On 2021-04-26 18:39:17, user William Alexander wrote:
Question: in Figure 4B, a vaccination rate of 5000 per day is purported to reduce daily deaths and total cumulative infections over rates of 8000 and 12000. Why is your model predicting this non-intuitive result?
On 2021-08-10 16:09:56, user Vilkus wrote:
On 2021-08-10 21:39:41, user Paul Gordon wrote:
Hi,
Thanks for posting. I am trying to reconcile the text and Figure 1, but am having trouble. The B.1 graphs appear to be identical to the B graphs, even though the stated fold-changes at the top of each NT graph are different between B and B.1. Secondly, the text highlights a very large changes in Kappa neutralization efficacy, but it is marked in the Figure 1a B.1 graph as not statistically significant. Could you please clarify?
Cheers,
Paul
On 2021-08-11 20:20:37, user amalio telenti wrote:
Please check the text: purifying selection = negative selection
On 2021-08-21 16:43:18, user Mark J Kropf wrote:
A good many issues are of question in regards to this work, after mulling it over a good time. Firstly, evolution is always going on. If one is defining mutations in the most general sense, no treatment alters that rate. However, if one means by mutation the generation of some particularly problematic change causing a variant, then perhaps the logic dealt with here is relevant. Evolution is not a process which can be terminated or quelled, though it may be channeled and controlled! Secondly, a period of about 5.5 months can give some possible resonance to the supposed finding, but the ability to alter progression needs to really have significant follow up. Is the process of some unfavorable change (i.e my latter use of 'mutation' above) really limited or is it only impeded and delayed? A true ability to confirm requires a longer period of analysis and the current argument conclusion may be somewhat presumptuous in its statement. Thirdly, I am concerned that the numbers may yet be a bit too small for the conclusion reached, though running a study with the proper enrolled numbers for such comparisons is probably too problematic to be practical.
I believe there is some evidence here, but perhaps not so complete as to be given the full impact that the conclusion provides. It is likely, but it is not confirmed to nearly the extent that I might desire for such a paper.
On 2021-08-12 01:05:28, user SkylarkV wrote:
CDC and FDA won't act on increasing calls for mRNA boosters for the J&J vaccinated unless the data support it, yet researchers appear to be simply ignoring J&J in their research, so those data can't be obtained. So much for for #HealthEquity!
On 2021-08-15 00:21:45, user Covid Hospitalist wrote:
This abstract of this pre-publication is highly irresponsible. There is no clear delineation between 'infection' and 'illness'. This is going to be taken out of context as 'vaccine failure' by multiple groups and news media sources. The drop in prevention of 'infection' ei detectable virus on PCR is important. AND without the data showing that it is still exceptionally effective at preventing hospitalization, is reckless. The authors need to fill in the rest of the blank... they quote the ability of the vaccine to decrease illness/hospitalization from the wild-type "wuhan" strain EUAs in the intro, but then completely leave it out of the results portion of the abstract??? How many antivaxxers/news media are actually scrolling down to table 7 to see that the rate of covid death for pfizer was 0/38,000(n rounded) and moderna 1/36000(n rounded). Seriously irresponsible headline grabbing abstract.
On 2021-08-20 12:18:37, user Jodi Schneider wrote:
Were there any differences in the underlying populations vaccinated with Moderna (mRNA-1273) and Pfizer/BioNTech (BNT162b2) in the Mayo Clinic Health System?
On 2021-08-13 16:42:49, user Dr. Jon wrote:
Isn't it pretty normal to assume those who have recovered from a disease are unlikely to get the same disease again?<br /> Why is this a controversy?
On 2021-10-17 22:54:41, user Rob Reck wrote:
If appears that there is no differentiation given to to the amount of time that passed since a subject contracted CoVid19. Waning immunity is an issue that has been studied. Certainly more study would be a good thing. But there is enough current data to know that it does happen. People who have had CoVid19 do get re-infected.
Given the existence of even a small number of reinfections, the claim that a person who previously was infected with CoVid19 need not be vaccinated is not supported by this study.
On 2021-06-13 21:16:52, user thomas wrote:
I am not in the health field (that may be obvious from the questions I have) but I am very interested in this study because my parents (in their 70's) both had and recoverd from covid. They have not received a vax yet.
Why wouldn't having the infection give immunity? Is there something about this specific virus, or this type of virus in general, that it wouldn't be expected to give immunity?
If infection doesn't give immunity, how will the vaccines work? I realize some vaccines are mRNA or viral vector, but at least the two Chinese ones, the Indian one, and a new one the French are working on are all based on using a dead/weakened virus. Shouldn't recovering from an actual infection work just as good as the simulated infection of a vaccine?
Is 1,359 subjects really considered small? How big where the sample sizes for the initial vaccine studies? What would be an acceptable size? My background is more in the social sciences, and we often see samples in the hundreds.
Is it really correct to assume that people who had COVID would be more careful afterwards? I know with my parents, they were almost consumed with fear about catching the disease, but once they did and recovered, much of that went away. I wasn't around to see their behavior, but just based on conversations, I find it hard to believe they were more careful.
When my parents saw the doctor after recovering, he told them they could not get the vaccine for at least 3 months and that they didn't need to get it until after 6 months. So this study seems in line with what the medical establishment was already saying (they had COVID back in March).
On 2021-08-13 17:27:45, user Chuck Crane wrote:
If you look at the questionnaire (the "supplementary materials" link) you find that the MD's and DVM's are "professional degree," and there is no "PhD" classification at all. It says "Doctorate," which includes Jill Biden's Ed.D. and so on. So the chart is deceptive.
D8 What is the highest degree or level of school you have completed?<br /> 1. Less than high school<br /> 2. High school graduate or equivalent (GED)<br /> 3. Some college<br /> 4. 2 year degree<br /> 5. 4 year degree<br /> 6. Master’s degree<br /> 7. Professional degree (e.g. MD, JD, DVM)<br /> 8. Doctorate
The paper is not in sync with the questionnaire, saying, e.g., "Those with professional degrees (e.g., JD, MBA) and PhDs were the only education groups without a decrease in hesitancy, and by May, those with PhDs had the highest hesitancy." I can't see how an MBA could look at the question and check "Professional degree" instead of "Master's Degree."
Think the paper needs a good proofreading.
Participation bias is a big issue. They asked a lot of people to participate, but only a small percentage did. The rather inane attempt to correct for this is to assume that if a particular class of respondents is under-represented, just assign responses from that class more weight, according to their proportion of the population ("post stratification adjustment").
On 2021-08-14 00:52:29, user Meredith Olson wrote:
Those with a doctorate who choose to spend time on facebook and are also willing to take the time to fill out the survey there are a particular subset of people with doctorates.
On 2021-08-15 02:02:46, user bcwbcwbcw wrote:
An online survey, where anyone can claim to have a PhD and no tests or controls for whether that's true? If you're anti-vax what better way to claim credibility than to lie and claim to have a PhD? In other past surveys , 6% of PhD's said they are Republican, yet the hesitancy results for PhD's are nearly the same as the strongest Trump supporters. (statistically possible but very unlikely.) (https://www.pewresearch.org... ) If I was a reviewer, I would ask see the breakdown of Trump support versus education level. If not consistent with other studies, the educational attainment data should be discounted.
I took this survey and it likely has some use as far as changes in totals over time but PhD's not really.
Let me give you a data point from a lab with about 1500 PhD's and tech staff. Everyone I've asked is vaccinated and I've asked everyone I'm in contact with.
On 2021-08-15 10:02:06, user Anna Z. wrote:
This paper is circulating among no-vax groups and used as a prof that educated people don't get the vaccine because they are not fooled by the government.<br /> How did you make sure that the survey was not circulated among no-wax groups that on purpose answered to obtain this result?
On 2021-10-05 10:08:23, user Samantha Hester wrote:
Members of the trans community are raising questions about your new exclusion criteria that eliminated people who self-identified as unicorns. Unicorns belong to the otherkin community and their responses could be in good faith.
Please review this post from a trans advocacy organization for more details:
On 2021-08-16 15:59:43, user A. Jamie Saris wrote:
There are some excellent comments below that I will not rehash, but I agree that this pre-print "as is" would not survive peer review without some serious revisions. Unfortunately, as this site is Open Source, this "study" is appearing in a lot of anti-vaxx rants on social media (it's been cited twice to me on Twitter so far today). It would be a great help if there were some printed caveats on sites like this (especially around topics where pseudoscience to outright quackery is rife) to dissuade people from taking VERY provisional results (from a flawed study with a modest number of participants) as "settled" science "proving the effectiveness" of Ivermectin.
On 2021-08-17 14:22:29, user Jon Hillman wrote:
'Taken together our results indicate that state implemented NPIs can lead to less COVID-19 cases and mortality.' How much less? CDC concluded it was no more than 1.8% reduction in cases: https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e3.htm Quantifying the reduction matters to anyone concerned with objectively measuring NPI cost vs outcomes.
On 2021-08-17 14:26:39, user Andrew Sefton wrote:
In the research, how were those previously infected by COVID-19 categorized? As unvaccinated? Excluded?
Specifically, I am interested in the viral loads of those previously infected by COVID-19 as it relates to:<br /> "Delta viral loads were similar for both groups for the first week of infection, but dropped quickly after day 7 in vaccinated people."
On 2021-10-05 22:04:39, user Brooke wrote:
It would be useful to know what sorts of samples were used for PCR testing — were they nasal swabs or saliva?
On 2021-08-18 18:37:59, user Sumit Rawat wrote:
On 2021-08-18 18:40:47, user Sumit Rawat wrote:
News article by ET health <br /> https://indianewsrepublic.c...
On 2021-08-20 23:58:21, user Chris Raberts wrote:
This model ignores the wave form observed repeatedly over the past year and a half. Covid infection is not a never-ending exponential function. Terrible.
On 2021-08-21 00:31:20, user Sonia Villapol wrote:
This article has been published in "Scientific Reports" on 09 August 2021, a Nature Publishing Group's open access research journal: <br /> https://www.nature.com/arti... <br /> Thanks! <br /> - Sonia Villapol
On 2021-08-21 19:03:01, user Jonathan C wrote:
Hello,
Thanks for an interesting analysis. CDC estimates a far higher infection rate (36.77/100k, <br /> https://www.cdc.gov/coronav... "https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/burden.html)"), <br /> at a similar rate for the 0-17 y group, although they do not seem to show data for the 12-17 y group).
Am I correct in interpreting your assumption that the infection rate for the <br /> investigated COVID-19-related period was at a far lower <10%? (and that 2.5% of all COVID-19 cases should represent males aged 12-17)
Or is there some information missing regarding your analysis?
On 2021-08-24 07:21:17, user Red wrote:
This paper is missing one very crucial piece of information: 6-month adverse event followup. Table S3 still reports only adverse event counts up to 1 month after the second dose, but nothing about longer followup periods. This is a violation of a commitment from the study's protocol where it was stated that 6-month safety data will be reported (section 9.5.1). And the only reason I can think of why such a data was not reported is because it suggests the treatment is not as safe as it is claimed.
On 2021-08-04 07:40:42, user Mike wrote:
I'm curious about the HIV infected patients. There were exactly 100 in both vaccine and placebo group. If you look at the co-morbidity tables, no other co-morbidity is balanced in that way. I suppose it's possible that this occurred by chance but it's a very small one if so. Also, why did they include HIV+ patients in the study at all, if they exclude them from all reporting of deaths and adverse events? The HIV+ can lead long lives these days, it's not quite clear to me why they are being treated separately here, especially as it should hopefully be clear if they died of AIDS.
On 2021-08-05 17:53:36, user pedro paulo castro wrote:
It doesn't seem right that a much lower number of subjects from the vaccinated group came down with COVID 19, but the same number died as in the placebo group, which seems to indicate therefore a higher proportion of deaths among those who contracted COVID 19 AND were vaccinated. There is a conspicuous lack of what would have been a very useful breakdown of the instances of death, in such a way that we could see, for both groups, what number of deaths was among those who had COVID or those who didn't have COVID. This prevents us from seeing whether a subject had COVID, but had his or her death reported as, say, cardiac arrest, for example, which might change the context a bit.
On 2021-10-03 02:28:34, user OBS wrote:
How come this preprint (and the very recent publication of this in NEJM) both say 15 deaths vaccine vs. 14 deaths placebo, but the FDA briefing document for the booster shot (which summarizes the safety of the primary 2-dose series, see page 7), says 21 deaths vaccine vs. 17 deaths placebo?
https://www.fda.gov/media/1...
21 vs. 17 doesn't seem to be an update of the 15 vs. 14 result, since the booster FDA briefing document specifies March 13, 2021 as the data cutoff date corresponding to the 21 vs. 17 result, and that is the exact same cutoff date mentioned in this preprint / NEJM article. So why the discrepancy- what is going on here?
On 2021-08-26 07:10:10, user William Brooks wrote:
To help readers clearly see the difference in infectiousness before, during, and after the various interventions (i.e., the states of emergency, school closures, and GoTo travel campaign),the authors should add the start and end points of the interventions in Figure 2.
On 2021-08-27 02:57:37, user Jason Eshleman wrote:
The author's model assumes that the generation time for the variants is the same. This seems to run counter to observations of a markedly shorter incubation period with delta. This analysis absolutely needs to be rerun without that assumption. Are we seeing greater transmission between generations or are we seeing a fitness advantage due to a shorter generation time?
On 2021-08-27 12:30:01, user Nikos Salingaros wrote:
Hello everyone. Alarming results indeed. Are there any data on the visual complexity of the indoor environment in which these babies were raised? Our group is trying to relate low intelligence to the lack of mathematical stimulation coming from visual patterns. This is especially relevant since exposure to natural complexity such as outdoor plants is severely limited during the lockdown. The preferred architectural style today is minimalist: very different from the visual complexity of past generations, and this factor might contribute. How do we get some data on this possibility?
On 2021-08-27 22:08:03, user evasmagacz wrote:
To look at the data from a different perspective:
In your first dataset:
Model 1: n = 16000 <br /> In patients who were previously infected: <br /> There were 5 symptomatic re-infections per 10000;<br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously vaccinated, <br /> There were 124 symptomatic re-infections per 10000;<br /> 5 hospitalisations per 10000 and no deaths.
In your second dataset:<br /> Model 2: n = 46000<br /> In patients who were previously infected: <br /> There were 15 symptomatic reinfections per 10000; <br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously vaccinated, <br /> There were 105 symptomatic reinfections per 10000 <br /> 5 hospitalisations per 10000 and no deaths.
In your third dataset:<br /> Model 3: 14000<br /> In patients who were previously infected: <br /> There were 16 symptomatic reinfections per 10000 <br /> Less than one hospitalisation per 10000, and no deaths.
In patients who were previously infected and then vaccinated, <br /> There were 11 symptomatic reinfections per 10000 <br /> No hospitalisations per 10000 and no deaths.
On 2021-10-30 04:38:45, user Rn wrote:
The conclusions of this study stand in stark contrast to a report published today by the US CDC. https://www.cdc.gov/mmwr/vo...
Among COVID-19–like illness hospitalizations among adults aged >=18 years whose previous infection or vaccination occurred 90–179 days earlier, the adjusted odds of laboratory-confirmed COVID-19 among unvaccinated adults with previous SARS-CoV-2 infection were 5.49-fold higher than the odds among fully vaccinated recipients of an mRNA COVID-19 vaccine who had no previous documented infection (95% confidence interval = 2.75–10.99).
On 2021-08-28 18:17:03, user Squid Pro Crow wrote:
Despite the fact that I have no formal medical training, I think that I now have the real life experience to knowledgeably comment on this. My wife and I both had our second doses of the Phizer just under 5 months ago. Also my daughter and son-in-law had the Pfizer shots about 3-1/2 or 4 months ago. At the end of a 3 day stay of 2 grandkids i began to get a cough and slight fever, and lost my sense of smell and taste. So I got tested and it was positive, My wife has a cough and body aches and will be tested today. My daughter and son-in-law (in their low 40's) are also experiencing mild symptoms and will be tested today. The kids, of course had very minor symptoms for about a day, and are completely fine. So, assuming that the adults test positive, it seems evident that the delta strain does indeed spread rapidly and easily, and the vaccine(s) may not be as effective against it. HOWEVER, I feel that at my age, with asthma and possibly COPD history, I would be much worse off had I decided against the vaccine, as my symptoms are very mild now, except for the chest congestion that I have (which is already better) that I also get from just about every cold.
My main concern is that there is not enough focus on theraputics, and major health providers like Kaiser just expect even their at-risk patients like me to just sit at home and wait to see if their lips turn blue and they can't breathe, and make it to an E.R. for a company that is usually proactive about health care, this is just stupid. An apparently, this is the norm. There are some treatments that are effective if taken early, but our government and the health system that follows their dictates are afraid to prescribe safe drugs off-label that are semi-proven to be very helpful, like ivermectin, which I managed to get from a nearby Dr. It seems to be helping clear it up even faster--my sense of smell is even starting to come back.
On 2021-09-05 04:24:28, user Adriana Perez wrote:
Regrettable the matching of the groups requires to use conditional logistic regression for the analysis which the authors did not do otherwise they would have written it. The lack of control in the matching indicates that the results can not be trusted.
On 2021-08-29 20:54:09, user peter_wark wrote:
Thanks again Recovery trial.<br /> Participants admitted with COVID19; unable to maintain SpO2 <94% despite FiO2 0.4.<br /> Mean age 57yrs<br /> Primary outcome was intubation or mortality at d30.<br /> CPAP HR 0.72 (0.53-0.96) p=0.03<br /> HFO2 0.97 (0.73-1.23) p=0.85<br /> The number needed to treat for CPAP was 12 (95% CI, 7 to 105) and for HFNO was 151 (95% CI, number needed to treat 13 to number needed to harm 16).
On 2021-08-04 07:26:14, user oikoslibre wrote:
In the first chapter you talk about PCR.
I would like your opinion on the following document
https://www.fda.gov/media/1...
When I read this document , it becomes clear that this test is of no use at all
Positive results are indicative of active infection with SARS-CoV-2 but do not rule out bacterial infection or co-infection with other viruses. The agent detected may not be the definite cause of disease
In this document I also read: Since no quantified virus isolates of the 2019-nCoV were available for CDC use at the time the test was developed and this study conducted, assays designed for detection of the 2019-nCoV RNA were tested with characterized stocks of in vitro transcribed full length RNA.
Is it possible to write an article on this virus without the use of PCR data?
Do you have the isolated virus?
On 2021-08-05 18:41:36, user Ultrafiltered wrote:
With the probability of a PCR match of 1 with any sample comparison to a reference given 8 billion genotypes against strands of 30 to 50 mRNA, as DNA is expressed in any and all cells, the study only shows how many in the population are expressing a gene similar to a COVID phenotype, thus why the CDC has pulled its support of the PCR tests and going back to the process of isolation and identifying cells discovered through patient exam, similar to current Influenza like analoques. The basis of this paper goes to show that if you're sick with disease, you are sick with the disease and shed components, just like any other virus. The idea this effect is novel in this paper is superceeded by years of virology and research.
On 2021-09-16 07:33:29, user Chaos_14 wrote:
This study doesn't mention how many vaccinated vs unvaccinated people were tested.
"Notably, 68% of individuals infected despite vaccination tested positive with Ct <25, including at least 8 who were asymptomatic at the time of testing." (68% of what number?)
Since we know immune response, even with vaccines, decreases with age, it would be helpful to know the ages of the people in both the vaccinated and unvaccinated groups.
It would also be helpful to know the Ct in the samples of asymptomatic, <br /> unvaccinated people if there were any.
While it's beneficial to know that it's possible for infected vaccinated people to carry a viral load similar to infected unvaccinated people, this study left me with a lot of unanswered questions.
On 2021-08-06 23:22:56, user disqus_92pIDbtuHj wrote:
Hey, where's the full description of method and limitations? I get that this was published in medRxiv, a free distribution server for unpublished preprints that haven't been peer reviewed. It even states preprints "should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information".
This was a SMALL sample of 43 men... undergoing IVF and they served as their own self control. WHEN, was a sample after vaccination taken? WHEN was the baseline taken? HOW did they control for the effects of other variables... like the treatment recommendations these patients may have been following at the IVF clinic (especially since these were pulled Hospital IVF records)! They compared each man to his own baseline before and after vaccination, (14 men had male factor infertility, and 29 with normal spermogram results). Regardless all men were very likely receiving lifestyle, diet, or even medication recommendations! They also neglected to control season as a variable. Previous literature shows poorer sperm quality in Winter, and better quality in Spring. This design looked at two samples from each man somewhere between winter and spring. The same span of time for each man? No one knows!
On 2021-08-07 16:14:30, user Dmitry Pruss wrote:
Isn't it a time-of-testing confounding effect? In Israel, percent of positive tests increased from 0.1% in the beginning of the study period to 1.5% in its end, which would likely result in an artifactual increase of positive in those vaccinated (and tested) earlier...
On 2021-08-11 10:34:14, user Apriyano Oscar wrote:
I am sorry, I am just a layman. I want to ask about the 1.8% tested positive (608 people). Does it mean that the effectiveness of the Pfizer vaccine in this study is 98.2% ? And is this also the same as what is called as 'efficacy' ?
On 2021-08-08 19:23:45, user Sam Wheeler wrote:
So against delta, 1 dose of The Moderna COVID-19 (mRNA-1273) vaccine seems much more efficient than 1 dose of Pfizer Biontech?<br /> And no data about how efficient is Moderna with 2 doses against delta?<br /> What do we know about Janssen = J&J? Janssen is very efficient if you take into account it is given as a single-dose, and one can boost it by taking a booster or primer with other covid vaccine.
On 2021-08-09 15:03:31, user Disha Agrawal wrote:
Figure 3b is surprising and difficult for me to understand. The Y-axis for all figure 3 results should be Geometric Mean of the ELISA tests, as per the text. Assuming that to be so, Figure 3b is Antibody to N protein, which should not be induced by Covishield. Yet most Covishield/Covishield samples seem positive, as shown, with no difference from Covaxin/Covaxin. A possibility I considered is that most people were already infected, but then the Covaxin/Covaxin group should have been strongly boosted. Clarification from authors or others who were able to figure it out is welcome.
On 2021-12-01 22:44:50, user Tom wrote:
The susceptibility of Chilrden was estimated by PCR-Testing alone and has a high variance in the 95-CI. I guess the numbers may be even lower.
On 2021-09-14 21:28:12, user Alberto wrote:
23 vaccinated individuals, samples collected 5.2 weeks (average) after the second dose of the vaccine. No information about age, health, etc... compared to 10 individuals infected one year prior to taking the blood samples and 7 infected less than 2 months prior to taking the blood samples. Again no information about age, health, etc...
Conclusion: "Hence, immune responses after vaccination are stronger compared to those<br /> after naturally occurring infection, pointing out the need of the vaccine to overcome the pandemic".
Isn't that conclusion going well over the possibilities of this study? When in real world studies with cohorts of > 25.000 individuals it has been proven that the immunity acquired from infection is vastly superior to that from vaccination, how should we take these results?
On 2021-12-15 06:52:55, user MD PhD wrote:
Although it's a small sample size still it would be worthwhile to know the antibody response to booster/third dose in 6 months vs 9 months group post-vaccination. Additionally whether these groups received first and second shots at 3-4 weeks or 7-8 weeks interval will offer pertinent information since this basic difference rendered more antibody response in the latter groups as per studies (the point being that boosters might turn out to an immediate requirement for the 3-4 weeks vaccination interval group while the 7-8 weeks interval group might potentially be able to put it off for a month or so in light of prior studies showing a robust antibody response with delayed vaccination)
On 2021-09-16 13:24:58, user Theo Sanderson wrote:
The apparent pattern of back mutations at position 142 is an experimental artefact due to errors in some Delta sequences. It emerges from the fact that Delta has SNPs in the primer binding site for ARTIC amplicon 72 (in a previous ARTIC scheme) which often result in the failure to amplify this amplicon, containing the G/D 142 locus, from Delta samples. Small amounts of contamination from other genotypes (e.g. B.1.1.7) that are amplified normally at this location can then lead to an amplicon here (typically with reduced depth). This results in a final sequence which appears to have a back-mutation at this position, and phylogenetic analyses can tend to group such samples together on trees.
T95I is in this same amplicon.
It is likely that the Ct correlations observed here reflect the fact that the correct G142D call is much more likely to be detected despite the low efficiency of amplification for samples with higher viral loads.
On 2021-09-16 13:35:24, user David Brown wrote:
There is evidence that abdominal obesity in both humans and chickens is determined by the fatty acid profile of the diet; specifically, the linoleic acid content. Read pages 7-9 of this 2019 Master's Thesis. https://trace.tennessee.edu...<br /> For further comment regarding linoleic acid intake and vulnerability to COVID-19 complications, read these articles:<br /> https://www.medpagetoday.co...<br /> https://www.science.org/doi...
On 2021-09-17 17:04:42, user kdrl nakle wrote:
This would all be OK if we could rely on COVID reporting but we cannot. For example a continent of 1 billion people, Africa, on Wednesday reported 12,000+ cases while we have seropositivity in Kenya of 50%! Meaning, their numbers as reported, are a joke. India that reported some 33 million cases had more likely some 900 million cases. And similar things are happening throughout Asia, Latin America, and Eastern Europe. In other words, your statistics are a joke.
On 2021-09-19 12:46:53, user daan joubert wrote:
I rhink you are referring to the article entitled "Africa Dailye deaths.100k etc" showing the difference between the high incidence in the upper and lower parts of the continent compared to the equatorial region where Ivermectin is used against tropical parasites and there are few deaths. It seems to have been removed for some guessable reason.
On 2021-09-22 01:46:06, user jhick059 wrote:
Dear authors,
I believe your denominators (15,997 Moderna doses and 16,382 Pfizer doses) are off by more than a factor of 10.
Ottawa Public Health has 342,656 doses of Moderna and 485,178 doses of Pfizer between 2021-06-01 and 2021-07-31. Link: https://open.ottawa.ca/data...
You also state (pg. 6/20) that your data suggest a tenfold higher incidence than other papers estimating an incidence of 1/100,000. A tenfold higher incidence than 1/100,000 is 1/10,000, which is closer to the value you would obtain with the adjusted denominator.
Sincerely,<br /> Joseph Hickey
On 2021-09-22 03:14:20, user Norsksoul wrote:
It is a preprint article but they basically identified all vaccine recipients in Ottawa during the June 1 through July 31 study period. <br /> This was the denominator of the study group. <br /> Anyone from this study group that was admitted with Acute Myocarditis or Pericarditis within 1 month of a Moderna or Pfizer vaccine became the numerator. <br /> So 32 cases occurred in 32,379 vaccine recipients which comes out to a 1/1000 incidence. This study should be done in the 12-18 year old age range and the incidence would likely be even worse.<br /> But wait,....it gets even worse. <br /> That 1/1000 incidence is in a group of 32,000 men AND women. <br /> But out of 32 cases of myocarditis, 29 occurred in men. <br /> That’s 90%! <br /> They unfortunately don’t give the data on male/ female percentages in the study group denominator but if we assume a 50/50 split, then the male incidence is actually 29/16,189 or 1 in 558 males vaccinated. <br /> 1/558<br /> 1/558<br /> 1/558<br /> Let that sink in for a minute. <br /> This is reckless medical malpractice at its worst.
On 2021-09-23 06:52:46, user White Rabbit wrote:
There are several issues about the meta-analysis by Martinoli et al. for example they wrote they did a meta-regression in order to explain the the huge between-study heterogeneity affecting the results, but no meta-regression results appears anywhere. They observed a statistically significant publicaton bias ("We found an indication for publication bias (P=0.03)" ,page 10) a serious but unaddressed issue. Ther are also inconsistencies between the results and the conclusions, e.g. though they found that "Children and adults showed comparable SARS-CoV-2 positivity <br /> rates in most studies" (page 9)" the abstract reads "children are 43% less susceptible than adults".Furthermore in some tables and forest plots, they used as denominator the total of students and staff altogether instead of students only, to estimate the students incidence.
On 2021-09-23 15:49:33, user kdrl nakle wrote:
What is needed more is the distance between the shot and data collection. We need longer duration period for VE evaluation. Your time period is too short.
On 2021-09-23 18:14:58, user kdrl nakle wrote:
n-28, n=29, n=106 and no significant difference between 2.4x10^5 and 3x10^4? That is because your samples are small. I think that 8 fold increase would be significant if you got bigger samples.
On 2021-09-24 06:20:12, user Ella Lively wrote:
Where can I get the questionnaire for thus study please?
On 2021-09-28 15:09:49, user Tomas Maximus wrote:
Looks like the proportion of breakthroughs climbed dramatically as time went on, with breakthrough accounting for 17% of total new cases in July. Wonder what the August and September numbers showed.
On 2021-09-29 04:15:27, user Nikki wrote:
I work as an account Escalation Specialist/call center supervisor who takes over Escalated calls. I've never had issues with missing small details which are required to do my job. I caught covid in mid July, had a horrible experience with two weeks worth of severe vertigo, nausea, fever spikes, tons of phlegm, panic attacks. <br /> One month and a half after recovery, I've had 4 major fails which may ultimately end up costing me my job. <br /> My pcp, therapist, and boss appear to disregard this when I try to explain to them about the fogginess. <br /> As a very detail oriented person, I just don't miss those things.... never in my 15 years in callcenter experience.
On 2021-09-29 11:30:07, user kdrl nakle wrote:
Good example of making a paper focused on useless graphics instead on self-explanatory data.
On 2021-10-02 14:59:26, user Alberto wrote:
Thanks for the detailed report. I'd only like to ask about the last sentence included in the abstract: "The beneficial and protective effects of the COVID-19 vaccines far <br /> outweigh the low potential risk of neurologic and psychiatric reactions. Going through the paper I haven't seen anything that attempts to estimate these rinks vs. benefits in any way (let alone a systematic way, by age, risk of severe disease in case of COVID-19, etc...). It seems like a statement that's been added there arbitrarily and does not belong to a scientific paper that not actually evaluating any risks associated with the disease itself or the vaccine efficacy to prevent them.
On 2021-10-03 07:19:18, user Ruth Berger wrote:
That age and male sex are major risk factors is well known; mortality associations with pandemic wave should not be reported without factoring in varying levels of underdiagnosis (to my knowledge, it was larger in the first wave than the second) and age-specific vaccination rates.
On 2021-10-03 16:42:16, user Luke Yaldo wrote:
The peer reviewed, published version of this article can be found here: https://link.springer.com/a...
On 2021-10-04 06:54:30, user kdrl nakle wrote:
Simple yet important result, meaning we should definitely know Cp (Ct) value after getting tested. The next thing would be to investigate transmissibility but that is obviously much harder research.
On 2021-10-04 12:12:28, user LG27 wrote:
Why was there no stratification by prior infection? It's not even mentioned in the limitations.
On 2021-10-05 22:59:35, user MrMemeinator wrote:
So you're seeing the same thing we all saw in the UK and Israel months ago. Color me surprised...
On 2021-10-16 12:57:40, user gerryz wrote:
Can anyone tell me if there is evidence infection symptoms are milder in vaccinated? Articles?
On 2021-11-14 09:47:38, user Justbeenschooled wrote:
There are a few incorrect spellings in this document. But the study is excellent and should be peer-reviewed.
On 2021-10-06 16:56:58, user zega wrote:
Baseline is 1/100000/365days https://www.myocarditisfoun... vast difference, they an be off by 8000times and still below...
On 2021-10-07 22:22:53, user Robyn Schofield wrote:
"As the devices do not meet medical device electrical safety standards (EN60601) they were operated at a distance of >=1.5metres from any patient." Can the authors please clarify - what wavelength the UV was operating at, and whether this device has been tested for ozone production / loss rates. I assume that the EN60601 requires ozone production to be tested for? If ozone is being produced (or destroyed to odd oxygen) that this would need testing before deployment in a medical setting. Ozone, a respiratory irritant gas, will easily travel more than 1.5m (so distance should not be seen as useful in setting safety protocols for electronic air cleaning devices in a medical setting).
Are hospital rooms with no ventilation in line with current infection prevention and control or hospital design / operational guidelines in the UK? In Australia this would be in breach of both our hospital design and operating guidelines which require a minimum of 6 ACH for all hospitals.
The effectiveness of UV with air high flow rates has to be questioned (because the exposure time for bio-aerosols is short) - are the authors able to separate the effectiveness of the filtration over the UV features? Most literature on this point shows that in real-world operation the HEPA provides 99.97% of the removal of bio-aerosols from air and the advantage of UV is untested / unproven (this is particularly true at 1000m3/h flow rates this device is operating at). I assume this device will be noisy >65dB - can this please be specified.
On 2021-10-11 18:40:32, user Andrew T Levin wrote:
Diamond Princess Cruise Ship. The manuscript makes no reference to any epidemiological analysis of this episode, which informed seminal assessments of the age-specific infection fatality rate (IFR) of COVID-19.[1-4] Nonetheless, that evidence is particularly relevant, because the cruise ship’s passengers included 1231 individuals ages 70+ who were not merely “community-dwelling” but healthy enough to embark on a multi-week grand tour of southeast Asia. Following extensive RT-PCR testing, 335 passengers ages 70+ were confirmed to have been infected with SARS-Cov-2, and 13 of those passengers died from COVID-19 – an IFR of about 4%. Moreover, the strong link to age is underscored by the even higher IFR of 8% for passengers ages 80+. Given the size of that sample (which meets the 1000+ threshold used here), this evidence should certainly be incorporated into this meta-analysis.
Comprehensive Tracing Programs. The manuscript makes no reference to countries that succeeded in containing the first wave of the pandemic in spring 2020 through systematic tracing and testing of all contacts of infected individuals.[5] Such evidence is particularly relevant here, because the virus was contained within the “community-dwelling” populations of those locations and never spread to any elderly care facilities. For example, in the case of New Zealand, there were 256 infections and 19 deaths among adults ages 70+ -- an IFR of about 7%.
Hospitalized Patients. The manuscript cites a single study (published in July 2020) that examined the association between comorbidities and mortality risk of COVID-19.[6] However, that study was not able to distinguish whether comorbidities were linked to greater prevalence (the probability of getting infected) or to a higher IFR (the risk of mortality conditional on infection). Unfortunately, the manuscript makes no reference to any subsequent studies on this issue. In particular, a large-scale study of U.K. BioBank participants found that measures of frailty were indeed associated with higher mortality rates in the overall panel but not linked to mortality within the subset of hospitalized COVID-19 patients.[7] In effect, the prevalence of COVID-19 was markedly higher among residents of U.K. nursing homes compared to individuals of similar age living in the community, but the IFR was not significantly different. Those findings directly contradict a key assertion made at the start of this manuscript.
Prior Meta-Analysis of Community-Dwelling Populations. The introduction of this manuscript neglects to mention that an existing meta-analysis study (published in Nature in November 2020) was specifically focused on assessing IFRs excluding deaths in nursing homes.[8] That study estimated the link between age and IFR using seroprevalence and fatality data for adults less than 65 years old, and then showed that the model predictiions were consistent with data on fatalities among community-dwelling adults ages 65+. Moreover, that study used seroprevalence data adjusted for assay characteristics, and the results were obtained using a rigorous Bayesian statistical model that incorporated random variations in the time lags between infection, seropositivity, and fatal outcomes – a striking contrast to this manuscript, which uses rudimentary assumptions to address those issues.
Other Meta-Analyses. The introduction of this manuscript briefly refers to two other meta-analysis studies of the link between age and IFR.[5, 9] However, the manuscript then asserts: “Importantly, the vast majority of seroprevalence studies include very few elderly people.” (p.5) That assertion is supported by a single citation to the SeroTracker database, which provides comprehensive coverage of all existing national, regional, and local seroprevalence studies across the globe.[10] However, this assertion is completely incorrect as a characterization of the preceding meta-analysis of age-specific IFRs. As indicated in Levin et al. (2020, figure 5), that meta-analysis study included seroprevalence data on older adults (including narrow brackets for ages 60-69, 65-74, 70-79, and 75-84 as well as open-ended brackets for ages 60+, 65+, 70+, 80+, and 85+) from nine national studies (Belgium, France, Hungary, Italy, Netherlands, Portugal, Spain, Sweden, and the U.K.) and eight regional locations (Ontario, Canada; Geneva, Switzerland; Connecticut, Indiana, Louisiana, Miami, Missouri, and San Francisco, USA).[5]
On 2021-08-30 23:32:13, user Patrick Auta wrote:
Thank you
On 2021-10-14 17:12:39, user lbaustin wrote:
Has this been submitted to any of the MEDLINE indexed journals who publish on this topic?
On 2021-10-22 17:10:09, user Jeremy M Bartels wrote:
Can you clarify if Ct values decrease, does viral load go up or down?
On 2021-10-31 02:14:19, user Peter Jaji wrote:
So this shows vaccinated people spread the virus as much if not more then unvaccinated?<br /> Please enlighten
On 2021-12-01 22:19:03, user Kevin J. Black, M.D. wrote:
You'll want to cite and discuss this article:<br /> Snowden JS, Craufurd D, Griffiths HL, Neary D. Awareness of involuntary movements in Huntington disease. Archives of Neurology. 1998;55(6):801-805.
On 2021-05-27 10:15:21, user Qian Dong wrote:
Code is now available through: https://github.com/s0810110...
On 2020-12-03 21:43:53, user kdrl nakle wrote:
It could also be that association is purely coincidental. Meaning people that die more often are older people and they are also more likely to be vitamin D deficient. So you really have nothing here.
On 2020-04-05 22:12:33, user Soarintothesky wrote:
What delay was used for the time adjustment. A 10 day delay for cases>deaths in the Aneirin Bevan University Health Board in Gwent in South Wales shows a 22% CFR.
On 2020-06-04 00:48:58, user James Van Zandt wrote:
Vitamin C is a common supplement. I suggest you track whether patients had taken vitamin C (and how much) before or in the early stages of their illness. If it is helpful, then we would like to know when it is most helpful.
On 2020-04-10 00:26:48, user Brothers in arm wrote:
Curious to know why the BCG vaccination last only about 20 years. I have had mine as an infant, without any further boosters. Still my skin tuberculin test remains reactive after almost 50 years. The reaction subsides before follow up check on day 3. This is read as negative for active TB. I assume the slight reaction as due to having had BCG, and I still have immunity. People who never had it do not get any reaction or erythema. Maybe any immunization can confer some cross immunity?
On 2020-04-02 07:42:02, user japhetk wrote:
I don't know why, but medrxiv keeps deleting my warning comments.
So, I write brief comments again.
This study doesn't control important variables as kept suggested in comments and probably the findings are due to spurious correlations.
The one of uncontrolled important variable is "when the infection spread in the country". This study should have used the measure like "number of deaths or patients 10 days after 100th patients were detected in the country". Other analyses are doing that.<br /> The second uncontrolled important variable is "how long the country advanced BCG measure". UK, for example, advanced the BCG measure for more than 50 years. So, majority of nations are experienced with BCG.<br /> The third uncontrolled important variable is GDP. You can see the most of nations without BCG is Western rich countries which can do more tests, which are popular from tourists.<br /> I did analyses controlling these variables, and all the correlations between the length of BCG measure with coronavirus data (how fast the 100th patients were detected in the country, number of patients or deaths ten days after the 100th patients were detected in the country) are all insignificant. They did not even show the statistical tendencies.
Many people have wrong ideas how effective BCG is though this preprint. Somebody has to give warnings. Please do not delete this warning.
On 2020-04-04 09:25:59, user Oleg Gasul wrote:
The main proof will be if you found statistic of dead by reason of virus with BCG and without.
On 2020-06-30 08:50:43, user Simon Liebing wrote:
I have 2 questions to the study:<br /> What explanation have the authors that only 2 of 5 indicators are positive?<br /> Why the virus vanishes after March 2019 again?
On 2020-06-30 11:18:06, user Kevin McKernan wrote:
Interesting work. Great to see the qPCR replicated at another lab and spike in controls.<br /> It would be very helpful to sequence the Amplicons to see if any variation exists that can augment the phylogenetkcs of the disease.
On 2020-06-30 14:50:46, user Munir Hazbun wrote:
On 2020-06-30 21:40:24, user David Sbabo wrote:
"Anonymized raw data were extracted from the abovementioned databases on<br /> July 2, 2020."
July 2?
On 2020-07-01 22:29:58, user John wrote:
Loneliness is prevalent in COVID-19 crisis. Patients with Coronavirus are more lonely during the pandemic. Interesting findings for health psychology, psychological impact, public health, epidemiology and psychiatry.
On 2021-12-31 13:45:45, user Allan Lorentzen wrote:
What does negative effectiveness mean?
On 2021-12-28 00:53:06, user Drew wrote:
Two issues need to be corrected for in the data before any real conclusions can be drawn. First, is there a relationship between age stratification, higher vaccination status and higher symptomatic disease - i.e., Simpson's Paradox. Second, was there a behavioral reason that impacted the results? For example, if vaccinations were required for admittance to crowded venue during the initial spike in Omicron cases, it would have skewed the results toward negative effectiveness.
On 2020-07-07 14:29:11, user Anika Knuppel wrote:
This article has been accepted for publication in the International Journal <br /> of Epidemiology, published by Oxford University Press.
On 2020-07-07 18:11:59, user Tajemniczy don Pedro wrote:
Makes sense. https://pubmed.ncbi.nlm.nih...
On 2021-01-26 03:42:24, user Terran Melconian wrote:
Thanks for sharing this very interesting article.
On page 6, for the definition of the x and z transforms, they are both given as sin(2*pi*t/tau). One of them is presumably meant to be a cosine, right?
On 2020-06-21 11:55:23, user Dirk Monsieur wrote:
Rough estimate: 1% infected at 12th of March; chances that 84 random people are not infected: 0,99^84 = 43%<br /> I'm not a statistical expert, but I think a power analysis would be good.
On 2020-07-13 22:41:50, user Jim Coote wrote:
I share the concerns expressed in the previous 2 comments. Surely the decrease in antibody levels would be entirely expected after the primary response. The acid test would surely be whether there was a good secondary response to any Covid19 based antigen. Any analysis of that should examine the cell based response as well as the humoral.
Considering the concerns likely to be raised by their findings, I think it is a serious omission not to compare the data to antibody levels typically seen following primary responses to infections on which we have solid information on long term immunity, (both weak and strong). However this would be a completely academic consideration provided a good secondary response to Covid 19 antigen / virus was seen.
On 2020-04-17 15:25:24, user Dr. James R. Baker wrote:
Interesting approach and pretty convincing, but it does not take into account the number of asymptomatic infections associated with COVID. That is really substantial; some estimates of 30-50 percent. That would then double your number, wouldn't it?
On 2019-11-12 00:51:39, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 10, 2019
Monday, November 11, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,287, of which 3,169 confirmed and 118 probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> 411 suspected cases under investigation;<br /> No new cases confirmed;<br /> No new deaths of confirmed cases have been recorded;<br /> 3 people healed from the CTE in North Kivu in Mabalako;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths;
NEWS
Awareness and vaccination day for Beni mototaxi drivers with the support of Unicef S / Coordination MVE Beni, Wednesday 06-11 - 2019 HIVUM room
• There were many, about three hundred, the drivers of Mototaxi Beni invited to a day of awareness and vaccination against Ebola Virus Disease this Wednesday, November 06, 2019 in the HIVUM room.
• This day is welcome for the city of Beni during this period of EVD epidemic which, unfortunately, displays a lethality of 86.3% among motorcyclists, as pointed out by Dr. Pierre ADIKEY, Coordinator of the response of Sub Coordination of Beni.
• Thus, in his presentation, he focused his message on the risk of transmission of EVD among motorotaxi drivers and the conduct to be held in the exercise of their craft to protect themselves and the community.
• He asked bikers more often to respect the measures of prevention, namely: washing hands regularly, stopping at checkpoints, not being bribed to divert checkpoints, not carrying suspicious parcels and reporting and / or direct any suspicions of illness to colleagues or the community.
• In order to circumscribe the day, Dr. P. ADIKEY traced the path of the last Motard who died of EVD before his death confirmed at the CTE. To close his presentation, he made a reminder of the various events that prevented the teams of the response from working: among other things the days of the dead city, the fire of the vehicles of the riposte, the destruction of the structures of the care, the cases of resistance and others whose bikers were part of it.
• Dr. Bibiche MATADY, as Epidemiologist and Chair of the Monitoring Commission, introduced to the participants the importance of accepting to be listened to if you are in contact with a case, to let yourself be followed for the entire period indicated and to orient in a management structure as soon as the first sign appears. She also emphasized the collaboration between the bikers and the teams of the response.
• To justify this day again, one of the 3 Hikers shared his testimony and urged his colleagues to collaborate and follow the recommendations of the response teams starting with vaccination.
• Vaccination is one of the preventive measures against EVD, said Dr Adonis TERANYA, the Chair of the Immunization Subcommission. In his presentation, he explained the evolution of the vaccination protocol, the current targets, the side effects and the action to take in the event of an adverse event. Before calling for the voluntary vaccination of participants, he spoke about vaccines currently used in the DRC.
• In his words, the President of Bikers reiterated to the Coordinator the commitment of his organization and all its members to support the interventions of the response, while affirming its availability to any solicitation for the fight against the disease to Ebola virus in the city of Beni and its surroundings.
• The day ended with the vaccination of 100 Bikers and some of their dependents.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-15 16:53:09, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT NOVEMBER 13, 2019
Thursday, November 14, 2019
• Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,193 deaths (2075 confirmed and 118 probable) and 1067 people cured.<br /> • 527 suspected cases under investigation;<br /> • 1 new case confirmed in North Kivu in Mabalako;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from ETCs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths;
NEWS
Ebola Virus Disease Response Co-ordination Announces Three Road Traffic Accident in Bunia, Ituri
• The overall coordination of the response to the Ebola Virus Disease epidemic in North, South Kivu and Ituri was informed on Thursday 13 November 2019 of the tragic traffic accident between two motorcycles, one of which carried three agents of the riposte;<br /> • These three officers, who work for the Epidemiological Surveillance Commission at the Point of Entry and Control, were returning from Bunia to Mambasa, where they are respectively delivering;<br /> • This accident occurred around Marabo in Bunia on the evening of Wednesday 13 November 2019;<br /> • The balance sheet reports an officer who died at the scene and two others who were seriously injured, including one in a coma. The two wounded were taken to the Nyakunde Reference General Hospital in Ituri for appropriate care;<br /> • The overall coordination of the response sends its deepest condolences to the grieving family and expresses all its compassion and solidarity to the injured officers, while wishing them a quick recovery.
Effective start of Johnson & Johnson vaccination in two Goma health areas
• Ebola vaccination with the Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday, November 14, 2019 in two Karisimbi health areas in Goma City , North Kivu Province;<br /> • The Epidemic Response Coordinator for Ebola Virus Disease in North, South Kivu and Ituri. For this purpose, Prof. Steve Ahuka Mundeke visited the vaccination sites to inquire about the evolution of activities in the field. He was satisfied with the work of the teams;<br /> • He took the opportunity to invite the population of the targeted areas to be vaccinated in order to protect themselves from the resurgence of the Ebola virus;<br /> • Several people were present in Majengo and Kahembe health areas to get vaccinated. The first person to be vaccinated is a Kahembe community leader who has been protected against the Ebola virus today and also in case of a possible new Ebola outbreak. This community leader has appealed to all residents of his community and sites targeted to come take this second vaccine. "This is an opportunity not to be missed, because it is said that prevention is better than cure, " he said;<br /> • The logistics of this vaccination are provided by the international non-governmental organization Médecins Sans Frontières of France (MSF / France).<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, this second vaccine, called Ad26.ZEBOV / MVA-BN -Filo , is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine complements the first, the rVSV-ZEBOV, the vaccine used until then in this epidemic. Manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018, it was recently approved.
Closing of the training workshop for media professionals in Beni on the role and responsibility of journalists during public health crises
• The Deputy Mayor of the city of Beni, Muhindo Bakwanamaha Modeste, closed this Thursday, November 14, 2019 in Beni in the province of North Kivu the training of media professionals on the role and responsibility during public health crises;<br /> • The coordinator of the Beni Ebola Ebola response sub-coordination, Dr. Pierre Adikey, on behalf of the Coordinator-General of the Response, Prof. Steve Ahuka, wished to see these kinds of trainings be organized, not only in other sub-Coordination of the response, but also throughout the Democratic Republic of the Congo so that journalists from all over the country are ready to face any possible epidemic crisis;<br /> • This training, he said, is part of the zero-case Ebola strategy and strengthening the health system of tomorrow;<br /> • The focal point of Beni's journalists, Moustapha MULONDA, reaffirmed the commitment of journalists to combat Ebola Virus Disease through various programs and publications disseminated and published by their respective media thanks to the new tools acquired during this period. training;<br /> • This training was organized by the Ministry of Health in collaboration with the World Health Organization and benefited from the facilitation of the overall coordination of the response, UNICEF, CDC Africa and MSF.
VACCINATION
• Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 251,637 people have been vaccinated;
• Vaccination with the second Ad26.ZEBOV / MVA-BN-Filo vaccine, produced by Janssen Pharmaceuticals for Johnson & Johnson, began on Thursday November 14, 2019 in Goma. This vaccine was approved on 22 October 2019 by the decisions of the Ethics Committee of the School of Public Health of the University of Kinshasa and 23 October 2019 of the National Ethics Committee;
• Until then, only one vaccine was used in this outbreak. This is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee in its decision of 20 May 2018 and which has recently been approved.
MONITORING AT ENTRY POINTS
• A 27-year-old woman from Butembo for Goma, an escaped suspect from Makasi Hospital in Butembo, North Kivu, was intercepted at the Kanyabayonga checkpoint in Kayna. When she was intercepted, she experienced signs such as fever at 38.4 ° C, severe asthenia, abdominal pain and vaginal bleeding. It was sent to the KAYNA Transit Center.
• Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 116,622,388 ;
• To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-30 17:00:40, user Guyguy wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 27, 2019
Thursday, November 28, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,309, of which 3,191 are confirmed and 118 are probable. In total, there were 2,201 deaths (2,083 confirmed and 118 probable) and 1077 people healed.<br /> • 443 suspected cases under investigation;<br /> • 5 new confirmed cases, including:<br /> o 4 in Ituri in Mandima;<br /> o 1 in North Kivu in Mabalako;<br /> • 2 new deaths of confirmed cases, including:<br /> o 2 new community deaths in Ituri in Mandima;<br /> o No deaths among confirmed cases in CTEs;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Three members of the Ebola Virus Epidemic response killed during an attack in Biakato, Ituri
• Following the attack on the sub-coordination of the Biakato response in Ituri on the night of Wednesday 27th to Thursday 28 November 2019, three members of the Ebola response teams in this sector lost their lives ;<br /> • It is a provider and a driver of the vaccination committee and another driver;<br /> • In addition to these three deaths, there are 7 wounded and 6 others with psychological disorders and extensive material damage.<br /> • A good number of these teams from Biakato were evacuated in three waves to Goma. As soon as they arrived, they were greeted by a coordination team led by Prof. Steve Ahuka, general coordinator, who also visited the wounded before going to inquire about the security conditions and accommodation of evacuees. He did not fail to comfort them.
VACCINATION
• The vaccination commission is in mourning. A service provider and a driver of his team were killed on the night of Wednesday 27 November 2019 following attacks at the Biakato base in Ituri;<br /> • 2nd day without vaccination activity with the 2nd J & J vaccine following the disorders initiated by young people related to the security situation in Beni;<br /> • 724 people were vaccinated, until Tuesday, November 26, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,373 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.
MONITORING AT ENTRY POINTS
• Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 121,159,810 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2021-10-10 03:44:51, user kdrl nakle wrote:
The results on T-cells is quite murky here, without much explanation. You really need a bigger sample to be able to see this better, your sample of 46 is too small.
On 2020-01-25 23:16:22, user White_Runner wrote:
There is another study that puts the R0 value in 1.4 to 2.6, still very high.<br /> However not apocaliptic levels like the one described here.<br /> Also, this R0 value can change in time as long as the adequate restrictions are taken in place.<br /> So, yeah guys, expect the best, have some precautions and happy lunar new year.
On 2020-02-12 18:53:53, user S.T wrote:
Table 2 has a typo(I presume). It states the time window is from 25th December 2010**
On 2022-01-28 20:26:41, user Hussein Turfe wrote:
Was there any relation found between those who had THC in their urine and coming into the ED stating that they had a suicidal ideation?
On 2022-01-28 20:33:22, user Mohamad Kabbani wrote:
Fantastic article! Very informative and the ideas are easy to understand. This is a good baseline to get a better understanding on how different things have become during and after covid-19. We can learn what a pandemic can do to a population and compare it to this data as a reference point.
On 2020-03-22 15:56:46, user Sinai Immunol Review Project wrote:
Main findings<br /> The authors characterized the immune response in peripheral blood of a 47-year old COVID-19 patient. <br /> SARS-CoV2 was detected in nasopharyngeal swab, sputum and faeces samples, but not in urine, rectal swab, whole blood or throat swab. 7 days after symptom onset, the nasopharyngeal swab test turned negative, at day 10 the radiography infiltrates were cleared and at day 13 the patient became asymptomatic.
Immunofluorescence staining shows from day 7 the presence of COVID-19-binding IgG and IgM antibodies in plasma, that increase until day 20. <br /> Flow cytometry on whole blood reveals a plasmablast peak at day 8, a gradual increase in T follicular helper cells, stable HLA-DR+ NK frequencies and decreased monocyte frequencies compared to healthy counterparts. The expression of CD38 and HLA-DR peaked on T cells at D9 and was associated with higher production of cytotoxic mediators by CD8+ T cells.<br /> IL-6 and IL-8 were undetectable in plasma.<br /> The authors further highlight the presence of the IFITM3 SNP-rs12252-C/C variant in this patient, which is associated with higher susceptibility to influenza virus.
Limitations of the study<br /> These results need to be confirmed in additional patients.<br /> COVID-19 patients have increased infiltration of macrophages in their lungs{1}. Monitoring monocyte proportions in blood earlier in the disease might help to evaluate their eventual migration to the lungs.<br /> The stable concentration of HLA-DR+ NK cells in blood from day 7 is not sufficient to rule out NK cell activation upon SARS-CoV2 infection. In response to influenza A virus, NK cells express higher levels of activation markers CD69 and CD38, proliferate better and display higher cytotoxicity{2}. Assessing these parameters in COVID-19 patients is required to better understand NK cell role in clearing this infection. <br /> Neutralization potential of the COVID-19-binding IgG and IgM antibodies should be assessed in future studies.<br /> This patient was able to clear the virus, while presenting a SNP associated with severe outcome following influenza infection. The association between this SNP and outcome<br /> upon SARS-CoV2 infection should be further investigated.
Relevance<br /> This study is among the first to describe the appearance of COVID-19-binding IgG and IgM antibodies upon infection. The emergence of new serological assays might contribute to monitor more precisely the seroconversion kinetics of COVID-19 patients{3}. Further association studies between IFITM3 SNP-rs12252-C/C variant and clinical data might help to refine the COVID-19 outcome prediction tools.
References<br /> 1. Liao, M. et al. The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.02.23.20026690.<br /> 2. Scharenberg, M. et al. Influenza A Virus Infection Induces Hyperresponsiveness in Human Lung Tissue-Resident and Peripheral Blood NK Cells. Front. Immunol. 10, 1116 (2019).<br /> 3. Amanat, F. et al. A serological assay to detect SARS-CoV-2 seroconversion in humans. http://medrxiv.org/lookup/d... (2020) doi:10.1101/2020.03.17.20037713.
Review by Bérengère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai
On 2021-10-29 15:37:00, user Rogerblack wrote:
I find refreshing the repeated ''these associations did not survive correction for multiple comparisons'.<br /> An interesting paper.
On 2020-10-22 21:07:13, user Lee Jimmy wrote:
How does this relate to the nursing home outbreak in the La Crosse area ?<br /> James T. Lee, MD PhD FACS FIDSA FSHEA
On 2020-05-20 16:50:49, user Peter Ellis wrote:
Table 1 presents the data, showing 40 positive tests and 689 negative tests, i.e. an average prevalence of 5.49% across the course of the study. Elsewhere in the manuscript, the sensitivity is given as 100% (meaning none were missed) and the specificity as 98.3% (meaning there is a 1.7% false positive rate.
This being the case, can the authors please explain:
1) Why the caption for Table 1 reports 789 patients given that 40 + 689 = 729?
2) How they adjusted for false positives. 40 / 729 = 5.49%, which minus the 1.7% false positive rate leaves around 3.79% positive across the course of the whole study.<br /> [A Bayesian adjustment would be more accurate, this will suffice for now]
3) Given that the true positive rate in the samples they measured is around 3.79% across the whole study, how do they calculate a population prevalence of 4.6% at the start, rising to 7.1% at the end of the study. The methodology for this is entirely lacking.
On 2020-04-22 02:20:27, user Mike wrote:
This was certainly an interesting paper. It's done a lot of work and the findings are notable. IMHO it warrants as much attention as the pro-HCQ study via Dr. Raoult. While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required, but it is worth the read.
Observations/Questions:
1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease”<br /> 2. "we cannot rule out the possibility of selection bias or residual confounding”<br /> 3. demographic: 100% male, 66% black, median age ~70 (59 youngest)<br /> 4. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 5. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated? <br /> - What was the quantity/frequency of the treatments? <br /> - Were the treatments consistent between hospitals?<br /> 6. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that and what does that suggest?<br /> 7. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 8. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not accustomed to. The Conclusions portion (and page after) seeming to address topical issues of the times. Perhaps this introduces my own subjective bias, but I infer potential for analysis/deciphering bias when the study shows awareness of other controversial studies being conducted, rather than being a standalone independent study of its own; essentially, it leaves me to question motivations of the author, rather than that motivation being scientific discovery. I don't mind such commentary in the Discussion section, I'm just not as accustomed to seeing it in the Abstract.
On 2022-10-28 07:00:48, user Sujoy Ghosh wrote:
This manuscript has now been published as follows: <br /> Ghosh, S., Roy, S.S. Global-scale modeling of early factors and <br /> country-specific trajectories of COVID-19 incidence: a cross-sectional <br /> study of the first 6 months of the pandemic.<br /> BMC Public Health 22, 1919 (2022). https://doi.org/10.1186/s12...
Kindly update the link in medrxiv. Regards, Sujoy Ghosh
On 2022-11-07 06:03:04, user Daniel Corcos wrote:
I don't see any adjustment for the date of infection. There is a high probability that nirmatrelvir treatment was used on average at a different time, against infections with a different ratio of viral variants.
On 2022-12-01 05:07:56, user Amir Hossein Nikzad wrote:
Hi medRxiv, this pre-print is published on Schizophrenia in July 2022. Here are the link: https://www.nature.com/arti... and the doi https://doi.org/10.1038/s41.... Regards, Amir.
On 2022-12-12 06:18:02, user Stephanie Byrne wrote:
This article has been accepted for publication in the International Journal of Epidemiology, published by Oxford University Press. A DOI and link to the published article will be available soon.
On 2022-12-29 19:11:31, user tshann wrote:
Given the stated benefits of these vaccines, why are we doing modeling studies rather than real RCT's. It's been over 2 years with these products, when will we see the science instead of more modeling studies?
On 2023-01-23 00:20:10, user Stephen Akar wrote:
I think this is a fantastic work by this great team. I am working on YF in Nigeria.
On 2023-01-23 11:55:19, user Yang Wen wrote:
This manuscript has been reviewed and published on Frontiers in Psychiatry.
Please cite the latest version of this manuscript via https://doi.org/10.3389/fps....
On 2023-03-10 18:13:37, user Kirsten Wiens wrote:
This manuscript has now been published online at Lancet Microbe: https://doi.org/10.1016/S26...
On 2023-03-29 14:07:37, user YUE BAI wrote:
Hi,
Could you please link the preprint to the published article (https://ieeexplore.ieee.org...? "https://ieeexplore.ieee.org/document/9670745)?") Thank you!
On 2023-04-16 07:02:33, user Basheer wrote:
The paper is published at https://www.eurekaselect.co...
On 2023-09-06 12:08:36, user Hema Chaplin wrote:
This article has been accepted for publication in PLOS One (Open Access) on August 9 2023 and can be found here https://doi.org/10.1371/jou...
On 2023-10-22 13:47:09, user Rahma Menshawey wrote:
This research has now been peer reviewed and published here: <br /> https://bnrc.springeropen.c...
On 2023-11-21 02:18:06, user Marco Confalonieri wrote:
The finding that high glucose levels can predict glucorticoids (GCs) benefit surprised most of us. All we who performed the included RCTs thincked to hyperglycemia as an adverse effect of GCs, not paying attention to glucose blood level at admission. Nevertheless, there are several reports pointing out hyperglycemia but not diabetes alone associated with increased in-hospital mortality in community-acquired pneumonia (BMJ Open Diab Res Care 2022;10:e002880). It should be noted that AI doesn't have the same prejudices than human researchers.
On 2023-12-05 02:49:44, user Author wrote:
This article is now published on Journal of Psychopharmacology: https://doi.org/10.1177/02698811231211219
On 2024-01-11 13:59:49, user Mahalul Azam wrote:
This work now published in https://doi.org/10.15294/ke...
On 2024-02-22 14:38:06, user Albee Messing wrote:
Please provide more clinical information about the proband.
On 2024-02-26 19:05:34, user Pia Ostergaard wrote:
Final peer reviewed, published version can be found here: https://pubmed.ncbi.nlm.nih...
On 2024-06-02 22:51:12, user Hellen Paskaleva wrote:
p. 3, line 10: Clostridium is gram-positive, not gram-negative.
On 2020-04-16 00:27:24, user Adam Danischewski wrote:
China has a BCG Vaccination policy and there may be other aspects that may cause Chinese results to differ from the United States.
On 2024-10-16 16:27:20, user CDSL JHSPH wrote:
I quite enjoyed this article. I found it very interesting as it proposed significant thoughts to how we can improve antibiotic treatment. I wanted to comment about some of the thoughts I had while reading this article. I first wanted to see if the results that were found in TB could be translated into other bacteria infections, such as staph. or strep. species. I also wanted to see if the results found for antibiotics in this article could be translated to other pathogenic treatments including antivirals or antifungals. Finally, in terms of future approaches could we see a systemic or ordered approach when it came to treatment duration whether bacterial, viral, or fungal in nature, or is it mostly going to be drug/ species specific?
On 2024-10-23 00:04:57, user Mohammad Shah wrote:
Hello!
Thank you for sharing this preprint. I really enjoyed reading it. Your application of techniques like MCP-Mod and FP for duration-ranging trials provides valuable insights into detecting duration-response relationships much more effectively than traditional approaches. I also appreciate how you highlight the risks of underestimating the MED in smaller sample sizes and suggest using conservative thresholds to mitigate those risks—this is such a critical point.
One thing that really stood out to me was how you clearly lay out the limitations of traditional duration-response methods, while proposing model-based techniques, like MCP-Mod, as a better alternative. Your comparison of different models and how they behave with varying sample sizes and regimen responses is especially insightful for optimizing TB treatment duration.
Like others have mentioned, it’d be fascinating to see how this approach could be applied to other chronic diseases, such as HIV or hepatitis. Is that something you’re considering or perhaps already working on? Additionally, applying these model-based techniques to real-world patient data, where comorbidities and adherence issues add more complexity, seems like a natural next step. It would be interesting to see how that plays out in practice.
I also found your discussion on model selection particularly thought-provoking. Your suggestion of using MCP-Mod alongside Fractional Polynomials under different assumptions opens up an exciting possibility for integrating multi-model approaches in early-phase trials. I wonder if combining these models, maybe in a hybrid MCP-Mod/FP approach, could improve adaptability, especially in trials with more heterogeneous patient populations—those with comorbidities or fluctuating adherence, for example.
Lastly, your use of simulations to predict treatment efficacy in the face of sample size imbalances touches on a key challenge in trial design. Have you thought about how this framework might be extended to adaptive trial designs? It seems like interim analyses could help adjust treatment durations dynamically based on early patient responses, which could make trials even more efficient.
Overall, this was a great article, very informative and forward-thinking!
On 2020-05-01 10:56:16, user Ivan Berlin wrote:
Rentsch CT et al. Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years. <br /> medRxiv preprint doi: https://doi.org/10.1101/202... version posted April 14, 2020<br /> Comment of the results concerning smoking related issues. Corrected Version. Please ignore the previous one.<br /> Ivan Berlin, Paris, France<br /> The title is somewhat misleading. Only 3789 persons were tested for SARS-CoV-2, no data on the 2,022,438 are reported.<br /> Data are extracted from the Veteran Administration (USA) Birth Cohort born between 1945 and 1965 electronic database. Between February 8 and March 30, 2020, 3789 persons were tested for SARS-CoV-2. Among them 585 were tested SARS-CoV-2 positive (15.4%) and 3204 SARS-CoV-2 negative. (Remark: the authors frequently confound testing for SARS-CoV-2 and having the disease: COVID-19 +.)<br /> Testing used nasopharyngeal swabs, 1% of the testing samples was from other unspecified sources. Testing was performed “in VA state public health and commercial reference laboratoires”, page 7. No further specification about the testing method is provided. Data are analyzed as if no between test-sources variability existed. However, it is unlikely that between test-source variability would influence the findings.<br /> It seems that only individuals with symptoms were tested, however this is not clearly stated.<br /> Data extraction included diagnostics by diagnostic codes of comorbidities, non-steroid inflammatory drug (NSAID), angiotensin converting enzyme inhibitor (ACE) and angiotensin II receptor blocker (ARB) use, vital signs, laboratory results, hepatic fibrosis score, presence or absence of alcohol use disorder and smoking status.<br /> Smoking status data, never, former, current smokers were extracted using the algorithm described in McGinnis et al. Validating Smoking Data From the Veteran’s Affairs Health Factors Dataset, an Electronic Data Source. Nicotine & Tobacco Research, Volume 13, Issue 12, December 2011, Pages 1233–1239, https://doi.org/10.1093/ntr... used for HIV patients. According to this paper, the algorithm correctly classified 84% of never-smokers 95% of current smokers but only 43% of former smokers. The reported overall kappa statistic was 0.66. When categories were collapsed into ever/never, the kappa statistic was somewhat better: 0.72 (sensitivity = 91%; specificity = 84%), and for current/not current, 0.75 (sensitivity = 95%; specificity = 79%). Thus, classification error cannot be excluded in particular in classifying former smokers. <br /> In unadjusted analyses (Table 1) factors associated significantly with SARS-CoV-2 positivity were: male sex, black race, urban residence, chronic kidney disease, diabetes, hypertension, higher body mass index, vital signs but not NSAID or ACE/ARB exposure. It is to note, that among the laboratory findings, severity of hepatic fibrosis was associated with positive SARS-CoV-2 tests. <br /> Among those with positive SARS-CoV2 alcohol use disorder was reported by 48/585 (8.2%), versus 480/3204 (15%) among those with negative SARS-CoV-2 test. Among those with alcohol use disorder, 9.1 tested positive. <br /> Among SARS-CoV-2 positives there were 216/585 (36.9%) never smokers vs 826/3204 (25.8%) among SARS-CoV-2 negatives. 20.7% tested positive among never smokers. Among SARS-CoV-2 positive persons 179 (30.6%) were former smokers vs 704 (22%) among SARS-CoV-2 negatives. 20.3 % tested positive among former smokers. Among SARS-CoV-2 positive individuals 159 (27.7%) were current smokers vs 1444 (45.1%) among SARS-CoV-2 negative individuals. 9.9% tested positive among current smokers. Expressed otherwise, among SARS-CoV-2 negative individuals, there were less never smokers, less former smokers and more current smokers. Among individuals with SARS-CoV-2 positivity there were 338/585 (61%) persons with smoking history (former + current smokers=ever smokers) and among those with SARS-CoV-2 negativity 2149/3204 (72%) were ever smokers. <br /> COPD, known to be strongly related to former or current smoking, was more frequent among SARS-CoV-2 negative (28.2%) than among SARS-CoV-2 positive (15.4%) individuals.<br /> In multivariable analyses (Table 2), male sex, black ethnicity, urban residence, lower systolic blood pressure, prior use of NSAID but not ACE/ARB use and obesity were associated with SARS-CoV-2 positive test; current smoking (OR: 0.45, 91% CI: 0.35-057), alcohol use disorder (OR 0.58, 95% CI: 0.41-0.83) and COPD (OR: 0.67, 95%CI: 0.50-0.88) were associated with decreased likelihood of SARS-CoV-2 positive test. No association with age and SARS-CoV-2 positive test was observed. The association with hepatic fibrosis with SARS-CoV-2 positive tests remained significant in the multivariable analysis and the authors point out (page 15) that the “pronounced independent association with FIB-4 (fibrosis) and albumin suggest that virally induced haptic inflammation may be a harbinger of the cytokine storm.”, page 15. <br /> The main risk factors for hospitalization or ICU among SARS-CoV-2 positive persons are those that associated with worse clinical signs (status). This is expected: clinical decision about severity is based on current clinical signs and not on previous history. <br /> Neither co-morbidities, nor smoking status or alcohol use disorder were associated with hospitalization/ICU. Surprisingly, age was inversely associated with hospitalization (Table 4) among SARS-CoV-2 positive individuals.<br /> Conclusion
To the best of our knowledge, this is the first report showing that there are less current smokers among SARS-CoV-2 positive persons. However, looking at smoking history (former + current smoking=ever smokers), less subject of classification bias, the difference seems to be less. It is not known what is the percent of former smokers who were recent quitters; duration of previous abstinence from smoking is a crucial variable in assessing associations with smoking status. There is no report of biochemical verification of smoking status. <br /> It is not known when smoking status is reported with respect of the SARS-CoV-2 testing. It is likely that individuals with clinical symptoms stopped smoking some days before testing and considered themselves as former smokers.
The fact that alcohol use disorder, which is frequently associated with tobacco use disorder, is also less frequent among SARS-CoV-2 positive individuals raises the question of the specificity of the smoking finding and raises the contribution of substance use disorders overall i.e. the finding about current smoking is part of a cluster of various previous or current substance use disorders e.g. cannabis use, potentially associated with SARS-CoV-2 negative test directly or through associated health disorders e.g. hepatic disorders as a consequence of alcohol use. <br /> COPD as well as current smoking are being reported to be more frequent among SARS-CoV-2 negative individuals raising the possibility that reduced respiratory function (entry of SARS-CoV-2 is by the respiratory tract) is associated with lower likelihood of SARS-CoV-2 positive tests. <br /> It seems that all individuals included were tested because they had symptoms suggestive of COVID-19. It is surprising that only 585/3789 (15.4%) tested positive. This should be discussed.<br /> The paper does not report on analyses of smoking by clinical signs/co-morbidities interactions. It is likely that former smokers or those with alcohol use disorders are more frequent among individuals with comorbidities. Based on previous knowledge about smoking associated health disorders, one can assume that more severe clinical signs were associated with current smoking or among recent quitters; the smoking x clinical signs interaction is not tested. <br /> The authors conclude on page 14 “To wit, we found that current smoking, COPD, and alcohol use disorder, factors that generally increase risk of pneumonia, were associated with decreased probability of testing positive. While they were not associated with hospitalization or intensive care, it is too early to tell if these factors are associated with subsequent outcomes such as respiratory failure or mortality.”<br /> The reduced current smoking rate among SARS-CoV-2 positive individuals is an interesting but preliminary finding. It is likely that it is part of a more complex symptomatology and not specific to current smoking. Smoking status should have been assessed on a more detailed manner. The current findings, from a retrospective, cross sectional analysis, are insufficient to support the hypothesis that current smoking protects against SARS-CoV-2 positivity.
On 2020-04-16 21:17:49, user Sinai Immunol Review Project wrote:
Key findings:
The authors wanted to better understand the dynamics of production SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia and the correlation of virus-specific antibody levels to disease outcome in a case-control study paired by age. The retrospective study included 116 hospitalized patients with COVID-19 pneumonia and with SAR-CoV-2 specific serum IgM and IgG detected. From the study cohort, 15 cases died. SARS-CoV-2 specific IgG levels increased over 8 weeks after onset of COVID-19 pneumonia, while SARS-CoV-2 specific IgM levels peaked at 4 weeks. SARS-CoV-2 specific IgM levels were higher in the deceased group, and correlated positively with the IgG levels and increased leucocyte count in this group, a indication of severe inflammation. IgM levels correlated negatively with clinical outcome and with albumin levels. The authors suggest that IgM levels could be assessed to predict clinical outcome.
Potential limitations:
There are limitations that should be taken into account. First, the sample: small size, patients from a single-center and already critically ill when they were admitted. Second, the authors compared serum IgM levels in deceased patients and mild-moderate patients and found that the levels were higher in deceased group, however even if the difference is statistically significant the number of patients in the two groups was very different. Moreover, receiving operating characteritics (ROC) curves were used to evaluate IgM and IgG as potential predictors for clinical outcome. Given the low number of cases, specially in the deceased group, it remains to be confirmed if IgM levels could be predictive of worst outcome in patients with COVID-19 pneumonia. The study did not explore the role of SARS-CoV-2-specific IgM and IgG in COVID-19 pneumonia.
Overall relevance for the field:
Some results of this study have been supported by subsequent studies that show that older age and patients who have comorbidities are more likely to develop a more severe clinical course with COVID-19, and severe SARS-CoV-2 may trigger an exaggerated immune response. The study seems to demonstrate that the increase of SARS-CoV-2-specific IgM could indicate poor outcome in patients with COVID-19 pneumonia, however given the very small sample size, the results are not yet conclusive.
Review by Meriem Belabed as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.
On 2020-04-16 22:12:24, user Amy E. Herr wrote:
During the COVID-19 pandemic, we are grateful for the authors’ urgency in assessing N95 respirator decontamination methods. It is in this spirit of collegiality that we draw attention to an aspect that could (unintentionally) cause confusion: the PS19Q thermopile sensor mentioned in the Methods section does not appear to be suited to detect the virus-killing UV-C light emitted from the source. The authors are aware of the possible confusion and are working diligently to check into and, if needed, address the concern.
As background: from the manufacturer’s specifications, the PS19Q thermopile sensor mentioned in the preprint appears to only detect wavelengths as low as 300 nm, which is above the UV-C germicidal wavelength range (<280 nm). Low-pressure mercury UVGI bulbs emit a 253.7 nm peak [EPA]. 260 nm is the peak UV-C germicidal wavelength for inactivating virus via DNA and RNA damage [Kowalski et al., 2009, Ito and Ito, 1986]. The germicidal efficacy arises primarily from the UV-C dose, with the UV-B dose (280-320 nm) providing significantly lower germicidal efficacy. At 300 nm, UV light is ~10x less effective at killing pathogens than at 254 nm [Lytle and Sagripanti 2005]. UV-A dose (320-400 nm) is considered minimally germicidal [Kowalski et al., 2009; Lytle and Sagripanti 2005; EPA]. We are concerned about the potential adverse health outcomes that might stem from use of the PS19Q thermopile sensor not matched to the UVGI wavelengths for N95 FFR decontamination.
As best practices, all researchers working on UV-C methods are encouraged to use a calibrated, NIST-traceable, UV-C-specific radiometer to report not just UV-C irradiance, but also UV-C specific dose, as a minimally acceptable UV-C dose of 1.0 J/cm^2 is sought on all N95 FFR surfaces. For additional detail from the peer-reviewed literature, please see the 2020 scientific consensus summaries on N95 FFR decontamination at: n95decon.org
Again, we thank the authors for their timely research and quick action to confirm suitability of their experimental design, all of which aim to better inform decision makers working to protect the health of heroic front-line healthcare professionals during the COVID-19 pandemic.
References cited: <br /> • Manufacturer’s specifications, the PS19Q thermopile sensor: https://www.coherent.com/me...<br /> • EPA: ULTRAVIOLET DISINFECTION GUIDANCE MANUAL FOR THE FINAL LONG TERM 2 ENHANCED SURFACE WATER TREATMENT RULE: https://nepis.epa.gov/Exe/Z...<br /> • Kowalski et al., 2009: https://link.springer.com/c...<br /> • Ito and Ito, 1986: https://onlinelibrary.wiley...<br /> • Lytle and Sagripanti 2005: https://www.ncbi.nlm.nih.go...
On 2020-04-16 22:28:29, user Quandary wrote:
interested in states, my own and others. How to find them in this chart?
On 2025-05-21 23:31:22, user Yanning Liu wrote:
This article has recently been published on Radiology https://doi.org/10.1148/radiol.241597
On 2025-06-15 21:35:28, user CP wrote:
Great paper! The text makes reference to a "Supplementary Notes" section that doesn't seem to be in the PDF - is this part of the material that will be made available after peer reviewed publication? Sorry if this is a naive question; I'm new to preprints.
On 2022-01-26 22:15:44, user Siguna Mueller, PhD, PhD wrote:
Does the "fully vaccinated" group ALWAYS include those with (partial) natural immunity (i.e., those previously infected? This is at least what Table 1 says: these belong into the same group. Yet, throughout, this group is referred to as the "fully vaccinated." This does not seem to affect the conclusion that vaccination is in large part responsible for driving O's increased transmissibility (because the incr. OR is seen for the booster group as well). Apart from this, I am struggling to see how the other results are obtained. I seem to be missing how the factor of previously infection gets incorporated in the study. It would be helpful if this could be made explicit, please. Thanks!
On 2025-08-10 00:53:33, user Nihal wrote:
these guys have a great paper
On 2022-01-29 19:43:45, user A Thumb wrote:
Where is the VE data for 2 doses against severe outcomes with omicron in the abstract? Annoying omission.
On 2022-02-03 17:13:18, user Brian R Wood wrote:
Has the study accounted for the fact that if Omicron has less severe symptoms than Delta or COVID-19 Classic, the number of reported infections is likely to be significantly lower? Additionally, I would speculate that those who got vaccinated and boosted are also more likely to be tested than those who did not, but just speculation, no data to back it up.
On 2020-05-13 01:25:02, user Joe Exum wrote:
N.C. is projected to need more ICU units than it has capacity in August? Is this correct?
On 2022-02-08 21:10:34, user Sara wrote:
Thank you for your comment, unfortunately, I did not receive your comment once you replied. 1- we are in the era in the big data, more projects are aimed at generation of large cohort that we can depend upon to derive our clinical decision. <br /> The analysis used the data from US, the model will be deployed and can be used after that to predict the survival time of small cohorts. <br /> 2- We investigated the hazards assumption, we agree with you, we should add the results in the manuscript<br /> 3- SEER database identify the surgery as the surgical removal of the tumour.<br /> 4- I agree with you on the grade, it was on the old grading system for glioblastoma which is mentioned on SEER guidelines. Updated version will be posted and will update the analysis removing this one<br /> 5- we agree with you, we will change it in the updated comments<br /> 6- It is not insane! Developing models that consider these cases is a challenge. These models will be deployed for survival prediction of different cases of glioblastoma with different survival times.
7- we are developing a model that can be used for the routine data "we use", in this case US cancer data. We have a model that performed well so it can be deployed in the future for the clinical use for our routine data. the model is trained on large sample size that we believe it will achieve accurate prediction results for any routine data. The deployment of the model and its use in clinical practice is the goal. I hope you see the full picture.
Thank you for your comments.
On 2022-02-09 01:07:23, user Avi Bitterman wrote:
This paper dichotomizes a continuous variable to get a barely statistically significant result (P=0.044). But this is just dichotomania. Time to treatment is a continuous variable, not a binary variable. The appropriate test for this continuous variable is a regression along the continuous variable. Not a dichotomized sub-group analysis.
Using the same numbers this author uses from Table 1, we ran a regression which failed to show a significant effect of treatment delay on outcome P=0.13
Aside from being the appropriate test, another advantage of a regression here is it avoids the possibility selective dichotomization along the proposed moderator variable to get the desired result (a barely significant P value the authors just so happen to have found).
I would also be happy to have a discussion with the authors to elaborate on the above as well as discuss numerous other critical errors with this analysis as well.
On 2021-06-23 21:55:50, user David Wiseman PhD wrote:
Summary:<br /> Regarding the continued and unnecessary confusion related to the Argoaic and Artuli comments.<br /> 1. These are in reality distractions from the central issue that the original NEJM paper remains uncorrected in NEJM as to shipping times. Although a secondary issue, also uncorrected is the "days" nomenclature that is the reason for confusion in the Argoaic and Artuli comments on this forum. Also uncorrected in the original paper is the exposure risk definition which were informed were also incorrect. Together, these issues controvert the conclusions of the original study.<br /> 2. The incorrect nomenclature for "days" in the NEJM paper as well as in a follow up work (Clin Infect Dis, Nicol et al.) inflates the number of "elapsed time" days. This has not been corrected by the original authors. We on the other hand have corrected this by providing the correct information in our preprint.<br /> 3. Dr. Argoaic seems to have been given a wrong and earlier version (10/26) of the data which, although contains a variable that is supposed to correct the above problem, does not. In fact one cannot come to any conclusion that there is a discrepancy based on this incorrect 10/26 version, unless you have some preconceived notion.<br /> 4. Other post hoc analyses reported in follow up works (including social media) by the original authors looking at time from last exposure, or using a pooled placebo group, although flawed for a several reasons, when examined closely, nonetheless support our conclusions that early PEP prophylaxis with HCQ is associated with a reduction of C19.
Detail:<br /> Any confusion about "days" would disappear once the original authors correct the NEJM June 2020 paper as well as a follow up letter in Dec 2020 Clin Infect Dis (see upper red graph in Nicol et al. pubmed.ncbi.nlm.nih.gov/332... "pubmed.ncbi.nlm.nih.gov/33274360/)"). These errors inflate the "DAYS" by 1 day because the nomenclature for describing "days" was incorrect. As far as we know those corrections have not been made in the journals where these errors appear and in a way that can be retrieved in pubmed etc..
As far as we can tell, anyone who has cited the NEJM paper (NIH guidelines, NEJM editorial, many meta-anlayses etc., our protocol in preprint version) also misunderstood the "days" to mean the inflated figure. So the authors need to correct this. As far as we know we are the only ones to do this. After we were informed of this error by the PI (who was unaware of the problem himself) we described this problem very clearly in our preprint, distinguishing between elapsed time and the day on which a study event occurred. For the benefit of those who remain confused, we will endeavor to make it even clearer in a future version. You can read our correspondence log referenced in the preprint to verify that the incorrect "days" nomenclature was unknown to the PI, at least until 10/27 when he informed us about it.
You are confusing "DAY ON which an event occurred" with "DAYS FROM when an event occurred." For example the original NEJM Table 1 says "1 day, 2 days etc." for "Time from exposure to enrollment". This falsely inflates the number of elapsed time days by 1, and as the authors informed us (documented in our preprint), this really means DAY ON which enrollment occurred, with Day 1 = day of exposure, so you need to subtract 1 from the days to get elapsed time FROM exposure. The same error is repeated in Nicol et al. (note: we discuss other unrelated issues relating to time estimates in our preprint).
To confuse matters further, the problem is not even corrected in the dataset linked (datestamp 10/26/20) in the Argoaic comment. In column FS there is a variable "exposure_days_to_drugstart." This appears to indicate elapsed time (ie DAYS FROM) when it actually means the "DAY ON" nomenclature. We were only informed of the nomenclature error on 10/27/20 and later provided with a new version of the dataset on 10/30 where an additional variable "Exposure_to_DrugStart" (column GR) was provided that corrects this error by subtracting 1 from all the values.
Why the Argoaic comment does not link to the correct 10/30 version is unclear, but in this incorrect 10/26 version, the values for the new variable "Exposure_to_DrugStart" (column GR) are IDENTICAL to those in the "exposure_days_to_drugstart" (column FS) variable (they should be smaller by 1). Accordingly, unless Drs. Argoaic and Artuli had a preconceived notion (without checking the data) that some alteration had occurred, it is impossible to draw such a conclusion (albeit one that is incorrect for other reasons) from this incorrect 10/26 dataset. A number of colleagues have downloaded the 10/26 dataset from the link provided in the Agoraic comment, and have verified this problem.
So in addition to the original data set released in August 2020, as well as the three revisions (9/9, 10/6 and 10/30) we describe in our preprint there is this incorrect 10/26 version. I don't know how many people this affects but it would be appropriate for them to be notified that the version they have may be an incorrect one. An announcement on the dataset signup page covidpep.umn.edu/data would also be in order (nothing there today).
Regarding the possibly higher placebo rate of C19 on numbered day 4 (18.9%). This is matched by a commensurate change in its respective treatment arm, yielding RR=0.624 similar to that for numbered days 2 (0.578) and 3 (0.624), justifying pooling. We don't know if the 18.9% represents normal variation or has biological meaning.
Although they used enrollment time data (completely irrelevant to considering whether or not early prophylaxis is beneficial), the original authors (Nicol et al.) in a post hoc analysis, used a pooled placebo cohort to compare daily event rates (red bar graph). This would mitigate possible effects of an outlying value in the placebo cohort. We applied this same pooled placebo method to the data that correctly takes into account shipping times. This method is still limited because it may obscure a poorly understood relationship between time and development of Covid-19. Although at best this would be considered a sensitivity analysis, we did it to answer the Artuli question. This approach yields the same trends as our primary analysis. Using 1-3 days elapsed time of intervention lag (numbered days 2-4) for Early prophylaxis, there is a 33% reduction trend in Covid-19 associated with HCQ (RR 0.67 p=0.12). Taking only 1-2 days elapsed time intervention lag, we obtain a 43% reduction trend (RR 0.57 p=0.09). This analysis appears to reveal a strong regression line (p=0.033) of Covid-19 reduction and intervention lag.
We also looked at the post hoc analysis provided by the original authors (Nicol et al.) that used “Days from Last Exposure to Study Drug Start,” a variable not previously described in the publication, protocol or dataset, so we have no way of verifying it from the raw data. As in a similar PEP study (Barnabas et al. Ann Int Med) this variable has limited (or no) value, as we are trying to treat as quickly as possible from highest risk exposure, not an event (ie Last Exposure) that occurs at an undefined time later. (even the use of highest risk exposure has some limitation, which the authors pointed out to us and which we discuss in our preprint). Further the Nicol analysis used a modified ITT cohort, rather than the originally reported ITT cohort. with these limitations, pooling data for days 1-3 and comparing with the pooled placebo cohort (yields a trend reduction in C19 associated with HCQ (it is unclear which "days" nomenclature is used) after last exposure from 15.2% to 11.2% (RR 0.74, p=0.179).
Taken together with these "sensitivity" analyses inspired by the original authors' methodology, suggests that this is not an artifact of subgroup analysis. It could be said that any conclusions made by the sort of analyses conducted by Nicol are equally prone to the "subgroup artifact" problem. (also note that in our paper, the demographics for placebo and treatment arms in the early cohort match well).
Mention has been made elsewhere of two other PEP studies (Mitja, Barnabas) which concluded no effect of HCQ. It is important to note that the doses used in these studies were much lower than those used in the Boulware et al. NEJM study. Further, according to the PK modelling of the Boulware group (Al-Kofahi et al.) these doses would not have been expected to be efficacious (the Barnabas study used no substantial loading dose). So citing the Mitja and Barnabas studies to support claims of HCQ inefficacy in the Boulware et al paper is unjustified. On the contrary, taken together three studies suggest a dose-response effect. We discuss this in detail in our preprint.
Lastly it is important to note the since the original NEJM study was terminated early, the entire original analysis can be thought of as a subgroup analysis, with all of the attendant problems referenced by the original authors (and us). There is certainly a great deal of under powering and propensity to Type 2 errors, among the issues inherent in a pragmatic study design. The study was not powered as an equivalence study and so no definitive statement can be made that the HCQ is not efficacious. Along with the still uncorrected (in the original journal) issues of shipping times, "days" nomenclature and exposure risk definitions, there are are certainly many efficacy signals that oppugn the original study conclusions,and controvert the statement made in a UMN press release (covidpep.umn.edu/updates) "covidpep.umn.edu/updates)") that the study provided a "conclusive" answer as to the efficacy of HCQ.
_________________<br /> Please note that despite our offer to Dr. Argoaic to contact us directly to walk though the data to try to identify any issues, we have not been contacted.That offer is still extended to anyone who remains confused. We have also attempted to locate both Drs. Argoaic and Artuli to try to clear up their confusion, but these names do not exist in the mainstream literature (i.e pubmed, medrxiv), nor do they appear to have any kind of internet footprint.
With regard to Table 1 of our preprint, the reason why there are no patients for “Day 1” is that there were no patients who received drug the same day as their high-risk exposure. This is consistent with the PIs comment on 8/25/20 (p10 of email log) (at a time when he thought that there was a “Day zero”) “Exposure time was a calculated variable based date of screening survey vs. data of high risk exposure. Same day would be zero. (Based on test turnaround time, I don’t think anyone was zero days).”
We notice an obvious typo in the heading for the second column of our Table 1, which says “To”. But it should say “nPos”, to match the 5th column (and other tables). It is patently absurd that there should be a category of “1 to 0” days or “7 to 5” days etc. “From” makes no sense either and these typos have absolutely no effect on the analysis, interpretation or conclusions. This will be corrected in a later version.
On 2022-02-09 11:30:32, user Felix Schlichter wrote:
The authors explain that the data was gathered from community testing. They further note that mass testing has been available to "Dutch citizens experiencing COVID-19 like symptoms or who have been in contact with someone testing positive for SARS-CoV-2".
If one assumes that the inmune status affects the intensity and probability of exhibiting symptoms, wouldn't the sample be biased? Even if the real odds of being positive for individuals with primary vaccionation and booster were equal, the ones with booster would be underrepresented as they would not test as often if they tend to exhibit less symptoms. Is this not a limitation of the study?
Could the authors not show the results separated by the reason for testing (contact vs symptomatic) to account for this limitation? if the reason for testing was having been a contact, this limitation would not be there.
On 2022-02-09 18:08:12, user Syncope wrote:
Peter A. McCullough purposely forgot to disclose his involvment in Topelia Therapeutics https://web.archive.org/web...
On 2025-11-11 03:32:18, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.
Here are our highlights:
In the week after the Jan 7 ignitions, virtual (clinic) respiratory visits jumped 41% in highly exposed areas and 34% in moderately exposed areas, totaling 3,221 excess visits, a clear, short-term signal health systems can act on.
Virtual cardiovascular visits rose by ~35% across exposure groups in that first week (~2,424 excess visits), pointing directly to surge planning for virtual care during wildfire weeks.
On the day of ignition (Jan 7) in highly exposed areas, outpatient neuropsychiatric and injury visits were about 18% higher than expected, evidence that mental-health demand starts immediately, not just respiratory care.<br /> The exposure framing is reproducible: simple proximity bands (<20 km vs >=20 km within LA County) applied to a 3.7-million-member health system and a five-category visit dashboard (all-cause, cardiovascular, injury, neuropsychiatric, respiratory) that others can copy.
Scaled to all LA County residents, the estimates imply ~16,171 excess cardiovascular and ~21,541 excess respiratory virtual visits in the week after ignition, strong justification to expand virtual capacity during major fires.
On 2022-02-17 20:55:31, user RT1C wrote:
Table 3 (bottom) contains HR for boosted vs. non-boosted at various times (<6, 6-9, >=9 months). Aside from the minor labeling issue (hopefully not actual analysis issue!) that 6-9 months and >=9 months are not distinct subsets, overlapping at 9 months, I don't see how you could have made this analysis in the first place unless you have incorrectly defined POIC. You wrote, "we defined the proximate overt immunologic challenge (POIC) as the most recent exposure to SARS-CoV-2 by infection or vaccination." That means POIC for boosted subjects would be time since the booster dose as that is the most recent vaccination. Yet, considering how recently boosting began, how could you have boosted subjects with 6-9 or >=9 months POIC? (In your text you wrote, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days).")
On 2022-02-17 21:29:51, user RT1C wrote:
You state, "For those boosted, the median time to being boosted was 16 days prior to the study start date (IQR -38 to 6 days)." Is that a typo or did you truly mean a positive 6? i.e., did you mean -38 to -6 days, or -38 to 6 days? If the latter, you actually included subjects who were vaccinated with boosters after the study period began? If that's the IQR, then I assume the full range extends much further into the study range. Those are VERY recently boosted. In your discussion, you should not say, "boosting with a vaccine designed for an<br /> earlier variant of COVID-19 still provides significant protection against infection with the Omicron variant." without also providing a time associated with that. For example, you might add to that sentence "for a period of at least 1 month" or whatever. It seems important to stress the limitation of the study in this manner, to avoid giving the impression that the booster provides long-lasting protection against infection when that is not shown by your study.
Finally, on a related matter, how did you treat individuals who tested positive before 7 days after their booster? If, as some research suggests, vaccination temporarily increases susceptibility to infection (for about 2 weeks), by including subjects who were vaccinated within the study period, you may have biased findings against those without boosters.
On 2025-11-26 01:10:16, user Alda wrote:
Congratulations for all the colaborators!! <br /> When will be published ? (perhaps 2026)
On 2022-02-24 11:08:09, user AgentMMK wrote:
This study really looks very dubious.
On 2022-03-03 12:42:31, user Dan Bolser wrote:
This paper seems to be published here:<br /> https://pubmed.ncbi.nlm.nih...
but the preprint doesn't link it.
On 2022-03-04 16:06:11, user Tracy Beth Høeg, MD, PhD wrote:
The peer reviewed version including numerous international datasets estimating rates of post vaccination myocarditis is now available. We have included risk-benefit calculations for children with a history of infection and used overall infection hospitalization risks (rather than just 120 days risks) both pre and during omicron. http://doi.org/10.1111/eci....
On 2022-03-28 18:14:47, user August Blond wrote:
Dear colleagues,<br /> I am having difficulty understanding figure 3, the two graphs that are plotted with GFP/EGFR.<br /> Zooming in on the four ovals - red, blue, black, green - I see that the scattered-plots are themselves contained in a smaller perfect ovoid.<br /> Can you explain how you manage the computer processing of your samples?<br /> In reference 13, the method for doing multiplex FACS, these close to perfect ovals do not appear. There are still points that are not perfectly integrated into the "virtual" geometrical structure.<br /> As is the case with all FACS using gating.<br /> Would it be possible to generate point clouds that have not been "artificially" modified after gating?<br /> Best regards,<br /> August Blond
On 2022-05-31 08:26:48, user Christoph Lippert wrote:
transferGWAS: A similar article from my group (accepted at OUP Bioinformatics): https://www.biorxiv.org/con...
ContIG: also related (accepted at IEEE CVPR): https://arxiv.org/abs/2111....
We would appreciate a reference.
On 2022-06-08 17:08:32, user Ted Gunderson wrote:
Should this be considered a scientific study or an advertisement?
What evidence is there that what the authors refer to as "(non-variola orthopoxvirus and monkeypoxvirus specific)" actually causes the disease that is currently being diagnosed all over the world as "monkeypox".
This is a paper funded by Roche that says "Our tests work!"
"ML and DN received speaker honoraria and related travel expenses from Roche Diagnostics."
Roche has gotten lots of press recently about their monkeypox tests.
On 2022-06-09 20:11:19, user John Doe wrote:
Interesting paper that confirms and complements prior molecular findings on this devastating malignancy. A strength of this study is the inclusion of a relatively large series of patients (n = 47) considering the rareness of the disease. The results suggesting a diverse origin of BPDCN are of special interest, and the figure on potential therapies against the disease is visually appealing. However, data analysis and data interpretation have certainly problems and inconsistencies. In particular, the results on CNV pathogenicity produced by X-CNV are highly questionable and dubious, and I would strongly advise against using those results to guide data interpretation. Among deleted regions (suppl. data) classified as non-pathogenic by X-CNV are: 1p36.11 (ARID1A), 5q33.1 (NR3C1), 7p12.2 (IKZF1) and 9p21.3 (CDKN2A–B). All these are well-known tumor suppressors with demonstrated pathogenicity in numerous human cancers. Besides, prior studies back up the recurrent deletion and pathogenicity of these cancer genes in BPDCN [refer to papers by Lucioni M et al. Blood. 2011;118(17), Emadali et al. Blood. 2016;127(24), Bastidas AN et al. Genes Chromosomes Cancer. 2020;59(5), Renosi F et al. Blood Adv. 2021 9;5(5)].
Puzzling enough, despite claiming the use of the X-CNV results to determine pathogenicity of CNVs, it appears that the authors chose to highlight anyway some deleted and gained regions classified as non-pathogenic by X-CNV (ARID1A, CDKN2A) as well as other regions not even formally called by GISTIC (e.g. TET2). This is even harder to comprehend considering that 7p12.2 (IKZF1) is clearly one of the most conspicuous peaks in the analysed cohort (Figure 3A); yet, completely ignored in the text and figure!? Quite baffling. In short, the paper would greatly benefit and improve from re-interpreting and discussing the data considering the existing literature on BPDCN genetics.
On 2020-04-21 21:10:27, user Bruno Vuan wrote:
Article says, page 7,
"This study had several limitations. First, our sampling strategy selected for members of Santa Clara County with access to Facebook and a car to attend drive-through testing sites. This resulted in an overrepresentation of white women between the ages of 19 and 64, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample population by zip code, race, and sex to match the county. We did not account for age imbalance in our sample, and could not ascertain representativeness of SARS-CoV-2 antibodies in homeless populations. Other biases, such as bias favoring individuals in good health capable of attending our testing sites, or bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain."
In summary sample has
Overrepresentation white woman 19-64<br /> Age imbalance not accounted <br /> Partial weighting by zip code, race and sex<br /> Biased favoring good health individuals and those seeking antibody confirmation
Conclusion: "overall effect of such biases is hard to ascertain"
Additionally
There is no discusion on sampling effect by facebook ads, as answering rates, impact of facebook ads algorithm which is optimized to get maximum amount of answers. It is well known that this convenience samples are non probabiistical, so this has to be included in error range evaluation, (1)
On 2022-06-24 22:03:50, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint. This certainly represents a large amount of work and careful consideration!
I have some questions / comments:
1) Is there a way for me to calculate enhanced scores for myself?
For example, I would like to learn more, but I was not very satisfied with the PRS that I listed for my own genomic sequence in this blog post:
https://cdwscience.blogspot...
2) In the blog post link above, there seemed to be a noticeable disadvantage to the PRS without taking the BMI into consideration for Type 2 Diabetes.
In this paper, age is an important factor in Figure 1 for the PRS.
If other non-genetic factors are known, do you have a comparison for non-PRS models? <br /> For example, I wonder how performance of age + BMI (+ other established factors) compares to the plot for Type 2 diabetes in Figure 1.
3a) I see that the percent variance explained is sometimes provided (such as Supplemental Figure 5), but sometimes it is not.
For example, in Figure 3, the effect per 1 SD of PRS is higher for LDL cholesterol than height. However, how does the ability to predict an individual's height from genetics alone compare to the ability to predict an individual's LDL from genetics alone?
After a certain age (as an adult), the exact value for my own LDL has varied more than my height. However, I was not sure how that variation by year compared to others and/or the variation over decades.
In general, I would like to have a better sense of how absolute predictability compares for height versus disease scores. I also understand that there are complications with binary versus continuous assignments, but it is something that I thought might be helpful.
3b) I see AUC statistics in Supplemental Figure 2, described as for AUROC. However, am I correct that some of the cases are not well balanced with controls?
If so, should something like AUPRC be provided (possibly as a complementary supplemental figure)? I believe the idea is described in Saito and Rehmsmeier 2015; the application is very different, but you can see the inflated AUROC values in Figure 1A of Xi and Yi 2021. I expect that there are other good ways to illustrate the differences with PRS in cases and controls of varying proportions, but that was one thought.
In the context of genomic risk, I might expect that high predictability in a small number of individuals may be preferable over a small difference in low predictability in a large number of individuals. There is emphasis on thresholds like top/bottom 3% (in many but not all figures), which I thought might be consistent with that opinion.
So, I think something like Figure 1 was helpful. In order to try and capture how false positives change when sensitivity increases, I am not sure if something similar for positive predictive value might help? I would consider that very important if the PRS might be used for screening purposes.
4) In the Supplemental Methods, I believe that you have a minor typo:
Current: 100,000 Genomes Project (100KGP). The 100,00 Genomes Project, run by Genomics England,<br /> Corrected: 100,000 Genomes Project (100KGP). The 100,000 Genomes Project, run by Genomics England,
Thank you very much!
Sincerely,<br /> Charles
On 2022-08-01 22:06:02, user Saiful Islam wrote:
The authors also need to clarify masks vs respirators as they provide different level of protection.
On 2022-08-06 11:55:02, user Dieter Mergel wrote:
I have a question concerning the following passage:
"Previous work demonstrated that vaccination reduces severe COVID-19 and hospitalisation 46 and also the risk of Long COVID 7, 47. However, we did not observe evidence of qualitatively different symptom clustering in vaccinated vs. unvaccinated individuals, with either alpha or delta variants."
Does it mean: <br /> (a) Vaccination does not reduce the risk of Long Covid.<br /> or<br /> (b) Vaccination reduces the risk of Long Covid, but if (!) vaccinated people get Long Covid, then (!) the symptoms are similar to those of unvaccinated people.
On 2022-08-14 15:08:34, user Peter J. Yim wrote:
The trial registration at ClinicalTrials.gov listed three primary endpoints:<br /> 1. Number of hospitalizations as measured by patient reports. [ Time Frame: Up to 14 days ]<br /> 2. Number of deaths as measured by patient reports [ Time Frame: Up to 14 days ]<br /> 3. Number of symptoms as measured by patient reports [ Time Frame: Up to 14 days ]
The publication reports the outcomes for none of those endpoints. (the endpoints were changed after publication on ClinicalTrials.gov)
This article is close to irrelevance on the question of the efficacy of ivermectin in COVID-19.
On 2022-09-06 20:45:26, user Benjamin D. Horne wrote:
Now published in BMJ Nutrition Prevention & Health https://nutrition.bmj.com/c...
On 2021-05-21 21:38:46, user lbaustin wrote:
Has this been submitted to a MEDLINE indexed journal?
On 2021-05-24 16:53:32, user Gustavo Bellini wrote:
Congratulations on the work! It would be interesting to analyze the action of vitamin D in the MHC complex, MICA / MICB.
"Indeed, immune cells significantly up-regulate vitamin D receptor (VDR) transcription upon activation and proliferation (reviewed in [28]). In turn, through the binding of VDR, vitamin D induces the expression of anti-proliferative/pro-apoptotic molecules, thereby evoking immune tolerance 29, 30. Interestingly, recent data showed that MICA stands as a VDR-sensitive molecule, through which vitamin D renders tumour cells susceptible to NK cytotoxicity 31. According to this view, in our patients the gene expression of MICA in T cells was not associated with the up-regulation of TLR or ISG, as could have been expected, but paralleled levels of vitamin D instead. All these observations suggest that vitamin D could help to restore homeostasis of the immune system during flares, and that its deprivation may jeopardize MICA-dependent cell growth control."
In addition, the inverse relationship between circulating sMICA and vitamin D found in our cohort suggests that the vitamin could prevent MICA shedding. Alternatively, sMICA impairment of NK functions could promote the uncontrolled proliferation of immune cells which, in turn, would facilitate the depletion of vitamin D.
In summary, we propose a particular disease pheno-type characterized by the disruption of MICA-dependent cytotoxicity in patients with innate activation of T cells and possibly facilitated by low vitamin D levels."
"Basically all cellular components of PBMCs belong to the innate and adaptive immune system. Therefore, it is not surprising that the immunologically most important region of the human genome, the HLA cluster, also highlights as a “hotspot” in the epigenome of PBMCs.<br /> However, it is remarkable that the HLA cluster is also a focused region of the vitamin D responsiveness of the epigenome. This observation provides a strong link to the impact of vitamin D on the control of theimmune system.<br /> In conclusion, in this proof-of-principle study we demonstrated that under in vivo conditions a rather minor rise in 25(OH)D3 serum levels results in significant changes at hundreds of sites within the epigenome of human leukocytes."
The study below has shown evidence that the vitamin D endocrine system is dysregulated in sars-cov-2 infection.
On 2020-11-24 09:59:43, user Lee Rague wrote:
This paper has been recently published:<br /> Labrague LJ, De Los Santos JAA. Prevalence and predictors of coronaphobia among frontline hospital and public health nurses. Public Health Nurs. 2020 Nov 23. doi: 10.1111/phn.12841. Epub ahead of print. PMID: 33226158.
On 2020-12-01 21:55:11, user RRD wrote:
How does your model compare when it is based on a more infection disease (such as tuberculosis)?
On 2020-12-03 16:15:54, user Marc Rafael wrote:
Any news about peer review or publishing?
On 2020-12-07 04:30:15, user Murilo Perrone wrote:
Because the spreading of this information is so important, I suggest changing the license. See:
Why Sharing Academic Publications Under “No Derivatives” Licenses is Misguided
PS: Because of CC BY-ND I don't think I can comment on your maps in my youtube videos.
On 2020-07-21 15:39:16, user OxImmuno Literature Initiative wrote:
On 2021-12-13 11:53:14, user Undertow of Discourse wrote:
The summary of findings in the abstract is defective in relation to PIMS-TS. It says “ The overall PIMS-TS rate was 1 per 4,000 SARS-CoV-2 infections”. Rate of what? Occurrence of PIMS-TS? Hospitalization with PIMS-TS? Death from PIMS-TS?
On 2023-05-09 17:56:41, user Dr. Gerald Zincke wrote:
I am missing indication at which point in time after the vaccination an infected patient was counted to the vaccinated group.
(For the importance of this, please refer to Prof. Norman Fenton's description of the statistical illusion that can occur when vaccinated people are counted as unvaccinated for a period of time after the shot. https://youtu.be/Gkh6N-ZL3_k )
On 2021-08-14 17:37:30, user Uwe Schmidt wrote:
The study states a hospitalisation rate of 6% for children.
This rate needs to be strongly questioned as it is internationally significantly higher than any other rate observed. In fact, it is higher by roughly factor 10-12. E.g. in Germany, at the peak of the pandemic in week 51/2020, less than 100 children were hospitalised nationwide, 1/3 of them newborn, who just stayed in hospital a little longer. The number of positive tested children in that week was ~20,000. For July 2021, the number of hospitalised children is less than 10, no ICU.<br /> In England, one out of 200 (0.5%) children are hospitalised.<br /> In Israel, no patient below the age of 30 is in critical condition.
Questions for the authors:<br /> 1. Does the total number of children tested positive really consist of ALL PCR-positive or only a subgroup reported by certain institutions?<br /> 2. Of those 5,213 hospitalised, how many were hospitalised because of COVID-19 and how many because of other conditions?
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 2020-07-27 06:19:26, user OxImmuno Literature Initiative wrote:
On 2020-12-28 18:05:42, user Rogerio Atem wrote:
The 3 preprints of this series on COVID-19 epidemic cycles were <br /> condensed into a single article that summarizes our findings using the <br /> analytical framework we developed. The framework provides cycle pattern <br /> analysis, associated to the prediction of the number of cases, and <br /> calculation of the Rt (Effective Reproduction Number). In addition, it <br /> provides an analysis of the sub-notification impact estimates, a method <br /> for calculating the most likely Incubation Period, and a method for <br /> estimating the actual onset of the epidemic cycles.
We also offer an innovative model for estimating the "inventory" of infective people.
Check it at:
(Revised, not yet copy-edited)<br /> https://doi.org/10.2196/22617
On 2021-01-17 21:18:23, user Arturo Sanchez-Lorenzo wrote:
The paper has been published:
On 2020-08-11 14:22:58, user David Curtis wrote:
Hi.
Just to say that you might want to consider citing this paper, which also analysed exome sequence data from ADSP:
https://onlinelibrary.wiley...
Regards
On 2020-08-12 11:44:27, user My Opinion wrote:
In my opinion...this supports the explanation why certain facilities (e.g. nursing homes, prisons, cruise ships, church gatherings) experience large numbers of individuals who become infected....I have never believed that the primary mode of transmission was a cough or sneeze....in some prison facilities....we have seen 80% of the population inside the facility become infected, including prison guards....the virus spreads too efficiently to blame it on a cough or sneeze....for example, we know that small pox can be spread through exhaled respiration...this research appears to be the first published study to definitively prove COVID-10 can float in the air and infect people quite distant from the infectious source (17-feet)....this explains how large numbers of people can become infected quickly...it is in the air...Thomas Pliura, M.D., Le Roy, IL
On 2021-06-13 20:25:12, user Gaurav Pandey wrote:
This article has now been published at https://www.nature.com/arti...
On 2020-07-08 11:38:25, user peter kilmarx wrote:
Congrats on your bibliometric analysis. Here's a reference for you: Grubbs JC, Glass RI, Kilmarx PH. Coauthor Country Affiliations in International Collaborative Research Funded by the US National Institutes of Health, 2009 to 2017. JAMA Netw Open. 2019 Nov 1;2(11):e1915989. doi: 10.1001/jamanetworkopen.2019.15989.
We found that publications coauthored by US-affiliated and non-US-affiliated investigators had a higher mean citation index (1.99) than those whose authors were only US affiliated (1.54) or non-US affiliated (1.35).
On 2024-07-24 16:07:33, user Jim Woodgett wrote:
A sobering study! I have a couple of questions about the population evaluated and timing of the study. In Methods the "Pandemic" group (G1) included subjects with scans before and after pandemic onset (N =404; 247 female), further split into "Pandemic–COVID-19" (G3, N = 121; 75 female) and "Pandemic–No-COVID-19" (G4, N = 283; 172 female). So there were 121 who had (at least one?) Covid-19 infection and 283 who had no infection. This seems an unusual sampling ratio given known serological analysis and overall penetrance of infection. How long after infection were the MRIs performed and at what point were subjects classified as Covid infected or not (presumably, the majority became infected during the study)? Were there sufficient subjects and data to assess degree of brain aging vs multiplicity of infection? Is there data on subjects self-reporting long Covid effects?
On 2021-01-27 06:59:12, user Peter Hessellund Sørensen wrote:
In the graph showing mortality vs COVID19 cases as a function of T cell imunity. In Singapore 95% of the cases were in migrant workers in their 20s and 30s. Similar problems are probably present in the other countries in the sense that the way of counting cases and deaths is not the same and different population groups are infected in different countries. <br /> Allready with Singapore removed the statistical significance of the graph has vanished.
On 2021-09-26 18:18:34, user wolvverinepld wrote:
How long was the period from the second dose to the study?
Pfizer study (Qatar)
Asymptomatic infection protection<br /> Highest 63.7% at 0-4 weeks<br /> 0% by 15 weeks
Symptomatic<br /> 49.6% at 15 weeks<br /> 0% by 20 weeks
On 2021-01-31 03:17:24, user Seyed Moghadas wrote:
It is now published in Clinical Infectious Diseases
On 2020-04-30 19:12:43, user Sinai Immunol Review Project wrote:
Main findings<br /> This report describes the use of systemic tissue plasminogen activator (tPA) to treat venous thromboembolism (VTE) seen in four critically ill COVID-19 patients with respiratory failure. These patients all exhibited gas exchange abnormalities, including shunt and dead-space ventilation, despite well-preserved lung mechanics. A pulmonary vascular etiology was suspected.
All four patients had elevated D-dimers and significant dead-space ventilation. All patients were also obese, and 3/4 patients were diabetic.
Not all patients exhibited an improvement in gas exchange or hemodynamics during the infusion, but some did demonstrate improvements in oxygenation after treatment. Two patients no longer required vasopressors or could be weaned off them, while one patient became hypoxemic and hypotensive and subsequently expired due to a cardiac arrest. Echocardiogram showed large biventricular thrombi.
Limitations<br /> In addition to the small sample size, all patients presented with chronic conditions that are conducive to an inflammatory state. It is unclear how this would have impacted the tPA therapy, but it is likely not representative of all patients who present with COVID-19-induced pneumonia. Moreover, each patient had received a different course of therapy prior to receiving the tPA infusion. One patient received hydroxychloroquine and ceftriaxone prior to tPA infusion, two patients required external ventilator support, and another patient received concurrent convalescent plasma therapy as part of a clinical trial. Each patient received an infusion of tPA at 2 mg/hour but for variable durations of time. One patient received an initial 50 mg infusion of tPA over two hours. 3/4 patients were also given norepinephrine to manage persistent, hypotensive shock. Of note, each patient was at a different stage of the disease; One patient showed cardiac abnormalities and no clots in transit on an echocardiogram, prior to tPA infusion.
Significance<br /> The study describes emphasizes the importance of coagulopathies in COVID-19 and describes clinical outcomes for four severe, COVID-19 patients, who received tPA infusions to manage poor gas exchange. While the sample size is very limited and mixed benefits were observed, thrombolysis seems to warrant further investigation as a therapeutic for COVID-19-associated pneumonia that is characterized by D-dimer elevation and dead-space ventilation. All four patients had normal platelet levels, which may suggest that extrinsic triggers of the coagulation cascade are involved.
The authors suspect that endothelial dysfunction and injury contribute to the formation of pulmonary microthrombi, and these impair gas exchange. Pulmonary thrombus formation has also been reported by other groups; post-mortem analyses of 38 COVID-19 patients' lungs showed diffuse alveolar disease and platelet-fibrin thrombi (Carsana et al., 2020). Inflammatory infiltrates were macrophages in the alveolar lumen and lymphocytes in the interstitial space (Carsana et al., 2020). Endothelial damage in COVID-19 patients has also been directly described, noting the presence of viral elements in the endothelium and inflammatory infiltrates within the intima (Varga et al., 2020). One hypothesis may be that the combination of circulating inflammatory monocytes (previously described to be enriched among PBMCs derived from COVID-19 patients) that express tissue factor, damaged endothelium, and complement elements that are also chemotactic for inflammatory cells may contribute to the overall pro-coagulative state described in COVID-19 patients.
References<br /> Carsana, L., Sonzogni, A., Nasr, A., Rossi, R.S., Pellegrinelli, A., Zerbi, P., Rech, R., Colombo, R., Antinori, S., Corbellino, M., et al. (2020) Pulmonary post-mortem findings in a large series of COVID-19 cases from Northern Itality. medRxiv. 2020.04.19.20054262.
Varga, Z., Flammer, A.J., Steiger, P., Haberecker, M., Andermatt, R., Zinkernagal, A.S., Mehra, M.R., Schuepbach, R.A., Ruschitzka, F., Moch, H. (2020) Endothelial cell infection and endotheliitis in COVID-19. Lancet. 10.1016/S0140-6736(20)30937-5.
The study described in this review was conducted by physicians of the Divisions of Pulmonary, Critical Care, and Sleep Medicine, Cardiology, Nephrology, Surgery, and Neurosurgery and Neurology at the Icahn School of Medicine at Mount Sinai.
Reviewed by Matthew D. Park as part of a project by students, postdocs, and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2021-02-04 09:44:55, user Sepp271 wrote:
Taking into account the 7-day incidence of that region (Munich) and the number of tests taken, about 1 or 2 positive cases would have been expected when similar testing would have been done in general population. Taking dark number of incidence into concern this figure goes up to roughly 2 or 3.
Therefor within this study one can not state that the observed number of positive cases of 2 found in primary schools, kindergartens and nurseriesis is significantly different from the infection numbers in the general population.
It would have helped if the authors had made a strict comparison of both groups including statements about the confidence interval.
On 2021-10-02 06:16:24, user Not Ready to Panic Dog wrote:
Since low Vitamin D levels are associated with increased incidence of cancer, heart disease, diabetes, and various auto-immune, neurological and inflammatory disorders, how did you account for the patients’ comorbidity influence on disease progression? https://pubmed.ncbi.nlm.nih...
On 2022-01-13 13:16:50, user Zacharias Fögen wrote:
Table S9 and S8, community median income, number of cases in <50,000 is higher in S9 than in S8, which is impossible. Same but reversed for 50,000-99,999, maybe exchanged numbers?<br /> Why in Table 3 did you use log increase for median income? that makes no sense to me, as you are using steps of 50,000 each.
However, more importantly, <br /> Table S3: HR Age per 1y increase =1.05 , that's not plausible as COVID-19 risk increases exponentially (doubles every 6-7 years). Using a linear regression on a nonlinear variable is not a fitting model. you could have used log age.
On 2020-09-01 09:40:12, user Roland Salmon wrote:
This is a thorough piece of field epidemiology, although like much field epidemiology today, the data substantially comes from existing information sources. As a former director of the Communicable Disease Surveillance Centre Wales (CDSC), I am pleased that Public Health Wales, via CDSC staff, past and present, produces work of this quality.
The study demonstrates, persuasively, that much of the problem with infection in care homes, resulted from the care home's size, rather than from receiving infected patients, discharged from hospital. Nevertheless, I do not think that it should be stated ("Research in context"p.3) that "Our analysis found no effect of hospital discharges on care home outbreaks once care home size had been adjusted for" (my underline). In fact, as the discussion section makes clearer (p11), the observed hazard ratio is 1.15 and the effect could be as high as 1,47 (Table 2), although the result is not statistically significant at the 5% level. (It would be interesting inter alia to know the actual probability of this, the most probable estimate of hazard of 1.15.) Table 3, looking at the risk of outbreaks, by care home capacity, further, implies that the effect of discharges might be particularly marked in the smaller homes (<10 beds) where I calculate that the crude relative risk of an outbreak in the post hospital discharge risk period is 3.2. compared with around 1.2 for larger homes. Anyway, an intervention that reduced the risk of outbreaks, in this vulnerable population, by some 15% would be considered by most people as well worth having.
It's thus important to reflect whether the failure to demonstrate an effect of this size merely reflects a lack of statistical power, some of which could be due to misclassification of the outcome. The study authors recommend, in "Conclusions and recommendations" (p12), that, "further analyses should investigate the risk where discharges were confirmed or probable cases of Covid-19, and also consider additional evidence on likely chains of transmission that may become available from sources such as.....viral genetic sequence data". This is an important supplementary piece of work. In addition, the risk from hospital discharges, unlike that from home size, does not extend over the whole period of the study. I note that 16 outbreaks that occurred before certain homes received any discharges are included in the dataset so homes, therefore, enter the study before they are at risk of any infection introduced by receiving patients discharged from hospital. Secondly, homes remain in the study after 2nd May, when universal testing of hospital patients for SARS CoV2, prior to discharge to care homes, is introduced. Thus, from, a few days after this until the 27th June, the study's end date, effectively, risk from hospital discharges is eliminated whereas the risk from home-size remains. The authors consider this and report that they fitted their model, with a factor for the two time periods (before and after 2nd May). They tell us that, "this factor was found not to be significant, and did not significantly alter the hazard ratios". Whilst I understand that any alteration of the hazard ratios was not significant at alpha =5%, I would like to actually see the change in the observed hazard ratios. It might be expected that the hazard of receiving hospital discharges was higher in the period up to 2nd May, than in the period from 2nd May to the study's end.
I was curious as to why Cox's Proportional Hazard was the test used. I don't altogether see that the risk of outbreaks following introduction, by hospital discharge is particularly time dependent, given how readily and for how long SARS CoV 2 can spread in institutional settings. Thus, I don't really see why that risk factor could not be expressed as a categorical variable (outbreak, no-outbreak) which would allow a much simpler analytical approach. I, frankly, also, don't understand the detail of the sensitivity analyses, presented, for choosing different at-risk time periods which, I feel, for a general readership, certainly, merits being explained more fully.
Finally, I think that the discussion section could be more robust. If home size is the issue, then shouldn't the authors be saying that larger homes need to consider having dedicated areas, facilities and staff for smaller subsets of their residents. Maybe larger homes should have more stringent planning requirements. I also think that rather more should be made of the contribution of hospital discharge (notwithstanding it's failure to achieve conventional 5% levels of statistical significance) than the rather anodyne paragraph at the foot of page 11 which bears all the hallmarks of the dead hand of the corporate public relations department.
Nonetheless, overall, this is an accomplished piece of epidemiology with important practical implications.
Dr Roland Salmon
On 2021-07-04 05:23:23, user PriyankaPulla wrote:
Major protocol violations occurred at the largest site of the Covaxin phase 3 trial, a private hospital called People's Hospital, which recruited 1700 participants. These violations are documented extensively by multiple media outlets. And these violations raise questions about the integrity of the Phase 3 trial data. They also raise questions about the sponsors' attitude to due process, and the independence/training of the DSMB: both sponsors (the Indian Council of Medical Research and Bharat Biotech) responded to the allegations with cursory dismissals, while the DSMB remained mum.
Further details here: https://www.thequint.com/co...
I am listing a few of the documented irregularities:
Investigators admitted in a video-recorded press conference that they didn't give participants a copy of their informed-consent form during their first visit, unless participants explicitly asked for it. This strongly suggests that the investigators weren't trained in Indian legal requirements or Good Clinical Practices.
Investigators allegedly advertised the trial as a vaccination drive in communities of poor and illiterate people.
Dozens of participants say the trial team did not contact them to record solicited adverse events. These participants often didn't have their own mobile phones (mobile phones are the mode through which solicited adverse events were to be collected, as per trial protocol). Even though these participants came from poor communities, investigators didn't foresee the fact that they may not have their own mobile phones, and may be hard to contact. Nor did they attempt to contact them in their homes in the days following the doses.
People's Hospital recruited a record 1700 participants in 1.5 months (no other Covaxin trial site in India managed such numbers). In contrast, another government-run Covaxin site in Bhopal struggled to even recruit a few hundred participants, and was, therefore, excluded from the trial. This supports the allegation that People's Hospital misadvertised the trial as a vaccination drive.
Many participants told media outlets that they suffered Covid-like symptoms post jab, but the investigators never called them to collect this information. Nor did the participants know where to report their symptoms. This raises questions about how well Covid cases were recorded.
Participants say they were denied medical treatment at People's Hospital when they fell sick. This, again, raises questions about how well the investigators captured adverse-events.
When one participant at the Bhopal site died, investigators ignored his family's version of the participant's symptoms in their causality analysis. In the family's version, the participant suffered from very severe symptoms (vomiting, dizziness, weakness) for 7-8 days before death, while the investigators claimed he was fine during solicited-adverse event monitoring, and died suddenly.
The dismissal of the family's version of events, when the family was present during the participant's death (but the investigators weren't), raises serious questions about how Serious Adverse Events are investigated. No post-mortem report or causality analysis was shared with the family despite multiple requests. Further, the family alleges that the deceased participant received no phone calls from the investigators to record solicited adverse events in the days leading up to his death.
The investigators could easily have shared proof of their claims by sharing a record of the phone calls with the family. They haven't.
Despite the above serious concerns (which are supported by video testimony from participants broadcast on multiple media outlets, specifically NDTV), the trial's government sponsor, ICMR, and Bharat Biotech, denied all allegations in a cursory manner. Further, the preprint makes no mention of them, or explain how these irregularities were handled.
This raises questions about overall data integrity in Bharat Biotech's phase 3 trial. Bharat Biotech has been under substantial pressure from the government to roll out Covaxin fast, which may explain why the company is overlooking such data integrity issues. More details here: https://www.livemint.com/sc...
Reviewers of this paper, and licensing authorities, including the World Health Organisation, must investigate these allegations thoroughly.
On 2020-09-08 12:00:16, user Wendy Olsen wrote:
I noted that the assumptions going into this model are a consistent proportion of Overseas and Home students, and a similar size student body, as last year. In addition the cases arriving at UK campuses would be over half from UK Home Students. So even if the assumption of consistent proportion from Overseas turns out untrue, there is still the problem that having more UK Home students will bring more cases into the campuses. I also noted the summary, written by the authors:
Their core estimate is that "81% of the 163 UK Higher Educational Institutes (HEIs) have more than a 50% chance of having at least one COVID-19 case arriving on campus when considering all staff and students. Across all HEIs it is estimated that there will be a total of approximately 700 COVID-19 cases (95% CI: 640 - 750) arriving on campus of which 380 are associated from UK students, 230 from international and 90 from staff. This assumes all students will return to campus and that student numbers and where they come from are similar to previous years. According to the current UK government guidance approximately 237,370 students arriving on campus will be required to quarantine because they come from countries outwith designated travel corridors. Assuming quarantining is 100% efficient this will potentially reduce the overall number of cases by approximately 20% to 540 (95% CI: 500 - 590). Universities must plan for COVID-19 cases ... and ... reduce the spread of disease. It is likely that the first two weeks will be crucial to stop spread of introduced cases. Following that, the risk of introduction of new cases onto campus will be from interactions between students, staff and the local community as well as students travelling off campus for personal, educational or recreational reasons.
"COVID-19 has resulted in the on-campus closure of HEIs across the UK in March 2020 (1). Since that point universities have been working predominantly as virtual establishments with most staff working from home. Autumn sees the start of the new academic term with the potential return of more than 1.5 million UK and almost half a million international students (2).
"The COVID-19 pandemic continues ... approximately 1000 new cases reported each day in the UK, 25,000 across Europe and 250,000 worldwide ((3) accessed 28/03/20). There have been a number of outbreaks of COVID-19 reported in universities in the USA (The University of North Carolina, Notre Dame in Indiana, Colorado College, Oklahoma State and University of Alabama (4)) where the national infection rate is approximately 10 times higher than the UK (3). advice ...(5, 6). However, it is currently unknown to what extent COVID-19 will be brought to campus by staff and students whether from the UK or abroad."
On 2021-10-23 16:32:56, user CDSL JHSPH wrote:
I really enjoyed reading about this topic and what the implications drawn by your results could mean to the medical field in regards to the development of clinical traits associated with height. Although you do draw many parallels between specific clinical traits and height, I was left confused about which height range you were drawing your associations from. I see that you do provide the average height of the individuals in the sample (individuals of approximate 176 cm height); however, are the associations being measured effective on all heights above this number or is there a specific height range in which we begin to see the development of these traits? I would suggest to clearly define this in your Introduction section in order to provide better context of which height range are significantly showing associations with each of the clinical traits detected. Further, just as my colleague below, I was wondering if you plan on publishing this study in a journal of genomics or statistical science? Your paper contains advanced vocabulary on both of these topics, and although the findings are incredibly interesting to any science-oriented reader, I do feel that it is perhaps a paper that is better aimed towards an audience with a background in genomics or statistical science. But other than this, congratulations on this paper, it is incredibly thought-provoking!
On 2021-10-27 15:17:33, user Edward Jones wrote:
I find this study very biased considering they use the 16.7% with such a small sample size, usually you'd discount that number. Also no consideration was given to the type of virus being investigated, the paper is regarding SARS COV and yet you quote 16.7% inaccuracy in Ebola virus. Furthermore, the statement saying that uninfected individuals will be in risk of exposure is nonsense. A false positive would mean they may have to isolate, having the opposite effect.
On 2020-10-25 19:08:24, user Daniel Haake wrote:
Dear study team,
Thank you for your study, which shows that the risk of COVID-19 death increases significantly with age. To improve the quality of the study I have some comments regarding the statistical analysis of the study. In the following I would like to go into it.
You write that antibodies are formed in 95% of people after 17-19 days. In contrast, 95% of deaths are reported after 41 days. That is a difference of 22-24 days. Nevertheless, you take the number of deaths 28 days after the midpoint of the study. Why do you take a later point in time than you yourselve have determined? Even with this approach, you are 4 - 6 days too late and overestimate the number of deaths. Why even this would be too late, I will explain in more detail below.
The 41 days were given for the USA. But what is the situation in other countries? In Germany, for example, there is a legal requirement that the death must be reported after 3 working days at the latest. Of course there can also be unrecognized deaths in Germany, where it takes longer to report. But this should be the minority. If we transfer however this fact of the USA to other countries, in which the risk of the long reporting time does not exist in such a way, you take up too many deaths into the counter of the quotient with. This leads to a too high IFR.
Counting the deaths 28 days after the study midpoint is also problematic because in the meantime, further deaths may appear in the statistics that were not infected until after the infected persons identified in the study became infected. This is because not all deaths take as long to report. These are then deaths that are not related to the study. You yourself write that the average value of the report of a dead person lasts 7 days with an IQR of 2 - 19 days. These figures speak in the statistical sense for a right-skewed distribution in the reporting of death figures. This in turn means that the majority of the deceased have a rather shorter reporting time. The procedure leads to a too high number of deaths. This is a problem especially with still existing infection waves, even with already declining infection waves.
You write: “The mean time interval from symptom onset to death is 15 days for ages 18–64 and 12 days for ages 65+, with interquartile ranges of 9–24 days and 7–19 days.”<br /> If we assume the 3 days reporting time for Germany, we receive 18 days for the age 18-64 and 15 days for 65+. In contrast, 95% of the antibodies are formed after 17-19 days, which is about the same or later than the time when the dead appear in the statistics. For other countries this may be different and would therefore need to be investigated. In any case, a blanket assumption from the USA is not possible for studies outside the USA.
Since the mean time interval from onset of symptoms to death is 15 days for the age 18-64 with the interquartile range of 9-24 days, but the midpoint of the range would be 16.5 days, this suggests a right-skewed distribution in the values. The same applies to the mean time interval from the onset of symptoms of 12 days with interquartile range of 7-19 days for the age 65+, where the midpoint of this range is 13 days. This also speaks for a right-skewed distribution of the values. This would mean that the majority of the values would be below the mean value in each case, making shorter times more likely. This also shifts the time too far back. Therefore it would be better to assume the median value, because it is less prone to outliers.
Your example infection wave from figure 1 also shows the problem with this procedure. As you say, antibodies are formed in 95% of people after 17 - 19 days. Now you have an example study with the median 14 days after the start of infection. At that time, only a few of the infected persons have formed antibodies at all, since just 14 days before the infection wave starts with low numbers and then increases. Only 4 days before is the peak of the infection wave. This means that the time period, which is very strongly represented, cannot have developed any antibodies at all. This leads to the fact that only very few infected persons are recognized as infected. In your example, 95% of the deceased are now infected, but only very few of the infected. This leads to a clear overinterpretation of the IFR.
Due to the problems mentioned, the number of deaths should therefore be taken at the median time of the study. Of course, it would be best if the studies took place immediately after the end of a wave of infection, where the death rates are stable and the expression of antibodies is complete.
You write: "A potential concern about measuring IFR based on seroprevalence is that antibody titers may diminish over time, leading to underestimation of true prevalence and corresponding overestimation of IFR, especially for locations where the seroprevalence study was conducted several months after the outbreak had been contained.“
You have made many assumptions about the death figures and adjusted the death figures (upwards) accordingly. Here you find that the antibodies disappear over time and that this can lead to an underestimation of the number of infected persons. However, you do not adjust the number of infected persons upwards, unlike your approach to adjusting the death figures. For example, a study by the RKI found that 39.9% of those who tested positive for PCR before did not develop antibodies (https://www.rki.de/DE/Conte... "https://www.rki.de/DE/Content/Gesundheitsmonitoring/Studien/cml-studie/Factsheet_Bad_Feilnbach.html)"). From this, we could conclude that the antibody study only detected around 60% of those previously infected and that the number of infected persons would have to be adjusted accordingly. But you have not done that. I can understand that you did not do that. I wouldn't have done it either, because we don't know how this is transferable to other studies. But in adapting the dead, you have transferred such assumptions to other studies. This should therefore also be avoided. There, too, we do not know how transferable it is. If you only make an adjustment in the dead, but not justifiably in the infected, this leads to an overestimated IFR.
You write in your appendix D: "By contrast, a seroprevalence study of Iceland indicates that its tracing program was effective in identifying a high proportion of SARS-CoV-2 infections“.
In my opinion this is a wrong conclusion. In my opinion, it is not the success of the tracing program, but the number of tests and thus fewer unreported cases. To date, Iceland has performed almost as many tests as there are inhabitants in Iceland. Therefore they could keep the number of unreported cases lower. Other countries did not test as much. Therefore the results are not easily transferable to other countries. The PCR tests only show the present, but not the past and not the untested.<br /> You write it yourself: „(…) hence we make corresponding adjustments for other countries with comprehensive tracing programs, and we identify these estimates as subject to an elevated risk of bias.“<br /> Nevertheless, you leave these studies in meta-analysis, although for the reasons mentioned above this leads to severe problems. The figures for countries with tracing programs should therefore not have been included. The estimated number of unreported cases is not known and cannot be taken over by Iceland.
You sort out some seroprevelence studies. These include Australia [63], Blaine County, Idaho, USA [67], Caldari Ortona, Italy [72], Chelsea, Massachusetts, USA [73], Czech Republic [75], Gangelt, Germany [79], Ischgl, Austria [81], Riverside County, California, USA [98] , Slovenia [101] and Santa Clara, California, USA [116]. For the most part, these studies are sorted out because there is no age specification for seroprevelence. Since this is the study's investigation, this is of course understandable. However, these studies in particular have shown calculated IFR values between 0.1% and 0.5%. At the same time, you leave the numbers of PCR tests from countries with tracing programs in the meta-analysis. As already mentioned, this is not correct due to the unknown dark figure and the transfer from Iceland is also not possible, as described before. This leads to the fact that studies with low values are sorted out, but at the same time uncertain numbers with high values are left in the study. This shifts the calculated IFR value upwards in purely mathematical terms.
It is precisely the outliers upwards that cause problems in the calculation. Since the numbers are rather small (in a mathematical sense), there can be no deviation as strong downwards as upwards. This means that there may be studies that deviate perhaps 0.2 percentage points downwards, but other studies that deviate upwards by 1.2 percentage points. This is a problem for the regression, because the regression then leads to too high values. Therefore, outlier detection should be performed upstream and the outliers should be excluded. You can also make it easier by taking the median value, since it is less susceptible to outliers. But then you would have only one value.
You write: “The validity of that assumption is evident in Figure 3: Nearly all of the observations fall within the 95% prediction interval of the metaregression, and the remainder are moderate outliers.”<br /> You can see it in figure 3, but due to the logarithmic scale it is difficult to estimate the ratios. Better suited is Figure 4, which would be desirable for the different age groups to be able to make a better estimation there. Figure 4 shows that many studies are outside the confidence interval, often to a considerable extent and to a greater extent also towards the high IFR values. Looking at the values and the confidence interval, these studies must have significant z-scores, which would show that these are clearly outliers that should not be considered. This leads to the fact that the regression will be brought further in the direction of high values, which results in too high IFR values.
In Appendix Q you write: "In the absence of accurate COVID-19 death counts, excess mortality can be computed by comparing the number of deaths for a given time period in 2020 to the average number of deaths over the comparable time period in prior calendar years, e.g., 2015 to 2019. This approach has been used to conduct systematic analysis of excess mortality in European countries.[159] For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium“
I understand why you want to do this. But there are some dangers involved. The above statement may be true for Belgium, but it cannot be transferred to other countries in a general way. Especially since you cannot say in general terms that every dead person above average is a COVID 19 dead person. Mathematically, this would mean that there have been COVID-19 deaths in some of the last few years, because there have been periods with more deaths than the average. This makes the average straight. Especially since, as I said, you can't simply say that every death above the average is a COVID-19 death. The majority will be it, but not necessarily everyone. Thus, even cancer operations that did not take place or untreated heart attacks due to the circumstances and unnoticed visits to the doctor may have contributed a share. Whether this is the case, we do not know without a study. A blanket assumption that every death above the mean value is a COVID-19 death is not correct. From the statement "For example, the Belgian study used in our metaregression computed age-specific IFRs using seroprevalence findings in conjunction with data on excess mortality in Belgium", we could also conclude that the number of reported COVID-19 deaths is correct and can therefore be used as the numerator of the quotient for calculating the IFR. <br /> If you take this as a blanket assumption, how do you deal with those countries that do not have excess mortality but have several thousand COVID-19 deaths in the official statistics? Would you then correct the number of COVID-19 deaths downwards, perhaps even to 0? Certainly not.
You write: "We specifically consider the hypothesis that the observed variation in IFR across locations may primarily reflect the age specificity of COVID-19 infections and fatalities.“
It is also possible that the variation in the calculated IFRs occurs due to still different dark figures. If, for example, the PCR tests are taken in countries with a tracing app, but an IFR based on Iceland is calculated there, this can lead to incorrect and too high IFR values. Also the adjustments of the death rates themselves or the late time of the death rate determination 4 weeks after the study center can lead to this high variance.
In Table 1 you write that on July 15 there were 8 million inhabitants with a projected 1.6 million infections. According to my research there are 8.4 million inhabitants. You calculate the 1.6 million infected on the basis of the 22.7% infected in the study. However, the blood samples were taken between April 19 and 28, so the infections occurred before or until the beginning/middle of April. So you now take the number of infected persons from the beginning/mid-April or from April 24 (study midpoint) and insert them for July 15, i.e. just under 3 months later! In the meantime, however, not only people have died, but have also become infected and formed antibodies. They thus increase the numerator of the quotient, but leave the denominator unchanged, although the denominator would also be higher. So you shift the IFR upwards here as well.
The study on Gangelt, which was not taken into account, shows a similar picture. You write that at the end of June there were 12 deaths and therefore the IFR rises to 0.6%. That is 8 weeks (!) after the study center. This does not take into account that in Germany the deaths must be reported after 3 days. If you have proceeded in this way when calculating the other IFRs from other studies, this suggests that the IFR values are too high.
You calculate the IFR of influenza based on the CDC figures for the 2018/2019 influenza season and indicate the IFR as 0.05%. Firstly, it should be said that statistically it is never good to look at just one value. The average of a time series should be considered. You calculate the value by looking at the estimated deaths and looking at how many were estimated to be symptomatically infected with influenza. You use a study according to which about 43.4% of cases are asymptomatic or subclinical (95% CI 25.4%-61.8%). You then take the mean value from the confidence interval with the value 43.6% and use this figure to calculate how many people were probably infected with influenza. Statistically it is not correct to take the average value of 43.6%. The value of 43.4% must be taken. Due to the small difference, this does not make much difference, but it shows the statistically imprecise consideration that runs through the study and generally leads to an IFR that is too high or, in the case of influenza, too low.
Now a statement on the selection of the 2018/2019 flu season, the CDC writes: "These estimates are subject to several limitations. (...) Second, national rates of influenza-associated hospitalizations and in-hospital death were adjusted for the frequency of influenza testing and the sensitivity of influenza diagnostic assays, using a multiplier approach3. However, data on testing practices during the 2018-2019 season were not available at the time of estimation. We adjusted rates using the most conservative multiplier from any season between 2010-2011 and 2016-2017, Burden estimates from the 2018-2019 season will be updated at a later date when data on contemporary testing practices become available. (...) Fourth, our estimate of influenza-associated deaths relies on information about location of death from death certificates. However, death certificate data during the 2018-2019 season were not available at the time of estimation. We have used death certification data from all influenza seasons between 2010-2011 and 2016-2017 where these data were available from the National Center for Health Statistics. (…)
The CDC writes the same for the 2017/2018 season, so the values, which were always only estimated anyway, were estimated even more due to missing data. Therefore we should have considered the figures for the seasons 2010/2011 to 2017/2017. If we calculate the IFR of influenza in this way and also use the confidence interval to calculate the number of people potentially infected per season, we get an IFR of influenza of 0.077%, ranging from 0.036% to 0.164%. Every single year prior to the 2018/2019 season was above the 0.05% and the average of 0.077% is also 54% above your reported value. This means that influenza is still not as lethal as COVID-19 has been so far, but the factor is not as high as suggested by your study.
It should also be noted that it is not possible to compare an IFR calculation that is equally distributed over age with an IFR of influenza that is not equally distributed over age. You do not do it directly, but by naming these numerical values, this has been taken up by the media. The IFR just indicates the mortality per actually infected person. Therefore the IFR of the actually infected persons of COVID-19 must be compared with the IFR of influenza. You can of course calculate a hypothetical IFR assuming that every age is equally likely to be infected. In this case, however, the calculation must be performed not only for COVID-19, but also for influenza.
I hope I can help you to improve the study in terms of statistical issues. I remain with kind regards.
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 2020-05-24 21:18:10, user helgarhein wrote:
Thank you for your impressive study. I would like to ask, would you be able to check retrospectively serum 25-hydroxyvitamin-D levels (25(OH)D) in blood samples of the hospitalised covid-19 patients? The findings are probably interesting and might explain some of the excess mortality in people with dark skin types and those who are overweight. I suspect the lower the 25(OH)D level was, the worse the outcome will have been, as found in many observational studies https://www.bmj.com/content....
The crucial point is to understand that the full beneficial functioning of vitamin D will only appear after a blood 25(OH)D level of around 100 nmol/l (40 ng/ml), unlike the erroneous definition of sufficiency of 25 nmol/l (10 ng/ml) by NICE and SACN. https://www.grassrootshealt...
Vitamin D is the substrate for a pleiotropic seco-steroid hormone with multiple gene regulating functions in the immune system and sufficiency will most likely have beneficial influence on the covid-19 illness progression, suggested by 30 experts recently: https://www.bmj.com/content...
A sufficient 25(OH)D level is mainly derived from UVB rays on our skin, or vitamin D supplementation. However, lighter skin types have adapted to be more efficient in using the scarcer sun light of Northern areas, and dark skin types will need much longer sun exposure to produce the same amount of 25-hydroxyvitamin-D https://pubmed.ncbi.nlm.nih... <br /> as do overweight individuals because fat tissue accumulates it. A large number of human diseases are linked to deficient 25(OH)D levels (osteomalacia, depression, diabetes, autism, cancers, infections, inflammatory bowel diseases and many others) and vitamin D deficiency is a worldwide problem.
I have recently retired from over 30 years working as GP in deprived areas of Edinburgh. I have seen many clinical improvements in my patients after rectifying their deficient vitamin D levels, as well as clear differences in 25(OH)D levels in different ethnic groups https://www.ncbi.nlm.nih.go...<br /> 24/5/20 Helga Rhein https://scotsneedvitamind.com
On 2021-12-04 13:02:08, user Kiwinka74 wrote:
Hi, I think at the very end of the paper there is a typo with the term primary infection, as it reads instead as 'primary reinfection'.
On 2020-07-18 09:34:46, user Richard Harrison wrote:
Useful paper. Good to see physics being applied to droplets and virus particles, although conclusions re aerosols will obviously be affected by air flows in any particular room.