone revert gung ho to their previous pattern of behaviour (we're at herd immunity, we're safe!) then infection takes off again.
On 2020-07-29 17:52:54, user arkay808 wrote:
Say what? Nowhere has seen 1% of their population dead. Not even close.
On 2024-10-12 09:28:56, user Alireza Mani wrote:
This manuscript has now been published. Please see the published version at: https://www.nature.com/articles/s41598-024-74351-2
On 2021-04-23 13:52:16, user Dijon Mustard wrote:
The title says "up to 12 months", the abstract says "at least 12 months". Please change the title to accurately reflect the paper's findings.
On 2022-06-28 04:59:17, user Mike Rogers wrote:
Congratulations on this very interesting study, which supports the evidence from other observational and preclinical studies that bisphosphonate therapy has a protective effect against respiratory infections. Regarding the mechanism by which bisphosphonates may confer this protection, we are surprised that you did not mention our paper published late last year in eLife, which demonstrates that the bisphosphonate zoledronate directly targets alveolar macrophages in the lung, inhibits the mevalonate pathway in these cells and boosts immune responses in vivo in mice. In our paper (Munoz et al 2021,Bisphosphonates have actions in the lung and inhibit the mevalonate pathway in alveolar macrophages. eLife 10:e72430, doi.org/10.7554/eLife.72430) "doi.org/10.7554/eLife.72430)") we suggest multiple routes by which inhibition of the mevalonate pathway in alveolar macrophages may confer beneficial effects against lung pathogens, including viral infections and SARS-CoV-2.
On 2020-03-14 11:12:53, user Ray Phoenix wrote:
How about virus riding on tobacco smoke particles? The odor of tobacco smoke is frequently detectable 10 to 15 meters away from the source. The particles are emitted directly from the lungs through the mouth. Why could a virus not be carried along? What would the half life of those viruses be? Also, tobacco has been shown to facilitate infection for other viruses.
On 2021-07-29 07:51:21, user Portal Cedip wrote:
I am surprised that a country that was punished so badly by COVID-19, due to its nihilism, purely academic debates which misled the point even after recognizing that SARS-Cov-2, get the boy sick kills children and young people, but -you know Winston- their finest hour will not come until they get sick and die in numbers that do not even represent the TOTAL burden of the disease (just 6,340 boys, of which 700 got the PICU and oh, maybe 13 died, eventually more. Who cares? Just another non caucasic problem. Sense of safety for a far away condition that colonize, infects, make CYP get hospitalized, complicates 10% and kills with a lethality of 2% ONLY. I saw my pediatric unit got exhausted due to the large number of teleconferences with boys we could not hospitalize. The crisis was burning out or infecting our teams. We were under attack but the non-traslational sweatless sirs were complaining about us being hysterical and overplaying our hands with our small patients. And our government made the impossible. A country ranked 27 in Health Services got top 10 in number of cases and deaths / 100, 000. We did not see our boys dying in front of us. But got overwhelmed at all ages. Our 19 million people´s country got 130.000 CYP infected, Three thousand were hospitalized, half of them had a critical trajectory or came back from home with TIMPS. One hundred died. Eighteen had less than 1 yo. That´s crude data. Most of it occurred during the second wave, after we naivly thought we had gotten rid of the virus (Christmas 2020). But the virus gave itself a gift from England: the variant Delta, which seized the country for 4 additional months. Now is calmed again. You trust that it got surrended to vaccination, a plan that already involves more than 65% of the population. <br /> NO<br /> I do not.
My best wishes. With personal regards from the very south of the world,
Ricardo
On 2021-01-21 12:22:19, user Michael A wrote:
Thank you for this important trial. Please include specific data on what ‘usual care’ was given to patients. Comparing dexamethasone use between treatment and placebo groups would be important. If those rates were similar your conclusion would be even stronger.
On 2024-10-03 03:31:24, user Max Lamparth wrote:
This paper is nowpublished through the CoLM conference proceedings with new results: https://openreview.net/forum?id=1pgfvZj0Rx
On 2024-10-04 13:44:45, user Pablo wrote:
Very interesting work! Our recent work ( https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00099-2/fulltext ) also explores a pathogen-agnostic mobility network to address the problem of sentinel site allocation in Brazil. How do you think this sort of approach could be translated into implementation policies in countries such as ours?
On 2025-11-17 12:57:52, user Gregor wrote:
On 2025-10-07 13:33:35, 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:
This study uses over 15,000 samples from COVID-symptomatic patients from 2020-2022. COVID and BioFire assay negative and positive samples were further analyzed with untargeted and semi-targeted metagenomic panels
16 (5%) of samples previously identified as negative were found to be positive for human respiratory viruses. Viruses included Influenza C, bocavirus, rhinovirus A and C, COVID, and one positive for human parvovirus B19
Twenty samples were found to be dominated by a single bacterial or fungal strain. 10 samples with high bacterial content were dominated by Pseudomonas azotoformans. 1 sample had fungal relative abundance of 72% (penicillium, aspergilis). Samples with bacteria/virus co-infection were also identified
On 2021-09-13 17:23:34, user Damon Hill wrote:
On 2020-07-09 17:58:50, user Jerry Lamping wrote:
This comment is about supply air grilles that are located at the end of ducts In the rooms. The exit dampers that are located in AHUs are not subject to this concert. Be careful about stating that the supply grilles were contaminated by virus that pass thru the air filters . You should discuss with a grille designer the possibility that the grille can entrain some room air as it passes the supply air over the louvers.<br /> I have found many dirty supply grilles that were depositing dust from the room on the louvers.<br /> Gerald Lamping<br /> Mechanical Engineer & IAQ Investigator
On 2021-03-03 17:21:39, user Maxim Sheinin wrote:
It seems that an important limitation of the study is to treat Covid-deceased individuals as representative of the average within the population (as reflected by the use of actuarial tables), while we know that people with comorbidities (who likely face shorter life expectancy) are also more likely to die of Covid. While this analysis may be too complex, there's another easier and impactful one: nursing homes. As of early Dec 2020 ~39% of Covid deaths occurred in nursing homes (https://www.nbcnews.com/kno... "https://www.nbcnews.com/know-your-value/feature/39-covid-19-deaths-have-occurred-nursing-homes-many-could-ncna1250374)"). Nursing home residents face reduced life expectancy. For example this paper (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6143238/)") found ~2.2 years median life expectancy post-admission for nursing home residents with an average age of ~85, while the authors' analysis would have assumed at least ~4.5 years (Appendix table 1). Thus current analysis is likely to overestimate true YLLs
On 2020-04-10 22:18:29, user stephenkirby wrote:
We're seeing a lot of curves on Coronavirus right now, but I'm just wondering, are there similar stats of influenza this year that would be helpful for people's perspectives?
On 2020-10-16 16:08:03, user COVIDscience wrote:
These data seem to contradict a previous study published in JCI by Yanqun Wang and colleagues, where increased IgG titers towards OC43 spike were associated with more severe disease outcome (https://doi.org/10.1172/JCI... "https://doi.org/10.1172/JCI138759)"). The time after infection at which the sera from the mild (outpatients) and severe (inpatients) cohorts were obtained is not specified in the study from Martin Dugas and colleagues. Potentially, these are not similar in the different patient groups.<br /> Given the complexly linked kinetics of antibody titers in COVID-19 patients towards SARS-CoV-2 and other coronaviruses (https://doi.org/10.1101/202... "https://doi.org/10.1101/2020.10.12.20211599)") this may change our perspective of these data.
On 2022-08-15 10:05:00, user james hurley wrote:
I congratulate the authors on their protocol for a ‘Systematic Review and Meta-Analysis of Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients’ [1]. That there are already over 40 published articles with “Systematic Review”, “Meta-Analysis” and “Selective Digestive Decontamination” in the title indicate that this is a vexed topic and the definitive publication is yet to appear. <br /> A simple reading of recent Cochrane reviews appears to indicate that SDD lowers both infection incidence and mortality in this patient group, whereas four other interventions do not [2-7]. However, what are the substantial areas of doubt and how can these be best addressed [8]?<br /> May I make some suggestions that might increase the chance that their proposed Systematic Review might be definitive?<br /> Firstly, is the mechanism of action of how Selective Decontamination of the Digestive Tract decrease infection and mortality in invasively ventilated patients understood? Are the animal studies undertaken in mice in the early 1980’s, from which the term ‘Selective Decontamination’ originated, still regarded as valid? Is the term “Selective Digestive Decontamination” a triple misnomer? Several have proposed that the term ‘Control of Gut overgrowth’ as a more accurate term to describe the presumed mechanism [9, 10].<br /> Second, is it true to state that the “Uncertainty about the effectiveness of SDD is due to concerns about the generalisability of RCTs with limited internal and external validity.”? Why did the use of SDD fall out of favour among neutropenic patients in the 1990’s? Is there a potential for rebound infections? Will this proposed systematic review address the question of rebound? Is there a possibility that SDD is ineffective among ICU patients? Is there a possibility that SDD and the rebound effect on its withdrawal is harmful? <br /> Thirdly, the authors will need to confront data inconsistencies between various versions of the published SDD trials that appear in the two Cochrane reviews of this topic [2, 3]. The earlier review obtained ‘Intention to treat’ data from several of the authors of the primary SDD studies which differs from the ‘on treatment’ data as published. The latter often excluded patients who died before completing the four days regarded as necessary to achieve ‘Selective Decontamination’. As a consequence, there is both survivorship bias and an underestimation of infection and mortality incidences in the ‘on treatment’ data. In addition, will the authors use the original data for the study groups as randomly allocated or will they use the adjusted data as published?<br /> Fourth, the authors propose a subgroup analysis comparing the results for “Individual patient vs unit level randomisation (i.e. cluster and cluster/cluster-cross-over).” However, their hypothesis is that the effect is unidirectional, i.e. they expect a benefit to be “,…greater in individual patient randomised trials compared to unit level randomised trials.” This expectation is a restatement of the ‘Stoutenbeek’ postulate, stated in the first SDD study undertaken in the ICU setting, that there would be a contextual effect of using SDD in the ICU context and that this effect would be beneficial to any concurrent control groups patients and, as a consequence, bias downwards the estimates of the SDD intervention within individual patient randomised trials [11, 12]. Stated otherwise, this postulate implies a herd effect similar to that of herd protection from vaccination within a population. <br /> This postulate creates several difficulties for this proposed systematic review. By raising this postulate, does this invalidate the Stable Unit Treatment Value Assumption (SUTVA) that is fundamental to valid estimates of effect size from concurrent controlled trials? If the SUTVA is questioned here, will this invalidate the estimates from the proposed systematic review? Moreover, given this postulate and proposed subgroup test, will the test be one-sided, with the expectation that the effect is uni-directional [only beneficial effect possible], or two sided?<br /> There is evidence that the results of individual [i.e. concurrent control] patient randomised trials of SDD differ to those of unit level [or historical control; i.e. non-concurrent controls] randomised trials and that the SUTVA is questionable for these studies. This has only been addressed in first and second meta-analyses on this topic both published 25 years ago [13, 14]. These indicate that the effect is greater in the former, i.e. contrary to the direction postulated by Stoutenbeek. There is further and more recent evidence for this discrepancy. On the one hand, the three largest subsequently published studies of SDD versus either standard care or SOD, which were all undertaken using unit level randomization [i.e. and non-concurrent controls], showed absolute differences in bacteremia and mortality [before any statistical adjustments] of less than 5 percentage points [15-17]. On the other hand, the most recent Cochrane review of the studies of SDD in this population, which included mostly trials using individual patient randomization [i.e. and concurrent controls], showed absolute differences in pneumonia and mortality of five percentage points or greater [3]. <br /> Will the proposed protocol use the unadjusted data or the adjusted data from these trials? Does the data adjustment account for the Stoutenbeek effect?<br /> Finally, to provide a definitive review, the authors will need to explain why event rates [pneumonia, bacteremia, candidemia and mortality] are generally higher among control groups within trials using individual patient randomization [i.e. with concurrent controls] versus control groups within trials using unit level randomization [i.e. with non-concurrent controls], versus control groups from studies of interventions other that SDD, and versus groups of studies without an intervention. Moreover, why is it that the event rates in the SDD intervention groups are similar to intervention groups from studies of interventions other that SDD in this patient group? The higher event rates are apparent in closer scrutiny of the summary results of the five Cochrane reviews [3-7]. On the one hand, the median control group event rates for pneumonia and mortality [18] are highest within the control groups of studies of SDD versus control groups of studies of other interventions and yet, on the other hand, the event rates for the intervention groups are paradoxically similar to intervention groups of studies of other interventions.<br /> I wish the authors well and hope that they succeed in providing the definitive systematic review of this topic over the arc of time [19].<br /> References<br /> 1. Hammond NE, Myburgh J, Di Tanna GL, Garside T, Vlok R, Mahendran S, Adigbli D, Finfer S, Goodman F, Guyatt G, Venkatesh B. Selective Decontamination of the Digestive Tract in Invasively Ventilated Patients in an Intensive Care Unit: A protocol for a Systematic Review and Meta-Analysis. medRxiv. 2022 Jan 1.<br /> 2. Liberati A, D'Amico R, Pifferi, et al: Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care. Cochrane Database Syst Rev 2009; 4: CD000022.<br /> 3. Minozzi S, Pieri S, Brazzi L, Pecoraro V, Montrucchio G, D'Amico R. Topical antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving mechanical ventilation. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: CD000022.<br /> 4. Wang L, Li X, Yang Z, Tang X, Yuan Q, Deng L, Sun X. Semi-recumbent position versus supine position for the prevention of ventilator-associated pneumonia in adults requiring mechanical ventilation. Cochrane Database Syst Rev 2016(1). DOI: 10.1002/14651858.CD009946.pub2.<br /> 5. Gillies D, Todd DA, Foster JP, Batuwitage BT. Heat and moisture exchangers versus heated humidifiers for mechanically ventilated adults and children. Cochrane Database Syst Rev. 2017(9). DOI: 10.1002/14651858.CD004711.pub3.<br /> 6. Bo L, Li J, Tao T, Bai Y, Ye X, Hotchkiss RS, Kollef MH, Crooks NH, Deng X. Probiotics for preventing ventilator-associated pneumonia. Cochrane Database Syst Rev. 2014(10). DOI: 10.1002/14651858.CD009066.pub2.<br /> 7. Zhao T, Wu X, Zhang Q, Li C, Worthington HV, Hua F. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2020(12).<br /> 8. Hurley JC Selective digestive decontamination, a seemingly effective regimen with individual benefit or a flawed concept with population harm? Crit Care. 2021;25(1).<br /> 9. Silvestri L, Miguel A, van Saene HK. Selective decontamination of the digestive tract: the mechanism of action is control of gut overgrowth. Intensive Care Med. 2012;38(11):1738-50.<br /> 10. Hurley JC (2020) Structural equation modeling the “control of gut overgrowth” in the prevention of ICU-acquired Gram-negative infection. Crit Care 24(1):1-2.<br /> 11. Stoutenbeek CP, Van Saene HK, Miranda DR, et al: The effect of selective decontamination of the digestive tract on colonisation and infection rate in multiple trauma patients. Intensive Care Med 1984; 10(4):185-192.<br /> 12. Hurley JC. Incidences of Pseudomonas aeruginosa-associated ventilator-associated pneumonia within studies of respiratory tract applications of polymyxin: testing the Stoutenbeek concurrency postulates. Antimicrob Agents Chemother. 2018;62(8):e00291-18.<br /> 13. Vandenbroucke-Grauls CM, Vandenbroucke JP. Effect of selective decontamination of the digestive tract on respiratory tract infections and mortality in the intensive care unit. The Lancet. 1991;338:859-62.<br /> 14. Hurley JC. Prophylaxis with enteral antibiotics in ventilated patients: selective decontamination or selective cross-infection?. Antimicrobial agents and chemotherapy. 1995;39(4):941-7.<br /> 15. de Smet AMGA, Kluytmans JAJW, Cooper BS, et al: Decontamination of the digestive tract and oropharynx in ICU patients. N Engl J Med 2009, 360:20–31.<br /> 16. Oostdijk EA, Kesecioglu J, Schultz MJ, Visser CE, De Jonge E, van Essen EH, Bernards AT, Purmer I, Brimicombe R, Bergmans D, van Tiel F. Notice of retraction and replacement: Oostdijk et al. effects of decontamination of the oropharynx and intestinal tract on antibiotic resistance in ICUs: a randomized clinical trial. JAMA 2014; 312 (14): 1429-1437. JAMA 2017; 317(15):1583-4.<br /> 17. Wittekamp BH, Plantinga NL, Cooper BS, et al: Decontamination strategies and bloodstream infections with antibiotic-resistant microorganisms in ventilated patients: a randomized clinical trial. JAMA 2018;320(20):2087-2098. <br /> 18. Hurley JC Discrepancies in Control Group Mortality Rates Within Studies Assessing Topical Antibiotic Strategies to Prevent Ventilator-Associated Pneumonia: An Umbrella Review. Critical care explorations. 2020;2(1).<br /> 19. Pizzo PA. Management of patients with fever and neutropenia through the arc of time: a narrative review. Ann Intern Med. 2019;170(6):389–97.
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 2020-08-17 06:39:53, user Jesper Markmann wrote:
A big difference between Sweden and Danmark, and the UK, US, and southern Europe is labor market rules and culture. In Denmark and Sweden people would stay at home, if they have the slightest symptoms. In the latter group of countries, more people would be inclined to go to work, in spite of symptoms in fear of loosing their source of income.
On 2024-08-02 13:43:41, user Paul Levay wrote:
This preprint has been superseded by:
Levay P, Finnegan A (2024) The NICE search filters for treating and managing COVID-19: validation in MEDLINE and Embase (Ovid). Journal of the Medical Library Association, 112(3): 316-328. https://jmla.pitt.edu/ojs/jmla/article/view/1806
On 2025-09-16 13:48:30, user Tengfei Zhou wrote:
We are pleased to announce that our manuscript has been accepted for publication in Nature Communications and will be available online soon. The DOI for the article is https://doi.org/10.1038/s41467-025-64035-4 . Thank you for your interest in our work!
On 2020-04-23 13:15:53, user behrouz pirouz wrote:
It would be a big mistake if someone think "there are several regions across the north with similar climates". What do you by that! Even in Humid subtropical climate the weather parameters are different such as Temperature, wind and humidity.
I reference to our previous "peer-reviewed articles" that is in accordance with the current article:
Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis, https://doi.org/10.3390/su1...
Development of an Assessment Method for Investigating the Impact of Climate and Urban Parameters in Confirmed Cases of COVID-19: A New Challenge in Sustainable Development, https://doi.org/10.3390/ije...
I wonder how a person that opened an account 2 days ago come to a scientific page about COVID-19 and put a comment! I hope medrxiv not allow this repeat and put a prosecute on the fake account like this.
On 2020-05-14 15:31:20, user Emanuel Papadakis wrote:
At the statistical level there are two major flaws: What are you using the chi-sq test for? You test a hypothesis. State the hypothesis. Your data for families B and C show rather random infections. Given the 14 day window of symptoms and the date of the start of the lock down the members of the B and C families may have been infected randomly outside of the restaurant. Also, why are the stories of all of these people accurate? You do have something to say but since you do not state the two hypothesis for which you test. Also your table at the end violates the assumptions for the test. The main assumption is independence and these people belong to families so the number of patients in total, namely 5 does not constitute 5 independent cases. Honestly, you try to establish causality and this is difficult because you have families. But your work despite these setbacks is interesting at the exploratory level. But you have no statistical significance as you treat your data because tests can be applied under certain conditions.
On 2020-04-04 10:53:48, user Statistics wrote:
almost 80% at the cross test(!)... so what about Iris diagnosis? Tuberculosis was over 80 % back in the 50s... so if anyone has time to evalute (complete time table please) I will appreciate; also check the completeness of the waldeyer throat ring of the infected; just for the interest...have thanks and praises
On 2020-08-19 17:08:34, user diymasks wrote:
Graph 3.1 shows the impact the change in HSE mask policy had from April22d. Strong data collecting, would like to see more.
On 2020-08-15 01:08:32, user leon islas wrote:
Great work. I have only one question, how representative are the serum cytokines measured, of the cytokines in the lungs and other organs? Are they truly representative? If not, this would be another limitation of your study.
On 2021-09-10 18:07:48, user dm wrote:
After watching the delta varient spread effectively between vaccinated individuals it would be irresponsible to not cite the following from the study:
"This study has several limitations that must be considered. First, the study cohort size is small, thus making it hard to draw firm quantitative conclusions. Second, our study cohort is biased towards breakthrough infections that were detected in our on-campus screening programs (saliva-based RTqPCR at UIUC, nasal swab-based LAMP assay at NU). Finally, enrollment in this study concluded before the arrival of the Delta variant at either study site. It remains unclear how well the effects of vaccination on viral infection dynamics that we describe apply to Delta variant breakthrough infections, given the unique features12 and enhanced transmissibility13 of this variant relative to the viruses we captured here."
On 2020-03-25 21:03:54, user Sinai Immunol Review Project wrote:
Summary of Findings: <br /> - Clinical data from 116 hospitalized CoVID-19 patients analyzed over 4 weeks for correlation with renal injury. Comorbidities included chronic renal failure (CRF) in 5 patients (4.3%). <br /> - Approx 10.8% of patients with no prior kidney disease showed elevations in blood urea or creatinine, and 7.2% of patients with no prior kidney disease showed albuminuria. <br /> - Patients with pre-existing CRF underwent continuous renal replacement therapy (CRRT) alongside CoVID-19 treatment. Renal functions remained stable in these patients. <br /> - All 5 patients with CRF survived CoVID-19 therapy without progression to ARDS or worsening of CRF.
Limitations: <br /> - Renal injury biomarkers in patients with incipient kidney abnormalities not tabulated separately, making overall data hard to interpret. It will be critical to separately examine kidney function (BUN, urine creatinine and eGFR) in patients that developed any kidney abnormalities (7.2~10.8% of cohort). <br /> - No information on type of CoVID-19 therapy used across cohort; will be useful to correlate how treatment modality influences kidney function (and other parameters). <br /> - Invokes previous clinical-correlation studies that indicate low instances of kidney damage [1,2], but those studies did not track longitudinal urine samples for acute renal injury markers and viral shedding. <br /> - CRRT in patients with CRF is standard therapy irrespective of CoVID-19 status; it will be important to compare clinical parameters of these patients (n=5) with virus-naïve CRF patients (none in this study) to make any meaningful conclusions.
Importance/Relevance: <br /> - This study argues that renal impairment is uncommon in CoVID-19 and not associated with high mortaility, in stark contrast to a concurrent study (https://doi.org/10.1101/202... ). If supported by further studies, it suggests kidney impairment is secondary to cytokine storm/inflammation-induced organ failure, and not due to direct viral replication. <br /> - Will be important to comprehensively characterize larger datasets of CoVID-19 patients across hospitals (meta-analyses) to conclude if kidney function is actively disrupted due to viral infection, and if renal disease is a major risk factor for worse CoVID-19 outcomes.
References: <br /> 1. Wang D, Hu B, Hu C, et al. JAMA 2020; published online Feb 7. <br /> doi: 10.1001/jama.2020.1585
Review by Samarth Hegde as part of a project by students, postdocs and faculty at the <br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-05-26 21:39:01, user Sam Wheeler wrote:
Interesting. But please note: in many countries that have given a lot of BCG vaccines, they have given also a lot of MMR vaccines. What if it is the MMR vaccine that helps? Or should an adult take boosters of both? With which schedule?
On 2025-03-30 09:52:42, user Isatou Sarr wrote:
Over time, immunity from both vaccination and previous infection can decrease, leading to an increased risk of breakthrough infections. This phenomenon is particularly noticeable as the immune response fades, and the virus continues to evolve.This waning immunity presents a challenge for public health strategies that rely heavily on initial vaccination or infection-induced protection. Boosters become crucial in reinforcing the immune system and restoring protective antibody levels, especially for vulnerable populations such as the elderly or those with underlying health conditions. Moreover, the emergence of new variants, often with mutations that allow them to evade existing immunity, further complicates the picture. These variants can spread more easily and cause illness in individuals who were previously protected, necessitating ongoing adaptation of vaccines and preventative measures to keep pace with viral evolution. Continuous monitoring of variant spread, vaccine effectiveness, and the duration of immunity are essential for informed decision-making and effective mitigation strategies.
On 2024-10-15 08:51:45, user Philippe Arvers wrote:
In France too, according to the BEH (No. 1 January 2021) ????????<br /> https://beh.santepubliquefrance.fr/beh/2021/1/2021_1_1.html <br /> Smoking cessation without any help is the most common, followed by #vaping and then #TSN
On 2025-06-04 05:27:15, user Sira Jam Munira wrote:
This paper is now published in the “Journal of public health research” and the link-
On 2020-07-02 18:07:08, user 3b wrote:
Interesting paper and idea.
However, the main result is based on a correlation of two time-series. Time-series violates the iid assumption of the statistical test used due to the autocorrelation inherent in such data.It would be nice to see the analysis redone using proper methodology.
Here's an accessible paper on the topic: https://link.springer.com/article/10.3758/s13428-015-0611-2
See this blog post for a simple demonstration of this using simulated data.
And Yule, 1926 who first described the problem.
On 2020-04-07 17:50:33, user japhetk wrote:
Brief comments.<br /> 1, Categorical classifications are apparently not appropriate, because it reflects the ratio of the nation which is experienced with BCG a little. I cited the case of UK which had advanced BCG for more than 50 years until 2005, and another example is Iran and Vietnam which introduced BCG in mid 1980s.
2, >>Assuming that BCG is protective against the infection, "1st day when the 100th cases is detected" is expected to be also delayed, as well as the spread of the virus after the day. <br /> It may be so, but it doesn't matter. Because, if the nation is robust toward coronavirus, whether or not the day of 100th infection is delayed, the increase of the patients after the day of the 100th patients should be also delayed. Further, without this control,you have no idea whether or not the virus is even brought into the country substantially enough to spread. It is out of question. And isn't that the very reason that you are testing the associations between the BCG and slope of the increase in the analysis of "BCG vaccination policy is associated with a lower COVID-19 growth rate"?? <br /> They are conceptually essentially the same analyses.Why are you introducing the apparently inadequate analyses using 3 categories of the slope (it loses information) if that is so inadequate and why aren't you intoruducing the analyses of the continuous numerical data, if that is not due to the fact that the later analyses yield insignificant findings.
In addition to these two big things, herd immunity is expected to exist.Therefore, considering these 3 factors, the analyses based on "how long the country has advanced the BCG vaccination measure" are theoretically inappropriate in testing this hypothesis.
I think your counterargument is getting more and more bizarre. If the strain of the BCG and the spread of TB is important, and if that is the reason you cannot introduce the analysis of how many years BCG is advanced in that country, for the very same reason, you should not introduce three category analyses (current, past, never). Because that is biased by the strain of BCG and TB. Why are you then introducing 3 category analyses ? and why aren't you introducing the analyses of how many years BCG has been advanced in that country, if that is not due to the fact that the later analyses yeild insignificant findings. <br /> I think this is evidence that you are just cherry picking the conceptually apparently inappropriate analyses (that even you yourself admit they are biased according to your counterargument) that support your conclusion and that shouldn't be how things work in science.
4, >>For example, even when we restrict the countries from 5000 to 15000 tests per million population, the effect remains.<br /> I don't know why you can control the household incomes and you can't do the same analyses that control the number of testing (rather than restricting the countries). It shouldn't be an arbitrary issue.<br /> You can easily do both,if the appropriate data of number of testing could be obtained (I heard it was difficult). I think the total inappropriateness of your main analyses are apparent (3 categories of BCG, which is biased according to your opinion, no control on the timing of the spread of the virus which you tried to control in subanalyses, but not in main analyses). It is even out of question, and you yourself admitted that the former is biased, and the latter is acceptable in your subanalysis.
5, >>Tourists from Asia: How about Japan and Taiwan, that had a lot of tourists from China and still have few patients who died.
A remarkable difference can be seen between Western Europe and Eastern Europe.<br /> Anecdotal evidence doesn't matter.The difference can be easily attributed to the number of tourists.<br /> Taiwan and other provinces of China could be robust to the virus, due to the highest alert and vigorous policies and not BCG, all that matter is the statistical test. <br /> I couldn't obtain the data of Chinese tourists, so I obtained the number of tourists from UNWTO, and controlled it.<br /> The number of tourists in the country is robustly associated with how fast the 100th case was detected after controlling the population (p = 0.001, partial correlation coefficient: -0.434), the number of cases 9 days after the detection of the 100th case (p = 0.009, partial correlation coefficient; 0.345), the number of 10 days after the detection of the 100th case (p = 0.006, partial correlation coefficient: 0.361)<br /> And after controlling the population, number of tourists, and
6, <br /> 6. Masks: In Asian countries, it might be a factor. However, are there any differences in the percentage of people wearing masks between Western Europe and Eastern Europe? I don't know, but maybe not.<br /> 7. Food: Could be. But exactly what kind of food can explain this big differences? In case you can think of any particular food that can distinguish Western Europe from Eastern Europe, please let us know.<br /> 12, The strength of individual rights and freedom (in other words, how strongly the nation can control the individual behaviors).
The difference of Eastern Europe can western Europe can be easily explained by the number of tourists.<br /> If you make a counterargument through cherry picking comparisons, then I also make a cherry picking comparison of "why on earth Israel which stopped BCG is not yielding number of deaths compared with Iran and Turkey." "If Denish strain is not that good why on earth Singapore and Ethiopia are controlling the death?", "And in china, why only in Wuhan, were there so many deaths? "In Korea, why on earth only in Taegu, did the virus spread substantially?"<br /> None of them have to be related to BCG. Because many factors apparently affect the spreads of the virus. Please stop sticking the idea that everything is about BCG. It is not and even if BCG works, the difference between particular nations can or cannot be attributed to BCG. So, only the appropriate statistical analyses matter. <br /> Nutrients of green tea and soy beans are cited as the potential substances to prevent coronavirus in in vitro preprint (https://www.researchsquare.... "https://www.researchsquare.com/article/rs-19560/v1)") and green tea and soy beans are more consumed in Asia. <br /> Further, amount of fat one takes also tended to be positively correlated with how fast the 100th case is detected (P = 0.103, partial correlation coefficient -0.231) after controlling the population, GDP and tourists. <br /> Yes, you are not able to know many many effects of numerous possible confounding variables that are already shown to potentially prohibit the spread of the virus in various types of ways, that is why this study cannot conclude anything regarding BCG which is stopped in Western countries, through this kind of study. In appropriate studies, such variables are obtained and controlled. Subjective feeling of unlikeliness or anecdotal evidence are not how things work in epidemiological studies.
14, As for TB, after controlling GDP, number of tourists and population, the association between the the number of onset of TB per 100 thousand people in 2018 showed no significant correlation with how fast the 100th case is detected (p = 0.210, partial correlation coefficient, 0.127), the number of cases 9 days after the 100th case (p = 0.472), the number of deaths 9 days after the 100th case (p = 0.744). <br /> I couldn't obtain the data of the total past experience of TB in the nation.<br /> If you used the same dependent variable (onset in 2018), then the correlations you found were probably due to the inclusion of many developing countries in which the 100th patient has not even been detected (meaning the virus hasn't spread substantially) because few tourists go there and developing countries mostly have TB, regardless of TB has an effect toward the virus or not.<br /> As I keep saying, this is why when the virus is spread should be controlled.Without this control, apparent no correlations can result in spurious correlations.
On 2020-12-13 13:26:48, user Sam Smith wrote:
A concern about the Córdoba study is that 25OHD serum levels were not measured, so we do not know if the treatment was associated with a benefit only in patients who were deficient. A randomized controlled trial will be needed to determine whether calcifediol will benefit hospitalized COVID-19 patients who are not deficient.
Sadly, calcifediol is sold in some European countries, including Finland?
On 2020-04-20 07:55:27, user Joseph Kirschvink wrote:
Very nice work, however we would like to point out a major error with regards to the ethanol decontamination. The mechanism that is commonly cited in the literature for the loss in filtration efficiency - disrupting the surface charges on the microfibers - is wrong. In our manuscript uploaded to medRxiv last week (Nazeeri et al.), we report a similar drop in efficiency after rising in EtOH/water solution, followed by simple air drying. On that point we agree. However, we noticed that the mass of the respirators was still significantly more than their original weight, arguing that simple air drying was not enough. When we dried them further in a vacuum, we were stunned to see that the filtration efficiency bounced back up to the N95 levels as the pressure dropped from ~ 13 down to below 6 mbar (0.6 kPa). This is typically the range where thin films of surface adsorbed water are removed from a room-temperature vacuum surface. The adsorbed water seems to be the cause of the loss of filtration efficiency. Hence, simple EtOH solutions can indeed be effective at both disinfecting AND RESTORING function to degraded N95 respirators that are based on melt-blown, corona-charged microfibers of polypropylene.
The Nazeeri et al. manuscript is here:
An Efficient Ethanol-Vacuum Method for the Decontamination and Restoration of N95 Polypropylene Microfiber Medical Masks & Respirators
Albert I. Nazeeri, Isaac A. Hilburn, Daw-An Wu, ViewKabir A. Mohammed, View D. Yovan Badal, View Moses H.W. Chan, View Joseph L. KirschvinkA direct link to the medRxiv manuscript is here: https://www.medrxiv.org/con... )
On 2020-05-22 15:55:00, user Jakub Kwiecien wrote:
Sure, but the denominator, i.e. INFECTED population cannot be greater than GENERAL population, thus IFR must be higher than 0.026% in general population.<br /> BTW. could you somehow justify the claim that most of infected individuals are asymptomatic?
On 2021-12-14 16:32:02, user Ole K. Fostad wrote:
What is the rationale for excluding 154 patients vaccinated with one dose <21 days before positive test? If they are hospitalized they shoud be counted as they have to be dealt with in the hospital-system too. There is no discussion nor justification for the exclution, it looks like this omission would result in survivorship bias.
On 2025-10-18 15:00:12, 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:
The combined biotic/abiotic approach can guide resource allocation, outbreak management, or pollution mitigation efforts specific to each region's exposure landscape.
Abiotic signatures help to identify the cause of biotic signals which could be driven by infectious disease outbreaks or changes in human hygiene, diet, or mobility.
This study supports a much larger-scale spatiotemporal range than most wastewater epidemiology efforts enabling the evaluation of seasonal shifts, regional differences, and post-pandemic changes in microbial and chemical profiles.
Wastewater surveillance has typically been a reactive infection-tracking tool. Continuously mapping exposures, as performed here, can identify risks before they manifest clinically, supporting not only intervention but prevention.
On 2020-07-09 10:05:48, user Nan Liu wrote:
Published version of the preprint:<br /> Liu, N., Chee, M.L., Niu, C. et al. Coronavirus disease 2019 (COVID-19): an evidence map of medical literature. BMC Med Res Methodol 20, 177 (2020). https://doi.org/10.1186/s12...
On 2021-05-28 15:56:20, user Paul Smeets wrote:
Impressive study that appears well done. However, I think the headline is overstated in the absence of information on which effect size of the 'brain health' measures is clinically/physiologically relevant; why couldn't there be a 'safe' level, like for many other substances? That might be below current recommendations but still... I.e., define 'safe' in this context. It would strengthen the paper and aid application of the outcomes in health policy if this, to my mind, crucial point were discussed.
On 2020-05-26 06:02:08, user Hooman Noorchashm wrote:
An open Letter to the editor-in-chief at JAMA on the potential efficacy of Cyclosporine in COVID-19 disease: https://medium.com/@noorcha...
On 2024-10-19 20:32:11, user CDSL JHSPH wrote:
I thoroughly enjoyed reading your paper and found it to be a significant contribution to the field of tuberculosis treatment. The exploration of model-based methods, particularly MCP-Mod, to enhance traditional qualitative approaches is both timely and necessary. Your findings highlight the effectiveness of these methods in detecting duration-response relationships, especially in small sample trials, which is a crucial aspect of optimizing treatment strategies.
I am particularly intrigued by the potential for these model-based approaches to be applied beyond TB. It would be fascinating to investigate whether the insights gained from your research could be translated to other bacterial infections, such as Staphylococcus or Streptococcus species, as well as to the treatment of viral and fungal infections. This could open new avenues for improving antibiotic and antiviral therapies.
Additionally, your suggestion to incorporate patient characteristics and risk factors into the analysis is commendable. I believe that further exploration of how different design parameters—such as sample size and time intervals—affect the accuracy of the model could provide valuable insights. Understanding how to integrate more patient-centric data into real-world clinical settings would enhance the applicability and scalability of your findings.
Thank you for your valuable contribution to this important area of research. I look forward to seeing how your work evolves and the potential implications it may have for personalized treatment strategies in the future.
On 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 2023-01-17 14:50:16, user Theo Sanderson wrote:
This manuscript has been retracted. The retraction notice reads: "The authors of this article were made aware of a technical oversight which invalidate their conclusions, and alerted the editorial office so it could be withdrawn."
On 2021-02-20 19:30:05, user Valerie DeLaune, LAc, CNMT wrote:
There is an assumption in the paper: "Third, the likelihood of exposure to SARS-CoV-2 may be dependent on vaccination status to a greater extent in the real world than it is in the context of a randomized trial. That is, vaccinated individuals may feel more comfortable participating in social situations that pose a higher risk for infection, whereas this bias did not exist by definition in the context of the observer-blinded clinical trials." I would think the opposite would be true. Those who are most concerned about getting the vaccine as quickly as possible would be those already taking the most precautions both before and after vaccination i.e. wearing a mask, physical distancing, so also would be less likely to contract COVID in the first place.
On 2021-12-03 00:34:25, user Sulev Koks wrote:
This manuscript is under review at Experimental Biology and Medicine.
On 2022-01-24 19:39:25, user Ilya Zakharevich wrote:
Unfortunately, going through the all the details, it seems that the conclusions have a very high chance to be complete bogus.
(A) The reported effects are small enough to be explainable by the (standard) confounding factors of heavy COVID infection. According to the paper, no matching has done according to the confounding factors.
(B) As Saar Wilf already noticed, there is another confounding factor: hospitalization on the day of swabbing. For example, in the group “age>=65” 17% of the unvaccinated cohort were hospitalized on the day of swabbing (or on in 2 preceding days).
If one removes this, then the studied effect on hospitalization is zero (less than ±½?). For example, in the cohorts as above, in the days 5–20 after swabbing 131 vs 126 people were hospitalized. Similar effect for ages 55–64 (but with the opposite sign!).
(C) Incredibly high mortality rate in the control group (like mortality 4.26% in males aged 55–64) is left without any comment.
???????????????????????????????
To add insult to injury, a completely unprofessional reply to the comment of Saar Wilf leaves the strong doubts in “who are the authors”.
Giovanna, one proves one’s credentials by pointing where in the paper the (obvious!) defects discussed in these comments were addressed. If they were not already addressed, your credentials are completely undermined.
On 2020-04-16 21:20:27, user daneekadoc wrote:
It's the Netherlands, not Denmark which had no BCG vaccination policy so far.
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 2023-01-14 13:18:53, user Basheer Marzoog wrote:
the paper is published https://www.eurekaselect.co...
On 2020-04-06 17:25:08, user t Darroll wrote:
Why wait to administer Chloroquine/hydroxychloroquine? My understanding of the initial studies of Chloroquine/hydroxychloroquine with azithromycin which was done by the Chinese and French showed that the most productive time to administer it was in the first 3-4 days of Covid-19. It seemed to work best given early in the disease process and then seemed to significantly shorten the disease time frame..
On 2021-09-21 04:34:22, user Shawn Mclean wrote:
Wheres the raw data for this survey? It would be great if it's made public so we can dig into it ourselves.
On 2020-06-28 20:38:28, user itellu3times wrote:
OK I'll say it, I find this entirely opaque, I cannot tell what you are even proposing, much less whether you found it or proved it.
On 2024-04-28 13:06:50, user Gina Dee wrote:
I’m so happy and relieved to see research being done to better understand TSW. My daughter suffered through TSW starting at the age of 2 and it was a nightmare. We had to struggle through with minimal support from doctors. I hope this study and further studies help to lessen the occurrence and better treat the condition.
On 2020-09-09 06:55:50, user Tomasz Kolinko wrote:
very little info about the death numbers underreporting - why was 15% used as an underreporting level?
Considering the article states IFR as very low, and serorevelence numbers seem right, perhaps the number of deaths in the region in severly underestimated?
On 2020-10-28 18:05:07, user Jean Dubois wrote:
Dr. Risch acknowledges past advisory consulting work with two of the <br /> more than 50 manufacturers of hydroxychloroquine, azithromycin and <br /> doxycycline.
That is litterally written in the study
On 2024-12-15 08:46:43, user Ujváry István wrote:
Note the correct chemical name:<br /> bis(2,2,6,6-tetramethyl-4-piperidinyl) sebacate
(BTMPS is a piperidine derivative; it is not a pyridine derivative!)
On 2020-04-22 22:06:16, user Marie Benz wrote:
Table 2 discrepancies which favored the non-treatment group, lack of randomization, lack of information on when treatment was begun as well as lack of number of doses completed make this paper unable to be interpreted since it is being heralded by news media as demonstrating that such treatment has now been proven ineffective. Clearly the jury is still out but the authors owe it to the country and the scientific community to point out in the media that this is not enough to conclude that HC or HC + AZ is ineffective or dangerous and that present therapies should not be altered one way or the other based on this report.
On 2020-04-22 23:15:48, user Wolfgang Wodarg wrote:
When I read, that most of the severe cases were observed among black patients, I wondered why the risks of patients with a G6PD-deficiency were not even mentioned. The prevalence of favism among patients with ancestors from countries with endemic malaria is about 20-30%.<br /> They will suffer from haemolysis, microembolia and a strong lack of oxygene carriers, when they get certain drugs like e.g. Hydroxychloroquine in a high dose for some days. (breathlessness without signs of pneumonia). Please read the longer comment with sources here: https://www.bmj.com/content...
On 2020-08-14 04:52:30, user Melimelo wrote:
Great paper, could you please say which hygroscopic materials you tested? (salt? rice? something else?)
On 2020-01-27 18:30:54, user Cyborg Gabe wrote:
In reviewing the supplementary model details, I note that the rate of exposure in each city is assumed to be directly proportional to the number of infected individuals in that city. However, if quarantine measures being taken in affected areas are at all successful, then this assumption will not be correct. Instead, a declining proportion of the infected will infect others as successful quarantines take effect. I suspect that implementing this change in the model would significantly change the model predictions, though it would require some method of estimating the success of the quarantines.
On 2020-01-25 03:20:14, user Xuefeng Ding wrote:
How is it determined? there is no data at all.
On 2020-05-15 13:20:10, user Melimelo wrote:
One more possible causal pathway is that many poorer countries with endemic malaria also have (or recently had) endemic tuberculosis, and national bcg vaccination programs to address TB. BCG vaccination may be protective against covid-19. See: https://www.medrxiv.org/con...
On 2020-03-31 16:17:23, user Johannes Opsahl Ferstad wrote:
Link to tool: https://surf.stanford.edu/c...
On 2021-08-27 01:55:40, user Private for now wrote:
Great work. We need more of this and updates vs variants for infection and serious disease efficacy correlates. Not sure how many people would want a personalized booster schedule created, but this type of data is foundational.
On 2021-12-13 14:29:16, user vepe wrote:
it looks like this study has a major flaw in the calculation of the covid cases
for example their data set contained 6846 cases in the cohort 12-17 (they applied the same logic for the other cohorts)
The 6846 number of covid cases for 12-17 was 2.5% of the total covid cases in their data set.<br /> Then they assumed the same infection rate as the adults at the time, 9.2% and normalized their total number of cases for 12-17 based on that:<br /> adjusted number covid cases = 6846*9.2/2.5 = 6846*3.7 = 25193
then they almost doubled the number of myocarditis cases on the premise that there would be cases that they would miss (e.g. people receiving care outside the TriNetX system)
so they end up with about ~12 myocarditis cases per 25193
so the biggest problem is that their estimated number of covid cases, is essentially the number of covid cases they were expecting to see in their data set and not the total number of covid cases associated with their data. Even if they were meant to estimate the number of covid cases they were expecting to see, this estimation is not accurate since the probability of a younger person ending up in the hospital is way smaller than adults.
In practice, based on that estimation of covid cases, the authors implicitly say that 2.5/9.2=27% of young people that get coronavirus, end up diagnosed/treated by health care provider. This looks like a big overestimation.
In practice, hospitalization rate for younger people looks like is closer to 2% as indicated below:<br /> https://www.aap.org/en/page...<br /> https://covid.cdc.gov/covid...
I think a more accurate estimation would have been to skip the normalization based on infection rates and estimate based on the probability a young person has to end up to a health care provider.<br /> example, covid cases = 6846*100/2 (instead of 6846*9.2/2.5)
Based on this estimation of covid cases, myocarditis risk would be higher in vaccination instead of infection for young people
On 2022-07-25 16:31:06, user Dr. D. Miyazawa MD wrote:
Please also refer to previous studies.
Hypothesis that hepatitis of unknown cause in children is caused by adeno-associated virus type 2 (08 May 2022)<br /> https://www.bmj.com/content...
Daisuke Miyazawa. Possible mechanisms for the hypothesis that acute hepatitis of unknown origin in children is caused by adeno-associated virus type 2. Authorea. May 16, 2022.<br /> DOI: 10.22541/au.165271065.53550386/v2
On 2020-06-04 15:25:54, user Andy Loveman wrote:
what other factors were considered: prevalence of O-type, A-type in the population at large; and underlying health factors compared in both groups?
On 2023-12-26 14:48:44, user Donald R. Forsdyke wrote:
LATE ONSET POST-VACCINATION MYOCARDITIS
The acceleration of SARS-CoV-2 vaccine research post-2020 was so rapid that preprint postings became the norm for many of us working in the field. This preprint of Watson et al. (1) describing 3 case histories is in line with previous preprints describing single case histories (2, 3). It now appears that late-onset post-vaccination myocarditis in elderly subjects can be either overt (symptomatic; 1, 2) or cryptic (not symptomatic; 3).
The cases described here (1) developed symptoms of myocarditis several weeks after vaccination and a few weeks after initiating anti-PD-1 treatment (Immune checkpoint blockade; ICB). The latter would have decreased constraints on autoimmune phenomena. The reported period following vaccination prior to symptom onset, coincides with that reported early in the pandemic by Guatam et al. (2) for a subject with a previous cardiac condition (prior morbidity). It also concides with the post-vaccination periods that preceded protracted, yet asymptomatic, transient dips in blood pressure (BP) in a normal subject, which has been attributed to myocarditis (3).
In the latter case, an episode of cardiac fibrillation during a run, prompted a retrospective analysis of blood pressure (BP) readings for the period when five sequential anti-SARS-CoV-3 vaccinations has been given (3). This resulted in the unexpected discovery of the extreme BP dips that progressively increased in extent with successive vaccinations. A cause-and-effect relationship was evident. The myocarditis was cryptic and was deemed likely to remain so, unless the subject had made excessive demands on cardiac function (e.g., vigorous exercise). Alternatively, the delicate balance between normal immunity and autoimmunity might have been shifted as in (1), or a comorbidity might have emerged as in (2).
The present preprint begins by stating that association between vaccination and myocarditis is rare and affects younger subjects (1). The other preprints suggest the existence of a vulnerable population-subset that may include many elderly subjects and may be less rare than is generally understood. A “crowd sourcing” follow up has been suggested (3,4).
1.Watson RA, Ye W, Taylor CA, Jungkurth E, Cooper R, Tong O, et al. Severe acute myositis and myocarditis upon initiation of six-weekly Pembrolizumab post-COVID-19 mRNA vaccination. medRxiv 2023; doi.org/10.1101/2023.11.24....<br /> 2.Gautam N, Saluja P, Fudim M, Jambhekar K, Pandey T, Al'Aref S. A late presentation of COVID-19 vaccine-induced myocarditis. Cureus 2021; 13: e17890.<br /> 3.Forsdyke DR. Cryptic evidence on underreporting of mRNA vaccine-induced cardiomyositis in the elderly: a need to modify antihypertensive therapy. Qeios Here<br /> 4.Forsdyke DR. Physician-scientist-patients who barketh not. The quantified self movement and crowd-sourcing research. J Eval Clin Pract 2015; 21: 1024–1027.
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 2022-02-21 00:22:33, user consalg wrote:
Log0=1, not zero. That distribution is discontinuous. Shouldn’t you recalculate means without counting in the samples that produce no foci?
On 2020-08-12 08:06:03, user Marek Kochanczyk wrote:
The source code to analyze data and generate figures as well as updated figures are available at GitHub: https://github.com/kochancz...
On 2021-08-16 19:12:41, user Paul Wang wrote:
Abstract(results) needs to be fixed: first sentence gives the exact same name to 3 of the 4 subgroups
On 2020-06-23 17:11:15, user Sam Lambert wrote:
Dear Charles,
Thank you for your interest in the PGS Catalog and the manuscript. The PGS Catalog is designed to be a source of scores and metadata describing their development and how well they’ve been reported to perform. Scores in the Catalog can then be downloaded and combined with other tools and services that calculate scores on individual-level data (similar to those mentioned in your blogpost). We are currently exploring ways to add population reference calculations and distributions (e.g. percentiles) to aid end-user applications and interpretation, and hope to add those features in the future. Do let us know if you have other questions, you can also e-mail us at pgs-info@ebi.ac.uk.
Best,
Samuel Lambert (on behalf of the PGS Catalog Team)
On 2020-10-26 12:37:26, user reader_DF wrote:
Perhaps not at 20% but this research brings very good points that it should not be as high as 60 or 70%. Second wave is in part a terrible misunderstanding about the relation between confirmed cases and REAL number infected. Most likely, the peak of first wave was much higher as underreporting was back then much higher. Deaths numbers are a good indicator. What we are seeing now is a bit of improvement in treatment (or do you believe in some tremendous breakthrough?) and a pseudo second wave whcih is likely less pronounced if we could look at all cases not just confirmed ones.
On 2020-06-26 16:11:55, user Kevin McKernan wrote:
Very interesting work. There are many data points suggestion earlier introduction.<br /> It would strengthen the manuscript if you could provide the CQs for your NTC for the late PCRs.
I would also suggest sequencing the amplicons to see if these are ancestral or SARS-CoV-2.
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 2021-05-25 17:36:04, user debernardis wrote:
As Juan Chamie first pointed out (https://twitter.com/AOlavar... "https://twitter.com/AOlavarria/status/1397202822672879621?s=19)") you inverted the results of Niaee 2020. This invalidates your conclusions re mortality. It's better to retract, probably.
On 2020-04-18 15:07:55, user Bertrand DELSUC wrote:
Were there periods where the RT-PCR status of patients toggled between positive and negative and then back from negative to positive during the follow-up period of this study? If yes, did you report the last day positive in the LDVP column? Thanks
On 2020-05-15 14:20:03, user Boonton wrote:
Has someone read the larger study? How do they verify so few outdoor cases? Given 14 days is a lot to remember for people who haven't been locked down, it doesn't seem clear to me how you know someone didn't get it from outdoors or an outdoor person didn't get it indoors?
On 2020-09-09 19:13:42, user Pascal wrote:
At alpha=1 the formula diverges. This means the formula cannot be right in my opinion. Do I oversee something?
On 2020-04-21 00:36:26, user Chao Jiang wrote:
Please feel free to let me know how to improve the manuscript here or @Chao_Jiang1. Thanks!
On 2020-11-26 12:13:59, user Dr Gareth Davies (Gruff) wrote:
Thank you for this fascinating analysis! It brings together a great deal of very useful information, and the data were presented in useful and transparent ways, and the tables and graphs especially helpful in understanding the data.
I would like to offer some constructive feedback concerning the statistics and their interpretation, as some results appear to have been misinterpreted and this undermines this excellent work.
The use of term "statistically significant" (18 occurrences included negatives) is especially concerning and goes against best-practice. P values and confidence intervals are frequently misinterpreted by both review authors and readers. A lack of evidence is not evidence of lack of effect. This is especially concerning where interpretations of dose, frequency and trial length are interpreted, as they give the impression that some were demonstrably effective whereas others were demonstrably not effective and the latter is not something this study could ascertain and should definitely not conclude or discuss.
(Best practice recommendations from Cochrane Handbook for Systematic Reviews of Interventions version 6.1 C.15.3.2: "Review authors should not describe results as ‘statistically significant’, ‘not statistically significant’ or ‘non-significant’ or unduly rely on thresholds for P values, but report the confidence interval together with the exact P value.")
There is a great deal of heterogeneity in the studies that cannot be measured by an I-squared metric but are important and will affect. Differences in study populations, sizes, country, latitude, age ranges, comorbidities, length of trial, method of assessing outcome, dosing freqency, % participants <25nmol/L, year of study etc. can all introduce very large unmeasurable confounding bias that may strongly influence results in ways that cannot be accounted for by software calculating CIs, P values, or I^2 measures. I would strongly urge great caution in interpreting these as meaningful.
For example, in the group of studies where dose equivalent > 2000 IU/d, the studies vary enormously in almost every attribute and yet the I-squared metric suggests only moderate heterogeneity which is very misleading. It is especially telling that in some studies the reported incidence of > 1 ARI in the intervention and control arms is wildly different across studies: ~17% (Rake 2020) ~74% (Camargo 2020); ~96% (Murdoch 2012), casting strong doubt on the reliability of the measure to capture the outcome of interest to the study.
Berman 2012 showed a small population (N=124) of patients in Sweden (latitude 60°N) susceptible to ARIs (assessed with symptoms, range 40%-60%) and with measured high-prevalence of D deficiency (11.45%) responded positively to >2,000 IU with an odds ratio of 0.43 (CI 0.21 - .88). Among others, these results are combined with Camargo 2020 in New Zealand (40°S) in a very large population (N=5,056) of healthy adults with low prevalence of D deficiency (1.8%) where (ARIs self-reported cold/flu incidence ~75%) with an odds ratio 0.90 to 1.16; and Lehouck 2012 (adults with chronic obstructive lung disease).
It's hard to see how the data from these trials can be meaningfully combined. It's no surprise the comined CI was large 0.84 to 1.31 (in truth it will be far larger since bias and measurement errors have not been accounted for), but the only interpretation possible here is that we cannot interpret anything from these combined data and more research is needed.
The same problem occurs when combining individuals with deficiency (<25nmol/L) giving a combined CI of 0.53 - 1.16. This is reported as "a statistically significant protective effect of vitamin D was not seen in those with the lowest 25(OH)D concentrations" which is then wrongly interpreted to mean evidence of no effect which is simply not the case. All this means is the statistical power was too low to detect an effect with high confidence. Arguably, there IS a detectable effect if we use a lower confidence threshold. (I'm not suggesting this, I'm merely pointing out how careful we need to be interpreting statistics).
Results with CIs crossing null can say nothing about the existence or non-existence of an effect and should not be reported or interpreted as such, especially if the ranges are large. The inability to reject the null hypothesis is not proof of the null hypothesis. It's just lack of study power.
Statements such as "Greater protective efficacy of lower vs higher doses" has no evidential basis and should be removed. This analysis did not show a greater protective effect at lower doses! It showed an effect at lower doses and had insufficient data at higher doses to investigate the question. The subsequent musing over potential mechanisms to explain this imagined difference should also be removed.
I would also strongly caution against multivariable meta-regressions on trial characteristics. There are simply too many potential unmeasured confounders and sources of measurement error to trust that this method will produce meaningful adjustments. There's no telling if this would properly adjust, or conversely introduce bias and loss of precision.
I think if these issues were addressed the study contributes some very important and useful results confirming the positive beneficial effects of vitamin D, and suggests more research could help to answer the questions where the data were insufficient to cast light.
Congratulations on the paper and I hope this feedback is helpful!
Best wishes,
Gareth
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 2021-02-19 01:42:51, user Oliver Cudziš wrote:
Voluntary? "All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived - Yes" What is this, areu all blind or what. Slovak nation was like experimental rabbit without knowing, congratulations you just made stage for Slovak national uprising 2, good luck.
On 2020-06-29 15:58:18, user JS wrote:
This preprint has now been published as:<br /> Phenotype and kinetics of SARS-CoV-2-specific T cells in COVID-19 patients with acute respiratory distress syndrome. Science Immunology 26 Jun 2020: Vol. 5, Issue 48, eabd2071. doi.org/10.1126/sciimmunol.abd2071
On 2020-05-13 16:17:09, user Algis Džiugys wrote:
Hi,
Because the number of Patients in Intensive Care Units depends on number of daily new infection cases, may be behavior of curve in Fig.3a can be explained by dynamics of daily new cases: https://www.medrxiv.org/con... (fig.11).
Best regards,
Algis
On 2020-10-29 21:32:27, user Dan Dan wrote:
I believe high dose angiotensin 2 type 1 receptor blockade would alleviate this phenotype as, for example, olmesartan dose dependeny blunts tgfb as well as inhibits the fibrotic response and cardiac remodelling.
On 2021-09-04 06:31:10, user Philological wrote:
In this version of the paper the “U” shaped response curve between covid vaccine hesitancy and education level is still mentioned. It is clear from the updated statements in this version that the data set was mortally compromised by respondents falsely linking PhD education with vaccine hesitancy. This resulted in an avalanche of anti-vaccination invective in social media and online news media, in many cases justifying covid vaccine rejection based on the relevant findings in the paper. All references to PhD’s should be removed.
On 2020-11-02 00:25:13, user Noriaki Kurita (????), MD, PhD wrote:
This paper was eventually published from BMC Nephrology.<br /> https://bmcnephrol.biomedce...
On 2021-07-15 16:14:14, user Tanavij Joob Pannoi wrote:
I am wondering about the total number of recruited participants divided by VOC, particularly in figure 2A, while, the results from linear mixed model were not reported elsewhere. Researchers should provide more details of study limitations since it could be published on social media, while, many audience might misinterpret or exaggerate the result.
On 2025-05-22 10:50:12, user Naoki Watanabe wrote:
We are pleased to announce that this preprint has undergone peer review and has been published in a formal journal. Please refer to the final version of the article.
Watanabe, N., Watari, T., Hosokawa, N., & Otsuka, Y. (2025). Alistipes Bacteremia in Older Patients with Digestive and Cancer Comorbidities, Japan, 2016–2023. Emerging Infectious Diseases, 31(4), 652.
On 2020-05-05 09:18:09, user ??? wrote:
You may be interested in my paper "Growth Mechanism of Coronavirus ( How to Stop Spreading of COVID-19)" that predicts at temperatures above 25°C Coronavirus should have difficulty in replication because its outer cover melts and its RNA core decays at temperatures above 25°C. For instance, it explains why people catch cold more often in cold winter than in hot summer. You can read the paper in OCN.
On 2021-07-21 20:55:48, user Meditate wrote:
Is anyone able to share what time period this study covered, I notice that the median followup was 258 days, and from the supplementary data. the serosurvey they are talking about is the one performed in August 2020. When did the study followup period end? April-May 2021? If this covered a reasonable time period of the second wave in Odisha, this would be great news for immunity against Delta.
On 2021-06-10 20:23:12, user John Jay wrote:
Sorry if this was already mentioned, but is there a discussion of how the demographics (ie age, co-morbidities, status before care, etc.) varied between the group given 3,000mg HCQ + 1,000mg AZM and all other patients in the study?
On 2020-10-15 22:40:24, user Marm Kilpatrick wrote:
Dear Dr. van Beek and co-authors,<br /> Thank for your this important work!<br /> In your Table 1 you appear to be grouping results for multiple assays together:<br /> Panbio™ COVID-19 Ag rapid test (Abbott), and Standard Q COVID-19 Ag (SD Biosensor);<br /> and COVID-19 Ag Respi-Strip (Coris BioConcept), and GenBody COVID-19 Ag (GenBody Inc)<br /> I *think* you did this because they had similar LODs but it'd be more informative if you could show results for each assay independently. <br /> It would also help to know the sample sizes for each of the assays in each group of patients.<br /> Finally, specificity is a potential issue with these rapid antigen assays. Did you test samples that were negative by PCR to determine this (acknowledging that PCR could miss viral RNA, especially if not done at the same time)?<br /> thank you,<br /> marm
On 2020-04-20 15:06:14, user PokeTheTruth wrote:
This article does not represent rigorous scientific study that conclusively proves the SARS-CoV-2 virus spreads from an asymptomatic person to susceptible (non-infected) people. The fact that active (symptomatic) carriers of the virus were in a closed environment (homeless shelter) sneezing, coughing, and exhibiting post-nasal drip prior to the 2-day study while they touched multiple surfaces everywhere cannot be overlooked as the collective "patient zeros." An analysis by an epidemiologist is only as good as the quality of data measured. Commingling active carriers with asymptomatic (inactive) carriers cannot be treated the same from a qualitative perspective and such information should be discarded from evaluation.
There is only one way to determine if asymptomatic carriers can infect a susceptible population and that is by a controlled experiment where one inactive carrier is introduced to a "clean" (non-contagious with any known airborne transmitted virus) group and observed clinically over the known incubation period
On 2020-06-21 20:05:22, user Jørgen K. Kanters wrote:
Please note that by some (yet) unknown reason one of the authors Claus Graff is omitted from the MedRxiv page, but correctly included in the pdf file. We will submit a revision tomorrov to correct it
On 2020-10-28 06:10:32, user DenSvenskeSkeptikern wrote:
My question is this; is this a cross-section study? They just tested cognitive abilities whose results were below the average otherwise and then conclude the cognitive decline must be chronic? I mean, you would need to do follow-up to even begin suggesting it is chronic, right?
If things improve even though it might take weeks or even months, then it isn't chronic is it? Also, wouldn't you suffer (maybe temporary) cognitive decline if you end up in the ICU because of how intense that is for your body no matter why you ended up there?
Is it likely my questions would be indirectly answered as the article goes through the peer-review process where they might realize the logical flaws or the possibly too weak evidence they base their conclusions on?
On 2021-09-24 01:01:18, user Harley Cole wrote:
This was the data used<br /> https://open.ottawa.ca/data... .
On 2021-08-27 11:08:39, user Lakesha Scout wrote:
Most people do not understand how their immune system works, and fall back upon the inept press as their inept narratives. Antibodies do not continue long term, as such a condition would in fact prove disastrous.
Long term immunity relies upon the creation of memory .. specifically memory B and T cells. These are the cells which identify later reemergence of a pathogen (such as SARS-CoV-2) in the body and mount a successful and rapid response to its demise.
There are two pathways to "Active" immunity (infection, and inoculation) <br /> Also there is "Passive" immunity which occurs through such things as a blood plasma infusion from a prior infected person who has existing antibodies. <br /> Any of these three paths, can provide immunity to a pathogen.
The false narrative being pumped by the media is that the only way to overcome a pathogen is through inoculation (vaccination).... that quite simply is not true, neither in medical sense nor in scientific sense. As a mater of medical fact, some inoculations (specifically mRNA) have been proven to enhance a pathogens capability to infect. This condition is known as Antibody Dependent Enhancement or (ADE).
On 2021-04-26 14:10:09, user Peter wrote:
"…patients receiving these drugs should be prioritized for optimally timed second doses."
But what is the "optimal" timing for a second dose?
It takes time for the immune system to respond to the first dose. In general, extending the prime-boost interval leads to a greater eventual response.
This paper suggests that patients on TNF blockers may not respond as well to a single dose, so delaying the second dose may leave them at risk for longer.
Perhaps the answer may be that such patients are likely to benefit from an additional, early booster dose, followed by a third dose (a second booster dose) when their immune systems have had more time to respond to the first doses.
On 2020-04-20 12:33:33, user Wouter wrote:
Hi Micke D,<br /> Please ask me again by the end of this week :-).<br /> I follow the #dead as most reliable statistic. There are large delays in data (1-2 weeks), and likely a considerable underreporting. For now, it looks like an exponential doubling time of 5 days is too high. Looking at the data since publication, it is too early to estimate if we still have an exponential growth (or is it more linear?), and if so, at which doubling time (8-9 days?).<br /> Best greetings,<br /> Wouter
On 2022-08-26 08:21:01, user Josh Atkins wrote:
Full article published at JNCI : https://doi.org/10.1093/jnc...
On 2021-02-14 17:53:25, user Bruce Aronow wrote:
Is Children's National seeing a recent rise in MIS-C cases and do you think this variant may be responsible?