On 2025-02-23 14:32:17, user Shin jie Yong wrote:
"Recently, a subset of non-classical<br /> monocytes has been shown to harbor S protein in patients with PVS [18]" - reference #18 cited Patterson et al. (2020), which might be an error since this study examined long covid participants only. My apologies if I'm mistaken, however.
On 2021-09-27 21:24:14, user pwlg wrote:
"Dror Mevorach, head of internal medicine at the Hadassah University Medical Center, and his colleagues identified 110 myocarditis cases among 5 million people in Israel who had received two doses of the Pfizer-BioNTech vaccine in the month before their diagnosis. That translates to about one in 50,000 vaccine recipients, a number that isn't concerning given the background rate of myocarditis in the general population where it is typically triggered by viral or bacterial infections." Science June 1, 2021.
On 2021-10-06 16:56:58, user zega wrote:
Baseline is 1/100000/365days https://www.myocarditisfoun... vast difference, they an be off by 8000times and still below...
On 2020-03-21 21:53:33, user Joel Joonatan Kaartinen wrote:
There's data showing that someone who gets the asymptomatic version of this disease tends to fight it off in under 10 days. So if asymptomatic covid-19 was the case for >90% of patients, it'd actually be quite plausible that all people aboard the ship had it and were totally virus free by the time they were finally tested.
That being said, until this paper gets some significant peer review, I'm not going to give it much weight, personally. Indirect analysis is easy to fumble in many ways.
On 2021-11-04 03:14:20, user Hiromichi Suzuki wrote:
We thank you for your comments, The reagent was currently approved in October, 2020. We changed the year of approval from 2021 to 2020, which is the mistake of description.
On 2020-04-22 07:37:25, user Dominique Joly wrote:
Major issue: the number of days between the first symptom and the beginning of hosp/treatments is not indicated.
On 2020-04-22 20:38:01, user David Swiff wrote:
Macrolides can prolong the QT and QTc interval and cause cardiac arrhythmias, including TdP, ventricular tachycardia, and ventricular fibrillation, via their effect on the IKr potassium channel.
On 2020-04-22 12:57:03, user Jorgen Schultz wrote:
Interesting - and chocking, I must say. I hope the report I have read on medRxiv is not final, because I am missing the following:<br /> 1) As I understand it, the combo-treatment is effective in treating patients with Coronavirus BEFORE it is "to late". Giving a treatment with known side effects during late stage infection is recommended by?<br /> 2) The screening done (on patients) prior to treatment - I must have missed it or? Just to compare: as I understand it, in IHU's treatment (besides patient-groups being not comparable) screening is done prior to any medication, and patients in risk form a kind of control group as best as can be in this effort to safe lives. <br /> 3) Dosage and duration of treatment?<br /> 4) Did patients with cardiovascular symptoms receive hydroxychloroquine? Likewise with patients showing symptoms of "Cytokine Storms"?<br /> 5) According to "Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies" by Ming Zhao, Hydroxychloroquine is mentioned for its effect to inhibit viral replication. Is that not very much prior to the case of the patients in this study? <br /> Did other - and more relevant drugs - replace the use of Hydroxychloroquine if the later stages of the infection?<br /> 6) Why study a subject you have already discarded and emotionally distanced yourself from (in "Background" it describes the use of Hydroxychloroquine as "anecdotal")?
As the French President said: We are at war. <br /> But here - another war seems to be fought!<br /> With human sacrifice and casualties as a result.<br /> Colatteral damage?
I am chocked and saddened by the loss of life during this study.
On 2020-08-24 18:08:50, user Frank Harrell wrote:
A discussion is taking place here: https://discourse.datametho...
On 2021-05-30 17:55:22, user fatlas wrote:
When the data for Niaee study was corrected for version 2 the weights given to the studies were also changed. The weights in figure 2 changed in a way that is hard to explain by the Niaee data fix alone.
Was any other parameter adjusted for version 2 than data for Niaee study? If so, on what basis was the other parameters changed?
On 2020-07-16 18:54:38, user Marm Kilpatrick wrote:
This is a fantastic dataset. Could you possibly show the data relative to date of onset of illness, both for SARS-COV-2 & for other viruses? Also will the raw data be shared when paper is published? This could help quite a bit in syntheses on several key topics. Thank you!
On 2021-08-27 15:57:52, user Jacky wrote:
The study does not account for survivor bias (i.e., those who got COVID and died; however, hardly anyone--probably nobody--who got a <br /> vaccine died); the estimates they report are confounded and not <br /> interpretable. Also, it does not account for time differences of when <br /> the person was vaccinated and when when the person got COVID. If most <br /> individuals were vaccinated say 6 months ago and they are compared <br /> individuals that got Delta recently, then of course the latter will have<br /> more antibodies than the former (antibodies will wane in both groups). <br /> Thus, this study has sever methodological challenges.
On 2021-09-12 15:11:23, user Ben Veal wrote:
I don't think you've read the paper very carefully. Socio-economic class is matched between the treatment groups, and also used a covariate in the model (i.e. the results take economic class into account).<br /> What makes you think there is a difference in the amount of immunocompromised people between the treatment groups? Important health risk factors are accounted for.
On 2021-12-03 13:45:03, user Ben Veal wrote:
Those are not flaws, they are limitations (which are also mentioned in the paper). You have not highlighted any errors, you have just mentioned one possible way that the results could be misleading if certain circumstances were met.
On 2021-10-11 16:26:26, user sentner wrote:
It's not relevant because this study isn't about survivability of the disease. It's about how likely one is to be infected a second time.
On 2021-08-26 20:37:24, user David Anfinrud wrote:
This is mostly common sense. But again to get people to understand that those that had COVID are better protected and do not need the vaccine you have to have a study. This information is just a repeat of science seen over the Decades. Vaccines help but the best protection is natural immunity
On 2021-09-11 16:32:55, user Chadwick wrote:
Red Flags all over the place, like there are 2.5x more immunocompromised in normalized comparison groups, being immunocompromised makes re- or breakthrough infection LESS likely, Economic class swings between comparison groups, being wealthier makes infections more likely...
On 2021-09-02 10:34:11, user Sally Holdener wrote:
Your comparison of Alabama vs Ontario is only surface deep as well. Very different climates. How many people live in air conditioned homes in Ontario vs. Alabama, for instance? Your bias is quite obvious just from your tone.
On 2021-09-15 06:59:54, user Henri van Werkhoven wrote:
We're not stating that natural immunity HAS to be inferior to vaccine-induced immunity. We're arguing why the relative risks found in the study, favoring natural immunity, are strongly overestimated. There may still be a smaller difference in favor of natural immunity (which I agree would make sense). Or there may be no difference at all. We don't know because we can't correct for these biases.
On 2021-02-18 23:34:23, user Max van Berchem wrote:
There is a mistake in the discussion part. Moderna is the one that had 30 severe cases, all in placebo and Pfizer 9 severe case in placebo and 1 in vaccine arm.
On 2023-09-14 09:01:23, user Chris Iddon wrote:
CO2 measurements are taken for one occupied day in November 2022 and compared to PCR positive cases during the period Aug21 to Aug22. There is no data presented here to suggest that the single day CO2 reading is representative of the room ventilation during the whole period of Aug21 to Aug22. Also there doesn't appear to be any record of how many of the occupants were PCR positive prior to Aug21 and therefore have some level of prior immunity, nor how often the occupants are tested by PCR.
On 2022-01-14 06:53:34, user Andy Bloch wrote:
The hazard ratios for ICU admission and mortality were unadjusted. This is not clear from the abstract, but the full text explains: "The observed number of patients meeting each of these endpoints was inadequate for multivariate analyses due to the absence of counts within multiple covariate strata." Considering that non-SGTF (Delta) were more likely to be 60 or older, nearly twice as likely to be unvaccinated (49.7% v. 26.6%) and about 1/3 as likely to have had 3 shots boosted (4.6% v. 13.4%) there should have been some adjustment or stratification made, perhaps using rates from other studies. The CDC is citing this study as showing a "91% reduction in risk of mortality" and that is very misleading since the figure is unadjusted.
On 2022-01-13 17:45:28, user T S wrote:
There is a calculation error in Table S8 for the Hispanic noSGTF. The percentage listed in parenthesis is incorrect and appears to be a transposition of the line above it for Black non-hispanic.
Also, I would hate for this article to appear biased upon publication but listing the actual value for increased risk of Omicron infection as compared to Delta for people with prior Covid diagnosis "4.45 (3.24-6.12) fold higher" yet listing a general statement of just "higher" when presenting that same data for prior vaccines leads an astute reader to assume the authors are using framing to present a bias to the data in favor of vaccines. With our top health officials playing politician by misrepresenting studies and dodging questions it is ever more important that actual scientists and peer reviewed studies are above reproach or we risk further deteriation of the rapidly declining public trust in scientists and studies.
"Among cases first ascertained in outpatient settings, adjusted odds of documented prior SARS-CoV-2 infection >=90 days before individuals’ first positive test during the study period were 4.45 (3.24-6.12) fold higher among cases with Omicron variant infections than among cases with Delta variant infections infection (Figure 1; Table 2). <br /> Similarly, adjusted odds of prior receipt of each vaccine series (1, 2, or 3 doses of BNT162b2/mRNA-1973, or Ad.26.COV2.S with or without a booster dose of any vaccine) were higher among cases with Omicron as compared to Delta variant infections."
On 2025-11-13 16:48:17, user Kevin Davy wrote:
Interesting paper! Is self-reported health in 80+ yo really "superaging"? The most common definition is ~ 80 plus year olds who have the memory capacity of individuals 30 years younger. Mortality has been studied as an outcome since the inception SRH measures. Don't quite a few studies report lower mortality among those who self-report better health?
On 2021-04-27 06:36:03, user Miguel Pedrera Jiménez wrote:
Hi, I'm Miguel Pedrera, from Hospital Universitario 12 de Octubre in Madrid, Spain. Great job, very interesting manuscript. We have recently published an article about a methodology to obtain useful data for clinical research from EHRs, applied to COVID-19. I am sending you the link in case it is of your interest: https://www.sciencedirect.c...
On 2025-07-25 12:31:33, user Evolutionary Health Group wrote:
We at the Evolutionary Health Group ( http://evoheal.github.io ) really enjoyed this paper.
Here are our highlights:
Evaluating co-occurrence across different surfaces in the built environment led to the identification of 15 pathogens as indicators for elevated SARS-CoV-2 abundance.
Methodology was flexible, including the evaluation of both cellular and viral genomes.
Study potentially lays the groundwork for improved hospital surveillance of antibiotic resistance gene abundance applying the same approach in a different context.
On 2020-03-12 20:49:50, user personfromreddit wrote:
Thank you for the well-designed and informative experiments and paper. The implications are critically important to managing COVID-19 patients and preventing further outbreak. However, in seeking publication for this paper I strongly recommend that the authors bolster their discussion -- specifically on the topic of in vitro vs. in vivo aerosolization. I am not a virologist, but I understand that just because SARA-CoV-2 can be aerosilized in similar systems that you used in your paper, that it may not mean there is clinical aerosolization and viral spread. While you touch on this in the paper and state that it is "plausible" that aerosol may contribute to spread, I think there needs to be more nuance to this discussion as to how likely this may be. To my understanding there are notable cases where in vitro systems such as the one in this study have found steady viral aerosols, but clinical aerosolization was not significant enough to present a means of viral spread (i.e. ebola).
I think discussing the topic of aerosolization in more detail are especially important for this paper during this time period -- one where many non-virologists and laypeople are reading high-brow virology literature. A more careful and detailed discussion of the implications of these findings will help prevent undue interpretations of these results.
On 2021-07-30 08:33:39, user Rob Leeson wrote:
The most important end point is death, as there is no pre hospital treatment for covid in the UK and the most at risk group was the over 65s surely an over 65s split 50-50 with placebo would have been more realistic. This looks a bit like the Tamiflu studies where there was a shorter time to recovery but NO protection for flu progressing to pneumonia and death.
NHS England removed the original link early 2021.
On 2020-05-02 07:17:53, user MichaelWoon wrote:
Do you think nobody in the test was positive?
On 2022-01-05 09:52:18, user Zacharias Fögen wrote:
Dear Authors,
Thank you for this in-depth analysis, yet you draw conclusions that lack physiological plausibility.
First, you draw conclusions from serum neutralisation to transmissibility. As any SARS-COV-2 variant replicates in nasopharyngeal epidermal tissue, and protection from these kind of viruses is solely reliant on sIgA or IgM but not on monomeric IgG, you cannot draw this conclusion.<br /> This has been well researched in animal coronavirus vaccinations and is also the reason why Polio vaccines are given p.o. instead of s.c. in endemic regions.<br /> Furthermore, you are describing the variant as hyper-transmissible, yet again, there is no physiological explanation for this. Entry into epithel cells is only a micrometer of the distance needed to move from one person to another. Changes in spike protein have no effect on the men-to-men transmission. <br /> Furthermore, you are relying on studies which do not control for differences in contacts between the group now infected with Omicron variant and those previously infected with Delta variant. There are less restrictions now in England and Denmark then when Delta variant arrived. Statistics also show that age group 20-30 is primarily infected with Omicron, and as those have the most variable contacts, there is clear indication of bias.
Best,
Zacharias Fögen
On 2021-09-28 18:41:49, user Tigist Workneh wrote:
You can find the published article on EJHS journal following the link https://www.ethjhealths.org...
On 2020-06-13 03:34:34, user kpfleger wrote:
Why are the 25(OH)D levels reported here (43 or 44 nmol/L w/ IQRs of 32 or 31 respectively for the n=580 C19+ and n=723 C19- UK Biobank cases) so much higher than those reported in Hastie et al, "Vitamin D concentrations and COVID-19 infection..." Diabetes Metab Syndr., 2020: https://pubmed.ncbi.nlm.nih...<br /> which reported median 25(OH)D of 29nmol/L w/ IQR 10-44 for C19+ & 33 IQR 10-47.<br /> This is a huge difference for 2 papers with online publication dates 2 days apart both pulling data from the same source.
See also D'Avolio et al, "25-Hydroxyvitamin D concentrations are lower in patients with positive PCR for SARS-CoV-2", Nutrients, 2020 and Meltzer et al, “Association of vitamin D deficiency and treatment with COVID-19 incidence”, medRxiv, 2020 for 2 different studies that found in contrast to the top level conclusion here, that low D was associated with higher C19 incidence. Discussion of all 4 paper of these papers in the "D deficiency might be associated with higher infection risk" of the review: "Low vitamin D worsens COVID-19": http://agingbiotech.info/vi...
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 2021-04-25 15:46:55, user Annette Toledano wrote:
Congratulations on advancing the understanding of the range of symptoms in hospitalized COVID-19 patients. I am surprised that pain was not a common symptom in the population you examined. I found elsewhere (Persistent neurologic symptoms and cognitive dysfunction in non-hospitalized Covid-19 “long haulers.” E. Graham, March 2021, Annals of Clinical and Translational Neurology), pain afflicted 43% of "long haulers." <br /> Are you aware of a review paper on the clinical manifestations of non-hospitalized COVIS-19 patients? <br /> It appears the Cov-Sars-2 virus affinity to target organs varies in different populations. In some people, the innate immune system's initial response in endothelial cells can lead to lung or vascular symptoms. In others, infected nerve cells can lead to pain.
On 2020-04-24 01:46:41, user Mike wrote:
Note: the original version has been previously discussed at length. medrxiv is redirecting that paper to v2 (this paper), making those comments are no longer available. This is a link to those comments: https://disqus.com/home/dis...
Below is my original comment with updates for this version of the paper
This was certainly an interesting paper. They’ve done a lot of work and the findings are notable. IMHO it warrants as much attention as the original pro-HCQ study via Dr. Raoult (~3/15). While it is entertaining, I will add that it is not conclusive, nor without fault. A double-blind study is still required.
It is important to note that this is version 2 of a previously released paper and it is much the same, with no major differences in the conclusions reached compared to v1. Therefore, my previous comment still holds true. Below I’ve included them followed by a new list of observations.
Observations/Questions (updated)
1. "hydroxychloroquine, with or without azithromycin, was more likely to be prescribed to patients with more severe disease” (p.12)<br /> 2. “as expected, increased mortality was observed in patients treated with hydroxychloroquine, both with and without azithromycin” (p.12) — I assume it’s expected because the patients given drugs were in a more severe state (and more likely die regardless of treatment)<br /> 3. "we cannot rule out the possibility of selection bias or residual confounding” (p.13)<br /> 4. demographic: 100% male, 66% black, median age ~70 (59 youngest); (Table 2, p.17)<br /> 5. uses PSM, which despite a common practice, could be considered controversial (https://gking.harvard.edu/f... "https://gking.harvard.edu/files/gking/files/psnot.pdf)")<br /> 6. Unless I missed it, I didn't see any specifics about how the treatments were administered.<br /> - How long before death were patients treated?<br /> - What was the quantity/frequency/duration of the treatments?<br /> - Were the treatments consistent between hospitals?<br /> 7. The rate of ventilation was less in HC+AZ (half of the HC and no-HC rates). Why was that? What does that suggest?<br /> 8. Although they were statistically insignificant, what was the result of the 17 women not included in the study?<br /> 9. Why does the paper seem to address political points? It seems like the Abstract is editorialized, which I'm not used to. The result seems to address topical issues of the times, having awareness of other similar studies being conducted, rather than a standalone independent study of its own. I interpret this as potential for some analysis/deciphering bias. I don't mind in the Discussion sentence as it's normal, I'm just not as accustomed to seeing it in the Abstract.
New List of Observations
1. There seems to be some bias in the number of healthy people with no-HC treatment, but left in the study. Those people are going to be unlikely to die to begin with. This is not a comparison of apples to apples.??
To clarify:<br /> ?- Dramatic difference in percentage of people people that had fever temperatures (38.1-39.0ºC / 100.58-102.2ºF); HC:11.3%, HC+AZ:11.5%, no-HC:7.6%. There’s ~4% difference between treated and untreated fever temps (more likely to die) in favor of untreated cohort. ?<br /> - Compare that with the percentage of people that had normal temperatures (35-37ºC/95-98.6ºF);HC:56.7%, HC+AZ:52.2%, no-HC:61.4%. There’s a 5-9% difference between treated and untreated normal temps (likely to not die) in favor of untreated cohort.
??So in this study, there was a larger proportion of people that did not have fevers, suggesting the data may be padded. In absolute numbers it's approx. a 40 person swing, which is a fairly large percentage in such a small study/survey. Similar observations are for systolic blood pressure and breaths per minute. There appears to be more healthy people? again.
2. Creatinine is created when muscles breakdown creatine. It’s a waste product removed by the kidneys. Levels are elevated when the kidneys begin to fail. Notice, there’s a much larger presence in the HC-groups, which suggests there was a larger percentage of these patients experiencing kidney failure.?
3. There are an awful lot of missing data in solely the no-HC group for statistically significant criteria. For instance Erythrocytes (red blood cells that transport oxygen), there 11.4% (!) missing in no-HC patients, yet that category has a P-value of 0.001 (<0.05 is statistically significant).??
Hermatocrit is the same way (missing 11.4% for no-HC). It’s also related to red blood cells, it is the ratio of red blood cells to the blood volume. Same missing amount for Leukocytes (white blood cells) test. And also Lymphocytes —white blood cells in lymphatic system, which transports fatty acids from the digestive system and white blood cells from the lymph nodes into the bones— not only has a lot of missing data, but the disproportional low count (<800 per mm^3) may warrant further investigation.
4. Even looking at the statistically significant Cerebrovascular disease, there are a much larger percentage of HC-only patients per its cohort.?
5. Table 4 describes a greater percentage of people using HC+AZ being discharged (recovering) w/o ventilation; 5% more than no-HC patients. Keep in mind that 30% of the no-HC patients were given AZ.
On 2020-12-05 11:14:49, user Robert Brown wrote:
A very useful study, but more questions than answers surely? I respectfully suggest an alternative one-line summary might read:
“Administration of a significant single oral bolus of Vitamin D3 (200,000iu) in COVID-19 patients, close in timeline to onset of ARDs, with Baseline 25(OH)D values averages of 21.0ng/ml and 20.6ng/ml, where significant numbers are on Corticosteroids and or PPI (which reduce magnesium and zinc (via zinc fingers), both ‘D’ co-factors), when almost all established risk factors were skewed against the treatment arm, did not result in any benefit in hospital stay or mortality.but did show tendency to reduced oxygen requirement.”
See lower post for detail.<br /> Erratum reference 2 is a TCT and not RCT – apologies.
On 2021-07-20 01:11:22, user The Davidtollah wrote:
From your pull quote: "our simulations"<br /> The study "proves" nothing but that these researchers plugged assumptions and numbers into a simulation and the simulation spit out results. Real-world analysis (such as in the study here at this site) and clinical studies show that masks do not work against airborne viruses. (Studies that are usually referred to that claim efficacy for masks are mechanistic, which, like simulations, can only show how something might work, and do not demonstrate whether they actually work or not.)
You are wrong, and your message demonstrates you don't know the difference between a simulation and an analysis of real-world results. Simulations must always be compared to actual data, and if they don't match, the simulation is obviously flawed and must be rejected.
On 2021-05-26 18:19:28, user rusbowden wrote:
Keep looking, Norbert-man. Seems you cherry pick, which is what I suspect these researchers of doing too. That's politics, not science. Aerosol can get through, which means the effectiveness is not 100%. Unless there is smart steering on board the tiny particles, or the mask material actually repels them, the mask will stop some. (It would be interesint for someone to find a smart mask material that draws the aerosol.) Note too, how vapor saturation of masks from the wearer's breath increases mask effectiveness. This is out there as well.
On 2021-05-30 17:20:05, user Badger Vanamburgh wrote:
It’s astonishing that anyone thinks that this study is in any way scientific, controlled, with conclusions based on anything but bias. It’s alarming that such nonsense writings like these are even taken seriously enough for someone to decide to publish it, spread it around the internet. The times of truth, fact and reality ruling the day, are behind us. They are destroying science and destroying our democracy. That is very sad indeed.
On 2023-01-03 01:06:03, user Myssi Graves wrote:
How can they say it was unexpected that increased doses would increase risk when there’s decades of evidence of this with flu vax. It was an obvious outcome to anyone educated on the topic. Sadly anyone who mentioned this possibility was vilified.
On 2021-06-04 19:32:52, user Anil Gulati wrote:
This article is published in DRUGS. DOI https://doi.org/10.1007/s40...
On 2020-04-03 12:58:04, user Kate wrote:
How can you find a correlation in an observational study? I'm not even touching on the issue of not controlling for any confounding variable
On 2020-04-13 15:17:54, user Advocatus Diaboli wrote:
It is one thing to talk about "observational studies" in general, and quite another to talk about a study on an ongoing and rapidly evolving global crisis.
They controlled for certain factors (e.g., wealth of the country, which can reasonably be considered a proxy for the development of the health care system). If they had other factors that they could have considered but did not, then I would agree that it is a weakness. However, given the paucity of data coming in, I seriously doubt that they had access to much more information across the full data set that could have allowed a more nuanced analysis.
They presented a hypothesis that seems both interesting and relevant (both in terms of projections of impacts and possible response options). As long as it is presented as a hypothesis and not a fact (the latter would be misleading), I believe it is better to have it out. Others can (and should) repeat the analysis, and with the progression of the virus we should get a clearer picture. If you are aware of data that they could have used but did not, you would be more than welcome to improve the analysis and publish your results.
On 2024-08-06 18:36:26, user Cindy wrote:
I would like to include some feedback regarding data analysis (full disclosure, I work for Olink), which I hope will be beneficial to both authors and others who are analyzing similar data from Olink:<br /> We recommend calculating Limit of Detection (LOD) according to manufacturer’s guidelines, specifically LOD for Explore HT should be calculated based on actual project data rather than use of estimates of LOD from unrelated validation data. <br /> The best practice is to use Olink Analyze functionality to determine the LOD for each project (available in the latest version!). <br /> Replacing values below LOD with LOD/2 is not recommended. It will artificially inflate coefficient of variation (CV) values. Instead, apply an LOD cutoff specific to the project data. Removing values below the project-specific LOD when calculating CVs ensures a more accurate representation of data variability.<br /> I am happy to coordinate any discussions with the Olink team to facilitate.
On 2021-01-23 16:10:09, user Prasad Kasibhatla wrote:
Do the top and bottom panels of Figure 1 represent different ambient conditions?
On 2020-04-14 10:07:38, user Rob wrote:
Thank you for this paper, as usually there is plenty of time to consider and/or analyse all angles within the context of Covid-19 I acknowledge the challenges. Beside, solid suggesting already mentioned. Please consider this as influencing the effect seen in relation to smoking and Covid-19 severity. Age (65+) are most risk of severe covid-19. Often smoking rate in 65+ is well below the national average. In the US ~14% is the smoking prevalence whereas 8.4% for 65+, in the UK ~16.5% is the national smoking prevalence whereas 10% 65+ (figures from CDC and ONS). Life Smokers die younger or likely to have comorbidities especially if they have smoked. Combines with other factors e.g under reporting and other mentioned already in the comment. Finally the effect size may be further attenuated by air pollution often severe in cities where most Covid-19 cases are found. Even if the paper shows a real effect, this would def change the effect size. In any case, it would not neccesarily show that smoking is protective
On 2021-08-05 01:41:37, user Tony Weddle wrote:
Those deaths are all cause deaths. In fact there were twice as many COVID-19 deaths in the placebo group as in the vaccinated group, though the figures are very low (1 and 2 respectively). By the way, there were 15 all cause deaths in the vaccinated group, 14 in the placebo group.
On 2021-08-18 18:30:23, user Steve Kirsch wrote:
There were two people in the placebo group who got the drug after the unblinding. The paper never talks about the cause of death from those two people. This is EXTREMELY important. Does anyone know?
On 2021-08-07 20:35:21, user vinu arumugham wrote:
Table S4 shows 4 deaths in the vaccine arm and 1 death in the placebo arm due to cardiac arrest. <br /> The probability that this outcome is a chance occurrence is 1.5%.
(((21999÷22000)^21996)×((1÷22000)^4)×(22000!))÷(21996!×4!) =0.0153 or 1.5%. <br /> So 98.5% chance that the vaccine CAUSED the excess cardiac arrest deaths.
Table S4 also shows 1 excess COVID-19 related death in the placebo arm.<br /> So to prevent 1 COVID-19 related death, the vaccine causes at least 3 deaths due to cardiac arrest.
On 2020-04-01 16:13:26, user Rosemary TATE wrote:
Hi Knut, I enjoyed reading your paper and found the models and your suggestions very interesting. However, I agree with Aaron, I was disappointed to find no evidence to back up the claim of the title. A much clearer justification should be made (with data and appropriate references) or the title should be changed. I suggest the latter as evidencing different strains seems to be a very secondary aim. <br /> I totally disagree with your that almost nobody considers epidemiological data for decision making. This is simply not true. Starting with John Snow there are far too many examples to name.
On 2020-03-29 10:45:38, user Bob O'Hara wrote:
Someof us had some problems with this manuscript, so we wrote a response: https://doi.org/10.32942/os...
Abstract: The ongoing pandemic of the severe acute respiratory syndrome <br /> coronavirus 2 (SARS-CoV-2) is causing significant damage to public <br /> health and economic livelihoods, and is putting significant strains on <br /> healthcare services globally. This unfolding emergency has prompted the<br /> preparation and dissemination of the article “Spread of SARS-CoV-2 <br /> Coronavirus likely to be constrained by climate” by Araújo and Naimi <br /> (2020). The authors present the results of an ensemble forecast made <br /> from a suite of species distribution models (SDMs), where they attempt <br /> to predict the suitability of the climate for the spread of SARS-CoV-2 <br /> over the coming months. They argue that climate is likely to be a <br /> primary regulator for the spread of the infection and that people in <br /> warm-temperate and cold climates are more vulnerable than those in <br /> tropical and arid climates. A central finding of their study is that <br /> the possibility of a synchronous global pandemic of SARS-CoV-2 is <br /> unlikely. Whilst we understand that the motivations behind producing <br /> such work are grounded in trying to be helpful, we demonstrate here that<br /> there are clear conceptual and methodological deficiencies with their <br /> study that render their results and conclusions invalid.
On 2020-04-18 04:34:06, user Zev Waldman MD wrote:
I agree with prior commenters that people who suspected that had or were exposed to Covid would be more likely to seek antibody testing. I see that participants were asked about prior symptoms, but it would also have been nice to ask about prior possible exposure concerns, If both numbers are very low, that would provide some reassurance about this possible bias.
I really wanted to address another issue: the calculation of the infection fatality rate, i.e., estimated deaths/cases. It seems that a lot more thought went into trying to get an accurate case count than an accurate death count. They seem to take it as a given that 50 people died of Covid in the county as of April 10; however, like case counts, there are multiple reasons to suspect this number of deaths might be higher:
People who actually died of Covid may never have been tested, and thus may not be included as cases or deaths
The doubling time of deaths that was used to project to April 22 is based also on reported deaths; if reporting of deaths is delays, the doubling time may appear slower.
I did appreciate the authors' efforts to validate the antibody testing. That's useful information.
I worry that, because these results support their prior beliefs, some readers may take these results at face value and push them for policymakers to use before they have been more widely vetted by the scientific community.
On 2020-04-17 20:25:02, user Mortal Wombat wrote:
Hold on, they made no adjustment for self-selection of symptomatic people in their study?
Researchers, at least please tell us the number of people shown the Facebook ad so we can have some sense of the potential for self-selection -- i.e. how many saw it but chose not to participate.
This seems problematic given the population demographic adjustments that were necessary. The researchers say that white women were heavily oversampled while hispanics and Asians were heavily undersampled, and that population adjustments led them to adjust the observed prevalence of 1.5% up to a population-weighted prevalence of 2.81%.
It would appear highly likely that the relatively affluent population (white women) would have the interest and capacity to be a roughly random sampling -- people with no prior symptoms just interested in knowing. Whereas for lower-income populations, there may be less ability to simply participate out of interest, and may have been a higher self-selection drive of the previously symptomatic to get themselves tested.
Thus I find the upward adjustment in the numbers quite suspect. To come up with an overall result that's _higher_ than the raw outcome of the study when you know that people who've been sick will be the most motivated to get themselves tested just seems perverse.
Did the study not even ask people whether they had been sick over the past couple months? Why not? That at least could've given some sense of whether self-selection was biasing results in the samples.
On 2020-04-19 14:45:07, user Tomas Hull wrote:
This study is not perfect but no studies ever are.
The study shows pretty close estimates of the much lower mortality rate than previously estimated, likely slightly above the seasonal flu of 0.1% and lower than the German study estimates of 0.37% of a town of 12,000 inhabits where an accelerated infection likely happened due to the town carnival 2 months earlier.
More similar study results will soon be published, including L.A. County, MLB organization from 27 cities, and many European countries, which will probably confirm that CoV2 is much more widely spread than initially thought, and with the infection mortality rate slightly above the seasonal flu of 0.1% and below 0.37% from the German town of Gangelt, where the much faster infection rate initially occurred due the the town festival in February.
On 2020-04-22 04:47:58, user G.O.B. wrote:
Comparing a nasopharyngeal swab viral PCR test to a qualitative serum antibody test is apples and oranges. The NP swab has a high, user dependent false negative rate (low sensitivity), as most of those who perform the test are only get a nasal swab and not reaching the nasopharynx.
Here’s an idea for a study: test serum antibodies on those with a history of negative PCR of NP swab - you’re likely to find a high false negative rate (likely due to improper swabbing technique)
On 2020-04-18 21:28:18, user Shiva Kaul wrote:
In the statistical appendix, the variance of the estimates of sensitivity and specificity seemingly have no dependence on the sample sizes. For example, if the empirically observed sensitivities \hat{r} of 91.8% and 67.6% were observed on samples 100x larger, the calculated variance would not decrease, though intuitively there would be less uncertainty.
Is there a missing factor of n, or have I just been sheltered-in-place too long?
On 2020-04-26 15:47:52, user DaveSezThings wrote:
The analysis has made a significant, basic error in handling the uncertainty associated with the specificity of the test. Leaving aside concerns regarding the applicability of the delta method, the mistake arises in that computing the standard error the values for Var(s) and Var(r) should be divided by the sample numbers used in the studies to establish these values, not the main study sample size n=3,330, which is used across all terms in the relevant equation in the appendix (it's on the middle of page 3). We can see this as the range of specificity (95% CI 98.3% to 100%) is sufficient to explain the observed data with zero genuinely positive cases.
Basically this destroys the conclusions which should now be along the lines of "unfortunately the test used for this study was not specific enough to support any conclusions beyond setting a maximum level of infection."
Stuff happens, time is short etc... the authors should just issue a correction. It'll be quick and easy and save a lot of irrelevant speculation,
On 2021-04-16 16:18:49, user S Wood wrote:
Since we first posted this a couple of results from direct statistical measurements have come out that are broadly in agreement with the paper's results on incidence and its timing relative to lockdown. Figure 1 of this REACT-2 report shows the reconstructed time course of symptom onset, which lags infection by 5 to 6 days. Reported ONS incidence reconstructions from statistical infection survey data tell a similar story. This paper, now published in Biometrics, also produces similar results on incidence and R, by a different approach.
On 2020-06-17 16:13:35, user Naama Geva-Zatorsky wrote:
This paper is under review in Experimental Biology and Medicine
On 2022-06-06 19:03:39, user Chris wrote:
During this period, the test positivity rate was about 7%. Some of those tests may be random (schools, needed for travel, etc.), but it seems odd that the rate of having COVID would be lower for people who go get a test than for people who just pick up the phone and answer a survey - even if it related to a 2 week period. Note the COVID tests would pick up people who were infected over some period in the past as well.
On 2020-09-17 11:50:43, user Brian Kennedy wrote:
Semi - comment, also a question.
I am an economist living in Bangkok, Thailand, so this is pretty far out of my area of expertise. Thailand was the first country to get the virus outside China, but it never took off here, at this date still less than 4,000 total cases, and 60 deaths. I think there were a variety of things that led to this, but clearly early and near universal mask usage was part of it.
Your paper looks very interesting, but I could not really follow all the math. So I will trust you on it :)
My question is one of emphasis by public health officials in the U.S. Why has there not been a push on the issue of Viral Load? It seems to me that it is very important concept - even if using the mask doesn't always prevent you from getting infected, it will still reduce the viral load, giving your body more time to deal with the virus, significantly increasing your bodies chances of fighting it off.
Why has this issue been (it seems to me) largely absent from the public sphere, and from the arguments public health officials use to promote mask usage? Note if it has been there and I am missing it from far away, just say so.
Thank you for helping a laymen in your field understand :)
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 2021-08-22 22:00:56, user MeandMyShadow2 wrote:
Link?
On 2021-08-12 19:41:47, user Steven Ramirez wrote:
I wonder if the study disentangled time since vaccination. If protection diminishes over time for both vaccines it needs to be controlled for.
On 2021-08-12 12:38:42, user Peter Colin wrote:
Has there been any analysis of vaccine effectiveness in (1) persons who tested positive for Covid prior to vaccination, versus (2) persons who did not test positive prior to vaccination (negative and/or untested)?
On 2021-12-23 12:09:50, user Matthew Nelson wrote:
This is a superb paper, especially the careful approach to CNV calling and the Bayesian methods used throughout. Searching through the supplementary material, there is one important piece of the story that appears to be missing. Where are the counts of de novo missense, PTV, and CNV mutations per gene? These were nicely captured in the Satterstrom and Kaplanis NDD publications. These are really important for understanding potential population sizes for prioritizing therapeutic discovery and development. Have I missed them?
On 2022-08-22 03:33:38, user BGThree wrote:
In Section 2.1 "Cell culture system to express SC2 proteins from synthetic mRNA-1273" the authors disclose incubation of mouse and human cells with 200uL vaccine, but do not disclose corresponding controls. Specifically, mouse and human cells should be incubated WITHOUT addition of vaccine. This control is needed to demonstrate the proteins extracted from lysed cells are transcribed from vaccine mRNA not innate mouse or human mRNA.
On 2022-01-02 22:26:52, user madmathemagician wrote:
I believe the dataset used in this article is
https://www.ecdc.europa.eu/...
but it is not linked in the article.
On 2020-10-19 16:03:32, user Rogerblack wrote:
This studies depression measure ASSUMES A HEALTHY PATIENT. 'little energy', 'trouble concentrating' 'moving slowly' = a minimum score of 3 due to physical symptoms of longcovid/fatigue.<br /> If very exhausted, this can easily rise into the 'severely depressed' range.
It is not unreasonable to use the PHQ-9 or similar as a screening measure of disease severity.
To use it in a patient population suffering from fatigue, concentration problems, ... is guaranteed to cross-read between those symptoms and anxiety - it is useless without a careful assessment of each question.
It absolutely cannot justify sentances such as 'A significant proportion of COVID-19 patients discharged from hospital experience ongoing symptoms of breathlessness, fatigue, anxiety, <br /> depression and exercise limitation at 2-3 months ' without much more work, as it will lead to the conclusion that treating depression may benefit the patient when there is no depression, and it's a scale artifact.
On 2022-10-24 17:42:56, user CDSL JHSPH wrote:
Dear Mekkes et. al.,
Thank you for sharing your work with us! Creating models to predict neuropathological diagnosis based on temporal signs and symptoms is very significant research, and I’m looking forward to seeing where this heads in the future! I enjoyed reading this paper, especially since it introduced me to techniques and concepts I was previously unfamiliar with. That being said, while reading I did notice some parts that I think could be given further clarity in order to make this paper more accessible to those not within the immediate field. There are a lot of abbreviated disorders mentioned, and I noticed that some were explained in the introduction however I could not find the proper matching terms for disorders abbreviated in later sections. I think it would be a great benefit if there were a word key with the disorders and their corresponding abbreviations. Especially since different disorders may be represented by the same acronyms, so googling it may not provide the reader with the correct one. Also, I was wondering if you plan to do another study focusing on optimizing these models to diagnose mental illnesses and psychiatric conditions in a separate paper? I understand the main focus on brain disorders and neurodegenerative diseases since those can be linked to prominent neuropathological changes, but when reading the abstract I was given the impression that mental illnesses would be focused on to a larger degree than I noticed in the paper. I would love to see any future steps you take with this, especially since the alterations in cognition and behavior associated with mental illnesses can be observed from live patients, and don't necessarily have to be inspected retroactively like from brain donors.
On 2020-04-10 14:54:38, user Neil Lancastle wrote:
For clarity: Figure 5 is countries with BIGGEST falls in mean growth... from the text on p7...'growth rates have fallen most compared to earlier period' and, excluding China, these countries are France, Spain, Switzerland, Italy, UK and Norway.
On 2021-10-01 11:24:21, user Mahalul Azam wrote:
This work now published in the Int. J. Environ. Res. Public Health with the DOI https://doi.org/10.3390/ije...
On 2020-08-12 20:39:28, user David Curtis wrote:
There may be dozens of groups around the world doing studies like this on 30 or 40 patients. Some of them are naturally going to show "statistically significant" results. There's an argument that it's unethical to perform clinical trials with small sample sizes. This study is impossible to interpret. If you'd done the study with 100 cases and 100 controls we'd have something useful.
On 2020-08-12 06:05:43, user Christian Heebøll-Nielsen wrote:
It seems most of the differences between groups could be explained by the difference in time since symptoms onset. Have you made any effort to control for that?
On 2021-05-21 22:52:28, user Ilmari Haavisto wrote:
The beginning state of medication of disease is very important. Was it equal to both groups?
On 2020-03-02 16:40:24, user Abed Ghanbari wrote:
We estimated that 18,300 (95% confidence interval: 3770 – 53,470) COVID-19 cases would have had to occur in Iran, assuming an outbreak duration of 1.5 months in the country, in order to observe these three internationally exported cases reported at the time of writing.
How did you reach to these numbers?
On 2021-12-02 13:07:03, user Max Daigle wrote:
Is this Peer Reviewed?
On 2021-09-19 18:43:06, user 4qmmt wrote:
While interesting and adding to our knowledgebase, and thus possibly important for something in the future, these quantitative titer studies don't seem to be useful for predicting outcomes or comparing protection. Many are now using these titer comparisons in vaccine studies in what appears to be the hope of predicting future immune response for both vaccine and or previous infection.
But the authors of the study Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection in Science point out the obvious and logical problem with these studies. pg 6:
While immune memory is the source of long-term protective immunity, direct conclusions about protective immunity cannot be made on the basis of quantifying SARS-CoV-2 circulating antibodies, memory B cells, CD8+ T cells, and CD4+ T cells, because mechanisms of protective immunity against SARS-CoV-2 or COVID-19 are not defined in humans.
pg 2:
A thorough understanding of immune memory to SARS-CoV-2 requires evaluation of its various components, including B cells, CD8+ T cells, and CD4+ T cells, as these different cell types may have immune memory kinetics relatively independent of each other.
On 2022-01-06 23:28:48, user Greg Nelson wrote:
This is encouraging, would love to see another paper comparing to the PCR negative population (2346 respondees - 951 with PCR positive (study population) = 1395 people) to see baseline frequency of symptoms
On 2020-01-24 20:05:36, user Warren Li wrote:
How could the ascertain rate be as low as 4.8-5.5%? I don't see clearly the basis or fitted evidences for this assumption?
On 2020-04-03 16:31:40, user Alexander Siegenfeld wrote:
These projections likely severely underestimate the number of deaths and hospitalizations because they assume that any state that has implemented three out of four interventions they consider (school closures, non-essential business closures, travel restrictions including public transportation closures, stay-at-home recommendations) will see an epidemic trajectory similar to that reported in Wuhan, China.
The Imperial College report released on March 30 that quantifies the impact of nonpharmaceutical interventions in Europe predicts that even with the complete lockdowns implemented by 10 out of the 11 countries studied, the number of new infections may still increase. Given that the response in even the U.S. states implementing all four of the interventions considered by IHME may be less effective than the European lockdowns, there is a distinct possibility that without action beyond that assumed by the IHME study, the rate of new deaths and hospitalizations may not only not peak and decrease as quickly as IHME predicts but may also continue to exponentially increase (albeit at a slower rate).
See our full comment here: https://tinyurl.com/yx8xxqsv
On 2020-04-02 10:24:38, user ansgarjohn wrote:
The output seems similiar to what Michael Levitt has observed. Here Netherland (growth rate slowing by 1% per day is the guesstimate) https://sinaas.blogspot.com...
On 2019-10-16 13:01:03, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT OCTOBER 13, 2019<br /> Monday, October 14, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,220, of which 3,106 confirmed and 114 probable. In total, there were 2,150 deaths (2036 confirmed and 114 probable) and 1033 people healed.<br /> 383 suspected cases under investigation;<br /> 2 new confirmed cases at CTE in Ituri in Mandima;<br /> No new confirmed deaths have been recorded;<br /> 1 person healed out of CTE in Ituri in Mambasa;<br /> No health workers are among the newly confirmed cases. The cumulative number of confirmed / probable cases among health workers is 161 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Governors of North Kivu and Ituri Raise Awareness on Ebola Virus Disease in Biakato, Ituri<br /> - The Technical Secretariat of the Multisectoral Ebola Epidemic Response Committee (CMRE) in collaboration with the Governor of North Kivu, Carly Nzanzu and Ituri, Jean Bamanisa, organized this Monday October 14, 2019 an awareness raising day on Ebola Virus Disease in Biakato, Ituri;<br /> - This tripartite awareness-raising aimed to share the experience of North Kivu on Ebola Virus Disease and to show that the movement of people between the two provinces can encourage further spread of this epidemic in the region, as much as the last four cases recorded in North Kivu (in Beni and Kalunguta) came from Biakato;<br /> - The governor of North Kivu has indeed responded favorably to the invitation of the Technical Secretariat of the CMRE because he wants to reinforce the surveillance in his province and refuses to see his province plunge into the epidemic;<br /> - To achieve their objectives the two governors were accompanied each by a strong delegation, where one finds the presidents of their provincial assemblies and some influential deputies of their respective countries;<br /> - In addition, the Ebola Virus Epidemic Response Coordination Team, which has been in the Mambasa Health Zone in Ituri for the past week, has been monitoring Bavalakaniki Control Points and Mabakese in this health zone.
VACCINATION
MONITORING AT ENTRY POINTS
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2019-11-17 04:20:48, user GuyguyKabundi Tshima wrote:
EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS AT NOVEMBER 15, 2019<br /> Saturday, November 16, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,292, of which 3,174 are confirmed and 118 are probable. In total, there were 2,195 deaths (2077 confirmed and 118 probable) and 1070 people healed.<br /> • 517 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • No new deaths of confirmed cases have been recorded;<br /> • No cured person has emerged from CTEs;<br /> • No health worker is among the new confirmed cases. The cumulative number of confirmed / probable cases among health workers is 163 (5% of all confirmed / probable cases), including 41 deaths.
NEWS
Goma opens leadership capacity building workshop for Ebola epidemic response to Ebola Virus Disease.
• The coordinator of the epidemic response to Ebola Virus Disease in North and South Kivu Province and Ituri, Prof. Steve Ahuka Mundeke, opened this Saturday, November 16, 2019 in Goma North Kivu a workshop on building the capacity of actors involved in the response against Ebola;<br /> • For four days, participants, coordinating and sub-coordinating officers from the response, the Ministry of Health, the World Health Organization (WHO), national security, CDC and DFID will be equipped with management skills epidemics before, during and after the tenth epidemic of Ebola Virus Disease, especially in the event of any outbreak;<br /> • According to Prof. Ahuka, this workshop will not only benefit this epidemic, but will help, through acquired skills, to cope with other epidemics or other crises in a collective and individual way. " Each participant will be able to use these skills in his daily life ," he concluded;<br /> • This training for the response officers, from 16 to 20 November 2019, is organized by the Ministry of Health in collaboration with WHO with funding from UKaid from the British people.
VACCINATION
• 93 people were vaccinated with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two Health Zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 252,835 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine complements the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this outbreak, manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. It has recently been approved.
MONITORING AT ENTRY POINTS
• Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 117,333,420 ;<br /> • To date, a total of 112 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.
As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:
On 2021-07-12 20:50:31, user David Epperly wrote:
Some good details. Should add diary info from each of the infected as available to include: recollection of interactions with patients 0a and 0b and 0b's recollection of all interactions with all infected. Each interaction should include distance, time, and environment. More info about tent structure, identity blurred photos of event wide-shots, and historical prevailing wind conditions for area. Help us understand how transmission likely occurred. Also state relationships of each infected (spouse, close friend, acquaintance, ...) to help understand likely interaction levels. It appears 0a and 0b are related. Tell us. Move info on Delta into a supplement and focus on details of the event. Create a supplement for additional event / relationship / transmission details. Capture more of what happened.
On 2020-11-18 00:52:30, user Peter James wrote:
I am very much concern with ethics of this research. The authors stated that the local authorities approved the study. Although it is good to have a local buy in in such research, I believe that it would have been prudent and ethically appropriate to seek proper ethics approval from a recognised body either from a university or from the national ethics committee. There are ethics committees or IRB. The most popular one is the one in the ministry of health and sanitation (Sierra Leone ethics review and scientific committee). I believe that those local authorities, many of which are less educated and have little or no idea about research ethics and so getting their consent does not equate to addressing the ethics concerns of such study. I think this research should not published given that the authors did not adhere to the tenets of good research ethics. As a Sierra Leonean researcher, I have observed such issues, especially during the Ebola outbreak where western researchers disguising as NGO workers tend to exploit our system or take advantage of the weak quality assurance system when conducting research in Sierra Leone. I believe such cannot be done in Italy.
On 2020-05-19 01:55:12, user Subhradip AIIMS New Delhi wrote:
Testis has a very poor expression of TMPRSS2 , a critical determinant for virus entry. I suppose the entire molecular machinery must be present to make possible the virus entry and replication. Drawing a conclusion based on just 1 -2 target proteins will be an over simplification.
On 2025-11-24 05:16:17, user WILLY CESAR RAMOS MUÑOZ wrote:
The paper has been published in the journal Acta Tropica. DOI: 10.1016/j.actatropica.2025.107919
On 2021-01-26 14:17:45, user Rune wrote:
How is sensitivity and specificity of 69.7% and 99.5% for RAT calculated?<br /> 87.0% of PCR sensitivity of 90%-95% equals 78%-83%.<br /> 98.6% of PCR specificity of >99% equals >98%.<br /> PCR values according to SST "Information om PCR test for COVID-19 til almen praksis".
On 2020-07-27 06:43:06, user OxImmuno Literature Initiative wrote:
On 2021-02-09 23:10:30, user Robert van Loo wrote:
Why do the authors talk about overdispersion as some infections seem to occur in clusters, and for me that would mean underdispersion.
On 2020-05-06 21:53:31, user laurent moulin wrote:
Looks very interesting. Small Typo on figure 3 (I Guess...) with inversion of case and death on the France panel.
On 2020-09-16 07:52:15, user C'est la même wrote:
While this abstract may lead to catchy speculative headlines "60,000 undetected cases", unfortunately few will actually read this paper and realise the statistical and methodological limitations, meaning this is only of "suggestive" quality evidence and thus not generalisable.
"Perhaps there were Zero SARS-CoV-2 in Australia by July" sounds less catchy - yet the results also suggests this too.
I wonder why none of the journalists reporting on this article in the Australian media understand how to assess and communicate the quality of epidemiological studies on a simple scale of suggestive-moderate-high?
On 2020-04-02 20:21:31, user drumfucius wrote:
I am an O positive nonsecretor. Wondering if there is any info regarding susceptibility in regards to secreter status.
On 2020-04-17 19:08:16, user Jacob G Scott wrote:
See github repo for live updating and opportunities to contribute data: https://github.com/TheoryDi...
On 2020-04-30 13:53:43, user Alan Beard wrote:
Could the authors clarify about Smoking status please . Is it <br /> Current Smoker only OR <br /> Current and Former Smokers(which would include current Vapers)
On 2021-11-15 13:29:28, user Gary Porter wrote:
How long does this reduction in transmissability last?
On 2025-04-17 08:53:34, user Nitzan Paldi wrote:
For this research project to be considered valid, a few items are missing and should be provided by the authors. The most pertinent outstanding issues are:<br /> 1) The analysis of wMel was done in the months July-October, but the 2023 and 2024 summer wMel prevalence data is missing. This is important because almost all previous studies, including those done in Niteroi, have shown a very large dip in Wolbachia detection in the summer. <br /> 2) The analysis done to demonstrate the impact on dengue compares an arbitrary 10 year-period and takes into account the extremely high 2013 outbreak in Niteroi to demonstrate how Niteroi moved from one of the highest dengue cities in the state of Rio to the lower part of the spectrum. However, the period from 2013 onwards marks a massive reduction of dengue regardless of intervention or non-intervention status, and is identical in almost all >100k population cities in a 100 km radius around Niteroi. Moreover, the adjacent city of San Goncalo, that had half the level of dengue in the 2024 outbreak, had an almost identical reduction since the 2013 epidemic and all of this without any connection to Wolbachia releases. <br /> 3) The authors compare the dengue cases of Niteroi in 2024 with other cities in the state, but do not make a comparison with the city of Rio, across the bay, where another massive Wolbachia project had no impact on the prevalence of dengue in 2024, and resulted in a massive dengue epidemic of 1300 cases per 100k population. Omitting, or more likely “conveniently forgetting” this project underscores the unacceptable “cherry picking” of this manuscript. <br /> When these comparisons with the adjacent cities of San Goncalo (no Wolbachia – low dengue, half from Niteroi in 2024), and Rio de Janeiro (massive Wolbachia project- identical high epidemic dengue to non-intervention areas) are both taken into consideration, the conclusion is that the Wolbachia project had no demonstrable impact on dengue prevalence.
On 2020-06-22 14:22:44, user Valentina Viego wrote:
Forthcoming in Ciencia e Saude Coletiva
On 2020-04-24 20:18:46, user Diego B. wrote:
This article has been published on JAMA Network: <br /> https://jamanetwork.com/jou...
On 2020-10-28 17:44:49, user Andrea Camperio wrote:
Here you finally can find our research on the covid pandemic revealing that the first wave which was early modeled by Giordano et al., .(2020 reference in the text),and influenced the Italian government decision toward lockdown, using SIDDARTHE algorithm, was dramatically worse than what actually happened.
This suggests that there is good hope against the pessimistic perspectives of this second wave will be disattended as well. We are still actively developing new strategies to counteract virus effects. We are in the brink of implementing vaccination, new medicines are becoming available, older ones have been rehabilitated, so there is good hope for winning new battles to defeat the virus.
My personal predictions are that when the whole country, 60 million people, will have been infected, 2% of them will need special and intensive care( data on present fraction of infected needing intensive care about 2%), that means 1.2 million people, if intensive care will be available and sufficient, only 2% or less will die (data on at present survival rate in intensive care with Sars-Covid19), that means around 12,000 people, once everyone is (and if) exposed to the infection. However, if the intensive care, wont be sufficient, then at present 40% of the worst cases (1.2 millions) will risk their lives, without intensive care support, equal to about 400,000 people.
All depends on the evolution of the virus. The virus is evolving, in two directions, as all other aereal virus that affect humans, such as flu. First direction, that we already have seen, the virus is evolving toward being less and less lethal, because harming the host means extinguishing its self as well. The second direction, however, is more dangerous, and it is going toward faster and faster diffusion in new human hosts.
All human flu are very fast spreading, within 3-5 month they affect a very large portion of the population (30-40%), and very low mortality, usually 8.000 to 12.000 lives every year, mostly old and fragile individuals (about 0.0002 % of infected individuals). The Sars-Covid19 virus at present is killing at 0,1 % without the support of intensive care, and 0,002 with the supplement of intensive care, which means that is between 1000 and 10 times more lethal than a normal flu. In other words, if the virus will affect the whole population, in ten years or more (very improbably slow) the rate of people in intensive care will be below 10.000 per month, and affordable by our present health system. On the other extreeme, if the virus will spread to the whole population in just one year (extremely fast given the present rate), there will be ten times more people needing intensive care that the ones available, which will mean, not more, but around, 400.000 people at risk of failing with present rates.
Hence my personal prediction is that this pandemic in Italy, will take between less 10,000 and 400,000 lives more, before transforming in a normal human flu, depending on virus evolution regarding the speed of infection and the decrease of lethality.
On 2021-01-12 09:56:32, user Hein De Waele wrote:
How far are you with the peer reviews. When do you expect publication?
On 2025-10-13 14:50:42, user James L.Meisel wrote:
This article has now been published. Please point towards its final publication:<br /> Meisel JL, Navedo DD, Opole IO, Cohen GM, Bernard SA, Carmona H, Nahas AH, Eiduson CM, Papps N. From Reductionist Skills to Meaningful Learning: Trust and Humility in Bedside Cardiac Assessment. Adv Med Educ Pract. 2025 Aug 5;16:1305-1316. doi: 10.2147/AMEP.S520398. PMID: 40791751; PMCID: PMC12335936.
On 2022-07-23 03:21:31, user Jodi Schneider wrote:
Thanks for an interesting paper.
Distinguishing post-retraction citation would be useful. I can't understand that now from what you write: You note that 893/1036 (86%) of citations did not identify that the RP was retracted or raise any concern - but some of these citations appear to be BEFORE the retraction. It appears that you looked at publication dates (two paragraphs later), but the info isn't explicit enough for me to, say, extract numbers for a meta-analysis of post-retraction citations.
When you do publish this, I recommend depositing the data - citation contexts, sources, and your ratings of them could be relevant for other researchers (like me!)
For Figure 1 clarify whether the blue correspond to preprints, online first articles, etc.
A flow diagram (analogous to a PRISMA diagram) would help in the methods for a concrete example of what this might look like, see https://doi.org/10.1371/jou...
In the methods when you say you "extract citation information" - are you talking about sentences from the papers (citation contexts)? Try to be more clear when you revise this.
On Google Scholar ranking, it's a black box but today the About page says "Google Scholar aims to rank documents the way researchers do, weighing the full text of each document, where it was published, who it was written by, as well as how often and how recently it has been cited in other scholarly literature." - https://scholar.google.com/... <br /> And for instance its language bias is widely recognized (see e.g. https://doi.org/10.3390/fi1... ) - not a problem for your study, but something to be aware of.
For background I'd recommend including Frampton's recent paper - which gets at the visibility of retracted publications: Frampton, Geoff, Lois Woods, and David Alexander Scott. “Inconsistent and Incomplete Retraction of Published Research: A Cross-Sectional Study on Covid-19 Retractions and Recommendations to Mitigate Risks for Research, Policy and Practice.” PLOS ONE 16, no. 10 (October 27, 2021): e0258935. https://doi.org/10.1371/jou...
There is also a recent analysis of reasons for retraction - which gets into the other angle on quality (e.g., has there been a rush to public)<br /> Rubbo, Priscila, Caroline Lievore, Celso Biynkievycz Dos Santos, Claudia Tania Picinin, Luiz Alberto Pilatti, and Bruno Pedroso. “‘Research Exceptionalism’ in the COVID-19 Pandemic: An Analysis of Scientific Retractions in Scopus.” Ethics & Behavior online first (June 7, 2022): 1–18. https://doi.org/10.1080/105....
These are likely the most interesting - though you can see other papers in my bibliography of Empirical Retraction Lit: the category "Analysis of Retracted COVID-19 articles" has 14 (there are more - the bibliography is so far only up to July 2021).
-Jodi<br /> https://orcid.org/0000-0002...
On 2020-08-27 13:31:15, user Joe Psotka wrote:
Using data from Florida creates misleading expectations because Florida's decrease in March and April was largely from Snowbirds' and part time residents' departure from the State. Some people estimate that one-third of Florida's winter population leaves in the Spring to avoid the summer heat.
On 2020-11-02 02:33:32, user Atomsk's Sanakan wrote:
The paper calculates IFR using COVID-19 deaths 4 weeks after the median time at which antibody levels were measured. That's consistent with other papers that use at deaths 3 weeks or more after the median time. For example:
https://www.thelancet.com/p... (with: https://www.thelancet.com/j... )<br /> https://www.medrxiv.org/con...
The paper also notes that IFR for SARS-CoV-2 is substantially more than that of seasonal influenza:
"These results also confirm that COVID-19 is far more deadly than seasonal flu; indeed, the World Health Organization indicates that seasonal influenza mortality is usually well below 0·1% unless access to health care is constrained."<br /> https://www.medrxiv.org/con...
"Using the midpoint of that interval, we estimate that the total U.S. incidence of seasonal influenza during winter 2018-19 was in the range of 45 million to 93 million infections and hence that the population IFR for seasonal influenza was in the range of 0.04% to 0.08%--an order of magnitude smaller than the population IFR for COVID-19."<br /> https://www.medrxiv.org/con...
That is consistent with sources such as:
"The current data in Europe are consistent with an IFR of 0.5–1.0%, which is many times higher than seasonal influenza (<0.1%)."<br /> https://www.ncbi.nlm.nih.go...
"The calculated COVID-19 infection fatality rate is 1.63%, which is 10 to 40 times more deadly than the seasonal flu (fatality rate 0.04%-0.16%)."<br /> https://news.ochsner.org/ne...<br /> [for: https://wwwnc.cdc.gov/eid/a... ]
"In summary, we estimate that the overall COVID-19 IFR ranges from 0.14 - 0.42% in low income countries to 0.78 - 1.79% in high income countries, with the differences in those ranges reflecting the older demography of high income settings.<br /> [...]<br /> Our estimates of the IFR of COVID-19 are consistent with early estimates and remain substantially higher than IFR estimates for seasonal influenza (<0.1% in the USA) [...]."<br /> https://www.imperial.ac.uk/...
World Health Organization, in October:<br /> "Several of these analyses have used published or pre-print seroepidemiologic results and they all converge around a point estimate of around 0.6%.<br /> That may not sound like a lot but that is a lot higher than influenza [...]."<br /> https://www.who.int/publica...
On 2021-06-25 00:23:11, user Otheus wrote:
While I would like to believe the results of this study, the details and the presentation of their numbers leaves much to be desired for the numerically astute/obsessed. A CI of "0 to infinity" means someone has not really done the statistics right at all. In the online preprint of the article, which may be an older version, the article cites -- somewhat deceptively -- provides the percentages in terms of the number of infections in one group with respect to the number of infections in another group. So, 99.3% of the infections were from people not previously infected and not vaccinated and 0.7% of infections were from vaccinated group. The problem with doing it that way is that you have a much larger population of people not infected and not vaccinated than the other sub-populations.
Imagine you have two drawers of socks, and in each drawer, there is the same ratio of white socks to red socks -- let's say 1 red sock for every 9 white socks, ie, 10% red. You then pull out 10 socks from each drawer. From the first drawer, you pull 5 red socks and 5 white socks, and from the second drawer, you pull 1 red sock. It would seem the first drawer had a greater percentage of red socks. In fact, the first drawer had 500 socks and therefore 50 red socks, while the second drawer had exactly 10 socks and only 1 red sock. The probability of this happening in each case is about the same. The number of red socks to socks drawn from the first drawer is 50%, but for the second drawer, it is 10%. But if you look carefully, the first drawer has 5x the number of red socks as the second. The ratio of red socks drawn to total *red* socks in that drawer is 10% in both cases.
At any rate, from the math given, I calculate that the rate of those got sick among those who were not vaccinated and did not have previous infection was about 10%, while the rate of those who got sick among those who either had been vaccinated or had a previous infection was about 1.2%. Since 0 people with a previous infection reported getting sick, we get 0%. What's significant here is that the population of those unvaccinated and not infected was 10x higher than that last group.
An important sentence from the paper sticks out: "Not one of the 2579 previously infected subjects had a SARS-CoV-2 infection, including 1359 who remained unvaccinated throughout the duration of the study." Those numbers appear high enough with respect to the lower bound of the infection rate (1.2%) to have enough statistical power. You'd expect to find at least 31 cases for the null hypothesis. It seems quite improbable to get 0 results unless previous infections provide very strong protection.
On 2021-07-13 23:48:27, user Meditate wrote:
Where is the reinfection by variant data available? Please share, would be grateful for the link
On 2020-08-13 12:21:38, user ArthurVandelay wrote:
This is a fascinating study ! To the authors: any preliminary speculation on the mechanism(s) of why there would be a protective effect of using ARBs ?
On 2020-08-27 21:59:05, user Thomas P. Dooley wrote:
This article has been peer reviewed and is "In Press" in Pulmonary Pharmacology & Therapeutics (an Elsevier journal).<br /> T.P. Dooley (co-author)
On 2020-05-21 19:46:25, user TS Francis wrote:
There are a lot of problems with this study making me embarrassed to have graduated from Columbia. The report repeats the obvious, that forced social distancing reduces the infection rate, and the report does this with impressive mathematical models but in total the research is misleading in a number of areas.<br /> The report states "a substantial number of cases and deaths could have been averted". This may be true in the measurement period, likely the cases and deaths occur after the measured period. In other words, you prove what we all know that the "control measures" slow down the virus but don't stop it. Even the data shows "control measures" don't stop the cases and deaths.<br /> Assumptions - You are only looking at a snapshot in time. Of course, social distancing slows the virus. Absent a magic cure or herd immunity, the virus will pick back up again after "control measures" are removed. There is an implied assumption that a person saved by "control measures" won't die from the virus soon after your measurement period.<br /> You are assuming Death is a good measure for public policy. Everyone will die, it is a given. Loss of life is what should be measured and this can be estimated based on Covid morbidity by age and life expectancy tables. At the same time you should estimate how much life was taken by your "control measures". Using data from Sweden and my state, I have done this and the loss of years of life from "control measures" far exceeds the loss of years of life saved. <br /> Obviously the objective of the research is to promote a certain public policy to save lives. But it does the analysis without looking at the costs which can be weighed using years of life. Overall, very impressive modeling but not useful except for promoting a biased agenda.
On 2020-07-06 16:20:42, user OxImmuno Literature Initiative wrote:
On 2020-05-26 21:50:22, user Sam Wheeler wrote:
Good paper, I downloaded the pdf.
We still don't have the answer: what if an adult has taken the BCG shot very recently. There are clinical trials that will answer this question, hopefully soon.
In many countries, medical doctors refused to prescribe BCG vaccines to adults even before covid-19, and pharmacies don't stock the vaccine at all. In which countries can an adult easily buy a BCG vaccine, and in which countries it is nearly impossible?
On 2020-08-20 18:03:31, user robert lindsay wrote:
Website needs some editing, spacing is off
On 2021-08-09 17:23:34, user vaxpro wrote:
author indicated "The sample size of our trial design meets the minimum safety requirement of 3,000 study participants for the vaccine group, as recommended by the FDA, and WHO guidance". Neither FDA nor WHO recommends sample size for an individual study. sample sizes of the individual studies are driven by the objectives. sample size for approval is a different matter. In fact, what FDA says in the referenced document is that "FDA does not expect to be able to make a favorable benefit-risk determination that would support an EUA without Phase 3 data that include the following, which will help the Agency to assess the safety of the vaccine:...ii. All safety data collected up to the point at which the database is locked to prepare the submission of the EUA request, including a high proportion of enrolled subjects (numbering well over 3,000 vaccine recipients) followed for serious adverse events (SAEs) and adverse events of special interest for at least one month after completion of the full vaccination regimen". also "FDA does not consider availability of a COVID-19 vaccine under EUA, in and of itself, as grounds for stopping blinded follow-up in an ongoing clinical trial". WHO guidance is for general vaccine development with major caveats highlighted in the guidance document. the selective and mis- interpretation of the FDA and WHO guidance is regrettable.<br /> immunogenicity is the primary objective of the study. it's curious why author chose to report full set of IgG data at all visits but NT data only at selected visits.post injection pain was reported in ~20% placebo (saline) subjects, which far exceeded any historic rates in this type of population. although no impact on the interpretation of safety for the active arm within this double blinded trial, the observation should be investigated for potential trial conduct issues and discussed.<br /> author stated "MVC-COV1901 has recently been granted EUA in Taiwan". however this is not a scientific issue for this trial, should not be included in trial report in a peer reviewed journal.
On 2020-04-10 17:40:13, user Sinai Immunol Review Project wrote:
Key findings<br /> The authors investigated the use of a commercially available form of heparin, low molecular weight heparin (LMWH), as a therapeutic drug for patients with COVID-19. Previous studies showed that in addition to its anticoagulant properties, LMWH exerts anti-inflammatory effects by reducing the release of IL-6 and counteracting IL-6.
This was a retrospective single-center study conducted in Wuhan, China. Forty-two (42) hospitalized patients with coronavirus pneumonia were included, of which 21 underwent LMWH treatment (heparin group) and 21 did not (control). The general characteristics of the two groups of patients were statistically comparable. Both control and LMWH had the same hospitalization time and there were no critical cases in either group.
This study found that treatment with LMWH significantly reduced IL-6 levels in patients in the heparin group compared to the control group. However, LMWH treatment did not have an effect on the levels of other inflammatory factors: CRP, IL-2, IL-4, IL-10, TNF-?, and IFN-?. Compared with the control group, patients in the heparin group had a significantly increased percentage of lymphocytes after treatment, further suggesting that LMWH treatment has anti-inflammatory effects and can reduce the lymphopenia associated with COVID-19.
Consistent with other studies in COVID-19 patients, they found that LMWH treatment can improve hypercoagulability. D-dimer and FDP levels (biomarkers of coagulation) in the heparin group significantly decreased from baseline after treatment, whereas there was no significant change in levels for the control group. Of note however, patients in the heparin group had a significantly higher level of D-dimer and FDP at baseline compared to the control group.
Importance<br /> Many studies have shown that severely ill COVID-19 patients have significantly higher levels of IL-6 compared to patients with mild cases and it has been proposed that progression to severe disease may be caused by lymphopenia and cytokine storms. The anti-inflammatory effects of heparin may help prevent or reverse a cytokine storm caused by the virus and thus delay COVID-19 progression and improve overall condition in patients. The pleiotropic effects of heparin may have a greater therapeutic effect than compounds that are directed against a single target, such as an anti-IL-6 therapy. This is because COVID-19 patients commonly have complications such as coagulopathy and endothelial dysfunction leading to cardiac pathology that may be mitigated by heparin treatment (Li J, et.al; Wojnicz et.al).
Limitations<br /> This study is limited by a small sample size (n=44) and a single-center design. Double-blinded, randomized, placebo controlled clinical trials of LMWH treatment are needed to understand the possible benefit of the treatment. Additionally, this study was unable to control for the dose and days of treatment of LMWH. Identifying the correct dose and timing of LMWH is a matter of immediate interest. Of note, patients in the heparin group received two types of LMWH, enoxaparin sodium or nadroparin calcium, which have been reported to have differing anticoagulant activity. The use of different LMWHs in the heparin group warrants further explanation.
Another caveat of this study is that the levels of D-dimer and fibrinogen degradation products were significantly higher at baseline for patients in the heparin group compared to those in the control group. Therefore, it is difficult to decipher whether some of the positive effects of heparin treatment were due to its anti-coagulation effects or direct anti-inflammatory effects. Future studies are that delineate the anti-inflammatory functions of heparin independently of its anticoagulant properties in cases of COVID-19 would be useful.
Lastly, this study did not discuss any side-effects of heparin, such as the risk of bleeding. Moreover, coagulation can help to compartmentalize pathogens and reduce their invasion, therefore anticoagulant treatments like heparin may have risks and it remains to be determined which patients would benefit from this therapy.
References:<br /> Li J, Li Y, Yang B, Wang H, Li L. Low-molecular-weight heparin treatment for acute lung injury/acute respiratory distress syndrome: a meta-analysis of randomized controlled trials. Int J Clin Exp Med 2018;11(2):414-422
Wojnicz R, Nowak J, Szygula-Jurkiewicz B, Wilczek K, Lekston A, Trzeciak P, et al. Adjunctive therapy with low-molecular-weight heparin in patients with chronic heart failure secondary to dilated cardiomyopathy: oneyear follow-up results of the randomized trial. Am Heart J. 2006;152(4):713.e1-7
Review by Jamie Redes as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-05-26 23:26:41, user Sam Wheeler wrote:
The medical staff can get the virus while commuting to work. Especially if you work place is the place where patients go for covid testing or treatment, so you share the bus or subway with sick patients.
On 2020-05-27 13:25:36, user Mohammad Ali wrote:
We welcome an open discussion about our study.
On 2020-09-18 15:06:23, user Giles Cattermole wrote:
This article has been published under the title "Accuracy of weight estimation methods in adults, adolescents and children: a prospective study" in Emergency Medicine Journal following peer review, 17 September 2020. It can be viewed on the journal’s website at 10.1136/emermed-2020-209581
On 2020-10-06 12:01:01, user Jani Ruotsalainen wrote:
I'm teaching systematic review methods on two occasions this autumn and I'm using this article as an example to be evaluated using the AMSTAR II tool. And yes, I'm doing this even though the authors do not in fact identify their work as a systematic review. My point is to show how a meta-analysis (MA) without all the supporting structures of a thorough systematic review does not really make much sense. AMSTAR II has 16 domains that one scores Yes or No. In some cases it also allows also Partial yes and some items might not be applicable when a review does not include MA. However, in the end there is no overall sum score. Anyway, according to my assessment, the manuscript as it is now scored two instances of Yes and fourteen instances of No. In other words, it didn't do too well. The biggest cause of problems is, in my opinion, the lack of a protocol published a priori that would have established the methods to be used in sufficient detail. Now it is impossible to tell if the authors deviated from their original plans along the way and what effect this might have had on their findings. Other problems include not using a satisfactory technique to assess the risk of bias of results extracted from included studies or its possible effects on results obtained with MA. The description of included studies is minimal and the description of excluded studies is nonexistent. There are also issues with the MA itself (proficiently examined by Jesper Kivelä on Twitter: https://twitter.com/JesperK... "https://twitter.com/JesperKivela/status/1291697936842338305)") and more. I'm happy to share my full assessment with the authors.
On 2021-06-10 17:59:48, user Aliya Amirova wrote:
Peer-reviewed and published article: https://openheart.bmj.com/c...
On 2021-09-21 07:44:32, user Martijn Hoogeveen wrote:
See our new preprint: Environmental factors and mobility predict COVID-19 seasonality. I.e., Solar radiation, allergens/allergies, mobility and temperature explain 87.5%(!!!) of COVID-19 reproduction number. https://www.medrxiv.org/con...
On 2025-04-30 07:16:59, user Frédéric Derbré wrote:
This preprint is now published on Cell Reports : https://www.cell.com/cell-reports/fulltext/S2211-1247(25)00219-0?uuid=uuid%3A08f5e5ab-1371-444d-b8cf-ba64672613f3 Is it possible to inform the readers on Medrxiv ?
On 2022-08-21 14:21:09, user Gerard Job wrote:
This article was published in the journal of clinical trials.....Author Info<br /> Gerard Job1,2*, Jennifer Okungbowa-Ikponmwosa1 and Yijia M1<br /> 1Department of Emergency, Jackson Memorial Hospital, Miami, Florida, USA<br /> 2Department of Emergency, Miami Dade Fire, Air and Ocean Rescue, Miami, Florida, USA
Citation: Job G, Okungbowa-Ikponmwosa J, Yijia M (202 1) Feasibility of Establishing a Return-to-Work Protocol Based on COVID-19 Antibodies Testing. J Clin Trials. 11:480.
Received: 22-Jan-2021 Accepted: 05-Feb-2021 Published: 12-Feb-2021 , DOI: 10.35248/2167-0870.21.11.48
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 2021-08-12 17:18:46, user Iñigo Ximeno wrote:
Although the observation is very interesting, it does not lead to the conclusion that it is the most prudent thing to continue with indiscriminate vaccination in the middle of a pandemic when the vaccine does not prevent the transmission of the pathogen.
It is not relevant how many mutations are detected but the importance of them. It is obvious that among people with some immunity the mutations for which the antibodies can neutralize the virus will not be spreaded and therefore those will not be detected. However the mutations that can evade the immunity, even if they are few, will be more virulent and letal.
On 2020-11-09 17:52:45, user Elena Criscuolo wrote:
The manuscript has been published on 16th October on Journal of Medical Virology as:<br /> "Weak correlation between antibody titers and neutralizing activity in sera from SARS-CoV-2 infected subjects"<br /> https://onlinelibrary.wiley...
On 2022-01-13 09:48:09, user zlmark wrote:
There seems to be some discrepancy between the actual calculations and the conclusions drawn in the Discussion section.
Assuming that Copenhagen data provides us with a more reliable estimate of the gatherings size distribution, as the authors themselves seem to suggest, limiting the gatherings of 100+ gives us about 40% reduction in the number of infections in a single infection cycle.
And given that Omicron mean serial interval is estimated to be around 2.2, this means that about 3 infection cycle happen in a week, and 40% reduction in single cycle leads to about 80% reduction in a week.
On 2020-03-27 13:17:39, user Sinai Immunol Review Project wrote:
Title: <br /> Serological detection of 2019-nCoV respond to the epidemic: A useful complement to nucleic acid testing<br /> The main finding of the article: <br /> This study analyzed the presence of anti-2019-nCov antibodies in serum samples utilizing a commercial automated chemiluminescent immunoassay. Samples included 228 patients admitted to a fever clinic in Shengjing Hospital, 3 of which tested positive for COVID-19 by nucleic acid detection, and 225 that tested negative (non-COVID-19). Other serum samples from outpatients with other diseases, from medical staff in the fever clinic, and from healthy subjects were included. Both SARS-CoV-2-specific IgM and IgG were detected in the COVID-19 patients, while single positivity of IgM or IgG were detected in very few samples from the other populations. In addition to the increase of anti-2019-nCov IgM 7-12 days after morbidity, the increase of IgG was detected in three patients with COVID-19 within a very short of time of IgM detection (0-1 day). <br /> Critical analysis of the study: <br /> The main limitation of this study is that only 3 confirmed COVID-19 cases were included, so that the relationship between anti-2019-nCov antibodies and disease progression could not be clearly defined. Another limitation is that the authors did not show the course of 2019-nCov specific antibodies in the cases with either IgM or IgG positivity for COVID-19 but without clinical symptoms.<br /> The importance and implications for the current epidemics:<br /> The detection of anti-2019-nCov antibodies can be an additional and complementary method to viral detection to identify patients infected by 2019-nCov virus, and can also be used to identify 2019-nCov exposed individuals regardless of symptoms. It may be also helpful to understand the course of individual cases with COVID-19 to predict the prognosis if more cases will be evaluated.
On 2021-09-15 14:55:00, user Donald Milton, MD, DrPH wrote:
This manuscript was accepted by Clinical Infectious Diseases and published online on 15 September 2021. https://academic.oup.com/ci...
On 2020-11-04 22:10:53, user stephan walrand wrote:
it is not an issue of quickly decline, it is a question of crossing down a certain blood level: it is a threshold triggering effect. <br /> When enough people cross down the threshold, when positive they become more contagious as their respiratory track is more invaded, and they contamine other people being under the threshold, who also become more contagious.<br /> It is similar to a nuclear chain reaction: under a critical neutron rate production you can control the reactor, a little bit above it is an explosion.<br /> But I will be happy to consider any other explanation for the correlation with the latitude.
On 2022-01-15 07:28:01, user Quis custodiet Custodientes wrote:
Can we avoid the term "breakthrough" infections? It's just infections. The fact that the vaccines are leaky is not an exceptional event, a "breakthrough". They are just not effective. e.g. based on data from the Quebec health service, the risk of infection was 9.2 times greater for the non-vaccinated in August 2021. It dropped to 0.7 around January 10, which means that the risk of getting infect is now greater for the double and triple vaccinated than for the non-vaccinated.
On 2022-01-23 20:43:42, user ELSA GRENMYR wrote:
Did you verify that all patients infected with VOC-Delta and VOC-omicron were SARS-COV2 naive, or could there be a mix of convalescent and naive individuals? How would the infectious viral titres look in a re-infected cohort?
On 2020-04-01 16:34:11, user Sinai Immunol Review Project wrote:
Study Description <br /> This is a randomized clinical trial of hydroxychloroquine (HCQ) efficacy in <br /> the treatment of COVID-19. From February 4 – February 28, 2020 142 <br /> COVID-19 positive patients were admitted to Renmin Hospital of Wuhan <br /> University. 62 patients met inclusion criteria and were enrolled in a <br /> double blind, randomized control trial, with 31 patients in each arm.
Inclusion criteria:<br /> 1. Age >= 18 years<br /> 2. Positive diagnosis COVID-19 by detection of SARS-CoV-2 by RT-PCR<br /> 3. Diagnosis of pneumonia on chest CT <br /> 4. Mild respiratory illness, defined by SaO2/SPO2 ratio > 93% or <br /> PaO2/FIO2 ratio > 300 mmHg in hospital room conditions (Note: <br /> relevant clinical references described below.)<br /> a. Hypoxia is defined as an SpO2 of 85-94%; severe hypoxia < 85%. <br /> b. The PaO2/FIO2 (ratio of arterial oxygen tension to fraction of inspired<br /> oxygen) is used to classify the severity of acute respiratory distress <br /> syndrome (ARDS). Mild ARDS has a PaO2/FIO2 of 200-300 mmHg, moderate is <br /> 100-200, and severe < 100.<br /> 5. Willing to receive a random assignment to any designated treatment group; not participating in another study at the same time
Exclusion criteria: <br /> 1. Severe or critical respiratory illness (not explicitly defined, <br /> presumed to be respiratory function worse than outlined in inclusion <br /> criteria); or participation in trial does not meet patient’s maximum <br /> benefit or safe follow up criteria<br /> 2. Retinopathy or other retinal diseases<br /> 3. Conduction block or other arrhythmias<br /> 4. Severe liver disease, defined by Child-Pugh score >= C or AST > twice the upper limit<br /> 5. Pregnant or breastfeeding<br /> 6. Severe renal failure, defined by eGFR <= 30 mL/min/1.73m2, or on dialysis<br /> 7. Potential transfer to another hospital within 72h of enrollment<br /> 8. Received any trial treatment for COVID-19 within 30 days before the current study
All patients received the standard of care: oxygen therapy, antiviral <br /> agents, antibacterial agents, and immunoglobulin, with or without <br /> corticosteroids. Patients in the HCQ treatment group received additional<br /> oral HCQ 400 mg/day, given as 200 mg 2x/day. HCQ was administered from <br /> days 1-5 of the trial. The primary endpoint was 5 days post enrollment <br /> or a severe adverse reaction to HCQ. The primary outcome evaluated was <br /> time to clinical recovery (TTCR), defined as return to normal body <br /> temperature and cough cessation for > 72h. Chest CT were imaged on <br /> days 0 and 6 of the trial for both groups; body temperature and patient <br /> reports of cough were collected 3x/day from day 0 – 6. The mean age and <br /> sex distribution between the HCQ and control arms were comparable.
Findings<br /> There were 2 patients showing mild secondary effects of HCQ treatment. More <br /> importantly, while 4 patients in the control group progressed to severe <br /> disease, none progressed in the treatment group.<br /> TTCR was significantly decreased in the HCQ treatment arm; recovery from fever <br /> was shortened by one day (3.2 days control vs. 2.2 days HCQ, p = <br /> 0.0008); time to cessation of cough was similarly reduced (3.1 days <br /> control vs. 2.0 days HCQ, p = 0.0016).<br /> Overall, it appears that HCQ treatment of patients with mild COVID-19 has a modest effect on clinical recovery (symptom relief on average 1 day earlier) but may be more <br /> potent in reducing the progression from mild to severe disease.
Study Limitations <br /> This study is limited in its inclusion of only patients with mild disease, <br /> and exclusion of those on any treatment other than the standard of care.<br /> It would also have been important to include the laboratory values of <br /> positive RT-PCR detection of SARS-CoV-2 to compare the baseline and <br /> evolution of the patients’ viral load.
Significance<br /> Despite its limitations, the study design has good rigor as a double blind RCT <br /> and consistent symptom checks on each day of the trail. Now that the FDA<br /> has approved HCQ for treatment of COVID-19 in the USA, this study <br /> supports the efficacy of HCQ use early in treatment of patients showing <br /> mild symptoms, to improve time to clinical recovery, and possibly reduce<br /> disease progression. However, most of the current applications of HCQ <br /> have been in patients with severe disease and for compassionate use, <br /> which are out of the scope of the findings presented in this trial. <br /> Several additional clinical trials to examine hydroxychloroquine are now<br /> undergoing; their results will be critical to further validate these <br /> findings.
Reviewed by Rachel Levantovsky as a part of a project<br /> by students, postdocs and faculty in the Immunology Institute at the <br /> Icahn school of Medicine at Mount Sinai.
On 2021-01-13 11:39:34, user carina brehony wrote:
hopefully a full published paper will acknowledge the laboratories, public health departments and the Health Protection Surveillance Centre that collected, validated and provided the data which was then made available publicly
On 2020-06-26 09:28:20, user Dena E. Utne wrote:
Al this study shows is that there wasn't a lot of Covid-19 around when they did the study. There wasn't enough Covid-19 around at the time of the study to make claims about the safety of gyms. It is disappointing to see the BBC summarizing the conclusions of this article, when the conclusions are not supported by the actual science. I don't think this article will or should pass peer review.
On 2021-01-19 21:13:39, user Richard Brown wrote:
Did the clinical data record the NSAID (particularly Ibuprofen and Naproxen) taken early (later less relevant). In our veterinary field early NSAID therapy (meloxicam, Flunixin, ketofen) is regarded as highly relevant in these situations.
On 2020-05-03 15:07:18, user RBIrides wrote:
Idaho might not like being referred to as west coast
On 2020-07-22 03:32:00, user Robert Kernodle wrote:
What if droplets are not the primary cause of deep lung infection, and aerosols are, as is currently believed, according to my research?
What if top tier research is a better choice for weighing the demolition of entire economies against the rationally assessed true gravity of an actual threat? We don't re-engineer society based on the sort of thinking that asks, "What if?"
We look at the best research, such as this:
Xiao, J., Shiu, E., Gao, H., Wong, J. Y., Fong, M. W., Ryu, S.Cowling, B. J. (2020). Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—Personal Protective and Environmental Measures. Emerging Infectious Diseases, 26(5), 967-975. https://dx.doi.org/10.3201/....
Our systematic review found no significant effect of face masks on transmission of laboratory-confirmed influenza.
On 2021-06-07 06:20:19, user Srinivasan Madhavan wrote:
What's the quantified value of Antibody. Which testing method was used...is it ELISA .or ECILA . How is Humoral response comparable between an infected- non vaccinated individual versus non infected vaccinated individual please.
On 2021-09-11 12:19:37, user William Brooks wrote:
This study finds similar results to studies looking at infections among South Asians in England [1] and foreign workers in Kuwait [2]: lockdown heightened the curve for groups with more crowded living conditions. The results also agree with those of the nearest thing we have to a lockdown RTC: higher secondary attack rates in asylum centres that mass-quarantined all residents in Germany [3].
Despite this, the authors claim lockdowns work. Like a pharmaceutical intervention, for a non-pharmaceutical intervention to be said to work, the intervention group (e.g. NY, CA) has to show significantly lower mortality and morbidity than the control group (e.g. FL, SD), which isn’t the case [4]. Also, for extremely authoritarian interventions to justify their many negative side-effects, hospitals in the control group would need to be overflowing like the models predicted, which has never come close to happening.
[1] https://doi.org/10.1016/j.e...<br /> [2] https://doi.org/10.1186/s12...<br /> [3] https://doi.org/10.1101/202...<br /> [4] https://doi.org/10.1101/202...
On 2020-02-20 09:21:38, user Linh Ngoc Dinh wrote:
Thanks for sharing your research.<br /> Just a small comment: In an introductory graph, you said "In jurisdictions outside China (and excluding Hong Kong, Macao and Taiwan) the CFR as detailed in the 13 FebruaryWHO Report [3] was 1/447 = 0.22% (95% confidence interval (CI) = 0.40% to 1.26%)."<br /> This is quite misleading statement, WHO has never mentioned an estimate of CFR outside China until now. I think what cite here is the information that there was 1 death and 447 confirmed cases outside China. You should make this point clear, because as a reader, I feel like the number 0.22% (95%CI: .4%-1.26%) is what WHO said. <br /> Also, I wonder how you arrived to that 95%CI as we have only 1 point estimate.
Thanks much!
On 2021-05-20 18:34:22, user Andrew wrote:
PEER REVIEWED UPDATED PAPER:<br /> https://clinmedjournals.org....
On 2020-07-06 16:29:22, user OxImmuno Literature Initiative wrote:
On 2021-05-11 17:34:07, user Joe Smith wrote:
Table 2 is interesting. It looks like, compared to 'other cancer (non-ADT)', the ADT patients had higher rates of infection (raw data). Looks like the table S2 suggests men taking ADT are more likely to get tested than...all non-ADT? all other cancer (non-adt)? I don't know what the comparator is for S2. But I don't see an OR listed in S2 for 'all other cancer (non ADT). Hopefully that was used in the creation of the Table 2, but it wasn't presented in S2.
I'm no statistician, but it seems like some of the covariates would interact; eg african americans may be less likely to get tested than white people overall, but maybe african americans with cancer are just as likely to get tested as white people with cancer. Hopefully someone can add some detail on this point, I'd like to know more.
I also note table 2 has a lower n than table 3, meaning some ADT+, COVID tested patients didn't have matching controls so were not included in this analysis (62/295~20%). For all subjects, a much lower % (6.7%, 1666/25006) were excluded from Table 2 for not having 5 matching controls. I wonder why it was so much more difficult to find matches for the ADT patients, and if this could have affected the outcome.
Finally, Table S3 is very important, and I believe should be added to the body. This table "Association between ADT use and SARS-CoV-2 positivity and COVID-19 severity among prostate cancer patients".
The percent of severe disease cases is very similar (22% vs 23% unadjusted) p~0.9 (propensity matched). So, when you are directly comparing the outcomes in men with prostate cancer, ADT does NOT result in fewer cases of severe disease.
Appreciate the timely work and sharing the prepublished article!
On 2025-10-02 15:15:56, user Soumik Mandal wrote:
This preprint has been published at: https://www.nature.com/articles/s41746-025-01972-w
On 2021-03-14 17:30:22, user Vikingman wrote:
Hi<br /> Would there be an updated version of the table in page 7 of the PDF other than 29 January? Thanks. Keep up the amazing work.
On 2021-08-04 15:55:17, user Hassan Rostam wrote:
The article has been publishet @ New Microbes and New Infections
On 2020-09-10 16:51:51, user Thomas Waterfield wrote:
Thanks Sunil. It was great to chat the other day.
We have produced a protocol that is currently with BMJ Open. The data presented here relates to the first clinic appointments (16th April to 3rd of July) for all participants. The symptom data was reported using RedCap data capture with retrospective reporting of illness episodes prior to the attendance from the beginning of the pandemic in February. In all instances the symptoms were reported without the participant knowing their antibody status. Data were entered by trained members of the research team.
On 2020-11-30 13:44:32, user Michael Jastram wrote:
The research HAS been published, that's why you can read it here. It has just not been accepted for peer-reviewed publication in a journal.
Note that there are a number of studies on Vitamin D are in process right now, see https://www.ncbi.nlm.nih.go...
So far it looks like Vitamin D has moderate positive effects, but not the "key role in COVID-19 outcomes" that the authors suggest – unfortunately so.
On 2020-06-30 12:35:03, user Dude Dujmovic wrote:
May 4 to May 12. That was sort of ages ago. OK as a snapshot I guess.
On 2021-06-14 22:46:28, user Kevin J. Black, M.D. wrote:
Please note, the title does not mention tDCS. You'll want to fix that.
On 2020-06-08 14:35:52, user Francesco Rossi wrote:
Dear Dr.Streeck, <br /> is it possible, with your experimental approach, giving an estimate of the percentage of people hospitalized and treated in ICU?<br /> The theoretical model supporting lockdown was published by Ferguson and collaborators in The Imperial College report 9 (https://www.imperial.ac.uk/..., hereafter ICR9). In this reports, authors extimated that in Covid-19 epidemic “optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over.” (ICR9). Therefore they concluded that “epidemic suppression is the only viable strategy at the current time.” (ICR9). They focused their prediction on UK and US, but they claimed that their prediction could be applied to other high-income countries (ICR9).<br /> However the model they proposed is controversial for several reasons (https://retractionwatch.com... ; https://pubpeer.com/publica... "https://pubpeer.com/publications/227C0B09C78E146F96F7D679348BF7#)").<br /> May be possible to extimate the percentage of people of Gangelt that would be hospitalized and treated in ICU over the total citizen of Gangelt extimated to be infected with SARS-COV2, according to the parameters used in table 1 of ICR9 reports (which are taken from Verity et al., 2020, https://www.thelancet.com/p...? "https://www.thelancet.com/pdfs/journals/laninf/PIIS1473-3099(20)30243-7.pdf)?")<br /> The parameters are “Under the China case definition, a severe case is defined as tachypnoea (>=30 breaths per min) or oxygen saturation 93% or higher at rest, or PaO2/FiO2 ratio less than 300 mm Hg.7 Assuming severe cases to require hospitalisation (as opposed to all of the patients who were hospitalised in China, some of whom will have been hospitalised to reduce onward transmission), we used the proportion of severe cases by age in these patients to estimate the proportion of cases and infections requiring hospitalisation.” (Verity et al., 2020)
On 2020-10-24 23:37:11, user Nando wrote:
Out of 110 cases, 27 created secondary exposures - of which 23 were in closed environment.
Conversely 71 cases were in closed environment and did not generate a secondary exposure.
As is, the data presented is statistically insignificant... it does not prove that closed environments increase the risk of COVID Exposure.
On 2020-04-26 12:58:30, user FeatheryHen wrote:
Interesting analysis, but I'm left wanting to know more about the source data. It would be good to see a summary of the different transmission events you analysed and what the characteristics were, other than enclosed or not. Is a link to source data available?
On 2021-12-03 03:11:46, user virtualcappy wrote:
How many of the reinfections in November were associated with omicron and what fraction of the total infections in November were from omicron vs other variants? Without that information the data does not seem to support the authors' conclusions that omicron is responsible for the increased risk of reinfection.
It stands to reason, risk of reinfection will eventually increase with time, as naturally acquired immunity wanes. Further, it is to be expected that the risk of reinfection will increase with a variant that has variation in sequence. The question is how much does each of these factors contribute to an overall reinfection rate and this paper doesn't seem to do much to answer that.
The authors also should suggest some plausible mechanism for the decrease in reinfection rate with time through the end of wave 3. Why would a mutated virus be less likely to reinfect? Seems more likely due to the dynamics of the immune response over time than genetic variation. Why then would the authors not consider that mechanism for the increase in reinfection rate in wave 4?
Anyway, the New York Times picked up on this preprint already and the headline suggests that "prior infection is little defense". This is a far cry from "substantial and ongoing increase in the risk of reinfection" which could be from multiple factors and 2-3x increase in risk of reinfection is not "little defense". The authors should provide better context to head off alarmism.
On 2020-10-03 15:16:25, user Dr. Amy wrote:
Thank you for this important contribution!! The particle number data points on the age and BMI graphs seem not to be the same. Eg The highest outlier superspreader who seems to be BMI 46 has 3000 particles and the highest outlier superspreader who is 63 has 3500 particles. 1) are all subjects’ particle numbers represented on both graphs? 2) what happens to the correlation coefficient if the outlier(s) is/are removed? 3) is it the same subject with different samples? Again, important information about droplet and aerosol behavior. It would be interesting to know if the same aerosol for Mation changes are noted with other respiratory infections or if SARS-CoV2 behaves differently.
On 2021-07-15 13:36:08, user Merlin Khoo wrote:
This laboratory study seems to indicate the need for a booster 3rd jab based on the latest variant as the virus mutates further and further from the original wild type
On 2021-02-22 15:49:56, user Lenisse M. Reyes wrote:
This paper has been provisionally accepted in the journal PLOS ONE.
On 2020-05-06 00:54:07, user Alessandro_Machi wrote:
We had a U.S. Senior flu epidemic in 2017-2018 that probably killed 100,000 seniors even though only 61,000 were reported. Flu and pneumonia deaths were being renamed among Seniors as death by Natural Causes, Respiratory Failure, Heart Attack on their death certificates. I have multiple links to stories about how badly the flu epidemic was during the 2017-2018 Senior flu epidemic. The problem was no numbers were being reported during the epidemic, thus allowing Emergency response to pretend there was no senior flu epidemic.
On 2020-04-21 18:19:21, user genomicsmarco wrote:
In Fig. 1, the third mutation in ZJU-6 is listed as "T1327C". Based on its position in the genome, there appears to be a digit missing from the sequence location. Also, the supplementary data does not appear to be available. Thanks!
On 2021-10-29 15:37:00, user Rogerblack wrote:
I find refreshing the repeated ''these associations did not survive correction for multiple comparisons'.<br /> An interesting paper.
On 2020-06-25 04:04:22, user Greg WHITTEN wrote:
Thank you for your work. I am curious, however, about some parts of your article.
First, I read your paper and could not see where you tried to control for the introduction of other virus-containment measures such as school closures, lock-downs, and physical distancing. Did I miss something in your paper?
Second, I have a question about your model #4 on page 9. You wrote "All<br /> regression coefficients were statistically significant in this model." The coefficient for the non-mask wearing rate in late April and early May is significant but negative. I.e., not wearing a mask in late April and early may reduces deaths on May 13th. Do you have any thoughts about this?
Third, did you consider performing a panel regression using deaths on all days, say, starting from March 31st (about 2 weeks after the March mask non-wearing rate) instead of relying just on deaths from May 13? Although you did explain why you chose May 13th, it may be better to use all death dates after, say, the incubation period for the virus.
Fourth, your section "Prediction of mask non-wearing rates" suggests that your regression analysis suffers from multicollinearity. Do you have any concerns about this?
On 2019-07-17 03:34:18, user Guyguy wrote:
EBOLA DRC - Evolution of the response to the Ebola outbreak in the provinces of North Kivu and Ituri on Sunday, July 14, 2019<br /> The epidemiological situation of the Ebola Virus Disease dated July 13, 2019:<br /> Since the beginning of the epidemic, the cumulative number of cases is 2,489, of which 2,395 confirmed and 94 probable. In total, there were 1,665 deaths (1,571 confirmed and 94 probable) and 698 people healed.<br /> 335 suspected cases under investigation;<br /> 12 new confirmed cases, including 6 in Mabalako, 4 in Beni, 1 in Katwa and 1 in Butembo;<br /> 10 new deaths of confirmed cases:<br /> 3 community deaths, including 1 in Mabalako, 1 in Beni and 1 in Katwa;<br /> 7 deaths at Ebola Treatment Center, including 4 in Beni, 2 in Mabalako and 1 in Butembo;<br /> 4 people recovered from Ebola Treatment Center, including 3 in Butembo and 1 in Beni.
Confirmed Ebola Patient from Butembo Supported at Goma Ebola Treatment Center
This Sunday, July 14, 2019, a pastor from South Kivu arrived in Goma after a short stay in Butembo. The 46-year-old pastor traveled from Bukavu to Butembo via Goma on Thursday, July 4 for an evangelistic mission. During his stay in Butembo, the pastor preached in seven churches where he regularly laid hands on Christians, including the sick. His first symptoms appeared on 9 July when he was still in Butembo. He was treated at home by a nurse until he left by bus for Goma on Friday, 12 July.
On the route between Butembo and Goma, the bus passed through 3 health checkpoints, namely Kanyabayonga, Kiwanja and OPRP. During the checks, he did not seem to show signs of the disease. In addition, at each checkpoint, he has written different names and surnames on the lists of travelers, probably indicating his desire to hide his identity and state of health.
As soon as he arrived in Goma on Sunday morning, he went to a health center because he did not feel well and started having a fever. No other patients were in the health center, reducing the risk of nosocomial infections of others. Nurses and doctors at the health center who recognized the symptoms of Ebola immediately alerted the response teams in Goma who transferred him to the Ebola Treatment Center (ETC). Around 15:00, the result of the lab test confirmed that he was Ebola positive. If his state of health permits, the patient will be transferred by ambulance to the ETC of Butembo to continue his care as of Monday, as provided by the procedure of the contingency plan.
It is important for people to stay calm. Due to the speed with which the patient has been identified and isolated, as well as the identification of all bus passengers from Butembo, the risk of spreading to the rest of the city of Goma remains low. Caution is still required. In order to avoid the contamination of additional people in Goma, it is urgent to break the chain of transmission by carrying out the following actions:<br /> Decontaminate the health center in which the patient has passed;<br /> Identify and vaccinate all contacts of the patient without exception;<br /> Track and limit contact movement for 21 days.<br /> Since November 2018, the Ministry of Health and the World Health Organization (WHO) have put in place an Ebola response planning and preparation system in the city of Goma due to the large influx of travelers from affected by the epidemic. The rapid detection of the patient by medical teams at the Goma health center proves the effectiveness of the city's preparedness activities to cope with the importation of potential Ebola patients. As part of this preparation, more than 3,000 health workers in Goma have been vaccinated and trained in the detection and management of Ebola patients.
In addition, the transport company has shown great professionalism in having a passenger register and making this register available to response teams to identify all passengers on the bus. The bus driver and the 18 other passengers have been identified and their vaccination will begin on Monday, July 15, 2019.
The collaboration of the entire population is necessary to prevent the spread of the epidemic in Goma. Beyond the medical arsenal, the Ministry of Health recalls that the response against Ebola is above all community.
As a reminder, the recommendations of the Ministry of Health are as follows:<br /> Follow basic hygiene practices, including regular hand washing with soap and water or ashes;<br /> If an acquaintance from an epidemic area comes to visit you and is ill, do not touch her and call the North Kivu civil protection hotline directly;<br /> If you are identified as an Ebola patient contact, agree to be vaccinated and followed for 21 days;<br /> If a person dies because of Ebola, follow the rules for safe and dignified burials. It is simply a funeral method that respects funerary customs and traditions while protecting the family and community from Ebola contamination.<br /> For all health professionals, observe the hygiene measures in the health centers and declare any patient with symptoms of Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect the sanitary measures advocated by the Ministry of Health, it is possible to ensure that this case of Ebola detected in Goma is a sporadic case that does not cause a new outbreak.<br /> Source: Ministry of Health press team on the state of the response to the Ebola epidemic in the Democratic Republic of Congo
On 2020-07-27 19:36:43, user Andrew Bowdle wrote:
Long et al reported SARS-CoV-2 RT-PCR testing of 20,912 patients[1]. There were 19,035 (91%) patients who tested negative. Of these negative patients, 626 were retested within 7 days, and 22 (3.5%) were positive. While not claiming to have measured sensitivity, the authors stated that “false negative results at the time of initial presentation do occur, but potentially at a lower frequency than is currently believed”. Some have interpreted this study as showing that the sensitivity of the SARS-CoV-2 RT PCR assay is high (>95%). This is an incorrect interpretation of the data shown in Long et al. High sensitivity means that, among people who actually do have SARS-CoV-2 only a small fraction will have a test result that is falsely negative. On the other hand, the 3.5% that Long et al report is an attempt to estimate something quite different, namely among all people with a negative test result, the fraction that actually does have SARS-CoV-2. This fraction being only 3.5% means that the vast majority of negative test results are true negatives, reflecting the fact that the prevalence of the virus is low in the population being tested. Therefore, a rate of 3.5% does not imply good sensitivity but rather low prevalence. In general, false negatives as a percent of negative results has to be less than the prevalence of the condition in the population being tested.
Consider a simple hypothetical example. Assume that the prevalence of SARS-CoV-2 in the population being tested is 10%, RT-PCR specificity is 100% and sensitivity is 50%. Then out of 10,000 people tested there will be 9,000 true negative results and 500 false negative results (half of the 1,000 people who truly have SARS-CoV-2). Thus, 500/9,500 = 5.3% of the negative test results are false negatives.
Upon retest, 250 of the 9,500 (2.6%) of the original negative test results will be positive, assuming the sensitivity is still 50% in the 500 people with SARS-CoV-2 who falsely tested negative the first time (which may not be true). The 2.6% corresponds to the 3.5% reported by Long et al. Thus, in this example, sensitivity is poor, yet the observed false negatives are only 2.6% of all negative results.
A number of reports have suggested a moderate sensitivity for SARS-CoV-2 RT-PCR of around 70% (30% false negative)[2]. Calculations similar to those in the example above show that if prevalence is 15%, specificity is 100% and sensitivity is 70% then the expected results are almost the same as those found by Long et al. Different combinations of prevalence, sensitivity and specificity also give results similar to Long et al. It is incorrect to interpret Long et al as showing that the SARS-CoV-2 RT-PCR test has good sensitivity.
References
Long DR, Gombar S, Hogan CA, et al. Occurrence and Timing of Subsequent SARS-CoV-2 RT-PCR Positivity Among Initially Negative Patients. Clin Infect Dis 2020;10.1093/cid/ciaa722.
Woloshin S, Patel N, Kesselheim AS. False negative tests for SARS-CoV-2 infection - challenges and implications. N Engl J Med 2020;10.1056/NEJMp2015897.
Posted by Kevin Cain PhD, Srdjan Jelacic MD FASE, Kei Togashi MD MPH, Andrew Bowdle MD PhD FASE
On 2020-06-30 08:50:43, user Simon Liebing wrote:
I have 2 questions to the study:<br /> What explanation have the authors that only 2 of 5 indicators are positive?<br /> Why the virus vanishes after March 2019 again?
On 2021-01-11 12:34:52, user Paul McKeigue wrote:
This paper has now been formally accepted by BMC Medicine, after several rounds of peer review.
On 2021-08-18 08:00:50, user Matt Huber wrote:
No it doesn't. Not in the same degree. That is false.
On 2025-06-12 12:45:41, user Juri-Alexander Witt wrote:
The article has now been finally published in CNS Drugs:<br /> https://link.springer.com/article/10.1007/s40263-025-01196-2
On 2021-03-05 23:06:05, user Minga wrote:
Several authors omitted to declare known links of interest with pharmaceutical firms. <br /> One of them declare not least than 24 pages of links with pharmaceutical firms on this official website : https://transparence.sante...., and nothing here. Such an offense to integrity put serious doubts about this pre-print.
On 2021-08-30 16:23:11, user Miriam Sturkenboom wrote:
In their discussion the authors erroneously claim to be the first to calculate incidence rates of TTS. EMA funded the ACCESS study to calculate background rates of AESI, including TTS in Europe. ACCESS reported the rates of TTS publicly on the EncePP website on a large population including hospital based data that are of crucial relevance for these rates. (http://www.encepp.eu/phact_... "http://www.encepp.eu/phact_links.shtml)"). The authors do not reference nor compare the rates with the ACCESS data. This is of scientific and public health relevance. The rates for several conditions differ substantially between the projects both of which run in Europe. It would be appropriate that the rates reported here are compared to rates for ACCESS to put the data that is relevant to monitor COVID-19 vaccines, in proper context and to understand the source of the differences.
On 2021-10-19 12:46:17, user student N wrote:
Line 178 & 181, "Supplementary Table 4". You meaned Supplementary Table 5?
On 2020-08-24 17:34:34, user Muhammad Shoaib Akhtar wrote:
Seems good work. However, biochemical analysis of remedesivir inside body is an important aspect and must be focused as well.
On 2021-05-12 11:20:59, user Rajesh K. Pandey, MD wrote:
Our arsenal of treatment options against COVID-19 remains very limited. For this reason re-exploration of the use of anti-inflammatory medications is imperative. A case in point is the use of colchicine against COVID-19. There is abundant data demonstrating colchicine's effects on lowering hs-crp in turn lowering risk of ACS. As supported by LoDoCo2 and Colchicine-PCI trials, Colchicine halved the risk of CV events vs placebo. In the COLCOT trial we saw a decrease in the combined primary endpoint of stroke; MI; CV death, CV arrest and resuscitation. In the COPS study, a benefit was seen in patients within 3 days of an MI. Overall amongst the above studies colchicine showed a benefit in CV outcomes. Therefore we take issue regarding the claim that colchicine increases the risk of PE. In our combined 50-60 years in practice, we have not had any incidents of PE in our 100's of patients treated with colchicine. Dr. Tracy Hampton's publication on February 2nd, 2021 in JAMA described the phenomenon that autoantibodies may drive COVID-19 blood clots, similar to patients with Anti-phospholipid syndrome. The statistical analysis of the COLCORONA trial may have done an injustice to known benefits of colchicine. We should continue to remain open minded on colchicine's potential therapeutic applications. Thank you.
On 2020-08-02 00:11:16, user Michael Verstraeten wrote:
I would like to make also a suggestion. <br /> 1. Calculate the amount of people infected in the whole population on the highest result of your research, by age category. (That's a simplification since there are also other relevant factors then age, like comorbidity factors but ok). <br /> 2. Add to this amount the results from the positive PCR-tests in the hospitals until that moment (Also a problem since there is a % of false negative results, but ok) <br /> 3. Estimate the amount of patients whit a general problem who were refused to get a blood test due to a suspicion of Covid - 19 and were not admitted to the hospital (if possible). And add them to the grand total. <br /> 4. From there on you can make an estimate based on the weighted average evolution of the deaths from de date corresponding to the testdate pn (a few days later then the test dates). It seems to be a relatively good assumption to consider that de evolution of the infections will be relatively equal to the evolution of the deaths / age category. <br /> 5. There is one problem however: we are not sure about the exact amount of deaths due to Covid. 73 % of people deceased in the care homes were not diagnosed and the tests have a big error margin. And diagnoses are maybe wrong due to extended diagnose protocols. Maybe it would be a good idea to calculate an average on the evolution of deaths and hospital admissions. Even if the latter depends on the admission policy.
Even with the uncertainties and the quite big error margin, maybe it will be possible to come with such an exercise closer to the real number of the infected population.
On 2021-09-24 13:28:53, user ovi wrote:
maybe they mean in their clinic not in the whole state? really we should not guess, the authors should come back and clarify this.
On 2025-10-15 17:57:20, user Pui-Yan Kwok wrote:
This paper is now published in Nature:<br /> “Population-specific polygenic risk scores for people of Han Chinese ancestry”<br /> DOI: 10.1038/s41586-025-09350-y<br /> URL: https://www.nature.com/articles/s41586-025-09350-y
On 2020-10-18 07:14:28, user Robert Clark wrote:
A key comparison was left out of the report, the effect of HCQ on patients specifically under invasive mechanical ventilation. This is a key category beyond just being “ventilated”. This is when the lung inflammation is so severe the patients have to be intubated, i.e., given a breathing tube inserted down the throat.
HCQ is a highly effective anti-inflammatory. Then it would be this case where it would be most effective for hospitalized patients. Note that the study was primarily focused on these drugs anti-viral effects. HCQ is the only one of them that also has an anti-inflammatory effect. Indeed it may be the only drug among all those being considered for COVID-19 that has both characteristics.
Note that in the RECOVERY trial it was specifically for THIS type of ventilation that the steroid dexamethasone was found to cut mortality, via its anti-inflammatory effect.
But in Table S1 of the Supplementary file to this SOLIDARITY report it states the “ventilation” discussed includes both invasive and non-invasive types:
The authors need to add to the report the effect of HCQ specifically for patients under invasive mechanical ventilation.
BTW, the report does show the result of Remdesivir on invasive mechanical ventilation patients in Fig. 3 of the report itself, showing null effect. But this was not a useful set of data anyway because Remdesivir does not have an anti-inflammatory effect.
The more important and relevant case of HCQ was not shown.
Robert Clark
On 2020-05-13 14:39:31, user Sinai Immunol Review Project wrote:
Main Findings <br /> - Study evaluated PBMCs of 63 italian patients with COVID-19 early after diagnosis (~5 days post-symptom start) using flow cytometry, and determined any association of inflammatory biomarkers with 28-days mortality.<br /> - Observed reduction in circulating lymphocytes (CTL, NK and NKT) and phenotypic changes in CD4+ T cells towards Th2 polarization, compared to reference intervals. Serum IL-6 levels not associated with monocyte counts, but correlated with Th2-polarized CD4+ T cell prevalence. <br /> - Detected enrichment of high-scatter atypical monocytes (in 11/14 subgrouped patients) with low CD14 and low HLA-DR MFI, but classical/non-classical monocytes abundance within normal reference range. <br /> - Lymphopenia, but also activation of T lymphocytes (defined as CD38+HLA-DR+ CTLs) reported to correlate with death within 28 days (more severe progression).
Limitations <br /> - All the analysis is using peripheral blood, which provides superficial global immune context but might not necessarily inform local inflammation in the alveoli or other organs. Concordant studies in BAL or rapid-autopsy tissue would be helpful <br /> - Activation of lymphocytes defined by CD38 and HLA-DR would need to be assayed functionally, potentially using IFN-g or Gzm-B ELISpot. <br /> - There is no breakdown of inflammatory markers across 28-day patient survival stratification in Table 3. Additionally, there is no correlation analysis shown for IL-6 levels (or other biomarkers) with the density of atypical monocytes. Such a multi-variate correlation study could tease out inflammatory biomarkers useful for prioritizing care on the hospital frontline.
Significance <br /> - Provides on-the-ground clinical report of early COVID-19 immunological characteristics from Milanese patients (despite being single-center small cohort of patients). <br /> - Results are in line with confirmed lympho-dysfunction observed in COVID-19 patients by multiple groups, and also support observations of atypical HLA-DR-low monocytes in severe disease (Giamarellos-Bourboulis et al. 2020). <br /> - Lymphocytopenia linked strongly to 28-day mortality stratification, in addition to <br /> standard clinical variates such as age, P/F ratio, LDH and CRP/Ferritin <br /> levels. Whether lymphocytopenia and associated innate-immune dysfunction<br /> is causal to adverse outcome is unclear, and will hopefully be revealed by prospective longitudinal clinical studies and potentially animal models.
Reviewed by Samarth Hegde as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2020-08-12 19:17:04, user Michelle Farina wrote:
Could not that be spread by droplets on inanimate objects? We know it remains on surfaces for 24-72 hours.
On 2021-03-08 08:44:37, user CD wrote:
"Our findings highlight the importance of monitoring how members of these known 501Y lineages, and others still undiscovered, are convergently evolving similar strategies to ensure their persistence in the face of mounting infection and vaccine induced host immune recognition ..."
The statement above makes it appear as if the SARS-cov-2 chooses where to mutate to escape host immunity, which is not the case. The pressure is put on the virus and mutations occur randomly, resulting in escape variants and some resulting in weaker variants
On 2021-12-22 03:16:08, user Kimihito Ito wrote:
Page 2: “Such methods often model the frequency of lineages using multinomial logistic regression [6,7]”
Ito et al. [7] does not use multinomial logistic regression. Instead, the paper [7] formulates the selective advantage using the ratio of the effective (instantaneous) reproduction numbers, which is called relative instantaneous reproduction number (R_RI).
On 2025-06-17 22:16:28, user Mamadu Baldeh wrote:
This article has been peer-reviewed and published: https://doi.org/10.1371/journal.pone.0324064
On 2021-12-24 07:45:25, user Jeff H wrote:
So assume the results you like (high VE for recent vaccination) are causal, but hand wave confounders at results you don't like (negative VE for distant vaccination)? Science?
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 2024-02-22 14:38:06, user Albee Messing wrote:
Please provide more clinical information about the proband.
On 2020-02-10 13:03:42, user nCoV.su wrote:
https://ncov.su/ - this site has human-readable statistics for 2019-nCoV
On 2020-07-25 19:31:23, user ???? ??? wrote:
It reflect the PK/PD pharmacological predication of efficacy <br /> The problem of LPV is complicated PK . Strong protein binding 98 % , extensive metabolism , long list of drug interaction. Therapeutic drug monitoring is mandatory to adjust dose in clinical setting. Moreover extrapolation of in EC50 to the current dose is not prefect. It was suggested to use PBA EC90 . the base line protein binding adjusted 90 % effective concentration. There is a debate about ability of current regimen to achieve Cmax > PBA EC 90 at lung tissues in severe cases
On 2021-03-14 07:19:10, user debernardis wrote:
Congratulations for this paper! I am excited that you could confirm the outcome of our observational study on disulfiram-treated patients in Northern Italy. Now waiting for the results of those two RCTs...
On 2021-09-12 09:56:05, user Rae Phillips wrote:
The initial results seem promising. I have 2 questions: 1. It's 18 months on from the beginning of the Novavax trial, are the original participants suffering any negative long term issues?<br /> 2. The spike protein in the Novavax vaccine pass through the blood brain barrier causing any detrimental effects on organs?
On 2021-06-19 21:00:25, user Elisabeth Bik wrote:
As pointed out on Twitter by @seqwave, in Figure 1, the 'Left orbitofrontal cortex (thickness)' and the 'Left superior insula (thickness)' plots are identical. Could the authors please check?
On 2021-05-13 22:21:36, user Jason wrote:
This paper should be removed for the lack of data, incorrect use of equipment as stated earlier, and improper testing conditions. Information such as flow rates, particle size concentration, and thermal conditions should have been presented, however are left out. As stated above, every other study conducted on this matter (which was done with correct equipment) shows very different results. This paper further spreads misinformation about mask efficiency and lacks any supporting scientific evidence or results to support the claims made.
On 2021-06-09 13:47:00, user Angela Kelly wrote:
‘Vaccine MRNA was not found in breast milk’ did you look for the spike protein it that may have already manufactured in the mother?
On 2021-10-07 20:59:57, user Mendel Singer PhD MPH wrote:
Vaccine reduces transmission by reducing cases. Yes, if a person was vaccinated AND gets COVID, they spread like the unvaccinated COVID cases. BUT, vaccination reduces the number of people infected, thereby reducing how many can transmit - so it reduces transmission. Even with Pfizer's waning immunity, after 6 months it still reduces infections by about half - so it is reducing transmission.
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 2020-09-27 03:43:34, user LB wrote:
It is well known that magnesium absorption is an issue with elevated gastric pH from PPIs. <br /> Please evaluate the possibility that the individuals who had a history of taking PPIs might have had magnesium deficiency, which altered their immune response to SARS-CoV-2.<br /> - Linda Benskin, PhD, RN
On 2020-12-28 08:48:28, user Disqus wrote:
The study design is somewhat confusing, a cross-sectional and prospective study that appears somewhat more similar to an ecological one than others. The authors crossed<br /> data from multiple sources including some known for their unreliability. Most surprising the authors states that they were unable to find a correlation between the opening of schools and the increase in Rt (par. "School closures did not alter the rate of Rt decline in Lombardy and Campania"), a statement that appears in stark contrast to the trend of Rt<br /> in Fig.6A and 6C showing a net increase after the opening of schools from an<br /> initial pre-opening value of approximately 1 for Lombardy and 1.5 for Campania,<br /> to a peak value of approximately 2.6 (Lombardy) and 1.9 (Campania).<br /> Curiously, the authors comment only on the descending part of the two curves but avoid<br /> commenting on the ascending phase following the opening of the schools.<br /> Finally, in spite of the medRxiv warning, one wonders why the authors decided to disseminate their unverified and not and not yest reviewed results, through the italian media (see for example the facebook pages of the authors for the links), suggesting the idea of a) somewhat solid and well establisehd general conclusions and b) the review process is a purely formal matter and not of substance.
On 2020-03-28 20:30:39, user adycousins wrote:
In Table 1 the estimate for the UK peak daily Covid-19 fatalities is 260 and a peak date of 5th of April, however 260 people died in the last 24 hours in the UK. Events seem have overtaken this study before its even reached peer-review.
On 2020-03-30 23:21:41, user Brian Coyle wrote:
They use Wallinga & Kretzchmar's age-based transmission matrix to model CoV2 transmission among age groups. The Wallinga estimates are based on influenza, and show "school-aged children and young adults will experience the highest incidence of infection and will contribute most to further spread of infections". This is not a useful model for CoV2, where children are not driving spread. This paper's estimates show isolating children has the biggest impact, which is almost surely not accurate.
On 2022-03-01 03:32:44, user Michal wrote:
Dear authors, very interesting preprint. I would like to bring attention to Supplementary Table 3 columns "DALYs / 100,000" and "YLLs / 100,000" which have exact same values - was this intended?