On 2021-05-23 07:26:54, user disqusWVOR wrote:
Fig.2 pg.25 graph indicates ~5% grade 3 (severe) systemic adverse effects with NVX 2nd dose vs. <1% with placebo. How was this addressed in the article other than pg.13 "similar frequencies of severe adverse events (1.0% vs. 0.8%)"?
On 2021-05-25 00:37:20, user Dr J wrote:
A glass of wine drinking with food slows the rate of absorption alcohol as has been shown by many studies. What is the effect of with food and without food in this study? Any difference or no difference?
On 2021-05-26 07:10:41, user Robert Clark wrote:
To the authors: with millions of lives at stake, you do not want to be on the wrong side of history on this.
The most ethical response considering the extreme importance of the issue is to go beyond just retracting and actually rewrite to conclude IVM by best available evidence does appear to have effectiveness as a treatment for COVID.
Robert Clark
On 2021-05-27 13:49:50, user unscientific science wrote:
This is the new version of the article. Please post your critique also there:
On 2021-05-26 16:03:04, user japhetk wrote:
Also, what is the percentage of people who were vaccinated (by the COVID-19's vaccine) in both groups? Also, how many people in both groups received the COVID-19's vaccine before the antibody test and tested positive?<br /> If I understand correctly, Greece started vaccinating the general elderly population on January 16 and the data lock of this study was on April 28, and the antibody test should have been completed by January 28 or earlier.<br /> I would like to know if the antibody test results that showed more infections in the BCG group were affected by the vaccination of COVID-19's vaccine.
On 2021-05-27 02:28:39, user Stel-1776 wrote:
It did not look at the effectiveness of masks, but the effectiveness of mask MANDATES. It should read "Mask MANDATES did not slow the spread". Why? Too many people who think they know better than professionals who dedicate their lives to studying this field. Too many people not wearing them, wearing them incorrectly, wearing the wrong type, not cleaning them, etc.
N95 masks are better, but there is solid evidence that regular surgical masks also reduce chance of spreading in the community.
This is supported by a systematic review (a review and critique of published studies to date) published in one of the most highly respected medical journals in the world.
"The authors identified 172 observational coronavirus studies across 16 countries; 38 of these studies specifically studied face masks and the risk of COVID-19 illness. The authors found that the use of either an N95 respirator or face mask (e.g., disposable surgical masks or similar reusable 12–16-layer cotton masks) by those exposed to infected individuals was associated with a large reduction in risk of infection (up to an 85% reduced risk). The use of face masks was protective for both health-care workers and people in the community exposed to infection."<br /> [Chu et al. COVID-19 Systematic Urgent Review Group Effort (SURGE) study authors. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis. Lancet. 2020]
On 2021-06-05 15:33:22, user Scandinavian Journal wrote:
Imo the twelve (13·5%) patients that had comorbidities associated with risk for severe disease [17] made a courageous contribution by accepting the possibility of ending up receiving placebo in the trial.
On 2021-06-09 18:09:15, user Paul Cwik wrote:
Peer Review in this case does not mean that peers reconduct the experiments. It simply means that others (with suitable credentials) have read and accepted the paper as having correctly followed the scientific methods. In other words, they are simply looking for errors in the paper, not re-doing and confirming the results.
On 2021-06-12 20:06:10, user Bill Wilson wrote:
The sample size could be larger but it is the best “science” we have so far.
On 2021-06-10 09:22:10, user cat's eyes wrote:
What were the baseline characteristics of the 37 patients who survived on HCQ compared to the patients who died? From Table 1 patients who survived were generally healthier and younger than those who died. Table 4 should provide adjusted and unadjusted hazard ratios. Also, did you test for interactions between HCQ/AZM and predictors such as age and steroid use?
On 2021-06-13 22:46:46, user Kenji Ikemura wrote:
Finalized peer-reviewed manuscript accepted by JMIR: <br /> https://www.jmir.org/2021/2...
On 2021-03-13 18:13:49, user Sean Patrick Murphy wrote:
This study focuses on hospitalized COVID patients. Many longhaulers were never hospitalized and some were completely asymptomatic. The authors attempt to address this issue with the likely erroneous statement - "Secondly, this is an initially hospitalised cohort so we cannot directly extrapolate to individuals who initial infection did not result in hospitalisation although there is no reason to suggest the effect would be any different." Patient-led research has demonstrated that there are clear subcategories of longCOVID based on symptomatology and to lump these all together is simply wrong.
On 2021-03-15 10:49:33, user Mav Rick wrote:
If NHS staff were not being tested when community prevalence was high, or only being tested once a week for a virus that van be infectious in 3 days the floodgates were open for staff both in hospitals and care homes to transmit the virus through asymptomatic/presymptomatic transmission.
The move to testing more staff 3 times a week was far too late, and not reliably implemented. A lesson not learned from first wave.The virus effectively went through an open door.
This testing policy failure was far more responsible for thousands of infections and deaths in care home and hospital settings than the unsafe discharges from hospital, but almost never reported on, or researched.
On 2021-03-24 14:52:53, user Pedro Negreiros wrote:
The paper is of good quality. The results need , however, be reproduced by other researchers.
On 2021-03-24 21:24:49, user Erik von Elm wrote:
Could the authors please clarify whether the study has been commissioned by a corporate client (as suggested at https://idalab.de/success-s... "https://idalab.de/success-stories/improving-rd-productivity/)") or not? In the manuscript they state that "no funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript."
On 2021-03-27 15:05:23, user Rogerblack wrote:
I note the severe concerns raised before the trial about inaccuracy of mental health scales used in this paper are not addressed at all in version 2. To find that comment, click on 'view comments on earlier vesions of this paper'.
In short, mental health scales with physically ill patients risk being akin to asking patients 'do you wobble when you stand up' and concluding that one-leggedness puts you at great risk of low blood pressure.
The measures used confuse 'I can't as I am physically unable to' with 'I cannot as I have anxiety/depression'
Emailed coresponding author and other two leads on 25th, raising these concerns.
On 2021-03-30 07:17:34, user Eunji Lee wrote:
This is a good study to supplement the results of previous studies that showed the high false-positive rate of PET in early cervical cancer for pelvic lymph node detection. In particular, it is impressive that this cause was evaluated by correlating with inflammatory changes after conization. However, it would have been better if other imaging evaluations, such as CT and MRI, were added to the analysis to provide a way to supplement this limitation of PET.
On 2021-04-06 07:47:45, user NAZ Mohamed wrote:
This article is now published in the International Medical Journal<br /> Full text is available at
On 2021-04-06 09:02:36, user Hieraaetus wrote:
1) An observational study on 90 patients from the end of 2020 compared with 90 patients treated during the first wave (Mar-Apr 2020): this is a bias! They should compare patients observed exactly during the same period. <br /> 2) In the paper there is no trace about the "Home-Therapy Algorithm": there is a list of allowed drugs but there is not an Algorithm that describes how use these drugs. Thus , the 90 patients did not underwent to a standardized treament.
On 2021-04-06 13:27:57, user Roseland67 wrote:
So,
Under what conditions Is a fully vaccinated person at risk of infection again?
And, can this fully vaccinated person, once reinfected, pass this infection on to others?
On 2021-04-10 18:48:39, user Daniel Haake wrote:
Regarding version 6 of your study, I have pointed out with my comment which statistical problems are present due to your study design, which leads to an overestimation of the calculated IFR (cf. https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.07.23.20160895v6?versioned=true#disqus_thread)"). Thank you very much for your reply to my statement. I think that an exchange is important, because this is the only way to get reasonable results. Therefore, please do not regard my comments as criticism, but as suggestions for improvement on how to achieve correct values. Since my statement is still valid with version 7, I answer to your answer, in which I comment here in version 7.
Here you seem to have misunderstood me. I meant that with your example wave of infections and starting the study shortly after the peak of the wave, there is the problem that antibodies have not yet been formed by many people by the time the study starts. By choosing the time of death then, you caught 95% of the deaths, but only a much smaller proportion of those infected. This leads to an underestimated numerator and thus an overestimated IFR.
Just because it was also done that way in the Geneva seropaevelence study does not automatically mean it is correct. So there are also very much studies where the study date was chosen for the number of deaths. For example:
https://www.who.int/bulleti...<br /> https://www.medrxiv.org/con... <br /> https://www.medrxiv.org/con...
?However, I agree with you that the Santa Clara County study should be taken with a grain of salt, as here the subjects were called via a Facebook ad and thus bias may have occurred.? As I said, I understand the idea of taking a later date for the number of deaths. However, the associated problems regarding the underestimation of the infected, which I wrote about in the previous answer, still remain.
It is still incomprehensible that you calculate a difference of 22-24 days, but then take a value 28 days after the study midpoint. This puts them 4-6 days behind your own calculation and thus automatically increases the IFR. Why do you elaborately calculate the difference of 22-24 days to determine the correct time, but then don't use that value??? Let me open up another example. Let's say we are testing at the peak of an infection wave. But now we count all the dead who showed up after a certain time, but we don't take into account that a large number of people still got infected after that. Some of the counted dead will also have become infected after the study. Then we have recorded all the dead, but not all the infected. Or do you want to say that all the dead are from the first half of the infection wave and none from the second part of the infection wave (especially since that would lead to an IFR of 0% for the second part of the infection wave). As you can see, it is problematic if you assume the number of deaths in the much later course, because you then choose the denominator of the quotient too small and arrive at an IFR that is too high.
In general, only deceased persons who are clear to have been infected before the latest time at which study participants may have become infected may then be included. This is not the time of the study, since the antibody tests can only be positive after some time following an infection.
Is it really "PCR testing per confirmed case", not "PCR testing per capita" that is the important parameter? Let us assume two example scenarios for this purpose. Let's assume that we test every resident and at that time 1% of the population is in the status where the PCR test is positive. Then we currently know from everyone what their status is. But then we would only get 1 positive tested person out of 100 tests performed. This test would then not be taken because of the too low ratio of tests per positive case. And this, although we would have tested even everyone. Now let's assume the opposite case. We test in a country where we don't know exactly where how many people are infected. Now we test in one region and assume that this result is transferable for the whole country. But actually this region is not as affected as other regions, we just don't know. Now we do 10,000 tests and find 20 infected people there. Then we come up with a ratio of 1 positive test per 500 tests performed. That test would then be included in your selection, even though the ratio of infected is actually higher. Therefore, it is just not the "per confirmed case" that is the important parameter. Because if there is a high number of cases in the country, you could now double and triple test everyone and know very well and still this investigation would be excluded. At the same time, however, studies can be included with few tests and thus a high statistical uncertainty for the reasons mentioned earlier.??
The comparison with South Korea is also problematic. 0 or 1 seropositive results are far too few to have any statistical significance. The statistical uncertainty here is simply too high. And, as already mentioned, the results of these investigations cannot be transferred across the board to the other investigations. ??
Including reported case numbers from countries that have a tracking system that works well for you leads to an overestimation of IFR.
That you screen out studies, based on recruitment I can understand. I think that is statistically correct. I also see the danger with recruitment that you can't get representative results. Therefore, it is also understandable that you want to see which studies are useful and which are not.<br /> Nevertheless, you just sort out the studies that have a low calculation of IFR and leave studies with high values in your study. This leads to a shift toward the high values. Furthermore, studies that are straight up deviant are more problematic because a larger shift is possible in that direction. Let's say there is a hypothetical virus with an IFR of actually 0.5%. Then we have a study with a value of 0.3% and a study with 1.5%. The high value in particular is further away from the actual value and thus shifts the calculated value upward. If you have an actual IFR of 0.5%, you can misestimate by a maximum of 0.5 percentage points on the downside and by 99.5 percentage points on the upside in theory. This is also not surprising because such distributions are right skewed. If I remove both, the study with the too low value and the study with the too high value, the actual value does not change. If I remove both, the calculated value shifts upwards, because a stronger shift is possible in this direction. This leads to an overestimation of the IFR.
You write in your reply that this is not relevant because reported deaths were used and not excess mortality. In Appendix Q you write: <br /> "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“. You may not have applied this to other studies. However, you are using a study that did. Accordingly, this is crucial and has an impact on your result.
You nevertheless calculate an age-specific IFR for COVID-19 and calculate the IFR as it would look if there were an equal distribution across age groups, which in fact there is not. At the same time, you say what the IFR is for influenza, which, as shown, you understate. After all, the comparability of numbers due to changing life circumstances do not change in a short period of time. Therefore it is no problem to use the IFR for influenza of several years. Thus you suggest a comparability of the numbers. It is not possible to compare an IFR that assumes an equal distribution of age groups with an IFR that does not assume an equal distribution. However, this is exactly what is being suggested. By the way, it is not only the media, it was also taken up by Dr. Drosten. For another reason the comparability is difficult. Namely, an IFR is compared of influenza, where we could already protect the vulneable groups to some extent by vaccination and also an infection could have been gone through in the past, which helps to fight the disease and can therefore lead to fewer problems. However, to be honest, one can of course argue here that this is just the way the situation is. Therefore it is also understandable for me if one nevertheless makes such a comparison. Then, however, by assuming an equal distribution over the age structure for both viruses, or the actual distribution for both. By the way, there is another problem. There is a comparison of an estimated IFR with a measured one.
With the studies to date, it is very difficult to estimate how high the IFR actually is. This is because there are problems with all methods. If you take antibody studies, there is the problem that antibodies are not detectable in all infected people. If you take the reported numbers of cases, there is the problem of the dark field. How could one calculate a clean IFR? By actually testing a certain proportion of the population as a representative group on a regular basis. For example, you can test 1 per thousand of the population every week and see if they are positive for COVID-19. Then look at how many people have died over time from the group of positives. Those deceased could then be autopsied by default to determine whether they died from or with COVID-19. In doing so, one must then determine what period of time after infection is still valid to count as a COVID-19 dead person. After all, is a person who died 10 months after infection still a COVID-19 dead person? After all, it is the elderly who are dying. But it is not atypical that they would have died over time even without infection. Now imagine that a 94-year-old dies 10 months after an infection. Can one then still say whether it was due to COVID-19? In this case, one would probably have to look at the medical history before and after COVID-19 and also see what symptoms the deceased had after the infection. Only with such a procedure it is possible to calculate a clean IFR. For a correct comparability with influenza, this procedure would also have to be used for the calculation of the IFR of influenza. If you are really interested in a scientific comparability of the IFR, you should proceed in this way.
On 2021-04-12 05:38:07, user ICUC wrote:
What were the results of these breakthrough infections? Were the symptoms severe? Did anyone need hospitalizations? Was there any death?
On 2021-04-16 16:04:08, user Dirk Van Essendelft wrote:
Just curious about the age distribution. The FE vaccinated cohort appears to be significantly older than any other cohort and also exhibits the highest B.1.351 infection rate. Is it fair to conclude that the vaccine is less effective against this strain or is it fair to conclude that the B.1.135 strain is more infectious for an elderly population.
On 2021-05-06 16:08:18, user leonolting wrote:
why is there no information about hospitalization at this point
On 2021-04-14 12:02:43, user ingokeck wrote:
Dear authors!<br /> Thanks a lot for publishing these interesting results as preprint! Reading it I arrived at a few questions and comments you might be able to answer to me:
(1) You have the gold standard to detect an infection: Viral cultures with confirmation of the viral agent via test. Yet you decided to use the less reliable RT-PCR as basis. Why? RT-PCR does not measure the existence of infectious virions, it only measures the existence and concentration of specific genes as RNA and DNA in a sample. There is a big issue with old gene material still „hanging around“ after all virions have been destroyed.
(2) Using your numbers from Figure 2 and the viral cultures as basis one can calculate that RT-PCR correctly detected 69% (77 of 112) of the cultured cases as positive and wrongly claimed 31% (35 of 112) to be positive. The BD test correctly identified 93% (66 of 71) of the cultured cases as positive and 73% (30 of 41) to be negative, but wrongly claimed 27% (11 of 41) of the cultured cases to be positive and 7% (5 of 71) to be negative. You clearly should not use RT-PCR as basis for the performance estimation!
(3) You call copies/ml a „viral load“. Why? This is not the definition of viral load. What you have is a concentration of gene copies. Viral load is defined by virions per host cells in a given volume. There is no simple relationship between viral load and gene copy concentration as the number of copies produced per virion depend on the host cells and the gene.
Thanks in advance for looking into this!
On 2021-04-20 15:53:48, user José F. Català-Senent wrote:
The manuscript has been published in the journal Biology of Sex Differences
On 2021-04-25 17:39:35, user Mikko Heikkilä wrote:
There are multiple errors in this systematic review and meta-analysis that have been reported to the authors already once the second version was published December 2nd 2020 and they have not been corrected to the third version either.
The intervention group total for the Aiello et al. 2010 paper is 663 and not 745 thus changing also the Relative Risk for that RCT.<br /> The third version has the mask and mask+hand hygiene groups separated but the numbers are still wrong. Aiello et al. subtracted the cases with previous symptoms so that the correct totals are 316 (367 in Ollila et al.) and 347 (378 Ollila et al.).<br /> The RRs for
On 2021-04-25 17:46:32, user Mikko Heikkilä wrote:
The RRs for the Macintyre et al. 2015 and Suess et al. 2012 are also not what they are in the original papers.
For the Cowling et al. 2009 Ollila et al. have used 18 events in an intervention group of 258. The orginal paper has three definitions for an event in the groups: RT-PCR confirmed, Clinical definition 1 (2 symptoms) and Clinical definition 2 (3 symptoms). There were 18 RCT confirmed, 55 Clinical def 1 and 18 Clinical def 2 cases in the intervention group.
On 2021-04-27 03:16:13, user vijayaddanki wrote:
Very interesting paper. Once you identified the mutations and found that these are unique variants, how did you determine the parent lineage? Did you use any programming tools or did you manually identify the parent lineage. I have a set of new unique variants (with a detailed list of mutations in the Spike protein), their GISAID Accession IDs, origin dates/locations and current dates/locations where it is prevalent. But I am very confused on how to submit it to get a new Pangolin lineage designation.
On 2021-04-27 13:40:52, user Patrick Gérardin wrote:
The paper has been accepted for publication by PLoS Neglected Tropical Diseases. Attached the URL towards the publication: https://journals-plos-org.p...
On 2021-04-27 22:06:58, user Tom Argoaic wrote:
I've looked over the public data set released by the Minnesota group, plus their later publication about their studies, and I can't figure out how to correlate the shipping times you used in this paper with their data set. Did you alter or adjust the shipping times in your paper? And if so, how? I didn't see any description of this in your methods, which makes me wonder where you came up with your numbers as I try to replicate the data you presented here.
Data sets I used, sent by their team:<br /> https://drive.google.com/dr...<br /> https://drive.google.com/dr...
Their paper that goes over their protocol and shipping times:<br /> https://academic.oup.com/of...
On 2021-04-28 10:31:51, user Steve Winter wrote:
In terms of Altmetric attention score, this potentially includes both positive and negative comments on social media. Did you account for this in your analysis? This is an important limitation when it comes to interpretation of Altmetric scores, and could be discussed in your manuscript.
On 2021-05-01 23:02:46, user Nick Day wrote:
The logistic fit for the B.1.1.7 lineage looks good. It is interesting to see that it works for such a range of (spatial, sampling, etc.) scales. It may also be of interest to see the logistic curve fits (for two individual mutations across all lineages) during 2020 for the initial spread phase of P681H and the saturation phase of D614G. The data for this is presented at https://www.biorxiv.org/con... - see also the comment there.
On 2021-05-06 19:34:30, user disqus_p0Pq7NxFg7 wrote:
Maybe I missed it, but you did not include a control group of individuals that had Covid and no vaccination. So, I curious how you can reach a conclusion that the vaccination improves immunity for individuals that had Covid.
On 2021-05-11 13:47:04, user Anestis Divanoglou wrote:
The manuscript was accepted for publication in EClinicalMedicine on May 6th 2021 and is currently in press
On 2021-05-12 01:30:10, user Heidi Connahs wrote:
Interesting paper! I have one comment though. I am noticing an increasing number of papers using the term post-exertional malaise (PEM) without providing any definition of what this condition represents. This is important because PEM is not a term widely known in the medical community and it has a distinct presentation. PEM is the worsening of a variety of symptoms following even minor physical or mental exertion and moreover, the severity of the impact is often delayed by hours or days and can take days, weeks or months to recover from. The reason why PEM is not widely known is because it is the cardinal symptom of the disease ME/CFS which has been significantly ignored and underfunded. PEM is unique to ME/CFS and any mention of PEM should really provide appropriate references to ME/CFS literature.
On 2021-09-16 11:02:10, user Jackie wrote:
Having trouble finding the D448 deletion in OFR1a ranked 8th in table 1 on any databases
On 2021-09-18 19:05:19, user OBS wrote:
These long-term results from Pfizer's clinical trial are quite informative- does anyone know of a corresponding preprint from Moderna containing their long-term (i.e. 6 months or so) clinical trial results, particularly regarding safety (total deaths, adverse events, etc.) Moderna has said it's efficacy at 6 months was 93% (a tiny bit better than Pfizer), and all 3 COVID deaths by 6 months were in the placebo group (also better than Pfizer's result of 1 COVID death with vaccine vs. 2 COVID deaths with placebo). This is not at all surprising considering that Moderna's vaccine dose is over 3x higher than Pfizer's. But what about Moderna's results for total deaths, how do they compare to Pfizer's? Surely, so many people here would like to know.
The following is stated in an article from 3 days ago on Moderna's website:
"Additionally, the Company shared a new analysis of follow-up through 1 year in the Phase 3 COVE study suggesting a lower risk of breakthrough infection in participants vaccinated more recently (median 8 months after first dose) compared to participants vaccinated last year (median 13 months after first dose). Manuscripts summarizing both findings have been posted to preprint servers and will be submitted for peer-reviewed publication."
However, no such preprint from the COVE study seems to show up- does anyone know how to find it?
On 2021-10-18 22:45:59, user Sir Henry wrote:
Table S3 of the Supplementary Materials shows 262 "severe" adverse events for the vaccine, compared to 150 for the placebo. The difference of 112 is too large to be a statistical fluke (p < 0.001) and is a multiple of the number of "severe COVID-19" cases (30) for the placebo (Table S6). In terms of "severe" outcomes (COVID-19 or adverse events), the vaccine appears significantly more dangerous than the placebo over the four month observation period.
On 2021-09-18 21:17:19, user Mike wrote:
This study did an awesome job! One of the reason it didn't have the political bias rather letting the science flow instead of putting on a show to sway people in a particular direction to fit a particular narrative. I predict more studies will bare this out, that natural immunity is better than vaccines but it doesn't mean people shouldn't get vaccinated if they want to but shouldn't be forced! Israel is an example of weakness with the Pfizer vaccine, most likely the other have this weakness too. One of the weaknesses of this vaccine and others is the fact that it's the same, viruses change. This is why the delta variant spreads easier than previous versions.
Back in August almost 60 percent of the hospitalizations in Israel were people at least 60 years old and fully vaccinated! Israel's government a great believer in vaccinations will be going on their 4th booster! I don't see a high rate of re-infections of those who already had Covid along with hospitalizations, but I do believe Covid is here to stay and re-infections will become common and some people will have major health issues with it. People 18 or younger which total so far 74 million Covid infections, 362 have died due to Covid complications.
On 2021-09-20 23:38:51, user BaboliDaboli wrote:
The study states: "Symptoms for all analyses were recorded in the central database within 5 days of the positive RT-PCR test for 90% of the patients, and included chiefly fever, cough, breathing difficulties, diarrhea, loss of taste or smell, myalgia, weakness, headache and sore throat." I understand this as follows: 90% of recorded symptomatic COVID-19 cases in the study were first recorded as positive on RT-PCR test. That would mean that any correlation between recorded number of positive RT-PCR tests, recorded symptomatic COVID-19 outcomes, and recorded COVID-19 related hospitalization outcomes may stem from the fact that positive RT-PCR test was a prerequisite for any other outcome to be recorded at all. As there were no fatal outcomes recorded, most hospitalizations recorded in the study might have been due to mild symptoms and a previous positive RT-PCR test (study fails to present the breakdown of hospitalizations by disease severity or duration of hospitalization). An earlier study of the link between hospital load and increased COVID-19 mortality in Israel (Rossman, H., Meir, T., Somer, J. et al. Hospital load and increased COVID-19 related mortality in Israel. Nat Commun 12, 1904 (2021). https://doi.org/10.1038/s41467-021-22214-z) found that between July 15th 2020, and January 1st 2021, on average, almost 60% of people were hospitalized while presenting mild initial clinical state. So, if most breakthrough infections or re-infections in the study never progressed past the mild clinical state, and if such patients wouldn't even be considered for hospitalization in Israel without a positive RT-PCR test, it is quite possible that all study outcomes depend directly on people taking a RT-PCR test and testing positive. While it may be difficult to assess and account for the difference between vaccinated and previously recovered people in terms of their inclination towards taking the test in case of mild or non-existent symptoms, any possible bias in terms of testing policies in Israel should be addressed and accounted for in the study. An example of such potential bias can be found on the Israel Ministry of Health "Testing for COVID-19" webpage (https://www.gov.il/en/departments/general/corona-tests) which states: "As a general rule, save for few exceptional cases, it is not necessary for confirmed patients or recovered patients to take a swab test for coronavirus, unless there is clinical suspicion for repeated infection with the virus." As clinical suspicion depends on severity and combination of symptoms typical for COVID-19, and as the set of symptoms related to delta variant seems to differ slightly from the set of symptoms related to previous variants, some or maybe even many reinfected people never got tested by RT-PCR as they had no symptoms or had only mild symptoms that never progressed beyond that. If that were the case the number of reinfections and related outcomes might be significantly underestimated in this study.
On 2021-10-04 13:50:29, user Steve Vlad wrote:
To the study authors:
As an epidemiologist, if I was reviewing this paper for publication I would send it back to you for major revisions or reject it outright. I would not even bother looking at the results.
The major issue is that you have conditioned study group entry by an event that happens at the end of the study. I.e. you have created a cohort of unvaccinated persons who must remain unvaccinated throughout the study. This is guaranteed to introduce selection bias, more specifically immortal-time bias. This further guarantees a biased estimate. This topic has been written about many times. Cf any of many articles by Sammie Souza at McGill.
Imagine someone in your unvaccinated cohort. Soon after the initial study date they develop an infection. 5 weeks later they have recovered and decided they should have had the vaccine, so they get one. Because you have insisted this group remain vaccine free you throw them out of the group and you lose their data. You have just thrown out an infection. Do this just a few times and it is guaranteed that your ‘vaccinated’ group is not reporting as many infections as it actually experienced. This easily accounts for the effect you report.
Note that this does NOT happen with the fully vaccinated/boosted group who must receive all vaccinations prior to study entry. You capture each and every infection with no drop out. Thus you’ve created a situation where you have non-random drop-out between the groups. That is selection bias.
To get around this problem you MUST use methods such as Cox proportional hazards modeling with time-varying exposure variables so that persons can move between cohorts based on exposure to the vaccine during the study period.
Hope this helps.
On 2021-10-08 13:13:27, user Richard S wrote:
Did the authors address multicollinearity of the highly correlated independent variables in their regression models?
On 2021-10-15 21:36:23, user Mary V wrote:
Please add results for the unvaccinated individuals who recovered from Covid prior to Feb 28, 2021. How does their risk of symptomatic infection during the delta uptick compare with the individuals who were fully vaccinated by Feb 28, 2021 and didn't have a positive Covid test before 6/1/21? Did one shot of the Pfizer vaccine improve the immunity of that group during the delta uptick? Other studies have shown a very strong immunity for those who have recovered from Covid symptoms. The data in this paper only supports a vaccine recommendation for people who've had asymptomatic cases of Covid. Hence the data for those who've had symptomatic cases needs to be added or your conclusion about the vaccine recommendation should be qualified.
On 2022-03-22 12:17:46, user panos101 wrote:
What's the status of this article. Has it been published?
On 2021-09-25 08:31:33, user Gavin Nichols wrote:
Is there any research that the vaccine hesitant will go for this older vaccine platform technology ?
On 2021-09-25 10:12:08, user Jan Podhajsky wrote:
I forgot to add that researchers allowed persons below 15yo to enter the survey without parental/guardian consent. This is illegal in Czechia.
On 2021-09-27 01:49:48, user DRamsey wrote:
Why was this withdrawn instead of just updating with corrected numbers?
On 2021-11-04 18:32:24, user Libres Penseurs wrote:
By looking roughly at the numbers, the authors seemed to be right. Ottawa area population is around 1 million. If Ottawa is aligned with the rest of Canada, 78% of the population received at least 1 dose since the beginning of the vaccination campaign. On the two months stated in the study, the increase was about 10% (overall Canada data). This means that around 100 000 people were vaccinated in the Ottawa area during those two months. Ottawa have a lot of hospitals so you cannot assume that everybody with adverse reaction to the vaccine will show up at the same hospital. The authors use 1/3 of that total number (32k). The 800000 number refers only to people having received at least one dose since the beginning of the vaccination campaign. <br /> Myopericarditis cases at one hospital for a period of two month cannot be used as a numerator on this number to calculate risk.
On 2021-10-01 15:38:22, user friidrottare wrote:
On 2021-10-03 07:16:11, user kdrl nakle wrote:
Sort of expected stuff, nothing surprising. Delta variant comes way ahead which is something we already know. So the real increase of airborne transmissions is a feature of Delta.
On 2021-10-03 07:33:26, user kdrl nakle wrote:
Ukraine's real disaster starts in 2021, in particular now with Delta variant since the country is in the bottom of European vaccination rates. 13% fully vaccinated versus 63% in EU. Absolutely catastrophic. Even worse than rather poorly vaccinated neighbors Slovakia, Romania, and Russia.
On 2021-10-05 01:50:50, user C.K. Franz, M.D., Ph.D. wrote:
For final version of this preprint: DOI: 10.1002/acn3.51416. <br /> Alternatively, see: https://pubmed.ncbi.nlm.nih...
On 2021-10-06 23:34:39, user kdrl nakle wrote:
Excellent, this is what we are afraid of and this is why we need updated vaccines.
On 2021-10-08 13:47:58, user Mazda Sabouri wrote:
The IFR formula on page 9 appears to assume that the number of excess deaths and Covid deaths are equal. Certainly possible in certain regions, but also not a proper assumption to make universally.
Also there does come a point where IFR exceeds PFR in a given region. Especially for a highly contagious virus that aggressive targets large numbers of vulnerable people each and every year. This study admits that certain regions in Iran have had 200%+ attack rates already.
On 2021-10-08 20:58:14, user AbsurdIdea wrote:
Referring to "However, if prior infection does not afford protection against some of the newer variants of concern, there is little reason to suppose that the currently available vaccines would either.": The mRNA vaccines are targeted specifically to the "spike". There appears to be no information as to what the infection used or uses to identify the virus, and thus while new variants have a "spike" and likelihood of maintained resistance there is zero evidence of what natural immunity would or would not recognise in a variant. Thus the argument provided has no substance.
On 2021-10-09 20:30:31, user j` wrote:
Are you under the impression antibodies are the immune system's only means of protection?
Have you reviewed previous studies showing robust immunity of mild infections?
On 2021-10-10 04:14:01, user kdrl nakle wrote:
Nice. So you are 20 times more likely to have COVID induced myocarditis than vaccine induced one. And on the top of that the COVID induced one will last longer.
On 2021-10-10 04:55:53, user kdrl nakle wrote:
One of many recent papers indicating that Moderna is ahead of Pfizer.
On 2021-10-10 05:19:33, user kdrl nakle wrote:
I don't know about UK but in case of US this is useless since the mitigation measures are so heavily politicized in the US to the point of absurd actions. For example Alabama, does not even report outbreaks in schools any more and does not quarantine nor test exposed students.
On 2021-10-18 00:30:55, user Prescott Comptr wrote:
hasn't there been enough time for a peer review?
On 2021-10-19 17:10:24, user Jeremy Gustafson wrote:
It's too bad they didn't break out a 5th group of previously infected and not vaccinated to compare their immunity similar to the study that was done in Israel.
On 2021-10-22 07:52:48, user Adrien MP wrote:
Hi,<br /> Could you make Tables 1 and 2 available in order one can take the full extent of the work presented here?<br /> Thanks
On 2021-10-24 07:24:16, user Otmar S wrote:
Dysguesia is the leading indicator which can also be seen for children regarding other studies (for example CLOCK). I can´t find the question/incidence of Dysgeusia for children in this study. I wonder why. No data?
On 2021-10-31 17:00:24, user wincap wrote:
You should include affect of vaccines triggering paraneoplastic syndrome, which occurs in 20% of cancer patients.
On 2021-12-01 14:08:31, user watcher wrote:
The authors mention that vaccination efficacy might be affected by underreporting of mild symptomps. This raises another issue, which was not accounted for in the model. Asymptomatic infected individuals may still transmit the virus, but due to the lack of symptoms will not alter their behavior. It should be assumed that the more prominent symptoms are, the more individuals will reduce their contacts, to protect themselves and others. As unvaccinated infections generally result in more symptoms, these individuals will likely reduce their contacts naturally. Symptoms alter the behavior of individuals and thus transmissions, which is not accounted for in the model, but could change the outcome quite a bit.
On 2021-12-07 17:04:15, user LizzyJ wrote:
''Pandemic modeling'' is quickly becoming the astrology of mathematics & physics.
Want to get a lot of media attention and citations? Simply code up a little model and write up a paper about your unscientific predictions. As in all models, the chosen values for the parameters in this simulation are naive assumptions. There is no sufficiently high-quality or complete data on vaccine status and mode of infection (e.g. infected by a vaccinated or unvaccinated person) currently being reported by hospitals or health departments.
Pandemic modeling is an abstract and theoretical mathematical exercise with very high bias and uncertainty based on the underlying assumptions, factors included or excluded, incomplete input data, very poor data quality with big non-random gaps in the data, etc. It should not guide public policy. Only clinical studies and real-world medical data should guide policy.
On 2021-12-05 11:00:28, user Professor Ritual wrote:
I actually like the modelling idea, a noble effort - but as things move fast the data used in the study is now outdated. Please remodel for the current data: 71% senior breakthrus and 50% adult breakthrus.
On 2023-03-08 12:30:58, user Carlos Oliveira wrote:
This study has been published on Frontiers in Public Health: <br /> Routine saliva testing for SARS-CoV-2 in children: Methods for partnering with community childcare centers<br /> Frontiers in Public Health, 11, 1003158 - February 2023<br /> https://doi.org/10.3389/fpu...
On 2022-01-12 17:15:59, user Rick Sheridan wrote:
This was a laudable effort and I congratulate all of the authors and the study’s primary driver for pushing this through. Am in agreement that insight from results is likely limited by the maximum dosage as stipulated by the NNHPD guidelines. I enclose here daily quantitative PCR results from a high-quality PCR vendor during my own Jan '22 experience with high-dose hesperidin in context of a documented SC2 infection during the omicron wave.
https://emskephyto.medium.c...
As can be seen in the data log made available, subject (100 kg male) was taking multiple grams at a dose, often successively within hours of each other. Critically for DDI, no other pharmaceutical drugs were taken concurrently. For independent auditing purposes, will be happy to disclose the PCR vendor, the collector, and the hesperidin nutritional supplement brand to any relevant reachout.
With what little one has to go on from the posted results, I would offer a model that during an active infection, the minimum possible Ct value is correlated to the sustained serum hesperetin glucuronide level during the 0.5 - 2 days prior to the nasopharyngeal sampling from which Ct is determined.
Addressing the standing issue that viral load has often peaked prior to trial enrollment, this challenge remains tolerable in context of a clinical trial because one can still show an accelerated viral load reduction in the experimental group as compared with control, sufficient to demonstrate the mechanism.
Rick Sheridan<br /> EMSKE Phytochem<br /> 11-Jan 2022
On 2022-01-12 20:21:08, user Mike B wrote:
Do we know it's VOC? Dogs detect Parkinson's using same technique, perhaps from minor expression of misfolded protein. Discrimination and scent memory by dogs is much more complex than we know. <br /> Implies we could build a molecular filter to mimic dog's nose. That however, is elusive.<br /> Fantastic study. Much ??? to working dogs, especially Belgian Malinois!
On 2022-01-13 12:47:09, user kdrl nakle wrote:
On Christmass Eve Dec 24, 2021 there was 948 in ICUs in California. Yesterday CA DoH posted 1903 in ICUs. What happened? Most of that in Southern California, imagine. This flies in the face of this paper. This paper lacks multivariate analysis as most infected by Omicron were double vaccinated and most infected by Delta were unvaccinated. That way these people make sensational paper without serious research. <br /> We had 2730 deaths yesterday in the US (NYT), now the 7-day-avg is approaching the height of Delta wave, currently 1825. The peak of Delta wave was 2087 on Sep 20. I actually expect Omicron will crash this Delta record shorty as it will crash ICU overall record in no more than two days from today. Currently 24,711 in ICUs on January 11, 2022.<br /> This sad attempt to make Omicron look mild will actually cost many lives for all the people that took masks off and are believing this "mild Omicron" propaganda. Wait until you see.
On 2022-01-14 20:11:27, user Boback wrote:
Why are the event number exactly the same between vaccine arms in Table 1 in multiple places. Calculated point estimates and CI also the same for those. Did you have a coding error with events? I wouldn't expect so many exact event counts to overlap.
On 2022-01-16 16:21:39, user Titan28 wrote:
I don't see ivermectin on the list of suspect drugs. Was it not tested?
On 2022-01-18 13:58:07, user William Henry Talbot Walker wrote:
Were all of the vaccinated participants studied for pre-existing cellular response or globulin levels before the vaccines were administered?
On 2022-01-21 05:16:30, user Victor Schoenbach wrote:
I found this article both valuable and important, but I do not understand the last two sentences ("Despite the small numbers of individuals included in this study, the findings are uniquely valuable because of the early detection of Omicron infection in frequent workplace Covid-19 testing to prevent spread. In real-world antigen testing, the limit of detection was substantially lower than manufacturers have reported to the FDA based on laboratory validation.")
The first of these sentences is confusingly worded; copy editing would help.<br /> The second sentence refers to a lower limit of detection. Perhaps I do not understand the technical meaning of that term, but I would have thought that meant that the real-world sensitivity of the antigen test was higher (virus detected at a lower level), whereas the article suggests the opposite.
On 2022-01-21 14:18:29, user Rosanna wrote:
It will be interesting to know which organs are mostly affected by these autoantibodies. No mention about it is done in the manuscript. Do the authors have this information? Are lungs more affected in patients that suffered a critical COVID-19? <br /> Also, do the authors have data in male patients?
Rosanna Paciucci, Ph.D. Faculty Attending, Vall d'Hebron University Hospital, Barcelona, Spain
On 2022-01-21 21:47:18, user Brian Roberts wrote:
Thanks for this most up-to-date study. The only "sensitivity" number that matters to the clinician is how the BinaxNOW compared to the PCR for the entire group. That number was not provided. Or how they compare in symptomatic vs. asymptomatic groups, since that information is known to the clinician. How they compare in groups defined by the PCR Ct counts is all but useless, since it is unknown information at the bedside.
On 2022-01-22 13:19:19, user Torsten Selle wrote:
Is it possible to extend the measurement series to smaller aerosols (5-2µm). I am asking in regards to aerosols that cannot be stopped by ffp2 or n95 masks.
On 2022-01-24 01:15:03, user Fergal Daly wrote:
The paper uses linear regression on a non-linear variable (cases/100k). Does it apply directly to cases/100k or is it against log(cases) which should be somewhat linear?
On 2022-01-28 20:32:03, user Ranya Srour wrote:
The article is a good baseline for future studies involving suicidal ideation and bar graphs are very clear and easy to interpret. By extension, the study addresses the question: is it necessary to wait for sobriety before defining a patient as suicidal?
Maybe discuss this question directly in the discussion more; since it is the main question it may be valuable to expand on the in discussion.
On 2022-01-28 20:32:54, user Dhuha Al-Rasool wrote:
Very interesting article! It would be interesting to see the impact of THC on the SI results considering that it does not wear off as quickly as alcohol.
On 2022-01-29 14:26:08, user Alberto wrote:
Thank you for this study. It's important to have this kind of study in a country like Greece where mortality has been very high in 2021 (compared to other European and worldwide countries and compared to itself in 2020) because we can appreciate the difference between the reality observed and the projected modeling based on the data that is available about vaccination status. The resulting model, which is incompatible with the reality observed worldwide, is a good measurement of the quality of the data available. I hope this can be looked at in more <br /> detail by more people thanks to this study.
On 2022-01-30 23:47:58, user HereHere wrote:
I'm a registered massage therapist in Ontario. We received no clear advice from our regulatory college about ventilation. I was waiting and waiting and waiting. They were very slow with recommending N95s and KN95s, and to my knowledge, still have not acknowledged that surface transmission is exceedingly rare. I don't know how they coordinate with Public Health Ontario, but you would think that, given we work in close contact with patients in typically small, poorly ventilated rooms, ventilation would have been given greater consideration.
On 2022-02-01 14:15:41, user Richard Reynolds wrote:
There is another reason, the most likely one, why immune cell aggregates were not found in the meninges in this study, which is due to the nature of the tissue used. This study used formalin fixed paraffin embedded tissues which are suboptimal for studying the delicate meningeal compartment. Cells are lost from the meninges at every stage of the embedding, cutting and section mounting stages, when compared to using snap frozen blocks. When you float FFPE sections on to a water bath before mounting them on slides you can actually see with the naked eye parts of the meninges floating away from the sections. Presumably this would also results in losing various components of the meninges, including immune cells. We dont find nearly as many immune cell aggregates in the meninges when we use FFPE sections and in order to see them in the FFPE sections we needed to change out protocols substantially to much milder procedures in order to better preserve the cellular components of the meninges.
On 2022-02-01 21:24:21, user Dylan wrote:
Would have been nice to see loss of taste and smell included given their pediatric prevalence per https://link.springer.com/a...
On 2021-10-18 16:39:30, user Kim Noble wrote:
Well-thought, provoking article. Congratulations to Dr. Gõni and her team.
On 2022-02-02 07:40:56, user Gerald Zavorsky, PhD, FACSM wrote:
I have no idea why studies like these are coming out all of a sudden. Politics should not be a part of science and I think these diversity studies and social injustice studies actually do a disservice to the minority groups in the U.S. There are several studies that show racial differences in lung function. I, for one, have published one of these studies (10.1186/s12890-021-01591-7) that demonstrate differences lung function after correcting for several factors. In order for these authors to truly test the hypothesis that reference equations should not be adjusted for race (i.e., no adjustment if you are African American, Hispanic, or Asians), then you would need to perform a Kappa Statistic and ROC analysis in both whites and the other ethnic groups. In addition, in each race/ethnic group, you would need a substantial proportion of individuals with CONFIRMED DISEASE. That is, confirmed disease via CT imaging or via strict criteria (i.e., GOLD criteria). Using a subjective assessment of breathlessness is not right, in my opinion. The authors have not used the objective criteria of FEV1/FVC ratio for definitive obstruction; they only used FEV1 and FVC separately, and according to ATS/ERS guidelines, it is strictly the FEV1/FVC ratio that confirms obstruction. Then, using the LLN criteria for FEV1/FVC, then one should assess the sensitivity, specificity, positive predictive value, etc., comparing reference equations for different races in those with confirmed disease and those without the disease OR at least confirmed obstructive pattern (FEV1/FVC < LLN). That is, what is the sensitivity in detecting lung disease (or confirmed obstruction via the FEV1/FVC ratio) in blacks when using the GLI reference equation for blacks? What are the false negatives in blacks when using the black reference equation? THEN compare these results against the same group using the prediction equations for whites. This is really the only way. Subjective scores of breathlessness do not confirm the disease. All that their tables and figures show that if you use a white reference equation in blacks you can falsely over-diagnose lung disease in blacks. In this case, 870-890 blacks had falsely low FEV1 or FVC when using the white reference equation. The authors data actually go against their conclusions. First, 9% of whites were below the LLN for FEV1 when the white equation was used. Similarly, 9% of blacks were < LLN for FEV1 when the black equation was used. To me, this shows that the equations for blacks correctly identify low FEV1 values in blacks, and the reference equations in whites correctly identify low FEV1 in whites. The proportions are the same! As well, their Figure 1b, the mortality for Blacks that had an FEV1 that was normal when using black reference equation (orange line) was the SAME as when using the white reference equation for whites (blue line). This shows that the reference equations for whites used on whites are just as appropriate as the reference equation for blacks used on blacks. Indeed, if anything, Figure 1b demonstrates that using the white reference equation in blacks underscored mortality in blacks by 5% (i.e., 5% of the deaths are missed in blacks when using white reference equations). Thus, in conclusion, the authors have this all wrong and have misinterpreted the data. We should be correcting for race.
On 2021-10-18 18:05:16, user Francis Bascelli wrote:
How can I access this data on UK Biobank? The data/code section says the data can be accessed though UK Biobank, but I am having trouble finding it.
On 2022-02-02 11:42:04, user Philip Ashton wrote:
Hello,
Thanks for posting this really fascinating paper, so much food for thought!
We looked at this in our journal club today, and one practical issue that came up is that we would like to know over what period and what season sampling was done at each site and how this relates to typhoid season at each site. Because Typhi is often seasonal and this could influence the results.
Thanks again!
Phil
On 2022-02-03 03:23:31, user Chris wrote:
Do we know why the vaccines cause myocarditis yet?
On 2022-02-03 08:04:13, user dgatwood wrote:
Any chance a future update to this article could include the VE data against hospitalization *prior* to the third dose of mRNA-1273 (for comparison purposes)? Even a citation would help.
On 2022-02-03 21:59:47, user Suzy Huijghebaert wrote:
Line 148: "susceptibility of potential secondary cases was highest among the unvaccinated"<br /> Yet, some % in Table 1 striked me, and Table 9 does not really confirm that in the OR values. So I checked a few numbers, as when it comes to transmission of the omicron it is not so much sex or age that will matter, but - in real life - rather the total number of people you are in contact with, in view of the speed of transmission and the fact kids are affected by this mutants as well. Neither does it matter - economic-wise- whether the secondary case is vaccinated or not (yet, I agree an interesting aspect to study).So, how did you define the "potential cases"? The potential cases (per group of index cases) were apparently much lower in the vaccinated group than in the unvaccinated, and just proportionally correcting for that parameter, suggests that the rough highest attack rate -t as would be in real life - would have occurred with both BA1 and BA2 among the fully vaccinated (62-63%), provided they would have been in contact with as many potential cases in their households as the unvaccinated. Please clarify what induces the divergence/where the divergence with the outcomes arise from. Another question: what was the proportion of omicron cases among the people having already received vaccine, yet not considered fully vaccinated and now counted among the unvaccinated in your unvaccinated sample? Already thank you for clarifying.
On 2021-10-20 16:54:26, user helgarhein wrote:
You reported a surprising result: in the group of 51health care workers who were replete (above 75 nmol/l) only 2 took supplements. I would not have expected so many people (49) to have replete 25(OH)D levels in indoor workers in Birmingham (52ºN) in May, without supplements. However we had in the UK an unusually sunny and pleasant spring in 2020. I guess many people used their free time to go outdoors, because it was so sunny and dry, cinemas, restaurants etc were closed and socialising happened mostly outdoors. I presume that the unusual finding of so many people with excellent 25(OH)D levels could be explained by having acquired those levels in the week or two before May 2020. But maybe a longer timespan with good vitamin D supply is needed to make really all immune actions work optimally?? Could this have skewed the curve to make it look U-shaped?<br /> But, as pointed out by Dr. Gareth Davies, the most important observation was missing: the out comes of those infected, the ICU admissions and the mortality rate.<br /> Helga Rhein, ?retired GP, Edinburgh
On 2022-02-07 21:44:51, user Isaac Tian wrote:
Hello, we're the authors of citation #20. We had a few suggestions after reading your work.
A practical deployment of the network would ideally use some other silhouette imaging method such as a CT scan or an RGB photo like you suggested in the Study Limitations section. The sentences that referenced our work didn't mention our attempt to estimate total and regional body fat using a 2D RGB camera image.<br /> Our study data, composed of 2D coronal and sagittal images coupled with 2-fold DXA composition measurements, may be relevant to validating your method on non-MRI inputs. Additionally, I believe our parallel effort in estimating body composition from 2D silhouettes should be cited and compared against. We also did estimate compartmental body fat as arm, leg, and visceral fat. Our initial model was not very flexible in pose due to the smaller training set available at the time, but we have since corrected for this.
I recomputed our errors as MAEs to directly compare against your results, and assuming an adipose tissue density of 900 g / L, this came out to 121 g and 151 g for males and females, respectively. This is about 3-4x less than the magnitude of error reported in your draft. An analysis on the RMSE may be appropriate as large scale data may inflate R2.
A collaboration may be appropriate once this draft has passed peer review in which your network is used as the pre-trained initialization to fine-tune on silhouettes segmented from another imaging source, such as our Shape Up! dataset which was stratified by age and BMI. This also addresses the bias concern you mentioned as your MRI dataset has an average age of 65.
Thanks for sharing your work!
On 2022-02-15 09:24:31, user Shelly L Miller wrote:
This paper has been peer-reviewed and published here: https://www.nature.com/arti...
On 2022-02-25 06:26:07, user Abhishek Mallela wrote:
As of February 24, 2022, Figure 5 in the published version of this manuscript is missing axis labels. Please refer to the preprint version of Figure 4 for the axis labels.
On 2022-04-10 09:05:20, user dyctiostelium wrote:
The manuscript describes an analysis made from a database of suspected and confirmed COVID cases, with information about whether they had received any COVID vaccine at least 14 days prior and in the case they were, which one of 7 different vaccines.
It is stated that "vaccination status, date and specific vaccine<br /> product was collected from evaluated persons as part of epidemiological follow-up of suspected COVID-19 cases" and table S1 includes a row titled "Follow-up - person days", but the design does not seem to involve any clinical follow-up, given that the persons had either been vaccinated or not at the time their data was included in the database.
Could the authors clarify what is the source of the numbers in the row "follow-up" of figure S1?<br /> Of note, when the person-day is divided by the n of each column it gives a number of 114 days for the unvaccinated and around 200 days for the 7 different vaccines.
On 2022-04-15 17:26:22, user Young Juhn wrote:
A version of this article has been accepted for publication in the Journal of the American Medical Informatics Association (JAMIA) published by Oxford University Press. A link will be forthcoming.
On 2021-11-06 19:24:57, user Eleutherodactylus Sciagraphus wrote:
: This preprint includes data from human subjects that are under ethical scrutiny. The<br /> majority of patients enrolled were not informed nor agreed on participating in the study. The Brazilian National Comission forResearch Ethics (CONEP) has been bypassed, documents have been tampered, and the situation is now under investigation.
References supporting this statement (both in English and in Portuguese):<br /> https://brazilian.report/li...<br /> https://www.emergency-live....<br /> https://www.dire.it/14-10-2...<br /> https://www.matinaljornalis...<br /> https://g1.globo.com/rs/rio...
On 2022-06-07 10:39:46, user M. M. Welling wrote:
For the 2 patients, both were vaccinated before the PET scans. The control patents were from 2019 thus uninfected and not vaccinated for COVID-19. Neuroinflammation can be initiated by the vaccination after liposomal transfer of the mRNA through the BBB. This needs to be discussed as well.issue
On 2022-07-11 07:19:17, user Thijs Blok wrote:
Question: <br /> - A PCR can stay positive for weeks after infection, is that taking in account?<br /> - Is a throat swap executed with the selftests ?
On 2021-11-23 20:58:11, user jackbutler5555 wrote:
Did the study include all samples from the formerly infected or just those hardy and viable enough for the study?
On 2023-06-29 09:40:33, user Nensi wrote:
The idea behind this study is truly interesting and highlights the critical importance of addressing the issues surrounding poor reporting and the quality of systematic reviews.<br /> However, some things could be changed to improve the quality of this study. Below you can find some of my comments regarding your manuscript.<br /> 1) The manuscript could use extensive language editing, as there are many grammatical and spelling errors. Many language editing programmes can be very useful for these purposes (Grammarly, Instatext etc.).<br /> 2) You begin the Methods section with the aim of your study, but you state that the aim was to do a study. You can see why that does not make sense. The aim of your study was to do a study. You should report here the specific purpose or what you wanted to assess (for example, the aim was to assess the reporting quality of systematic reviews published by authors from India from 2015 to 2020).<br /> 3) In Results, you decided to report the data in text and with two figures. However, when you have so much descriptive data, it could be presented more clearly with just a table. The table allows the authors to store large amounts of data in a small place, making it easier for the readers to go through the data and understand the results. <br /> 4) Another detail about presenting the results is that they should be written in the past tense instead of the present tense you used.<br /> 5) Also, when presenting descriptive data, it is recommended to report both absolute and relative numbers (for example, „Only 20 (15%) of the reviews have been registered in PROSPERO registry“).<br /> 6) You could benefit from using STROBE reporting guidelines for observational studies (https://www.equator-network... "https://www.equator-network.org/reporting-guidelines/strobe/)"). Reporting guidelines are handy in ensuring you have reported everything that needs to be written in an article.<br /> 7) There is also much room for improvement regarding the referencing. A small detail would be the in-text referencing where you put [1] after the full stop. So the general rule would be that if you use brackets, it should be placed inside the sentence, and if you want to put the number in superscript, it should be after the sentence. That could use a bit of tidying up. <br /> 8) Additionally, regarding the referencing, you have used different styles of referencing in the reference list and some of the references are not referenced correctly or at all. There are many programmes that can help you organize and write the references (EndNote, Zotero, Mendeley etc.).<br /> 9) And one more thing for future referencing, even though a study is methodological, it should be preregistered. All studies should be preregistered to promote open science and transparency in conducting scientific research.<br /> I hope these suggestions will help. Good luck with your work!
On 2020-05-26 22:31:23, user guost wrote:
Unfortunately there is absolutely no info about what kind immunoassay this LIAISON XL platform is. The company Diasorin website for is totally useless in that regard.
On 2020-04-24 07:57:39, user Sinai Immunol Review Project wrote:
Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking <br /> Mahapatra et al. MedRXiv [@doi:10.1101/2020.04.05.20054254v1]
Keywords<br /> • Drug prediction<br /> • Machine learning<br /> • Docking
Main Findings<br /> The COVID-19 pandemic has ravaged hospitals: the disease can present severe complications (acute respiratory failure in particular), and yet no specific drug exists to date. Time being of the essence, it is therefore essential to explore drugs already on the market for other indications. These drugs, however, must be tested in COVID-19 patients and thus selection of limited candidates is important. The authors argue that an important step in accelerating the selection of promising drugs can be done in silico.<br /> The authors use machine learning (ML), training their algorithm on a dataset obtained from in vitro targeting of SARS Coronavirus 3C-like Protease with existing drugs. The trained algorithm was then used to screen drugs available in the Food and Drug Administration’s Drug Bank. Using the Drug Bank dataset, the authors also performed a docking study -a process used to predict in silico the orientation and conformation of a molecule when bound to its receptor. Since SARS-CoV-2 spike protein is considered to play an important role in infection by binding ACE-2, docking was also applied to study the stability of drug-spike protein complexes. The results of the ML and docking were aligned, and antiretroviral Saquinavir was identified as a potentially promising therapy for COVID-19.
Limitations<br /> The authors train their algorithm on SARS Coronavirus 3C-like Protease, as inhibitors of this protein should prevent the virus from replicating in the host. However, the authors note that the most promising target seems to be SARS-CoV-2 spike protein. Moreover, the training dataset is the result of in vitro studies, and may have limited relevance in vivo.<br /> Overall, preclinical studies and then potential clinical trials would need to be performed before administering this drug to COVID-19 patients though, admittedly, clinical validation of an existing drug could happen faster than the development of new drugs entirely. Saquinavir has been studied in vitro by Yamamoto et al.[1], and shown little promise in SARS-CoV-2 treatment so far.
Significance<br /> Repurposing of existing drugs is can be advantageous to develop treatment strategies. An in silico approach could help identify potential therapies, although they must be confirmed in clinical trials before being administered on a large scale.
References<br /> Yamamoto et al. Nelfinavir inhibits replication of severe acute respiratory syndrome coronavirus 2 in vitro. BioRXiV preprint, 2020
Credit<br /> Reviewed by Maria Kuksin as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2024-04-28 02:04:59, user Ian Myles wrote:
Small correction, the Clinical trial number is NCT04864886
On 2024-04-27 18:42:07, user Kim Brumfield wrote:
Thank you to Dr. Myles and his team for doing this research. My daughter has 20 years of prescribed steroid use for eczema without informed consent of the risk of topical steroid addiction and withdrawal. The dermatologists she saw used step therapy which eventually resulted in tachyphylaxis. The topical steroids stopped working after reaching hichest potency and now she is suffering from "eczema on steroids" which is really topical steroid withdrawal. Please help us find the cure for this horrible iatrogenic disease.
On 2024-04-27 19:07:49, user Toby wrote:
So glad to see that this problem is being taken seriously. I have suffered from eczema all my life and from TSW as described here for many years. I hope this research will result in new treatments for this awful condition.
On 2024-04-27 19:50:44, user Eve Benson wrote:
I am starting my 8th year of TSW. The first five years were torture, the last few have been manageable. I can identify with every symptom listed in this study. I can also identify with the stress of seeing several "well-meaning" doctors whose only choice of treatment was putting me back on steroids, which I refused thanks to the education I received from online communities of thousands of people who were suffering like myself. More studies like this one are needed. TSW is real. Sufferers of TSW deserve appropriate medical care and care from practitioners who understand the disease and thus provide appropriate treatment. It is time for medical institutions to step up and address TSW.
On 2024-05-02 15:45:09, user Kelly Barta wrote:
This is such an important and groundbreaking study in its showing a differentiation between Atopic Dermatitis and Topical Steroid Withdrawal Syndrome, which has been one of the big debates within the medical community. Patients need and deserve acknowledgement and support from their health care providers, but understandably, this is unable to happen without the science to back up what we are seeing anecdotally in the eczema patient population.
More research is needed to determine how and when topical steroids are creating these dysfunctions in patients in order to better understand their proper use and prescribing guidelines. This research is CRITICAL to the over 31 million Americans living with eczema and over 300 million worldwide (not to mention the countless other dermatology patients), who are prescribed topical steroids to manage a skin condition.
On 2024-11-15 06:07:16, user Nina T ang wrote:
May I have access to the FFA dataset please?
On 2020-02-13 21:52:14, user reuns wrote:
I deduce from "case 13: no viral RNAs were detected until the fourth upper respiratory samples" that in the graphic U means negative
On 2020-03-25 19:31:48, user Charles Haas wrote:
My concern with their disinfection experiments is that there is no indication that they neutralized the disinfectant prior to culturing. This is an absolute necessity.
On 2020-04-22 20:47:32, user John Stevens wrote:
Hope this is widely understood - each tiny bump in efficiency makes differences in daily KPIs
On 2022-12-24 03:16:24, user ArCH wrote:
Published version<br /> https://www.frontiersin.org...
On 2020-05-28 08:18:17, user Philippe Brouqui wrote:
The paper of KIM et all reports on the response to treatment of hydroxychloroquine or<br /> lopinavir-ritonavir with or without antibiotic in a retrospective cohort study<br /> with comparison with standard of care. The evaluation has been carried out on<br /> moderate case only and no death were reported. They assume that HCQ and ATB is superior to both SCO and LR plus ATB in time to viral clearance, length of hospital<br /> stay, duration of fever and cough. Adverse event been significantly different<br /> from SOC but not different between the two arms of treatment and only<br /> mild. <br /> Methodology: Is appropriate for the aim<br /> -The study is relevant to the aims: treat patient early (non-severe disease) at the time to diagnosis to avoid complication and death<br /> - The study is relevant toward bringing new data in time to outbreak by using repurposing of drugs HCQ and LR <br /> -The study is relevant toward bias related to heterogeneity of care as a single center study.<br /> - Classification of patients referred to NIH Guidelines (NEWS Score) as mild, moderate and severe COVID<br /> - Definition of negative PCR and viral clearance is in adequation to previous published literature.<br /> -Treatment was done within the range of recommended dosage ; HCQ 200mg/ twice a day as well as for lopinavir/ritonavir 200/50 mg, however higher doses are generally used for HCQ (600mg/d). For antibiotics there were given as recommended
Ethics
-Data were collected through the patient medical file of the hospital blinded and using patient dataprotection
Outcome measures<br /> -Correspond to aims delay between treatment initiation and viral clearance, discharge from hospital and symptoms resolution
Statistical analysis
This is classical analysis methods relevant to aim
Confounding bias/ Limitation:<br /> -Those relative to retrospective study but well a posteriori controlled <br /> -As a retrospective study of the 358 patients (270) 173 mild and 97 moderate covid-19 cases were analyzable for completion of treatment and data availability. <br /> - 3/270 patients were still ongoing treatment at time of release (1%)<br /> - 97 moderate Covid-19 patients were categorized HCQ ATB SOC (22) LR ATB Soc (35), SoC (40) and analyzed for treatment and outcomes<br /> -A posteriori comparative analysis shows that the two groups HCQ and LR were identical in terms of comorbidity and other known factors that may weigh on the outcome.<br /> -Comparison with the Soc group alone showed that this last was less severely ill<br /> (significantly less pneumonia), and factor associated with poor outcomes, such<br /> as low lymphocytes count, and elevated CRP were associated with the treated<br /> groups. Interestingly dyspnea was more prevalent in this SOC group but we know<br /> that absence of dyspnea (silent hypoxemia) is rather than dyspnea linked to<br /> outcome. This suggest that if a difference in treatment exist it will probably<br /> be under evaluated. <br /> - Retinopathy to HCQ as never been reported in such short treatment and HCQ in serum returns to negative in 15 days after end of treatment (unpublished) <br /> The limitation of the study as been well reported
Interpretation of results: adequate except comparison LR/LR and ATB<br /> Time to clearance are shorter particularly in the HCQ and ATB group for which time to PCR > 35CTis 12 days in what is published elsewhere<br /> -Cough resolve significantly better in the HCQ ATB and fever in the two treated groups compare to SOC.
Adverse events were more frequent but only mild
The subgroup analysis LR versus LR ATB should be interpreted carefully as we don’t know interval time to onset of the LR only arm which may interfere with viral clearance of this subgroup.
Conclusion<br /> This study appropriately show that HCQ & ATB is better than LR & ATB than to SOC<br /> to shorten viral clearance, resolution of symptoms, and to shorten hospitalization duration in moderate form of COVID-19. The role of ATB alone to shorten viral clearance is overestimated
Note : P BROUQUI , has no conflict of interest with the industry concerning this review<br /> but has already published study supporting the efficiency of HCQ and azithromycin in COVID-19.
On 2020-04-23 05:41:01, user FrezzaLab wrote:
Is it possible that some of these kids had already been infected, before the lock down, and passed it onto relatives?
On 2020-04-07 01:06:33, user Cristian Reyes P. wrote:
In Chile BCG vaccine has been mandatory since 1949. Everybody is vaccinated. Over 90% of the population. You can study us. We still have the lowest mortality in the region.
On 2023-05-30 13:27:21, user Nils Yang wrote:
This paper has been published in Journal of Child Psychology and Psychiatry, see http://doi.org/10.1111/jcpp...
On 2020-07-24 16:43:50, user Kamran Kadkhoda wrote:
The correlate of protection is not inferred this way it is typically inferred through prospective vaccine trials in SARS-CoV-2-native volunteers.
On 2020-04-08 01:57:26, user Eliot Abrams wrote:
This just fits a gaussian curve. Absurd. Among other reasons, there is a second wave as soon as the current shelter in place restrictions are lifted.
On 2020-07-27 03:48:45, user Hagai Perets wrote:
A potential explanation for such dynamics could be related to co-infectio, see <br /> https://arxiv.org/abs/2007....
On 2021-12-22 14:03:31, user Simone Davies wrote:
I had these symptoms (vibrations for months, muscle twitching for days) after my all 3 of my vaccine shots. I had not had covid before I was vaccinated. Would be interested to know if this is true in others?
On 2023-11-07 14:15:21, user Nils Yang wrote:
This preprint has been published at Sleep: https://doi.org/10.1093/sle...
On 2020-06-09 17:41:58, user Hamid Reza Marateb wrote:
This is a hospital-based cohort whose results could not be generalized to the population. Moreover, these patients usually have commorbidity, and thus avoid smoking. Here are justifications.
On 2020-08-08 06:57:06, user Dr-Beesan Maraqa wrote:
Thank you for this study. I am struggling to find studies assessed the associations between stress and demographic factors, job title, and relation to social life.
On 2022-01-01 05:17:59, user Ardiana wrote:
N501Y and E484K signals high spread ability and original vaccine antibody evasion. But there were many variants like this and that couldn't beat Delta.
On 2020-04-15 11:57:26, user Renato Prandina wrote:
Duration and extent of immune protection will be critical to the novel betacoronavirus SARS-CoV-2 and will unfold in coming years. Some speculative scenarios...
On 2020-04-16 06:06:53, user Hellbound Reaper wrote:
SARS-CoV-2 is the virus name..COVID-19 is the disease name..you can't catch a disease. :P
On 2020-04-16 12:20:10, user Marlowe Fox wrote:
The tests on the efficacy of HCQ are confounded by multiple variables, including comorbidities, symptom onset, prescription drugs (RAAS inhibitors appear to play a key role in viral intensity), and testosterone/estrogen level, to name only a few.
Geneticists, epidemiologists, and other scientists have long used casual diagrams to clearly show variables that may potentially confound their results (1). The Wuhan study at the very least would need to account for the following:
HCQ <— comorbidities —> recovery<br /> HCQ <— symptom onset —> recovery<br /> HCQ <— drug prescriptions —> recovery
Adjusting for the confounding variable would essentially smooth out the flow of information between the treatment (HCQ) and the outcome (recovery), allowing for the inference of causal effects.
Assuming observable data is not available to adjust for confounding variables, a casual mechanism (mediator) could smooth out the flow of information from the treatment to the outcome (so long as the mediator is not influenced by confounder).
Luckily, multiple in vitro studies have been performed. One study posits that HCQ lowers endosomal pH which ultimately inhibits COVID from binding to ACE 2 and decreasing viral intensity (3).
HCQ —> endosomal pH —>glycosylation of COVID cellular receptor —> ACE 2 binding —> viral intensity —> acute lung injury
Another in-silico study posits that HCQ blocks specific protein sites on the host ACE2 cell, thereby thwarting its attempt to infect it and preventing the cytokine storm (over-reaction of the lymphatic system) that some posit is responsible for Acute Lung Injury (3). So here we have an entirely different causal mechanism:
HCQ —> BRD-2 receptor sites —> cytokine storm —> acute lung injury
Despite these problems, some believe that the p-values obviate the need to control for potentially lurking variables. However, they are subject to myriad influences, known as p-hacking. Whether it is the number of tests performed or the number of comparisons made, it increases the chance of finding a statistically significant p-value (4). Three professional statisticians co-authored a paper reviewing the validity of the Wuhan study (5). There were several issues with the data upon which the two significant p-values were based.
I suppose there is also a pragmatic argument: The p-values, along with existing studies and reports, are sufficient enough evidence to offset any concern for lurking variables in these urgent times. In other words, how much evidence is sufficient to warrant large scale roll-out of a low-cost treatment that may have a beneficial effect, from saving individuals who would have otherwise died to curbing its spread?
The consequences of large roll-out: manufacturing, scaling, distribution chains, and so forth could result in a tremendous diversion of resources. How many pharmaceutical manufacturers even have the capacity to roll out production of this magnitude? What if they all start scaling their labor to produce this particular drug. You can’t just put this genie back into the bottle. Not to mention the scientific energy/intellectual capital that would go to proving or disproving this proposed treatment. And why? Because scientific evidence demanded it? No because a tortured p-value and unpublished/unsubstantiated anecdotal evidence caught the attention of some in the media, and it has been over-popularized as a panacea. What about the risk that HCQ is not an effective treatment despite large investments in cash and resources that have been invested? Do you think the wheels of capitalism turn so easily? Investors will want a return and if that means continually touting an ineffective drug through spurious science, they will continue to do so. What about individuals taking HCQ as a prophylactic, believing themselves to be protected against COVID? Or COVID+ individuals taking HCQ and believing themselves to be cured? Or individuals who think: Well, if I get it—I’ll just take HCQ and be fine. This would increase the spread of COVID. From my perspective, the ignorance to viral transmission and the required precautions is widespread. This is just one more reason not to acquiesce to the new social norms of wearing face masks, social distancing, and abiding by shelter-in-place rules. Here, I think an understanding of cognitive psychology is important to anticipate the future behavior of a society in which a cheap and easy-to-manufacture cure is published in the media.
To sum up, HCQ's efficacy is not sufficiently proven to warrant a widespread roll-out, because it could result in several downstream consequences, from the diversion of resources (both manufacturing capabilities and intellectual capital) to increasing the risk threshold of individuals--who spurious believe in an easy and cheap treatment--thereby increasing the infection rate. One of two things needs to happen. Clinical trials that properly adjust for all potential comorbidities. Or the discovery of a causal mechanism (in vivo), which would obviate the need to control/adjust for confounders. For me, this would tip the utilitarian scales in regard to the potential benefits versus the risks.
References
On 2025-04-02 10:00:44, user Md Shahed Morshed wrote:
The published version can be found here: https://doi.org/10.3329/jacedb.v3i2.78642
On 2020-08-31 13:17:21, user Kamran Kadkhoda wrote:
Great work! This suggests the specificity of the Euroimmun assay is around 33%!
On 2019-10-30 08:25:55, user Marema wrote:
This paper is done to investigate how acute financial problems affect undergraduate students' clinical learning.The study used qualitative method to explore their experiences. I here for further information.
On 2020-05-06 06:53:53, user Hossein Mirzaei wrote:
Dear Georg<br /> i cant find table 1 in your article, Which you refereed to that for Main characteristics of patient.<br /> can you help me to find that?<br /> Thanks<br /> Hossein
On 2020-02-17 08:52:17, user Ellie_K wrote:
Once this paper has been peer-reviewed, could someone post here in the comments where (i.e. in which scholarly journal) it is published? Thank you!
On 2020-02-24 21:00:31, user Alexei Vazquez wrote:
As predicted https://arxiv.org/abs/cond-...
On 2020-03-04 13:30:42, user Bìtao Qiu wrote:
I think there are some wrongly put numbers on Table 3 (page 35), e.g. n = 59 for Immunodeficiency patients. It should be 3 according to the abstract and n = 3 on page 36.
On 2020-09-19 14:10:12, user kdrl nakle wrote:
Given the low infection rates you most likely have many false positives and the adjustment is probably of uncertain quality. Basically, it is not very accurate to use serosurveys in low infection areas.
On 2020-03-12 14:56:17, user Reza Amini wrote:
This is great job! I've done something similar to this earlier but I have DATASET shortage.<br /> "imreza.ir" .<br /> This was really surprising
On 2020-03-24 19:18:13, user Luis Cabrera wrote:
In the Extended Data 2, there is another standard curve, instead of "Primer, reporter molecules, target gene fragments, and guide RNAs used in this study. "
On 2020-03-26 15:11:11, user Sinai Immunol Review Project wrote:
Study description: Plasma cytokine analysis (48 cytokines) was performed on COVID-19 patient plasma samples, who were sub-stratified as severe (N=34), moderate (N=19), and compared to healthy controls (N=8). Patients were monitored for up to 24 days after illness onset: viral load (qRT-PCR), cytokine (multiplex on subset of patients), lab tests, and epidemiological/clinical characteristics of patients were reported.
Key Findings:<br /> • Many elevated cytokines with COVID-19 onset compared to healthy controls <br /> (IFNy, IL-1Ra, IL-2Ra, IL-6, IL-10, IL-18, HGF, MCP-3, MIG, M-CSF, G-CSF, MIG-1a, and IP-10).<br /> • IP-10, IL-1Ra, and MCP-3 (esp. together) were associated with disease severity and fatal outcome. <br /> • IP-10 was correlated to patient viral load (r=0.3006, p=0.0075).<br /> • IP-10, IL-1Ra, and MCP-3 were correlated to loss of lung function (PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury) with MCP-3 being the most correlated (r=0.4104 p<0.0001 and r=0.5107 p<0.0001 respectively).<br /> • Viral load (Lower Ct Value from qRT-PCR) was associated with upregulated IP-10 only (not IL-1Ra or MCP-3) and was mildly correlated with decreased lung function: PaO2/FaO2 (arterial/atmospheric O2) and Murray Score (lung injury).<br /> • Lymphopenia (decreased CD4 and CD8 T cells) and increased neutrophil correlated w/ severe patients.<br /> • Complications were associated with COVID severity (ARDS, hepatic insufficiency, renal insufficiency).
Importance: Outline of pathological time course (implicating innate immunity esp.) and identification key cytokines associated with disease severity and prognosis (+ comorbidities). Anti-IP-10 as a possible therapeutic intervention (ex: Eldelumab).
Critical Analysis: Collection time of clinical data and lab results not reported directly (likely 4 days (2,6) after illness onset), making it very difficult to determine if cytokines were predictive of patient outcome or reflective of patient compensatory immune response (likely the latter). Small N for cytokine analysis (N=2 fatal and N=5 severe/critical, and N=7 moderate or discharged). Viral treatment strategy not clearly outlined.
On 2020-05-18 12:43:08, user Sinai Immunol Review Project wrote:
Long period dynamics of viral load and antibodies for SARS-CoV-2 infection: an observational cohort study<br /> Huang et al. medRxiv [@doi.org/10.1101/2020.04.22.20071258]<br /> Main Findings<br /> The presence of serum IgM and IgG against SARS-CoV2 has been shown in several studies, however, a limited number of studies have shown the longitudinal relationship between viral RNA levels and antibody titers. This retrospective, observational study evaluated the dynamics of viral RNA, IgM and IgG specific for SARS-CoV2 proteins in patients with confirmed SARS-CoV-2 pneumonia over an 8-week period. <br /> Throat swabs, sputum, stool and blood samples from 33 patients with laboratory confirmed SARS-CoV-2 pneumonia were collected to analyze viral load and specific IgM and IgG against spike protein (S), spike protein receptor binding domain (RBD), and nucleocapsid (N). The demographics of the patients showed that 24 had respiratory symptom, two had symptoms in both the respiratory and the gastrointestinal tracts, one had gastrointestinal symptoms and six were asymptomatic. Chest CT revealed 27 patients had bilateral infiltrates and six had unilateral infiltrates. All the patients received antiviral treatment and atomized interferon during hospitalization. <br /> While viral load in throat swabs and sputum was higher at the symptom onset and undetectable by three weeks and five weeks respectively, viral load in stool started low but remained detectable for more than five weeks in many patients. The viral loads in sputum declined significantly slower compare to throat, so that the patients were divided into two groups based on load in sputum: short-persistence (viral RNA undetectable within 22 days, n=17) and long-persistence (viral RNA persists more than 22 days, n=16). The relationship between the persistence of sputum viral RNA and antibodies showed that short-persistence group had higher anti-S IgM, anti-RBD IgM and anti-RBD IgG levels compare to long-persistence group suggesting a potential protection by anti-RBD antibodies. The length of time from symptom onset to hospital admission was also associated with SARS-CoV-2 viral clearance. In addition, the higher seropositive rate for anti-S and anti-RBD IgM was seen in long-persistence patients. They suggest that delayed admission to the hospital resulted in higher seropositive and longer infection in patients with COVID-19.<br /> Limitations<br /> The separation of ‘low persistence’ vs ‘high persistence’ groups seems quite arbitrary, as only virus RNA levels for in sputum were considered, and viral loads in throat swabs and stool were not considered (these were no significant difference between the low vs high groups. The manuscript needs better and more detailed description of methods and figure legends. The same color codes for each patient could be used in figure 1, so that the readers could see if there was a trend in viral load between specimens in a same patient, and graphics for each patient will be useful to understand viral dinamics in the three types of samples for each person. The graphs on figure 5 seemed to correspond to one time point data, but there was no explanation which time point was used. In the results section, it was not clear which figures or tables were related to the text. The correlation between severity, viral load and persistency, and antibody titers could be analyzed.<br /> Significance<br /> It is important to understand the relationship between viral loads, disease progression and viral-specific antibodies in COVID-19 disease. More studies are necessary.
Reviewed by Miyo Ota as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-10-30 23:23:26, user Leonidas Palaiodimos wrote:
This article has been reviewed by peers and published at Hormones-the International Journal of Endocrinology and Metabolism
On 2020-03-31 16:17:23, user Johannes Opsahl Ferstad wrote:
Link to tool: https://surf.stanford.edu/c...
On 2020-04-22 04:36:02, user Paul Hue wrote:
Has Covid19 been truly isolated? Have its purported surface proteins been linked to genetic sequences in recovered genetic material from a true isolation?
On 2020-04-22 12:09:29, user Three-legged Crow wrote:
Where’s zinc mentioned in this study?
On 2020-11-18 21:50:14, user Hamid Merchant wrote:
Very interesting findings. Can you post the Ct values of all individual patients at different time points in a table as a supplementary data file please?
On 2022-10-24 19:47:27, user Camille Sawosik wrote:
The main goal of this study was to create a new model in order to diagnose brain disorders as they are often complex and get misdiagnosed. It seems that the researchers here have created a base computer model in order to diagnose brain disorders. The main critique here, as even the researchers point out, is that the model needs greater development and study before it can be applied in a clinical setting. At this point in development of the model, I would say that this paper has moderate significance, but could provide a breakthrough in the field if further development occurs. For now, the brain samples looked at all came from a limited number at the NBB. In the future, perhaps applying this model in other populations would continue to develop its significance within the field. The large majority of this paper relied on methods based in computer design. Coming from someone with a smaller background in these methods, I would have liked to see greater description of what they did. For example, at one point the FuzzyWuzzy library is referenced, and it would have been helpful to include some explanation of what this is. At some points as well, I found that the methods were essentially repeated in the results. The concept was interesting, but it was often hard to follow exactly what was done as this study seemed much more programing and computing based. In some sections, as well, tables or figures were referenced, but then those tables did not exist within the paper. Moving forward, other researchers should definitely use this as a building block for the future in order to build off of and develop a more advanced model. The findings here are interesting and provide a good framework for future extrapolation of the model. I found this paper interesting and hope for future development to get this idea into a clinical setting!
On 2022-10-27 19:45:52, user Indi Trehan wrote:
This article has now been published in The Pediatric Infectious Disease Journal -- doi: 10.1097/INF.0000000000003740
On 2022-11-10 06:25:14, user David Hinds wrote:
This paper has been published: https://doi.org/10.1038/s41...
On 2022-11-17 03:39:53, user M. Cunningham wrote:
The FDA EUA specifies that Paxlovid's window of availability requires the patient to be within both 5 days of symptom onset and the first positive test, whichever comes first. Is this not the VA's protocol? I only saw the testing aspect mentioned. I would think that this would further narrow the margins of error (eg: confirming early treatment). Additionally, Paxlovid is nirmatrelvir and the co-drug ritonavir. Since the press is already reporting this the same as a peer-reviewed finding, IMHO it's important to correct these omissions so that the public is not confused about the use of this medication. But I am enthusiastic to re-read the study when it has been evaluated and reviewed! Thank you for your research.
On 2022-12-02 16:36:24, user Mark Czeisler wrote:
Note from the authors:
A revised version of this paper was published in Annals of Internal Medicine on 29 November 2022 following peer review. Below is a link to the article, along with the PubMed citation.
https://www.acpjournals.org...
Czeisler MÉ, Czeisler CA. Shifting Mortality Dynamics in the United States During the COVID-19 Pandemic as Measured by Years of Life Lost. Ann Intern Med. 2022 Nov 29. doi: 10.7326/M22-2226. Epub ahead of print. PMID: 36442062.
On 2022-12-13 18:27:20, user João Duarte wrote:
This article is now peer-reviewed and published in Frontiers in Neuroscience, here: https://www.frontiersin.org...
On 2022-12-26 13:26:47, user y wang wrote:
You did not indicate the method of calculating the chi-squre.<br /> Actually, your method does not seem correct. <br /> One can google "Comparing Two Independent Population Proportions" and find the formula and calculator.<br /> Using the calculator, I found z=6.00, i.e., chi-squre=36 (not your 35.67).
On 2023-02-07 14:43:14, user E Edlund wrote:
Interesting read. Have you seen this similar study, focused on stroke? https://pubmed.ncbi.nlm.nih...
On 2023-02-18 07:18:25, user Anikó Lovik wrote:
This preprint has now been published in an extended and revised version in PLOS One: https://doi.org/10.1371/jou...
On 2023-02-28 18:38:31, user Stéphanie C. Thébault wrote:
Link to published article:
PLoS One. 2023 Jan 12;18(1):e0278388. doi: 10.1371/journal.pone.0278388
On 2023-03-30 21:55:59, user Daniel wrote:
Can the authors please elaborate on the changes made, in particular the effect on TLR4 response?
On 2023-04-21 12:49:03, user antonia peros wrote:
I believe that the topic of your research is very important and current, however, I have several methodological objections. <br /> Although the authors pointed out the limitations, they made quite strong conclusions and recommendations despite too small, non-randomized sample and a cross-sectional design without a control group.<br /> The use of the used instruments is very questionable when it comes to recalling satisfaction, self-esteem, and reduction in depression from 6 months ago. I also think that the fact that the respondents were familiar with the purpose of the research could have contributed to the recall bias.<br /> An important factor in your research could be how long the subjects exercised, and you did not collect that data. What their target is in the research is also vaguely defined. I recommend including some more objective criteria for that.
On 2023-04-21 15:29:37, user Erin Jonaitis wrote:
This manuscript has now been published in Brain Communications. The final version is available at https://academic.oup.com/braincomms/article/5/2/fcad057/7070362.
On 2023-05-06 19:13:10, user Bret Nestor wrote:
Hello, If you are interested in a peer-reviewed subsequent work with a distilled message, please refer to our work in The Lancet Digital Health: https://doi.org/10.1016/S25...
On 2023-05-18 19:00:12, user Dave Fuller wrote:
Please add final peer-reviewed citation as:
Lin D, Wahid KA, Nelms BE, He R, Naser MA, Duke S, Sherer MV, Christodouleas JP, Mohamed ASR, Cislo M, Murphy JD, Fuller CD, Gillespie EF. E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation. J Med Imaging (Bellingham). 2023 Feb;10(Suppl 1):S11903. doi: 10.1117/1.JMI.10.S1.S11903. Epub 2023 Feb 8. PMID: 36761036; PMCID: PMC9907021.
Thanks!!
CDF
On 2023-06-01 01:20:57, user Edmund Seto wrote:
This paper has been accepted for publication in the journal Science of the Total Environment under the title "Assessing the effectiveness of portable HEPA air cleaners for reducing particulate matter exposure in King County, Washington homeless shelters: Implications for community congregate settings"
On 2023-07-15 08:38:27, user readthecoran wrote:
Thanks for this paper. Your result may bne related to variance shrinkage of quantile averaging relative to equal weight combination of distributions. See https://onlinelibrary.wiley... For right skewed distributions medians tend to be lower than means
On 2023-08-08 10:45:14, user Robin Walters wrote:
Now published:
On 2023-09-14 11:31:45, user Indi Trehan wrote:
This has now been published: J Glob Health 2023 Jun 9;13:04065. doi: 10.7189/jogh.13.04065.
On 2023-09-20 17:48:48, user ASH wrote:
Why did the authors investigate the associations between poultry fecal matters and E.coli, instead of other more poultry-specific zoonosis, like Salmonella? E. coli is commonly found in the lower intestine of warm-blooded organisms, of which most are harmless...<br /> Why didn't the authors check the DHS data? Similar data can be found in the DHS data which is publicly available.
On 2023-11-04 15:16:53, user Clive Bates wrote:
Two problems here.
First is scalability. This doesn't sound like an intervention that would engage many veterans, nor does it seem likely to be affordable or practical at the scale necessary to achieve a turnaround in the aggregate burdens arising from smoking.
Tobacco-related deaths exceed those resulting from homicides, suicides, motor vehicle accidence, alcohol consumption, illicit substance use, and acquired immunodeficiency syndrome (AIDS), combined.
Almost all of that excess mortality is attributable to smoking not nicotine. Tobacco harm reduction approaches may deliver more and sooner - e.g. encouraging migration to smoke-free alternative forms of nicotine use such as vaping.
Second, it is quite possible that veterans with forms of PTSD are benefiting in some way from the functional and therapeutic properties of nicotine. Again, an approach to smoking cessation that does not demand nicotine cessation may achieve nearly all the health benefits of quitting smoking without demanding withdrawal from nicotine use.
The trial could at least consider an additional arm to assess the utility of encouraging vaping for smoking cessation. It might achieve more for less.
On 2023-11-13 10:04:59, user Theo Peterbroers wrote:
"The duration from the day of index vaccination to the day of the survey completion was a median of 595 days (Interquartile Range<br /> (IQR): 417 to 661 days; range: 40 to 1058 days)."<br /> That is at least one participant vaccinated before the start of the pandemic.<br /> EDIT Make that one person from early in the vaccine trials. How time flies.
On 2023-11-15 16:42:12, user jhick059 wrote:
This article was published in the peer-reviewed journal PLoS One on October 30, 2023 (citation below), but the medRxiv page has not yet been updated to reflect the PLoS One publication.
Citation: Hickey J, Rancourt DG (2023) Predictions from standard epidemiological models of consequences of segregating and isolating vulnerable people into care facilities. PLoS ONE 18(10): e0293556. https://doi.org/10.1371/jou...
On 2023-11-27 21:08:47, user Judith Mowry wrote:
The recent paper on peripheral vasopressors by Yerke doi.org/10.1016/j.chest.202... is an important reference for your research. It is vital to note that they changed their protocol to add very specific protocols and rules regarding IV site inspection, defined who was responsible. Also note that an antecubital site (or any joint) is avoided to minimize movement and extravasation risk. I wish you success with your research.
On 2023-11-27 22:00:33, user Christos Proukakis wrote:
We note the results of the analysis, and look forward to submitting a detailed response soon.
On 2024-01-27 21:34:52, user kasia bera wrote:
now published here: https://pubmed.ncbi.nlm.nih...
On 2024-02-20 21:32:15, user Wally Wilson wrote:
It would be handy if the authors could get the abbreviations for Borderline Personality Disorder (BPD) and Bipolar Disorder (BD) corrected
On 2024-04-25 03:20:17, user Lena Palaniyappan wrote:
Very interesting work. We observed a similar 'amelioration' effect using a cross-sectional design a few years ago (Guo et al., 2016). Since then we made several cross-sectional and a few longitudinal observations supporting the possibility of compensation and reorganisation after first episode psychosis (Palaniyappan et al., 2019a; 2019b), including one with the largest untreated sample we could access at that time (Li et al., 2022). These observations compel us to spare more efforts to understand the compensatory processes in psychosis (Palaniyappan et al, 2017, Palaniyappan & Sukumar 2020, Palaniyappan, 2021; 2023).
Guo S, Palaniyappan L, Liddle PF, Feng J. Dynamic cerebral reorganization in the pathophysiology of schizophrenia: a MRI-derived cortical thickness study. Psychological medicine. 2016 Jul;46(10):2201-14.
Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychological Medicine. 2023 Jun;53(8):3500-10.
Palaniyappan L. Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neuroscience & Biobehavioral Reviews. 2017 Aug 1;79:1-3.
Palaniyappan L, Das TK, Winmill L, Hough M, James A, Palaniyappan L. Progressive post-onset reorganisation of MRI-derived cortical thickness in adolescents with schizophrenia. Schizophr Res. 2019a Jun 1;208:477-8.
Palaniyappan L, Hodgson O, Balain V, Iwabuchi S, Gowland P, Liddle P. Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study. Psychological Medicine. 2019b Feb;49(3):412-20.
Palaniyappan L, Sukumar N. Reconsidering brain tissue changes as a mechanistic focus for early intervention in psychiatry. Journal of psychiatry & neuroscience: JPN. 2020 Nov;45(6):373.
Palaniyappan L. The neuroscience of early intervention: Moving beyond our appeals to fear. Australian & New Zealand Journal of Psychiatry. 2021;55(10):942-943.
On 2024-04-26 17:02:43, user Gary Goldman wrote:
We broadened our analyses (of IMRs) to explore potential relationships between childhood vaccine doses and NMRs (neonatal mortality rates) and U5MRs (under age 5-year mortality rates). Using 2019 and 2021 data, 17 of 18 analyses (12 linear regressions and six ANOVA and Tukey-Kramer tests) achieved statistical significance and corroborated the trend reported in our original study, demonstrating that as developed nations require more vaccine doses for their young children, mortality rates worsen. Please see https://pubmed.ncbi.nlm.nih...
On 2024-05-02 18:11:05, user Keith Robison wrote:
There is a great degree of interest in this preprint due to it being the first extended description of using the iCLR technology.
It would be very valuable to have details on how much Illumina short read data was generated from iCLR libraries and how much of that data contributed to the iCLR reads vs. what could not be used
It would also be valuable to report the read length distribution of the iCLR reads in greater detail - particularly since many interested parties cannot perform that analysis themselves on the clinical data sets
On 2024-05-28 13:49:19, user Jenn Ferris wrote:
This preprint is now published: https://www.neurology.org/d...
On 2024-11-08 19:59:59, user Andre Boca Ribas Freitas wrote:
Important Observations on Underreported Chikungunya Mortality in Light of Global Burden Analysis
Dear Authors,
I thoroughly appreciated your recent preprint on the global burden of chikungunya and the potential benefits of vaccination. Your work provides critical insights into the widespread impact of this disease and emphasizes the significant potential of vaccine interventions.
However, I wanted to highlight a critical issue that our research and that of others in the field have identified: the substantial underreporting of chikungunya-related mortality across many regions. While chikungunya is often categorized as a non-fatal disease, a growing body of evidence reveals severe and sometimes fatal cases that frequently go unrecorded by epidemiological systems. Our recent studies in Brazil documented excess mortality rates from chikungunya far surpassing those officially reported, with mortality rates up to 60 times higher than recorded by standard surveillance systems?Freitas et al., 2024?. Additionally, studies like those by Mavalankar et al. (2008) in India and Beesoon et al. (2008) in Mauritius underscore the elevated mortality associated with chikungunya during epidemic outbreaks, further reinforcing this critical gap in mortality surveillance.<br /> This growing evidence highlights the critical need for increased investment in molecular diagnostics, integrated surveillance, and more comprehensive mortality tracking for chikungunya. These measures are essential for aligning public health responses with the true impact of the disease and ensuring the full scope of chikungunya’s burden is addressed.
Thank you for advancing this essential conversation. Through improved surveillance and research collaboration, we can work toward effective strategies to mitigate the severe impact of chikungunya globally.
Best regards,
Dr. André Ricardo Ribas Freitas<br /> Faculty of Medicine, São Leopoldo Mandic, Campinas-SP, Brasil
Freitas ARR, et al. Excess Mortality Associated with the 2023 Chikungunya Epidemic in Minas Gerais, Brazil. Front Trop Dis. 2024. doi: 10.3389/fitd.2024.1466207.
Mavalankar D, Shastri P, Bandyopadhyay T, Parmar J, Ramani KV. Increased mortality rate associated with chikungunya epidemic, Ahmedabad, India. Emerg Infect Dis. 2008 Mar;14(3):412-5. doi: 10.3201/eid1403.070720. PMID: 18325255; PMCID: PMC2570824.
Beesoon S, Funkhouser E, Kotea N, Spielman A, Robich RM. Chikungunya fever, Mauritius, 2006. Emerg Infect Dis. 2008 Feb;14(2):337-8. doi: 10.3201/eid1402.071024. PMID: 18258136; PMCID: PMC2630048.
Manimunda SP, Mavalankar D, Bandyopadhyay T, Sugunan AP. Chikungunya epidemic-related mortality. Epidemiol Infect. 2011 Sep;139(9):1410-2. doi: 10.1017/S0950268810002542. Epub 2010 Nov 15. PMID: 21073766.
Freitas ARR, Donalisio MR, Alarcón-Elbal PM. Excess Mortality and Causes Associated with Chikungunya, Puerto Rico, 2014-2015. Emerg Infect Dis. 2018 Dec;24(12):2352-2355. doi: 10.3201/eid2412.170639. Epub 2018 Dec 17. PMID: 30277456; PMCID: PMC6256393.
Freitas ARR, Gérardin P, Kassar L, Donalisio MR. Excess deaths associated with the 2014 chikungunya epidemic in Jamaica. Pathog Glob Health. 2019 Feb;113(1):27-31. doi: 10.1080/20477724.2019.1574111. Epub 2019 Feb 4. PMID: 30714498; PMCID: PMC6427614.
On 2024-12-03 21:03:36, user xPeer wrote:
Courtesy review from xPeerd.com
This manuscript introduces DeepEnsembleEncodeNet (DEEN), an innovative polygenic risk score (PRS) model integrating autoencoders and fully connected neural networks (FCNNs) to address limitations of existing PRS methods. By disentangling dimensionality reduction and predictive modeling, DEEN enables the capture of both linear and non-linear SNP effects, improving prediction accuracy and risk stratification for binary (e.g., hypertension, type 2 diabetes) and continuous traits (e.g., BMI, cholesterol). Evaluation using UK Biobank and All of Us datasets highlights superior performance over established methods. While conceptually and methodologically compelling, areas such as interpretability, generalizability across diverse populations, and computational efficiency warrant further refinement.
Major Revisions<br /> 1. Interpretability and Practicality<br /> Black-Box Concerns: The complexity of the DEEN model limits its interpretability compared to simpler PRS methods like Lasso or PRSice. While the manuscript acknowledges this limitation, incorporating efforts to visualize model predictions (e.g., feature importance maps or SNP clustering analysis) would enhance its usability (Section: Discussion, p.16).<br /> Clinical Translation: The manuscript emphasizes the potential of DEEN for clinical utility but lacks discussion on the challenges of implementing deep learning models in healthcare. Addressing regulatory barriers and clinician engagement would add value (Section: Discussion, p.17).<br /> 2. Population Generalizability<br /> Demographic Bias: Both datasets used (UK Biobank, All of Us) consist predominantly of European-ancestry individuals. This limits the model's applicability to global populations. Expanding the discussion on efforts to improve DEEN’s cross-ancestry generalizability is essential (Section: Results, p.11).<br /> Validation Across Diverse Cohorts: While DEEN is validated on two datasets, additional external validations across non-European populations would strengthen claims of generalizability and reliability.<br /> 3. Comparative Analyses<br /> Missing Baseline Methods: Although DEEN is compared with multiple PRS methods, inclusion of additional machine learning benchmarks (e.g., gradient boosting models, convolutional neural networks for SNP effects) would better contextualize DEEN’s advantages (Section: Results, p.8).<br /> Risk Stratification Assessment: The risk stratification results are promising but need more rigorous evaluation metrics beyond odds ratios, such as net reclassification improvement (NRI) or integrated discrimination improvement (IDI).<br /> 4. Computational Efficiency<br /> Resource Requirements: DEEN’s reliance on high-performance computing resources (e.g., GPU usage) is noted but not sufficiently quantified. Providing benchmarks of computational costs and runtime against alternative methods is crucial for practical implementation (Section: Methods, p.19).<br /> Optimization: While grid search was used for hyperparameter tuning, exploring automated optimization frameworks (e.g., Bayesian optimization) could reduce computational overhead.<br /> 5. Data Filtering and Variant Selection<br /> Potential Bias from Variant Filtering: The preselection of SNPs based on p-values may exclude rare variants or those with small effects. A sensitivity analysis on SNP filtering thresholds would clarify the robustness of DEEN’s predictive power (Section: Methods, p.20).<br /> Minor Revisions<br /> 1. Typos and Formatting<br /> Figure Legends: Some figures (e.g., Figure 5) lack clear explanations of axes and statistical methods.<br /> Grammar: Line 124: Replace "similarly drive CRC progression" with "similarly drive progression."<br /> 2. AI Content Analysis<br /> Estimated AI-Generated Content: ~20-25%.<br /> Implications: Repetitive phrasing in methodological descriptions and literature summaries suggests potential AI assistance. While the technical content appears valid, manual rephrasing can enhance originality and scientific depth.<br /> 3. Statistical Reporting<br /> Insufficient Confidence Intervals: Odds ratio enrichment results lack 95% confidence intervals in several places, undermining statistical rigor (Section: Results, p.9).<br /> Inconsistent Metric Definitions: Terms like “improved R²” and “higher AUC” are used loosely. Precise numerical values and effect size comparisons would improve clarity.<br /> 4. Terminology Consistency<br /> Key terms like "dimensionality reduction" and "risk stratification" should be consistently defined and applied across sections to avoid ambiguity.<br /> Recommendations<br /> Enhance Model Interpretability:
Integrate explainability tools (e.g., SHAP values, visualization of autoencoder layers) to clarify how SNPs influence predictions.<br /> Discuss the potential for hybrid models balancing interpretability and performance.<br /> Address Demographic Bias:
Validate DEEN using datasets from underrepresented populations (e.g., African, Asian ancestries).<br /> Incorporate transfer learning techniques to enhance generalizability.<br /> Benchmarking and Evaluation:
Compare DEEN against additional advanced machine learning models for PRS.<br /> Introduce advanced evaluation metrics like NRI and IDI to strengthen claims.<br /> Refine Computational Analysis:
Provide detailed resource utilization benchmarks.<br /> Explore alternative hyperparameter optimization methods to improve training efficiency.<br /> Expand Data Analysis:
Perform a sensitivity analysis on variant filtering thresholds.<br /> Investigate the inclusion of rare variants to improve model robustness.
On 2024-12-20 20:46:20, user Jakub wrote:
You have stated: "We performed targeted metabolomics to quantify the absolute abundance of known uremic toxins, including (...) 4-ethylphenyl sulfate (4-EPS) (...) in plasma of this cohort. As expected, CKD and PAD+CKD groups had significantly higher levels of all these uremic toxins (Figure 3A)." Unfortunately, Figure 3A does not provide data on 4-ethylphenyl sulfate. May you add data on this solute?
On 2025-01-10 21:50:46, user Harold Bien wrote:
Fascinating article. Given that each individual VOC in Fig 1 appears to have significant overlap between each group and wide distributions, it would be interesting to learn how the various machine learning algorithms used each VOC and the resulting model. Could the authors provide more information on the ML algorithms used, how it was trained, and how the ROCs were constructed?
On 2025-03-17 20:22:07, user Adrian Barnett wrote:
A well planned and executed paper that highlights a serious and likely common issue in research practice.
On 2025-04-11 20:29:22, user Scott Olesen wrote:
Now published: https://doi.org/10.1016/j.vaccine.2025.127109
On 2025-10-09 02:52:59, user sid moose wrote:
I can’t tell, not a scientists here.. but did they test for whether or not the participants had the flue before the start of the study?
On 2025-10-15 20:49:15, user jpirruccello wrote:
This has been published; please see https://pubmed.ncbi.nlm.nih.gov/37019578/
On 2022-05-24 20:25:23, user Carol Taccetta, MD, FCAP wrote:
If a subject was still on immunotherapy at time of "recovery," the outcome of the adverse event cannot be considered as "resolved." It will also be important to follow these subject for relapse after therapy discontinuation, as immune-mediated conditions can sometimes relapse months, even years, after immunotherapy discontinuation.
On 2022-06-14 12:41:29, user Robert Clark wrote:
I was puzzled in Fig. 3 that the numbers for the severe cases was 39 for placebo and 51 for IVM. I thought this was measuring the comparative effect of ivermectin for the severe cases. But I see in the Supplementary appendix in eTable 1 that this is just giving the numbers in this category on entrance to the study.
But this raises another problem. For a randomized trial the number of severe cases assigned to the placebo and treatment groups should be close. Yet the IVM group got 24% greater number of severe cases. That’s a discomfortingly large difference for a randomized trial. Clearly this could create a bias against the treatment regimen.
Reviewing the further eTable 1of baseline symptoms, we see several categories of symptoms that would be key indicators of severe disease such as dypsnea, difficulty breathing, were assigned significantly more severe cases to the IVM group compared to the placebo group.
That shouldn’t happen in a randomized trial. I suspect something went wrong with the randomization. This could create such a serious bias against the treatment that a disclaimer should be placed on this study that its randomization procedure is being reviewed.
Robert Clark
On 2022-07-10 23:39:20, user Charles Warden wrote:
Hi,
Thank you very much for posting this preprint.
I consider the topics raised by this study to be important and interesting.
However, I have some comments and questions:
1) I agree that confirmation bias can be a contributing factor. However, I think true limitations in utility are also important. So, I am not sure if I completely agree with the statement "When results were not consistent with participant’s personal or family history, many participants found reasons to dismiss or discredit these results. This indicates a role for confirmation bias in responses to [self-initiated] PRS." For example, I might really want to understand the genetic basis for a disease, but the percent heritability explained by the PRS may be low and I could therefore be disappointed with the usefulness of a PRS due to a discordant result.
I have a blog post where I share my impute.me scores (along with others):
https://cdwscience.blogspot.com/2019/12/prs-results-from-my-genomics-data.html
I don't know if I would exactly say my response was "negative," but I certainly got the impression the PRS that I saw may have limited utility. In that sense, my view of the method was not positive, even if it did not evoke a strong emotional "negative" response.
Within that blog post, “ulcerative colitis” would be an example where there were different PRS for the same disease but very different percentiles (for the same SNP chip). So, I would consider that an example of the reaction that is described being due to something other than confirmation bias.
2) Did the interviewers respond when there were possible points of misunderstanding during the interview process?
It was acknowledged as a limitation in the discussion: "the researchers did not have access to participant’s PRS results and were unable to evaluate people’s understanding of their results".
However, it seems like that could be important. For example, there is a quote "Unfortunately, I do regret getting a PRS… I would have rather not known. I like uncertainty". Assuming that there were appropriate limitations to communicate, I believe a response from the interviewer might cause that quote to no longer reflect the subject’s opinion.
In general, there appears to be a noticeable emphasis on mental health in the article. My opinion is that this is an area where limitations are particularly important. If it helps, I think there are some additional details in this blog post for the book Blueprint.
In terms of my own impute.me results, I thought the "anxiety" PRS seemed reasonable (to the best of my ability to assess that). However, I also thought changes in conditions over time were important, and I thought there was potential for misuse.
3a) I think it is a minor point, but I don't remember receiving an invite to join a Zoom meeting for a discussion about my impute.me results.
I hope that I was one of the 209 candidates, but I was not sure if I could confirm that. I also noticed mention of categories like “medium” or “low” for one quote referencing a z-score of 2.5, but I only saw the continuous score distribution in the screenshots from my blog post.
3b) Perhaps more importantly, I tried to go back to sign in to check if I missed something.
In the Folkersen et al. 2020 paper, the link provided is for https://www.impute.me/. However, that link currently re-directs to a Nucleus website (https://mynucleus.com/).
Can you please provide some more information about the re-direction of the impute.me link?
For example, I submitted an e-mail to register on the new website, but I don't think I can see my earlier results anymore?
Additionally, I was confused when I couldn’t find the GitHub code provided with that paper: https://github.com/lassefolkersen/impute-me
4) Finally, but I don't think either of the 2 models that I see ("dismissed medical concerns" and "medical distrust") are a great description for myself. I think something like "curiosity" and "critical assessment" would be more appropriate for myself.
For example, I wouldn't say I distrust the healthcare system or medical research broadly, but I do think feedback and engagement is important. Thus, when I encounter problems, I submit reports to FDA MedWatch. Likewise, I contribute data/experience to projects like PatientsLikeMe.
Thanks Again,<br /> Charles
On 2022-07-11 04:26:35, user E Hansen wrote:
It would be useful if the authors could clarify if "unvaccinated" means "never injected", or if this group also includes subjects not yet defined as vaccinated, but who has received a vaccine within the last week/14 days. <br /> The same goes for the vaccinated group; does it include everyone who received a shot from the day injected, or only those who have passed the first 14 days after injection and then being consideres "vaccinated"? This was not entirely clear to me, anyway. <br /> Thank you
On 2022-07-29 09:25:01, user Dr. D. Miyazawa MD wrote:
Please also refer to previous studies.
Hypothesis that hepatitis of unknown cause in children is caused by adeno-associated virus type 2 (08 May 2022)<br /> https://www.bmj.com/content...
Daisuke Miyazawa. Possible mechanisms for the hypothesis that acute hepatitis of unknown origin in children is caused by adeno-associated virus type 2. Authorea. May 16, 2022.<br /> DOI: 10.22541/au.165271065.53550386/v2
On 2022-08-06 10:16:33, user Jef Baelen wrote:
No inclusion/exclusion criteria are mentioned? It includes a study from 2004, long before SARS-CoV-2 emerged. The Caruhel study was not performed on COVID-19 patients. The çelebi study used a cycle treshold cut-off of 38 and evaluated 20 parameters of which masks was only 1. The results of this study were wrongly extrapolated in table 1. Very dubious studies included in this meta-analysis!
On 2022-08-09 02:04:53, user Roel Ceballos wrote:
This is already published. The link to the publication is this https://mjst.ustp.edu.ph/in...
On 2022-08-09 12:40:13, user PhillyPharmaBoy wrote:
The authors conducted a thorough evaluation of the impact of ivermectin on SARS-CoV-2 clearance. On the surface their results differ from those of Krolewiecki, et al. (below). In a post hoc analysis these investigators found that ivermectin accelerated viral decay when drug concentration (4 hr) exceeded 160 ng/ml. It would be useful for the PLATCOV Group to mention this study and discuss potential reason(s) for the discrepancy.
On 2022-08-14 18:37:59, user Jason wrote:
Interesting manuscript, can see how it can easily be used in practice looking at the predictive variables chosen
On 2022-08-17 07:08:46, user Partho Sen wrote:
This paper is now published in iScience, Cell Press. Here is the link of the journal pre-proof. https://www.sciencedirect.c...
On 2022-08-29 13:47:58, user Titos wrote:
This was published in NEJM as https://www.nejm.org/doi/fu...
On 2022-09-14 16:05:23, user Roy Miller wrote:
There may be more evidence for this immunity than previously thought, Since Queen Elizabeth's death on September 8, 2022 the half-dozen British Royals have mingled with countless people, shaking hands, touching random surfaces, and breathing air all over the Scotland, England, and northern Ireland. I assume the Royals all have had their COVID injections and I have to assume that they have come in contact with the COVID virus on numerous occasions. Current thinking would lead me to assume the crowds gathered to see them would make transmission of the virus more likely.
So why hasn't COVID spread like wildfire over the grieving population? Perhaps it is too soon to tell. But since COVID symptoms appear 2 to 14 days after infection, at least a large uptick in the COVID infections should be noticeable by now But no such event has occurred according to the British Press. So I have to conclude that there are additional factors to consider such as the one postulated by this article.
PS: I have no medical training although my job involves helping the medical community take advantage of high technology.
On 2022-09-22 09:16:27, user Wera Pustlauk wrote:
Dear authors,
thanks for the valuable effort to set up a new assay for the determination of PPi.
Regarding table III samples remained somewhat vague to me. Clarification in the table header including the unit of the determined PPi might be helpful. Calculation of the standard deviation in addition to the average would make the data more roboust and would establish a more substantial link to the variability discussed in the paragraph before. Moreover, time differences in the addition of EDTA to the CTAD tubes as discussed in the text should be clearly stated for each sample in the table III as well.
In addition, a hands on protocol (stored in a repository or as supplement) allowing the direct usage of the assay based on the optimized procedure would make the usage more accessible.
Best regards,<br /> Wera Pustlauk