6,062 Matching Annotations
  1. May 2026
    1. On 2021-08-24 18:06:02, user Skeptic wrote:

      23andMe has an article about this on its website, in which the company listed the WRONG reference SNP number. According to this pre-print, it's rs7688383, but 23's 6/2/21 article claims it's rs7868383. In any case apparently the v.5 chip did not include this SNP as I can't find it in the raw data for any of the five kits I manage at 23.

      Kind of important to proof read, 23andMe, if you expect to develop and maintain credibility: https://you.23andme.com/p/8...

    1. On 2021-08-25 06:23:02, user L Wong wrote:

      This pre-print was submitted to the peer reviewed "Japanese Journal of Radiology" and was accepted on 6th of Jan, 2021. The content had been revised according to the reviewers suggestion and comment and the title of the article was revised as "Convolutional neural network in nasopharyngeal carcinoma: How good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?”. Readers can find the latest version of the article in the link:

      https://link.springer.com/a...

      .

    1. On 2021-08-26 05:47:36, user MarcoBonechi wrote:

      You assume 2-3 students being infected at the beginning. Out of 500 students. 2.5/500=0.005 i.e. 500 cases per 100k.<br /> That's 10x actual Aug-2021 US rate at 46 (https://www.nytimes.com/int... "https://www.nytimes.com/interactive/2021/world/covid-cases.html)").<br /> 18x the CA rate of 26.

      Your study has <10% chance of happening?

      Please explain.

      You should redo the study using several scenarios using randomized chances of a student being positive from outside.

      Then also randomize symptoms, as symptomatic cases will stop spreading or be caught altogether before reaching school.

      Then also randomize mask failure rate, badly worn masks, ineffective masks etc..

      Finally add testing with weekly or twice-weekly universal antigen with their success rate.

      You got to put more work!

    1. On 2021-08-26 16:37:49, user Larry Melniker wrote:

      The issue with Dr Hoffe conjecture is connecting D Dimer results, which are nonspecific, with serious ischemic events, which require specific testing results. He may be speculating on a True-True, but unrelated phenomena; otherwise D Dimer would be a routine part of ACS rule out work-up.

    2. On 2021-09-10 15:49:38, user skeptonomist wrote:

      The paper shows very conclusively that the vaccine reduces infection rate. Because the overall death rate among those infected is small (on the order of 1-2% at most), the expected number of deaths in the placebo group is not large enough for a meaningful test of how death rate is affected.

    1. On 2021-08-27 04:39:32, user William Brooks wrote:

      The authors use cumulative deaths from June 2020 but don't explain why they omit deaths before June 2020 (i.e., the <br /> whole first wave). Since many of the deaths during the omitted period <br /> occurred in states with strict NPIs such as Maryland (Fig.1a), this probably biases the results in favor of stricter states since they would have had smaller susceptible populations after the first wave than other states. Another study got around this problem by excluding northeastern states from the main analysis of the summer wave and including them in the analysis of the autumn/winter wave [1]. Because different NPIs were introduced/lifted at different times in different states, it would be interesting to see how consistent the correlation between NPI strictness and cases/deaths is during different waves.

      Also, Fig. 3a shows that case trajectories are clearly effected by geography, so rather than directly compare two states with different NPI strictness from different regions (Maryland and Tennessee), it might be more informative to compare two states with different NPI strictness from the same region (e.g., Louisiana and Florida).

      [1] https://escipub.com/Article...

    1. On 2021-08-27 17:40:44, user David Wells wrote:

      Table 1a shows that your 'vaccinated individuals' group exhibited higher rates of comorbidities. Comorbidities are therefore possibly correlated with vaccination. The model results show insignificant comorbidity effects, suggesting the possibility that your 'vaccination' effect is really (or partially) a case of stolen significance. Did you try removing the vaccination variable to find out if comorbidities then become significant? Or what if you matched on comorbidity rates, not just demographics?

    2. On 2021-08-28 02:02:35, user Jonas Ferris wrote:

      While it may be that natural immunity offers more protection than vaccine immunity, there seems to be some problems here:

      How can you adjust for the issue that some in the previously infected group died, presumably those most susceptible to symptomatic infection while the vaccinated group likely has many of these most susceptible still in the group?

      As the overall infection rate seems quite low (<2%) in the vaccinated group, though many multiples of the even lower numbers in the previously infected (and death screened) group (leading to sensationally high multiples of up to ’27-fold risk’) is it possible that many of these infected could have been deceased had they not been vaccinated?

      I understand there are adjustments for comorbidities, but there is no real way to determine who would have died from a group with comorbidities yet they may not exist in the previously infected group.

      Why are there so few people above 60 in the study (<5%) when this age groups is over 15% of the population over 16 and the very age group that is most likely to have serious symptomatic infection? How many went to the hospital from this group in both the vaccinated and previously infected groups?

      Early seekers of vaccines were likely more at risk of death from Covid than those that were not as worried and didn’t (or couldn’t) get a vaccine in Jan/Feb.

      Your two groups are basically those that were fearful of catching Covid and those that didn’t see it as much of a risk to them. These are groups that may have very different risks of testing positive for Covid even if they both received vaccines at the same time.

      Those that received a vaccine after almost a year of watching out for the virus may have acted in a more risky fashion after getting vaccinated - the pendulum swung even further than the no vaccine group (who may not have known they were somewhat immune)?

      Given these shortcomings, it seems like a more reasonable conclusion than natural immunity is 7 fold+ stronger than vaccine immunity after a few months, is that while both natural immunity and vaccine immunity offer similar substantial absolute protection from serious infection, for those in an age group already less likely to have serious infection, that has already made it through one infection without dying a significant population screening event of those most susceptible to serious Covid infection, symptomatic infection from Covid is less likely than for those that have self-identified as at risk and have been vaccinated for but not exposed to Covid.

      As fears of wanning immunity may lead to over consumption of limited resources of Covid vaccines globally, a conclusion that is more likely to lead to the unvaccinated seeking vaccination while discouraging the already vaccinated to seek an aggressive booster timeline would be more appropriate as opposed to one that could rationalize seeking natural immunity and encourage frequent boosters to the previously vaccinated.

    3. On 2021-08-28 14:41:37, user RC Cyberwarrior wrote:

      I have read comments based on medical studies that individuals who previously had SARS COV2 were 2 -4 times more likely to suffer adverse reactions to the covid vaccines, if vaccinated post initial infection. Some speculate this reaction was related to Antibody-Dependent Enhancement.

    4. On 2021-08-30 00:15:54, user chris amos wrote:

      An important paper and carefully conducted study, but it would be useful if the authors would provide a figure or table starting with the overall cohort size, indicating the total numbers of events according to vaccination versus infection or first vaccination among infected. Given the data that are provided I do not know how to accurately calculate a positive predictive value of having been vaccinated, which is another statistic that is of interest. Also, when the authors refer to the analyses as 'multivariate', I think the more accurate way to refer to the analyses is "multivariable". Multivariate would mean that multiple outcomes (vaccinated only, infected only or vaccinated and infected) are jointly modeled, but it seems like the comparison groups are analyzed in separate analyses.

    5. On 2021-08-30 22:42:54, user Chris Curry wrote:

      You would think this would be common knowledge seeing as all a vaccine does is simulate a person getting infected in order to force their body into building immunity to the virus. If natural immunity wasn't a thing then vaccination wouldn't be a thing either, but for some reason the country has decided that you have to be either "pro vaccine" or "anti-vaccine" without entertaining any sort of nuance.

    6. On 2021-08-31 22:30:55, user Fully wrote:

      Thank you for the interesting and easy-to-understand study - and the clear results: Recovered people are actually much better protected against the now predominant delta variant of Covid-19 and thus less contagious than vaccinated people, even if the infection occurred more than 6 months ago.<br /> Policymakers in Europe, who grant recovered people the same rights as vaccinated people for only 6 months after their infection, should now remove their 6-month rule based on your study results.

      Thank you for this from someone who has recovered since one year, who does not want to be vaccinated, because he did well with the disease - me.

    7. On 2021-09-02 09:13:39, user zlmark wrote:

      There are several issues with the way the cohort in this study have been formed - the most critical one is the age distribution:

      The 60+ group extremely underrepresented - the cohorts contain about 5-6% of people aged 60 and above, whereas they amount to about 31% of the vaccinated people in Israel. And since their own regressions show that the age is a major factor in infectability, such underrepresentation can seriously affect the risk ratio estimates.

    8. On 2021-10-16 15:43:33, user Alex wrote:

      Oh and on natural immunity, myself, partner and two children had COVID March 2020, both antibody tests came back May 2021 positive. Currently waiting for the results of an updated one…. We have also not had anything with similar symptoms since but in two weeks I fly to Barcelona with a tonne of red tape because I’m not vaccinated and I don’t find it fair…My partner loses her job along with 40 people in Hampshire social care next month because they opted for no vaccine, a big gap in care looking after our grandparents - good luck with that!- my point is,why is natural immunity not accepted??it’s simple to test for so questions need to be raised!

    1. On 2021-08-30 07:51:38, user Candice Chaplin wrote:

      It states that the GENECUBE® HQ SARS-CoV-2 (TOYOBO Co., Ltd.) reagent was approved in October, 2021. As a layman, I don't quite understand this.

    1. On 2021-08-30 14:40:54, user Nathan Johnson wrote:

      Hi Sean, table 2 is the attention getting graph with the large drop but it mixes tests at all different ages so it's harder to read. It'd be better to see a graph by time for separate groups of 3 months old, 6 month old and 12 months old (or similar). Since table 4 shows "Overall, we note no significant reductions in development trends." taking out the older groups who didn't drop should make the drop in 2021 even more dramatic, no? Also if masking was used in first few months in children born prepandemic without a drop, could point more strongly to prenatal cause.

    2. On 2021-10-08 05:09:07, user Anya Dunham wrote:

      Hi Sean and team, as a scientist and a mom of a 2020 baby, I read your paper with interest. Similar to Pasco, I wondered about the effects of masks. I am also wondering whether babies might have exhibited some form of a 'freeze' response, as some might have not left their homes or neighborhoods much... In an exaggerated example, I would probably do okay on a cognitive test in my home or your lab, but perhaps not so well if I were abducted by aliens... which a lab setting might feel like to babies born during the pandemic.

      Similarly, there could be a novelty effect. Given that babies learn by figuring out patterns and experiencing novel events, I wonder if everything in the lab visit was so new that babies who didn't 'freeze' had a harder time paying focused attention to the task at hand. (I see you already mentioned something similar below.) I imagine even following a shape with their eyes might be more challenging if baby is greatly distracted by the novelty of a visit. I can see my summer 2020 baby having this challenge, although he has amazing focus when playing independently at home. I think some measure(s) from the home environment taken by the family would be important here.

      Lastly, how did the families join the study? Did they self-identify? (As a side note, I would have liked to see more details in the Methods section - perhaps I am missing an Appendix?) At least where we live, getting an appointment with a pediatrician has been much more challenging during the pandemic. So I wondered if families who had some concerns around their babies' development (even subconscious ones) could have been more likely to join.

    1. On 2021-08-30 15:16:42, user Jeff Brender wrote:

      For those wondering about the decrease in PhD respondents from the last version<br /> From the Methods section<br /> "To be included in the analysis sample, participants had to complete the questions on vaccine uptake and intent, and report a gender other than “prefer to self-describe.”. This exclusion was made after discovering that the majority of fill-in responses for self-described gender were political/discriminatory statements or otherwise questionable answers (e.g. Apache Helicopter or Unicorn), and that as a group, those who selected self-described gender (<1% of the sample) had a high frequency of uncommon responses (e.g., Hispanic ethnicity [41.4%], the oldest age group [23.2% >=75 years] and highest education level [28.1% Doctorate]), suggesting the survey was not completed in good faith. "

    1. On 2021-08-30 16:11:49, user Eduardo Amorim ????????? wrote:

      Can you please explain how mf is calculated? You ms says "Mf was calculated as described previously [3]." But ref. #3 doesn't explain how mf is calculated -- at least I can't see it.

    1. On 2021-08-31 10:15:07, user Isatou Sarr wrote:

      Excellent paper,

      the route of therapeutic administration usually plays a pivotal role in immune cells activation, type as well as robustness. Mucosally induced immunological tolerance has become an attractive strategy for diagnostics and treatment of diseases, although there is a need to fully understand the dynamics of mucosal-tolerance immunotherapy as well as efficient antigen delivery and adjuvant systems.

      Additionally, the genetically diverse human subjects who also differ significantly in their mucosal flora, nutritional status and previous immunological/environmental exposure, all of which are factors that can been affect mucosal vaccine efficacy.

      On the brighter side of life :)))), if practical assays for assessing mucosal immune cells reactivity in research settings are developed as well as methods for predicting efficacy of candidate mucosal immunotherapeutics, harnessing the therapeutic potentials of the<br /> mucosal immune pathway can be a reality.

      Thank you.

    1. On 2021-09-01 04:18:17, user John Smith wrote:

      Surgical face masks at best have a 3.4 fold decrease in aerosols if worn perfectly, but in this case the typical imperfect fit would drop this down to about a 1 fold decrease. The math in this simulation is far off the mark compared to detailed peer reviewed experiments. Too many incorrect assumptions made in the simulation.

      https://www.sciencedirect.c...

    1. On 2021-09-04 19:24:55, user melanoficus wrote:

      Very encouraging results. I wish these investigators great success in their endeavours to find and implement beneficial treatment protocols that will save lives of those severely effected.

    1. On 2023-01-15 02:42:48, user Peter lange wrote:

      I agree with the other commenters. The description of training is inadequate to determine if the dogs are detecting acute and chronic stress, which canines have been trained to do with high reliability. Without further information the conclusions are unsupported by evidence presented.

    2. On 2022-01-13 17:30:19, user jetbundle wrote:

      How were the dogs trained? Were they trained on the sweat of infected (symtomatic or asymptomatic?) people or on isolated viruses?

      The authors should answer this. That makes the difference whether the dogs simply identify sick patients or whether it has anything to do with the virus.

    1. On 2023-08-08 19:34:44, user Xiaoping Liu wrote:

      The author has published this paper in PLoS One with a revised title: "Analytical solution of l-i SEIR model – Comparison of l-i SEIR model with conventional SEIR model in simulation of epidemic curves". PLoS One. 2023; 18(6): e0287196.<br /> Published online 2023 Jun 14. doi: 10.1371/journal.pone.0287196

    1. On 2021-12-25 16:37:09, user Markus wrote:

      In the light of the negative vaccine efficiency, why do they conclude that there is the need for massive rollout of vaccinations and booster vaccinations? The vaccines appear to undermine the natural immunity.

    1. On 2022-01-09 17:05:04, user rubenroa wrote:

      Any reason for the increased Odd in vaccinated people against other studies in Israel which conclude: "Vaccination with at least two doses of COVID-19 vaccine was associated <br /> with a substantial decrease in reporting the most common post-acute <br /> COVID19 symptoms."https://www.medrxiv.org/con...

    1. On 2022-01-10 23:20:22, user Litawor wrote:

      The previous version of this preprint additionally described adjusted <br /> analysis with important covariates related to vaccination status and <br /> vaccination timing. Why is that analysis omitted in this version? <br /> Matching does not remove the need for statistical adjustment.

    1. 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.

    1. On 2022-01-13 14:50:32, user Erik Petersen wrote:

      One of the findings that is going to be predominantly taken from this study is that, "vaccinated individuals have significantly lower IVTs." However, upon looking at the data in Figure 4A specifically, we see just under 3 (2.9?) FFU/ml in unvaccinated individuals compared to ~2 FFU/ml in vaccinated individuals. Would you please explain how this constitutes a "significant" reduction?

    1. On 2022-01-17 19:49:07, user AW wrote:

      Some errors in text and tables I’m afraid. In text you report the IRR for men <40 years as “7.60 (2.44 - 4.78)” for 3rd dose for Pfizer which clearly is nonsensical -looks you have used the 95%CI for second dose repeated in error. And you have reported the number of events as * for 3rd dose Pfizer in men under 40 years in table rather than number - should have a numerical value.

      Given these are probably the most important impactful data you present it’s a bit embarrassing to not get this right - but shows why peer -review is needed (and makes me wonder what else might be incorrect)

    1. On 2022-01-23 21:31:37, user maa jdl wrote:

      This paper is a total nonsense!<br /> Why applying the Benford law?<br /> There is no reason. And the paper does not contradict that!<br /> On the contrary.<br /> You just need to look at the data to understand WHY the Benford law doesn't apply!<br /> This is what I did and ONE simple picture can reveal it in a much clearer way than a long paper with a lot of references. This can be done with no references at all! The chi² test is useful there only to give numbers on what is obvious from the picture.

    1. On 2021-10-13 17:03:08, user constantinos schinas wrote:

      very interesting article. can you breakdown the calculation for the ie. <br /> 13,080 tests, 100 positives, 20% FNR and 0,8%FPR, in a way we can replicate it in an excel document? In two cases, stable 20%FNR and variable 0-40% FNR.

      thank you in advance

    2. On 2020-06-08 17:06:04, user Johann Holzmann wrote:

      Dear authors,<br /> Thank you for making the pre-print accessible, I read it with great interest.

      How do your findings regarding the presumptive false-positive rate of SARS CoV2 detection using RT-PCR relate with the very low RT-PCR positive rate as currently seen in many countries or regions with a very low prevalence of SARS CoV2?<br /> For example Australia runs between 30.000 to 35.000 PCR test daily for the last month and only gets around 10 positive assays per day. <br /> Other examples with a ratio of PCR assays per day to posiive assays of around 600-2000:1 are Iceland, Greece, Croatia, Thailand and certain parts of Germany (eg Sachsen-Anhalt, Mecklenburg Vorpommern) or Austria (eg Tirol).<br /> Wouldn't these data indicate a much lower false-positive rate than the one suggested in your manuscript?<br /> Thank you again for making your research accessible<br /> kind regards

    1. On 2022-01-27 21:22:51, user Michael Klar wrote:

      They have NOT done their homework:

      This investigation uses no suitable surrugate for human aerosols. These consist mostly of mucin5 and this is a hydrogel. Hydrogels behave differently than the one used Serum. This is reflected in the Shrinkage factor of 2.5 versus 4-5 in humans.

      The results of the preprint should not be evaluated, as another previously published study shows that the liquid composition is crucial for the inactivation rate:

      https://www.pnas.org/conten...

    1. On 2022-01-28 20:26:50, user Dylan Arroyo wrote:

      What happens to the patient with a suicidal ideation while they wait for sobriety? Are they restrained/sedated? Do they wait in the waiting room until they have sobered up before they can be seen by a social worker?

    1. On 2022-02-02 19:32:41, user Eric D wrote:

      This is on Sky News as<br /> BA.2 "More likely to infect vaccinated people"!

      SSI report is ambiguous<br /> https://en.ssi.dk/news/news...

      The headline<br /> "BA.2 is more transmissible than BA.1 but vaccinated persons are less likely to be infected and to pass on infection"<br /> contradicts a sentence that looks badly-written or edited<br /> "In addition, comparing the risk of household members being infected in BA.2 relative to BA.1 infected households, was higher in vaccinated and booster vaccinated than in unvaccinated, which suggests immune evasive properties of the BA.2 variant."

    2. On 2022-02-08 07:24:12, user Ole Stein wrote:

      Misleading and biased conclusions based on wrongfully datatreatment, where they have mixed vaxed with unvaxed, and so unvaxed included all vaxed less than 14 days since last shot and all vaxed include unvased post illness. Such mix is not just unetical, but makes the conclusion completely useless as it does not say anything about contamination between vaxed and unvaxed as they are mixed in their data input. The report should be discarded and removed as fraudulent science.

    1. On 2022-02-07 23:20:52, user A440 wrote:

      The report says: "The analyses were adjusted for [...] booster dose and time since last dose among the vaccinated."

      For those of us wondering whether to get a booster dose, it would be good to know more about how this adjustment was done.

    1. On 2022-02-22 02:12:34, user Juliet French wrote:

      Nice paper. You may want to check out one of our papers. Moradi Marjaneh et al, Genome Biology 2020. PMID: 31910864. Some similarities between yours and ours.

    1. On 2022-02-23 03:43:25, user Sam Wigginton wrote:

      Deaths in South Africa are still climbing steadily three months after the Omicron infection peak (according to Worldometer). The case fatality rate (assuming 3 week lag) appears to have risen from 0.7% three months ago to about 8% now. What's going on?

    1. On 2022-03-23 16:58:16, user Stefan Baeuml wrote:

      It would be interesting to have a follow up study in the presence of Omicron. In particular, given the increased likelihood of breakthrough infections, it would be interesting to see if the likelihood of the symptoms mentioned in the 'Results' section still remains within the background of people without SARS-CoV-2 infection.

    1. On 2022-04-11 18:32:10, user ReviewNinja wrote:

      Thanks you for this fast publication. This publication confirms:<br /> - that people can be reinfected with BA.1 after delta infection<br /> - that people can be reinfected with BA.2 after BA.1 (but that this seems rather a rare event)

      However, this publication shows some clear limitations that would need some discussion:<br /> - This publication gives an advice on testing policy, but does not discuss the testing policies at the moment of the study. This is important as this policy changed over the period of the study and was different during some periods for vaccinated and non-vaccinated individuals. Also a correction for testing behavior over time per age group would be useful. <br /> - The publication compares the number of reinfections (01/12 to 10/03) to the vaccinated population on 10/03. As the advice for vaccination for children 5-11 only came out on 15/12, this is an overestimation for the whole study period. The same is true for boosters in the younger age groups. <br /> Vaccination % at the different periods in the study: <br /> %For each age group (age in 2021) on: 01/12, 01/01, 07/02 and 10/03<br /> 5-11y (2 doses): 9 (probably most already 12y), 10, 20, 41<br /> 12-17y (3 doses): 0.5, 2, 11, 33<br /> 18-44y (3 doses): 7, 25, 70, 75<br /> 45-64y (3 doses): 13, 56, 88, 89<br /> The changing vaccination rate over time should be taken into account, or this comparison should not be made. Furthermore, most measured reinfections were in the first study period (<feb 7:="" 91="" of="" the="" 96="" reinfections).="" some="" other="" points="" to="" discuss:="" -="" the="" conclusions="" (and="" abstract)="" are="" rather="" strong.="" to="" advise="" a="" change="" in="" (pcr-)testing="" policy,="" at="" least="" reinfection="" versus="" residual="" pcr-detection="" should="" be="" compared="" discussed.="" reinfection="" during="" this="" short="" period="" measured="" in="" this="" paper="" is="" 0.16="" and="" 0.01%="" (with="" off="" course="" all="" biases="" and="" limitations).="" we="" know="" from="" a="" challenge="" study="" that="" 1="" 3="" young="" people="" (in="" these="" conditions="" in="" this="" small="" study)="" still="" test="" (low)="" positive="" after="" 28="" days="" for="" example="" (https:="" <a href="www.nature.com" title="www.nature.com">www.nature.com="" articles="" s41591-022-01780-9).="" -="" how="" was="" the="" n-gene="" cut="" off="" determined="" here?="" (it="" would="" be="" of="" added="" value="" to="" also="" confirm="" which="" percentage="" really="" resulted="" in="" detectable="" virus="" (specially="" for="" study="" period="" 2).)="" additionally,="" a="" look="" at="" the="" viral="" loads="" might="" be="" of="" added="" value,="" as="" done="" by="" the="" study="" by="" stegger="" (ref="" 9).="" they="" suggest="" a="" more="" transient="" infection="" upon="" reinfection="" with="" ba.2="" after="" ba.1.="" (would="" be="" nice="" to="" know="" the="" testing="" indications="" for="" these="" people="" as="" well.)="">

    1. On 2022-05-11 01:52:14, user bioRxiv wrote:

      This preprint is participating in the Comment-a-thon pilot initiative by bioRxiv/medRxiv at the Biology of Genomes CSHL meeting. You can enter the competition if you are registered for this conference by signing up using the link provided at the meeting. Remember to add #BoG22 to your comments.

    1. On 2022-06-14 13:31:27, user Peter J. Yim wrote:

      This comment is to clarify that the study showed an unequivocal benefit from ivermectin in COVID-19. From the abstract, the primary outcome considered in the study was: "...time to sustained recovery, defined as achieving at least 3 consecutive days without symptoms." The outcome did not reach statistical significance for that outcome. However, for the related secondary outcome "mean time unwell" the outcome was statistically significant and favored ivermectin.

      MTU was estimated "...from a Bayesian, longitudinal, ordinal regression model with covariates age (as restricted cubic spline) and calendar time." The principal finding of the study was that there was a statistically significant difference in MTU between the treatment and control groups: -0.49 (95% CrI: -0.82, -0.15) where CrI refers to "credible interval". The negative range of the 95% credible interval indicates that MTU was lower for the treatment group than the control group.

      The authors conclude that the trial "...did not identify a clinically relevant treatment effect ...". The magnitude of the treatment effect found in this trial may or may not be clinically relevant, but clinical relevance is not a statistical quantity and establishing it was not a goal of the trial.

    1. On 2022-06-18 09:09:00, user David Escors wrote:

      The preprint is still a work in progress before submitting it for publication. It has an error in the definition of the cohort and in Table 1. The correct statement defining the cohort previous to the correction of the manuscript would be :"The majority of patients were smokers, 75% male and the mutational status of the tumors was not evaluated in 96.4% of the patients".

    1. On 2025-11-11 14:07:07, user Evolutionary Health Group wrote:

      We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper.

      Here are our highlights:

      The authors applied metagenomic sequencing to samples from multiple wastewater treatment plants to characterize the diversity and abundance of antibiotic resistance genes. Using a standardized bioinformatics pipeline, they quantified ARG classes relative to total microbial DNA, and compared treatment efficiency across plants.

      They observed that water leaving the treatment plant still harbored a broad spectrum of ARGs, including multidrug-resistance genes.

      The authors describe wastewater treatment plants as both sources and potential intervention points for antibiotic resistance by emphasizing that improved engineering and coordinated antibiotic-management strategies could limit the spread of resistance genes in urban systems.

      These findings indicate that monitoring of municipal wastewater may serve as a real-time surveillance tool for community-level antibiotic resistance burden and inform outbreak preparedness.

    1. On 2022-10-24 11:51:28, user Indi Trehan wrote:

      This article has now been published after peer review: The Journal of Pediatrics 2022; 247: 147-149. doi: 10.1016/j.jpeds.2022.05.006.

    1. On 2020-04-06 19:55:46, user Sinai Immunol Review Project wrote:

      Clinical Characteristics of 2019 Novel Infected Coronavirus Pneumonia:A Systemic Review and Meta-analysis

      The authors performed a meta analysis of literature on clinical, laboratory and radiologic characteristics of patients presenting with pneumonia related to SARSCoV2 infection, published up to Feb 6 2020. They found that symptoms that were mostly consistent among studies were sore throat, headache, diarrhea and rhinorrhea. Fever, cough, malaise and muscle pain were highly variable across studies. Leukopenia (mostly lymphocytopenia) and increased white blood cells were highly variable across studies. They identified three most common patterns seen on CT scan, but there was high variability across studies. Consistently across the studies examined, the authors found that about 75% of patients need supplemental oxygen therapy, about 23% mechanical ventilation and about 5% extracorporeal membrane oxygenation (ECMO). The authors calculated a staggering pooled mortality incidence of 78% for these patients.

      Critical analysis:<br /> The authors mention that the total number of studies included in this meta analysis is nine, however they also mentioned that only three studies reported individual patient data. It is overall unclear how many patients in total were included in their analysis. This is mostly relevant as they reported an incredibly high mortality (78%) and mention an absolute number of deaths of 26 cases overall. It is not clear from their report how the mortality rate was calculated. <br /> The data is based on reports from China and mostly from the Wuhan area, which somewhat limits the overall generalizability and applicability of these results.

      Importance and implications of these findings in the context of the current epidemics:<br /> This meta analysis offers some important data for clinicians to refer to when dealing with patients with COVID-19 and specifically with pneumonia. It is very helpful to set expectations about the course of the disease.

    1. On 2023-12-12 14:56:15, user Tanmoy Sarkar Pias wrote:

      This paper has been accepted to an IEEE conference. A link (& DOI) to the IEEE Xplore will be added when this article is published. Please see the following copy right details of IEEE.

      2023 26th International Conference on Computer and Information Technology (ICCIT), 13-15 December, Cox’s Bazar, Bangladesh

      979-8-3503-5901-5/23/$31.00 ©2023 IEEE

    1. On 2023-12-19 12:39:03, user Christos Proukakis wrote:

      Response to: “Is Gauchian genotyping of GBA1 variants reliable?”

      Marco Toffoli1,2, Anthony HV Schapira1,2, Fritz J Sedlazeck2,3,4, Christos Proukakis1,2 *

      1. Department of Clinical and Movement Neurosciences, Queen Square Institute of Neurology, University College London, UK
      2. Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
      3. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
      4. Department of Molecular and Human Genetics, Baylor College of Medicine, TX, USA

      * To whom correspondence should be addressed: c.proukakis@ucl.ac.uk

      We recently described two methods for GBA1 analysis, which is hampered by the adjacent highly homologous pseudogene: Gauchian, a novel algorithm for analysis of short-read WGS, and targeted long-read sequencing 1. Tayebi et al have applied the former to WGS from 95 individuals, and compared it to Sanger sequencing 2. They report concordant genotypes in 85, while 11 had discrepant calls (we note that this leads to a total of 96). In addition, they report 28 false Gauchian calls in 1000 Genomes Project (1kGP) samples. Gauchian was developed because the homology of the GBA region requires a short read variant caller that does not rely solely on read alignments, and can identify specific variants known to be pathogenic. To understand the cause of these discrepancies, we reviewed their data, and conclude that they are mis-interpreting Gauchian results in 8 of the 11 discrepant samples, and incorrectly using Gauchian to analyze low-coverage 1kGP samples.

      Among the 11 (11.5%) samples with inconsistent calls with Sanger (Table 1), four (Pat_08, Pat_26, Pat_28 and Pat_58) were not called as the variants are not on Gauchian’s target list, which includes all ClinVar variants in December 2021. These variants, and any others, can be easily added (see Supplementary Information). Three other samples (Pat_75, Pat_76 and Pat_79) had low data quality resulting in large variation in sequencing depth across the genome, as shown by the median absolute deviation (MAD) of genome coverage: 0.269, 0.128 and 0.127 (three highest values among all samples). Gauchian recommends trusting calls in samples with MAD values <0.11, and produces a warning message if this is exceeded. In all three samples, the GBA1+GBAP1 copy number was a no-call (marked as “None” in the output file), indicating that Gauchian could not determine the copy number due to high coverage variation. Variants were not called because no further analysis was done beyond copy number calling. These should not be viewed as false negatives, as the warning message and the report of no-calls should prompt the user to obtain higher quality data or consider alternative sequencing. Among the remaining 4 samples with inconsistent results: Pat_03 had a Gauchian call of heterozygosity for p.Asn409Ser, while Sanger reports this as homozygous. Review of the IGV trace (Tayebi et al. Supp Figure 1) shows that at least 10 reads (around a fifth of the total) have the reference base, and therefore it is hard to conclude this is homozygous. Review of the Sanger trace (not provided) could determine whether there is a low peak representing the reference allele. We cannot provide a conclusion, and additional analysis is recommended. Mosaicism could be a plausible explanation, and this has been reported in GBA1 3,4, albeit not at this position. Pat_47 had a false negative p.Leu483Pro call. Pat_16 was indeed wrongly genotyped as homozygous for p.Asn409Ser, related to the adjacent c.1263del+RecTL deletion. Pat_92 had all expected variants called, but the heterozygous p.Asp448His was mis-genotyped as homozygous. In summary, there is one false negative and two wrongly genotyped variants (heterozygous variants called homozygous). Gauchian’s precision is therefore 98.9% (175 out of 177 calls are correct). Its allele-level recall/sensitivity is 99.4% after excluding alleles not on Gauchian’s target list, and samples which could not be analyzed due to high coverage variation. Alternatively, it can be calculated as 97.2% if only samples with high coverage variation are excluded, 96.2% if only alleles not on the target list are excluded, and 94.1% if all these samples are considered .

      Tayebi et al. concluded that Gauchian is not able to call recombinant variants without providing orthogonal evidence. In Pat_95, Pat_71 and Pat_16, they examined alignments in IGV and reported absence of supporting reads for Gauchian calls, but all recombinant alleles called by Gauchian were consistent with Sanger. This highlights that read mapping in this region is unreliable (variant supporting reads may align to the pseudogene), making interpretation of alignments in IGV very challenging. Gauchian is designed to untangle ambiguous alignments, locally phase haplotypes and make correct calls. Particularly, in Pat_95, they claimed that Gauchian called the expected RecNciI variant but got the mechanism of the recombinant allele wrong (gene conversion vs. gene fusion). This claim appears to be based on incorrect interpretation of IGV alignments, i.e. seeing 3’ UTR mismatches associated with GBAP1 does not necessarily indicate gene fusion, as they can be misalignments, or even part of the gene conversion. The RecNciI in Pat_95 is a gene conversion, as indicated by the normal copy number between GBAP1 and GBA1. Tayebi et al. claimed that this is a gene fusion without orthogonal evidence. In addition, they claimed that Gauchian misreported copy numbers in Pat_92, Pat_42 and Pat_72, again without orthogonal evidence. We validated Gauchian copy number gains by digital PCR in four cases 1. While particular recombinants could be prone to erroneous copy number calling, we do not know what “other techniques'' identified a different copy number in Pat_92. Orthogonal validation using digital PCR would resolve this. Finally, it is true that Gauchian does not have all possible recombinants on its target list, as it is designed to focus on recombinant variants in exons 9-11, because others are rare and detectable with standard callers.

      Tayebi et al. reported 4 samples where Gauchian missed variants in GRCh38 compared to GRCh37. Among these, two (Pat_35, Pat_75) were due to incorrect alignment settings that resulted in abnormally low mapping quality throughout the region. It is likely that ALT-aware alignment was on for all samples except these two. The remaining two (Pat_16, Pat_78) reflected an area of improvement for Gauchian to better call p.Asn409Ser, which is not a GBAP1-like variant, and can thus be called well by standard callers.

      We reported Gauchian calls of 1000 Genomes Project (1kGP) samples, validating some by targeted long reads 1. Gauchian called zero samples with biallelic variant in exons 9-11. However, Tayebi et al. reported a completely different set of Gauchian calls in the same samples (in their Table 4). This was caused by incorrect use of Gauchian on old low coverage WGS (median coverage <10X, https://ftp.1000genomes.ebi... "https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data/)"), rather than 30X (https://ftp-trace.ncbi.nlm.... "https://ftp-trace.ncbi.nlm.nih.gov/1000genomes/ftp/1000G_2504_high_coverage/data/)").

      We are grateful to Tayebi et al for assessing Gauchian analysis of this very challenging gene 2, but note that most discrepancies were due to incorrect use or misinterpretation of results. “No call” samples due to inadequate data quality cannot be considered false negative, as no calls are provided, and warnings of noisy coverage are given where applicable. Samples with inadequate coverage should obviously be avoided, as Gauchian is expected to perform at coverage >30X. Gauchian does not call variants not on its target list, which can be expanded. We provide updated recall (99.4%) and precision (98.9%) values. We have not seen any evidence of the alleged inability of Gauchian to call recombinant variants, and would welcome orthogonal copy number assessment of discrepancies. We show that Gauchian can be used for GBA1 assessment when coverage and data quality are adequate. We do note a limitation in genotyping p.Asn409Ser, a non-recombinant variant that can be called by standard variant callers, which we recommend running together with Gauchian for a complete call set. Finally, in clinical cases where absolute certainty is required, Sanger sequencing could be considered, with targeted long read sequencing another option 1,5–7.

      Table 1. Details on the 11 samples where Gauchian and Sanger are inconsistent.

      Gauchian calls Sanger Assessment,Tayebi et al. Our assessmentSample Copy Number of GBA1 and GBAP1 GBAP1-like variant in exons 9-11 Other unphased variants Genotype Prediction

      Pat_08 4 None p.Asn409Ser p.Asn409Ser/p.Gln389Ter False Negative Missed variant is not on Gauchian's target list

      Pat_28 4 None p.Arg535His p.Arg535His/Cys381Tyr False Negative Missed variant is not on Gauchian's target list

      Pat_58 4 None p.Asn409Ser, p.Arg296Ter p.Asn409Ser, p.Arg296Ter, c.203delC False Negative Missed variant is not on Gauchian's target list

      Pat_26 4 None p.Asn409Ser p.Asn409Ser/p.Arg502Cys False Negative Missed variant is not on Gauchian's target list.

      Tayebi et al.’s Supplementary Figure 1 shows no variant at p.Arg502Cys (c.1504C>T), but a different variant at the neighboring position, p.Arg502His (c.1505G>A), which is not on Gauchian's target list.

      Pat_75 None (No Call) NA NA p.Arg502Cys/p.Arg159Trp Missed Copy number is a no-calldue to high variation in depth so no further variant calling was performed. Coverage MAD 0.269

      Pat_76 None (No Call) NA NA p.Asn409Ser/p.Asn409Ser Missed Copy number is a no-call due to high variation in depth so no further variant calling was performed. Coverage MAD 0.128

      Pat_79 None (No Call) NA NA p.Leu483Pro/p.Arg502Cys Missed Copy number is a no-call due to high variation in depth so no further variant calling was performed. Coverage MAD 0.127

      Pat_03 4 None p.Asn409Ser p.Asn409Ser/p.Asn409Ser False Negative Gauchian call is supported by reads, see Tayebi et al.’s Supplementary Figure 1.

      Pat_47 4 None p.Asn409Ser p.Asn409Ser/p.Leu483Pro False Negative True false negative

      Pat_16 3 c.1263del+RecTL/ p.Asn409Ser, p.Asn409Ser p.Asn409Ser, c.1263del+RecTL False Positive Heterozygous p.Asn409Ser misgenotyped as homozygous as Gauchian did not know the exact breakpoint of the c.1263del+RecTL deletion, which is very close to p.Asn409Ser.

      Pat_92 7 p.Asp448His/p.Leu483Pro,p.Asp448His p.Asp448His/ p.Leu483Pro+Rec7 False Negative There is no false negative. Rec7 is reflected in the copy number call (copy number gain). This GBAP1 duplication does not have any functional impact on GBA, so Gauchian does not report it as a GBA variant. Heterozygous p.Asp448His misgenotyped as homozygous.

      Acknowledgements

      We are grateful to Xiao Chen and Michael Eberle for helpful comments. They are former employees of Illumina and current employees of Pacific Biosciences. This research was funded in in part by Aligning Science Across Parkinson's [Grant numbers 000430 and 000420] through the Michael J. Fox Foundation for Parkinson's Research (MJFF).

      Competing interests

      FJS receives research support from PacBio and Oxford Nanopore. AHVS has received consulting fees from AvroBio, Auxilius, Coave, Destin, Enterin, Escape Bio, Genilac, and Sanofi and speaking fees from Prada Foundation.

      Supplementary Information

      Add new variants to Gauchian’s config file

      The four new variants can be added to Gauchian’s config file as follows.

      For hg38, add the following lines to gauchian/data/GBA_target_variant_38.txt

      chr1 155236304 A GBAP G c.1165C>T(p.Gln389Ter)<br /> chr1 155236327 T GBAP C c.1142G>A(p.Cys381Tyr)<br /> chr1 155239989 CGGGGGT GBAP CGGGGGGT c.203delC(p.Thr69fs)

      Add the following line to gauchian/data/GBA_target_variant_homology_region_38.txt<br /> chr1 155235195 T 155214568 C c.1505G>A(p.Arg502His)

      For GRCh37, add the following lines to gauchian/data/GBA_target_variant_37.txt<br /> 1 155206095 A GBAP G c.1165C>T(p.Gln389Ter)<br /> 1 155206118 T GBAP C c.1142G>A(p.Cys381Tyr)<br /> 1 155209780 CGGGGGT GBAP CGGGGGGT c.203delC(p.Thr69fs)

      Add the following line to gauchian/data/GBA_target_variant_homology_region_37.txt<br /> 1 155204986 T 155184359 C c.1505G>A(p.Arg502His)

      Bibliography

      1. Toffoli, M. et al. Comprehensive short and long read sequencing analysis for the Gaucher and Parkinson’s disease-associated GBA gene. Commun. Biol. 5, 670 (2022).

      2. Tayebi, N., Lichtenberg, J., Hertz, E. & Sidransky, E. Is Gauchian genotyping of GBA1 variants reliable? medRxiv (2023) doi:10.1101/2023.10.26.23297627.

      3. Filocamo, M. et al. Somatic mosaicism in a patient with Gaucher disease type 2: implication for genetic counseling and therapeutic decision-making. Blood Cells Mol. Dis. 26, 611–612 (2000).

      4. Hagege, E. et al. Type 2 Gaucher disease in an infant despite a normal maternal glucocerebrosidase gene. Am. J. Med. Genet. A 173, 3211–3215 (2017).

      5. Pachchek, S. et al. Accurate long-read sequencing identified GBA1 as major risk factor in the Luxembourgish Parkinson’s study. npj Parkinsons Disease 9, 156 (2023).

      6. Graham, O. E. E. et al. Nanopore sequencing of the glucocerebrosidase (GBA) gene in a New Zealand Parkinson’s disease cohort. Parkinsonism Relat. Disord. 70, 36–41 (2020).

      7. Leija-Salazar, M. et al. Evaluation of the detection of GBA missense mutations and other variants using the Oxford Nanopore MinION. Mol. Genet. Genomic Med. 7, e564 (2019)

    1. On 2024-04-11 17:53:00, user eysen wrote:

      JMIR Publications and PREreview are pleased to announce our next Preprint Live Review on Friday, April 19 at 9am PT / 12pm ET / 4pm UTC which discusses this preprint

      Register Now at https://docs.google.com/for...

      The Live Review is hosted by two facilitators from the PREreview team with experience in moderating virtual collaborative review discussions. They will guide participants through a constructive discussion of the following preprint: Assessing the Incidence of Postoperative Diabetes in Gastric Cancer Patients: A Comparative Study of Roux-en-Y Gastrectomy and Other Surgical Reconstruction Techniques - by Tatsuki Onishi

      Live Review Details:

      WHEN: Friday, April 19 at 9am PT / 12pm ET / 4pm UTC

      WHO: The Live Review is hosted by two facilitators from the PREreview team with experience in moderating virtual collaborative review discussions.

      WHAT: The participants will be guided through a constructive discussion of the following preprint: Assessing the Incidence of Postoperative Diabetes in Gastric Cancer Patients: A Comparative Study of Roux-en-Y Gastrectomy and Other Surgical Reconstruction Techniques - by Tatsuki Onishi medRxiv: https://doi.org/10.1101/202...

      A review will be then written and published on PREreview.org within the following 2 weeks. Participants will have the chance to help compose the final review and be recognized as reviewing authors.

      HOW: To participate, please complete the following registration form. You will receive an email from PREReview with a link to a Zoom room and a passcode.

      More information on JMIRx-Med, the first pubmed-indexed preprint overlay journal: https://xmed.jmir.org/annou...<br /> https://xmed.jmir.org/announcements/457

    1. On 2024-05-03 15:04:37, user Tamy wrote:

      I am happy to see someone taking an interest in this horrific condition. My daughter has suffered for over 9 years and the mental toll, as well as, the physical toll it has taken on her overall well being has been life changing! Thank you for giving these sufferers some validation!

    1. On 2025-05-01 12:48:20, user Ravi Sharma wrote:

      Ladies and Gentlemen,

      in loving memory of my late, beloved mother, a type 2 diabetic since my birth, I dedicate this research to harnessing the beneficial power of gen-AI to banish GDM from the face of the earth.

      I salute my industrious and loyal research group for their dedication in this journey.

      Until our work is published and linked to this DoI, kindly cite this preprint as ...

      Edmund Evangelistaa, Fathima Rubab, Syed M. Salman Bukhari, Amril Nazir and Ravishankar Sharma. (2025). "Developing a GraphRAG-enabled local-LLM for Gestational Diabetes Mellitus." medRxiv preprint doi: https://medrxiv.org/cgi/content/short/2025.04.28.25326568v1

      With kind regards and best wishes, Ravi

    1. On 2022-01-27 21:10:24, user Siguna Mueller, PhD, PhD wrote:

      I find it difficult to see how many individuals were in each group. I may, or may not, be able to guess some proportions. For instance, Fig. 4 suggests that there were not many in the booster group, if any at all. (This is because boosters obviously were only rolled out not too long ago). Is the small peak at approx. 35 days since injection attributable to the booster group? If so, this makes me wonder if they were sufficiently many to be statistically relevant. Again, I find it hard to infer exact numbers of participants in the different groups. This info would really be helpful. Thanks.

    2. On 2022-02-14 04:09:55, user RBNZ wrote:

      "The estimates are furthermore adjusted for vaccine status of the potential secondary case interacted with the household variant, and the vaccine status of the primary case. "

      There is no information included as to how vaccination status adjusts the odds ratio.

    1. On 2022-02-03 14:15:23, user Matt Thrun-Nowicki wrote:

      Given previous studies’ evidence of a poor association between RAT results and viral culturability based on # of days after symptom onset, you guys might wanna wait to publish this paper until after those viral cultures result.

      In addition, your explanation of why booster’d HCW had higher positive RAT’s is a little baffling. If your explanation was correct, wouldn’t you expect to see the percentage of positive RAT’s among booster’d HCWers drop over time, and those of unbooster’d go up? What about confounders (like demographics of the booster’d vs unbooster’d)?

    1. On 2025-09-07 20:13:12, user S S Young wrote:

      Milojevic et al. 2014 had access to all emergency room visits for all of England and Wales for the years 2003 to 2008, over 400,000 myocardial infarction (MI) events, and over 2 million CVD emergency hospital admissions. They found no effect of CO, NO2, Ozone, PM10, PM2.5, or SO2 on heart attacks, hospital admissions, or mortality, their Figures 1 and 2.

      Milojevic, A., Wilkinson, P., Armstrong, B., Bhaskaran, K., Smeeth, L., Hajat, S. 2014. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: Case-crossover analysis of the MINAP database, hospital admissions and mortality. Heart (British Cardiac Society) 100, 14: 1093-98. https://doi.org/10.1136/heartjnl-2013-304963 .

    1. On 2020-04-19 16:51:41, user Sinai Immunol Review Project wrote:

      Neutralizing antibody responses to SARS-CoV-2 in a COVID-19 recovered patient cohort and their implications

      Fan Wu et al.; medRxiv 2020.03.30.20047365; doi:https://doi.org/10.1101/202...

      Keywords

      • Neutralizing antibodies<br /> • SARS-CoV-2<br /> • pseudotype neutralization assay

      Main findings

      In this study, plasma obtained from 175 convalescent patients with laboratory-confirmed mild COVID-19 was screened for SARS-CoV-2-specific neutralizing antibodies (nABs) by pseudotype-lentiviral-vector-based neutralization assay as well as for binding antibodies (Abs) against SARS-CoV-2 RBD, S1 and S2 proteins by ELISA. Kinetics of neutralizing and binding Ab titers were assessed during the acute and convalescent phase in the context of patient age as well as in relation to clinical markers of inflammation (CRP and lymphocyte count at the time of hospitalization). Across all age groups, SARS-CoV-2-specific nAbs titers were low within the first 10 days of symptom onset, peaked between days 10-15, and persisted for at least two weeks post discharge. In contrast to spike protein binding Abs, nAbs were not cross-reactive to SARS-CoV-1. Moreover, nAb titers moderately correlated with the amount of spike protein binding antibodies. Both neutralizing and binding Ab titers varied across patient subsets of all ages, but were significantly higher in middle-aged (40-59 yrs) and elderly (60-85) vs. younger patients (15-39 yrs). However, plasma nAb titers were found to be below detection level in 5.7% (10/175) of patients, i.e. a small number of patients recovered without developing a robust nAb response. Conversely, 1.14% (2/175) of patients had substantially higher titers than the rest. Notably, in addition to patient age, nAb titers correlated moderately with serum CRP levels but were inversely related to lymphocyte count on admission. In summary, the authors show that patients with clinically mild COVID-19 disease mount a strong humoral response against the SARS-CoV-2 spike protein. Compared to younger patients, middle-aged and elderly patients had both higher neutralizing and binding Ab titers, accompanied by increased CRP levels and lower lymphocyte counts. These patients are usually considered at higher risk of severe disease. Therefore, robust neutralizing and binding Ab responses may be particularly important for recovery in this patient subset. Conversely, patients who failed to produce high nAb/binding Ab titers against spike protein did not progress to severe disease, indicating that binding Abs against other viral epitopes as well as cellular immune responses are equally important.

      Limitations

      This study provides valuable information on the kinetics of spike protein-specific nAb as well as binding Ab titers in a cohort of convalescent mild COVID-19 patients of all ages. However, similar studies enrolling larger patient numbers, including those diagnosed with moderate and severe disease as well as survivors and non-survivors, especially in the elderly group (to rule out potential bias for more favorable outcome), are warranted for reliable assumptions on the potentially protective role of Abs and nAbs in COVID-19. Moreover, longitudinal observation beyond the acute and convalescent phase in addition to stringent clinical and immunological characterization is urgently needed. <br /> In their study, Wu et al. did not measure binding Abs against non-S viral proteins, which are also induced in COVID-19 and therefore could have added valuable diagnostic information with regard to patients who seemingly failed to mount both binding and neutralizing Ab responses against the SARS-CoV-2 spike protein. Likewise, while this study excluded cross-reactivity of nAbs against SARS-CoV-1, no other coronaviruses were tested. Of additional note, neutralizing activity of plasma Abs was only assessed by pseudotype neutralization assay, not against live SARS-CoV-2. Generally, while these are widely used and reproducible assays, in vitro neutralization of pseudotyped viruses does not necessarily translate to effective protection against the respective live virus in vivo (cf. review by Burton, D. Antibodies, viruses and vaccines. Nat Rev Immunol 2, 706–713 (2002)). Further studies are therefore needed to assess the specificity and neutralizing characteristics of these Abs to test whether they could be candidates for prophylactic and therapeutic interventions. In this context, setting arbitrary cut-off values (ID50<500 vs. a detection limit of ID50 < 40) and thus classifying up to 30% of patients in this study as “weak” responders does not take into account our currently limited knowledge regarding protective capacity of these nAbs and should therefore have been avoided by the authors.

      Significance

      This preprint is arguably the first report on neutralizing and binding Ab titers in a larger cohort of mild COVID-19 patients. Assessing Ab titers in these patients is not only important in order to confirm whether mild COVID-19 elicits robust nAb responses, but also adds further information regarding the use of plasma from mild disease patients for convalescent plasma therapy as well as vaccine design in general. Future studies will need to address now whether the nAb responses generated in mild disease will be protective or (functionally) different from nAbs generated in moderate and severe disease. The findings in this study are therefore of great relevance and should be further explored in ongoing research on potential coronavirus therapies and prevention strategies.

      This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2025-11-30 23:44:45, user Cyril Burke wrote:

      [Note: This is the second of several rounds of review of an earlier version of our combined manuscript, aiming to reduce ‘racial’ disparity in kidney disease. The comments were kindly offered by nephrologists, through a medical journal, and we remain grateful to them for the time and care they gave to improve our manuscript.

      We removed identifying features and included our responses, at the end of this comment. The changing title and line numbers refer to earlier versions.]

      August 3, 2022<br /> Dear Dr. Burke III,

      REDACTED.

      Reviewer #1: Cyril O Burke III et al submit a revised version of their intriguing , unusual paper.

      Overall, the paper remains extremely lengthy (the total , including clean and track versions and reply to reviewers is close to 200 pages !!) , whereas it contains relatively little original data.

      The authors speculate and comment a lot (and most of these speculations/comments will hardly be understandable by the expected audience, primary care physicians), and this will in addition distract the reader from the main key message (which is right in the opinion of this reviewer (see first round of review) and warrants more attention and studies.

      The race part is irrelevant for the key point (race does not change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be deleted in the opinion of this reviewer. In this respect, I completely agree with the comment of reviewer 2 in the first round.

      I can not resist quoting here the reply of the authors to reviewer 2. “This manuscript could be divided into three or four short papers, increasing the likelihood that any one of them would be read. However, different groups tend to read papers about screening for kidney impairment, racial disparities, cofactors in modeling physiologic parameters, or policy proposals to encourage best practices. Despite the appeal of perhaps three or four publications, we decided to tell a complete story in a single paper, but we are open to suggestions.”

      My reply to their reply: nobody would read the current paper , even partially. Shorten, shorten, shorten please and focus on the key message.

      Reviewer #2: Thank-you, once again, for the opportunity to review this lengthy “thesis-style” manuscript which discusses some important often over-looked topics. The under-use of serial creatinine measurements and over-reliance on often erroneous eGFR measurements is an important point which is easily missed by healthcare workers with potentially serious consequences. Likewise, the misuse of racial constructs in medicine (and elsewhere) is an important point.

      I am satisfied with this re-submission and the changes which have been made to the original manuscript.

      Minor points:<br /> 431: “creatinine inhibits several membrane transporters”. = Cimetidine

      502: “Because mGFRs have population variation as wide as sCr, with much greater physiologic variability compared to the relatively stable sCr and serum cystatin C”<br /> As mentioned previously the cited article compares the variability of sCr and cystatin C with CrCl, I agree with the authors that CrCl is a form of mGFR, however, probably one of the poorer forms and not what a reader will think of when mGFR is mentioned. In our current age of medicine when we talk about mGFR CrCl is seldom included, studies reviewing methods of mGFR will seldom include CrCl, however CrCl may be compared to one of the mGFR methods. Likewise, if a patient is sent for a mGFR, a CrCl will not be performed. In our current age of medicine mGFR refers to methods such as the clearance of iohexol, iothalamate, Cr-EDTA, inulin, DTPA, etc; the authors themselves mention this (line 539 – 540). I fully agree with the authors that mGFR is FAR from perfect and has many inaccuracies and imprecisions (which are often overlooked)- these are well published, some of which are cited in this manuscript. If the authors wish to use the current study as a source they should state the findings in a way that cannot be misinterpreted. For example: “CrCl has much greater physiologic variability than sCr and cystatin C …” – in this case the reader can determine for themselves whether they would use CrCl as a surrogate for mGFR. Alternatively, adjust the statement and use another source which has shown the variability that exists with what we currently refer to as mGFR method.

      670 – 719: As the authors specifically discuss age it would be prudent to briefly mention the short-comings, or considerations for interpretation, of serial creatinine measurements at a very young age which generally rise until late adolescence when steady muscle mass is achieved. Also note changes in creatinine and GFR from birth till 2 – 3 years.

      783 – 784: Consider re-wording the grammar makes this sentence difficult to read

      959 – 968: Note, editing has not been accepted (tracked changes still shown)

      1116 - 1121: “Using the opioid crisis as an example…. in, for example, the opioid crisis” – same sentence

      RESPONSE TO REVIEWERS:<br /> September 17, 2022<br /> Longitudinal creatinine, not ‘race’, signals pre-chronic kidney disease and decline in glomerular filtration rate

      We again greatly appreciate the reviewers for offering detailed comments and guidance, which we have endeavored to incorporate as best we could.

      Comments to the Author<br /> Reviewer #1: Cyril O Burke III et al submit a revised version of their intriguing, unusual paper.<br /> 1. Overall, the paper remains extremely lengthy (the total, including clean and track versions and reply to reviewers is close to 200 pages !!), whereas it contains relatively little original data.<br /> The authors speculate and comment a lot (and most of these speculations/comments will hardly be understandable by the expected audience, primary care physicians), and this will in addition distract the reader from the main key message (which is right in the opinion of this reviewer (see first round of review) and warrants more attention and studies.<br /> The race part is irrelevant for the key point (race does not change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be deleted in the opinion of this reviewer. In this respect, I completely agree with the comment of reviewer 2 in the first round.<br /> I can not resist quoting here the reply of the authors to reviewer 2.<br /> "This manuscript could be divided into three or four short papers, increasing the likelihood that any one of them would be read. However, different groups tend to read papers about screening for kidney impairment, racial disparities, cofactors in modeling physiologic parameters, or policy proposals to encourage best practices. Despite the appeal of perhaps three or four publications, we decided to tell a complete story in a single paper, but we are open to suggestions."<br /> My reply to their reply: nobody would read the current paper, even partially. Shorten, shorten, shorten please, and focus on the key message.<br /> We fundamentally agree and have worked to shorten the text; to clarify our understanding that ‘race’ may change with time, location, and self-identification; and to add a Table of Contents to make the Parts more accessible to interested readers. We comment a lot because, in highly racialized societies, like the US [1,2], it can be difficult to see beyond ‘race’ without explicit speculation about other possible explanations for difference, which we understand, may or may not pan out under investigation. One hope is that all clinicians will pursue explanations other than ‘race’, but this seems unlikely. Busy medical researchers have little time to develop expertise outside their area of interest, which may explain why ‘Commentary’ and ‘Perspective’ articles have failed to inspire an ethical ban on the misuse of ‘race’ in medical research, journals, clinics, and elsewhere [3]. We do not know whether a suite of articles can meaningfully contribute to ending misuse of ‘race’, where so many scholarly articles have failed, but after perceiving little change over four decades, trying something completely different seemed (almost) rational.

      1. Nunez-Smith M, Curry LA, Bigby J, Berg D, Krumholz HM, Bradley EH. Impact of race on the professional lives of physicians of African descent. Ann Intern Med. 2007 Jan 2;146(1):45-51. doi: 10.7326/0003-4819-146-1-200701020-00008. PMID: 17200221.

      2. Betancourt JR, Reid AE. Black physicians' experience with race: should we be surprised? Ann Intern Med. 2007 Jan 2;146(1):68-9. doi: 10.7326/0003-4819-146-1-200701020-00013. PMID: 17200226.

      3. McFarling UL. Troubling podcast puts JAMA, the ‘voice of medicine,’ under fire for its mishandling of race. Stat News. 2021 April 6 [Cited 2022 August 31]. Available from: https://www.statnews.com/2021/04/06/podcast-puts-jama-under-fire-for-mishandling-of-race/ <br /> Reviewer #2: Thank-you, once again, for the opportunity to review this lengthy “thesis-style” manuscript which discusses some important often over-looked topics. The under-use of serial creatinine measurements and over-reliance on often erroneous eGFR measurements is an important point which is easily missed by healthcare workers with potentially serious consequences. Likewise, the misuse of racial constructs in medicine (and elsewhere) is an important point.<br /> Thank you for again giving time for helpful criticism and comments on our manuscript.

      A. I am satisfied with this re-submission and the changes which have been made to the original manuscript.<br /> Minor points:<br /> B. 431: “creatinine inhibits several membrane transporters”. = Cimetidine<br /> Corrected.

      C. 502: “Because mGFRs have population variation as wide as sCr, with much greater physiologic variability compared to the relatively stable sCr and serum cystatin C”<br /> As mentioned previously the cited article compares the variability of sCr and cystatin C with CrCl, I agree with the authors that CrCl is a form of mGFR, however, probably one of the poorer forms and not what a reader will think of when mGFR is mentioned. In our current age of medicine when we talk about mGFR CrCl is seldom included, studies reviewing methods of mGFR will seldom include CrCl, however CrCl may be compared to one of the mGFR methods. Likewise, if a patient is sent for a mGFR, a CrCl will not be performed. In our current age of medicine mGFR refers to methods such as the clearance of iohexol, iothalamate, Cr-EDTA, inulin, DTPA, etc; the authors themselves mention this (line 539 – 540). I fully agree with the authors that mGFR is FAR from perfect and has many inaccuracies and imprecisions (which are often overlooked)- these are well published, some of which are cited in this manuscript. If the authors wish to use the current study as a source they should state the findings in a way that cannot be misinterpreted. For example: “CrCl has much greater physiologic variability than sCr and cystatin C …” – in this case the reader can determine for themselves whether they would use CrCl as a surrogate for mGFR. Alternatively, adjust the statement and use another source which has shown the variability that exists with what we currently refer to as mGFR method.<br /> We appreciate this comment and have both added another reference and added to the text an argument for reconsidering creatinine clearance. Many hospitals and some countries lack the resources for advanced mGFR filtration markers, which are only used for research or for screening related to kidney transplants. However, most laboratories have the tools for ‘quick-creatinine clearance’ (quick-CrCl), which may be an acceptable alternative to the classic mGFRs. If confirmed, a simple and affordable quick-CrCl might allow hospitals and laboratories worldwide an alternative measurement requiring fewer assumptions for another aspect of glomerular filtration.

      D. 670 – 719: As the authors specifically discuss age it would be prudent to briefly mention the short-comings, or considerations for interpretation, of serial creatinine measurements at a very young age which generally rise until late adolescence when steady muscle mass is achieved. Also note changes in creatinine and GFR from birth till 2 – 3 years.<br /> We have added a brief discussion of the diagnosis of CKD in infants, children, and adolescents.

      E. 783 – 784: Consider re-wording, the grammar makes this sentence difficult to read<br /> Done.

      F. 959 – 968: Note, editing has not been accepted (tracked changes still shown).<br /> Done.

      G. 1116 - 1121: “Using the opioid crisis as an example…. in, for example, the opioid crisis” – same sentence.<br /> Rewritten.

      We thank you.

    1. On 2022-03-01 05:15:31, user Nun Daled Yud wrote:

      clearly serial daily or twice daily testing is needed for patients who would benefit from the early antiviral treatments particularly aged care facilities .

    1. On 2022-03-09 02:39:17, user Peter J. Yim wrote:

      Vaccine efficacy based on vaccination registries is dependent on the completeness of the registries. Any missing or improperly registered data contributes to misclassification bias: https://drive.google.com/fi...<br /> This study relies on the Citywide Immunization Registry (CIR) and the NYS Immunization Information System (NYSIIS). However, no evidence is presented for the accuracy of those registries. As such, the VE estimates from this study should be regarded as uncertain.

    2. On 2022-05-05 12:39:51, user Robert Clark wrote:

      The data shows efficacy against infection becomes NEGATIVE after one month. Imperative to found out if at longer times this also happens for hosp./deaths. Review the data to found out.

      Robert Clark

    1. On 2022-03-23 02:12:03, user Guest wrote:

      Hello authors,<br /> Thank you for submitting a preprint of this interesting study on the virome to a public domain. I have a few questions regarding your methods and materials.<br /> First, the detailed description of sample collection was great, but I could not find any internal standards for the PCR steps, DNA extraction, or isolation of VLP. These might have been stated, somewhere else perhaps, but I could not identify them. However, for sample collection, how did you determine the location and type of wounds that would be tested? Was there a specific location or depth for chosen wounds or just all types stated that were within the frames of the criteria?<br /> Secondly, the methods for sample processing and DNA extraction are excellent, but I cannot seem to find any information regarding the primers used or the number of cycles performed while analyzing 16S rRNA. I could not find the total number of sequences obtained per sample, however, the quality reading for the viral reads was in-depth and well covered. I did not find any profile or 16S normalization or a total quantification of bacterial or bacterial numbers (like qPCR).<br /> Thirdly, I did not find anything about OTU abundance corrected for variance in copy numbers or variance in genome size. I also could not find any method details regarding coverage of communities measured or if there was any comparison to the dominate to rare. One last question, what do you define as ‘deep sequencing’ regarding this study?<br /> Overall, I found this article very interesting and a good read. Thank you for providing such excellent work with the virome. I have not seen many studies regarding the effects of the virome on human healing, host interactions, or composition until recent years, but this article provides a great starting point for these types of studies.

      SHSU5394

    1. On 2022-04-07 15:07:03, user Addi Romero wrote:

      A revised, updated version has been published as a correspondence in The Lancet Infectious Diseases. A link will be forthcoming. Meanwhile, feel free to have a look at the In Press, Corrected Proof: <br /> https://www.sciencedirect.c...

      Dynamics of humoral and T-cell immunity after three BNT162b2 vaccinations in adults older than 80 years

    1. On 2022-04-29 17:03:47, user Madhava Setty, MD wrote:

      Very interesting study. From where did the data on viral copies come from? Also, the odds of seroconversion in placebo vs treatment, stated as 13.67 at a given viral copy level, doesn't seem to be reflected in the corresponding plot (B).

    1. On 2022-05-21 01:10:31, user Fritz Stumpges wrote:

      You need to provide ground level readings for this test, for your group (1) without masks. Without this base, we don't know if your methods are just producing extremely high readings across the board!

    1. On 2022-05-30 22:36:58, user Stuart Turville wrote:

      Now published within this manuscript here:

      Congratulations<br /> Dear Stuart G. Turville

      We are pleased to inform you that your article has just been published:

      Title<br /> Platform for isolation and characterization of SARS-CoV-2 variants enables rapid characterization of Omicron in Australia

      Journal<br /> Nature Microbiology

      DOI<br /> 10.1038/s41564-022-01135-7

      Publication Date<br /> 2022-05-30

      Your article is available online here https://doi.org/10.1038/s41... or as a PDF here https://www.nature.com/arti....

    1. On 2020-05-20 00:33:44, user SizzMo wrote:

      It appears that the methods of administration of hydroxychloroquine were doomed to fail before even being undertaken. A review of the full study reveals NO mention of zinc, and suggests that hydroxychloroquine was administered alone or sometimes in tandem with azithromycin, and primarily to hospitalized patients in very late stages of illness. The omission of zinc and administration only in late stages of disease defeat the mechanism of action by which the hydroxychloroquine protocol works

      The primary mechanism of action in the hydroxychloroquine+zinc+azithromycin protocol uses hydroxychloroquine primarily as an ionophore for zinc, which then inhibits viral replication in the cell cytoplasm. Zinc is an essential component of this protocol, and omitting zinc appears to be a fatal flaw in all of the reviewed studies and case reports in this analysis. Furthermore, this paper repeatedly refers to hydroxychloroquine being administered to hospitalized patients. The mechanism of action is the inhibition of viral replication, which reduces viral load at early stages of disease. Giving this protocol in late stages of disease when viral load is already heavy and patients are already severely ill defeats the purpose of the protocol and practically guarantees that it will not be effective. The methods reviewed in this study overlook what is known about both the mechanism of action of viral inibitors, and the synergistic function of hydroxychloroquine and zinc in viral RNA replication, making it appear that these "studies" were designed to fail.

      Clinicians employing the complete hydroxychloroquine+zinc+azithromycin protocol at early stages of disease (mild to moderate illness) are universally reporting high levels of efficacy. <br /> Additionally, researchers in an NYU Langone retrospective analysis of more than 900 patients with mild-to-moderate illness who received the protocol with or without zinc also reported significant improvements in patients who received zinc. The NYU Langone study is currently undergoing peer review, and is available at this link: https://www.medrxiv.org/con...

    2. On 2020-05-23 22:06:14, user CKComments wrote:

      The authors dismiss the finding regarding the improvement in lung health in their summary. It's messed up lungs that kill patients, so it seems worth emphasizing.

    1. On 2022-07-18 12:29:27, user Loretta Lorenz wrote:

      Quite likely many person are vaccinated and infected in various sequences. My question is, if the SARS-CoV 2 Spike protein measurement differentiated beetween spike proteins originating from a vaccine against COVID-19 and the different Spike Proteins of the various SARS-CoV-2 mutations.

    1. On 2022-07-20 16:59:17, user Tania Watts wrote:

      The authors may want to note similar findings in our paper, Dayam et al. Accelerated waning of immunity to SARS-CoV-2 mRNA vaccines in patients with immune-mediated inflammatory diseases, JCI Insight, 10.1172/jci.insight.159721 April 2022. We show anti-TNF treated patients have lower Ab responses, no neutralization of Omicron and enhanced waning of T and Ab responses to SARS-CoV-2 mRNA vaccines after 2 doses.

    1. On 2022-07-25 16:31:06, user Dr. D. Miyazawa MD wrote:

      Please also refer to previous studies.

      Hypothesis that hepatitis of unknown cause in children is caused by adeno-associated virus type 2 (08 May 2022)<br /> https://www.bmj.com/content...

      Daisuke Miyazawa. Possible mechanisms for the hypothesis that acute hepatitis of unknown origin in children is caused by adeno-associated virus type 2. Authorea. May 16, 2022.<br /> DOI: 10.22541/au.165271065.53550386/v2

    1. On 2022-07-29 13:52:30, user Stuart MacGowan wrote:

      This is great work! A few years ago I worked on something similar - mapping missense variants to Pfams and defining constrained positions https://doi.org/10.1101/127050 . We also saw enrichment of pathogenic variants at constrained positions. Great to see this area moving forward!

    1. On 2022-08-04 17:25:32, user Paul Hunter wrote:

      Did you include date or week number in your model? During the study period there was a dramatic shift in the proportion of tests positive in Portugal from about 1 in 4.5 to 1 in 2 and that could explain your findings of a 3 x greater risk of hospitalisation associated with BA.5 infection irrespective of the actual risk . If you did not include week number then I think your conclusions are probably flawed.

    1. On 2022-10-05 13:49:05, user Merja Rantala wrote:

      Congrats for this preprint, it is an important summary what we know about protection of hybrid immunity and prior infection against cov19. However, I think that references and claims in the discussion should be checked. There was a sentence on page 13, first paragraph, claiming that covid survivors would have higher risk for dementia in addition to some other conditions. The reference cited was 36, which is not at all about risks for diseases after covid, but the other way around: risk factors for a severe covid outcome. So the ref need to be replaced. Moreover, we really don''t know at this stage whether risk for dementia is increased after covid or not, although has been under heavy speculation.

    1. On 2020-05-26 09:28:09, user David Sbabo wrote:

      5 counfounding factors with a p-value under 0.05, all in the same direction "higher chance of mortality for the no zinc group".

    1. On 2020-05-26 17:03:03, user Sinai Immunol Review Project wrote:

      The main finding of the article: <br /> Recent studies have diverged as to weather conditions are allied or not with the spreading of Covid-19. Through random-effects meta-regression analysis, this work aimed was to determine if elements linked to meteorology can influence SARS-CoV-2 incidence and the speed of its propagation. The number of Covid-19 patients and meteorological conditions at each Japanese prefectural capital city from January to April 2020 were collected. <br /> The results demonstrated a negative association between Covid-19 incidence and monthly mean air temperature (C) (coefficient -0.351), sea level air pressure (hPa) (coefficient -0.001) and the monthly mean daily maximum UV index (UV) (coefficient -0.001).

      Critical analysis of the study: <br /> The manuscript would benefit from a more thorough introduction and discussion of the results in the context of previous studies. The authors could explore more the results of the supplementary table 1 (wind speed, relative humidity and sunshine). The figure caption should be better detailed, explaining the characteristics of each graph.

      The importance and implications for the current epidemics: <br /> The transmission dynamics of SARS-CoV-2 depends on different factors, such as population density, demographic and clinical characteristics of the population, hygiene, local ventilation, etc., and the seasonality of SARS-CoV-2 is not yet known.<br /> The data of this manuscript suggest that higher air temperature, air pressure, and ultraviolet are associated with a lower incidence of Covid-19. Certainly, this study is a step in identifying which environmental factors can favor viral transmission.

      Reviewed by Bruna Gazzi de Lima Seolin.

    1. On 2020-05-27 02:34:42, user Aaron wrote:

      It would be a good addition to show the breakdown of patient demographics for those samples included in Figure 4 to show whether there are differences in the samples collected for each clade thus far. If there are any significant differences, those could be just as important as the viral sequence, if not more so. While I see that authors tried to control for these variables, it'd still be a good idea to show this information in a table in the main figures.

      Additionally, the differences in the rate of spread for each clade are probably much more attributable to the cities themselves that each clade is primarily associated with rather than any differences in the virus; there are major differences in the infrastructure and movement of individuals depending on the metropolitan area. I don't find it particularly surprising that any viral sequence(s) associated with NYC would spread faster than those found in Washington or Chicago. The differing responses of each city in shutting down public movement will also play a big role here.

    1. On 2020-05-28 12:04:39, user Mike Nova wrote:

      M.N.: Good study. It would be good to trace also the correlations with 1) Degree of Rat Infestations and 2) Centralised air conditioning and high power flush public toilets, producing the infectious aerosoles in these places.

    1. On 2020-05-28 16:48:14, user Megan Toohey wrote:

      So I had an antibody test that came back negative but I did have trace amounts apparently. The range was 1.4 and my result was 0.2. I did get sick for a week with severe migraine, dizzy, light headed, nausea, fever runny, nose (off and on but not bad) not really congested, no sob, or cough. Got tested twice for covid which came back negative each time. I work in a hospital so i am around covid a lot. I'm just looking for some insight on that 0.2 result. And if they mostly doing detected/not detected type testing doesn't that technically mean if its my system its been detected? I'm not a scientist, doctor, or nurse so I apologize if my question is dumb.

    1. On 2020-05-28 20:40:28, user Esmeralda R. wrote:

      Once accepted, this paper will be very important. <br /> This is a data that still in need in the community. Diabetes has been associated in many studies, but this work with 18.5K patient, from which 3.7K diabetic patients was/is in need. <br /> Real Gramas

    1. On 2020-05-29 03:44:38, user TE de la Belle wrote:

      It seems to me that there is no actual evidence that Covid-19 was ever more prevalent in the elderly than in any other age group. When testing subjects are chosen by self-selection, surely it is those suffering from the most severe symptoms who will be most likely to self-select and be tested. It is the elderly who are more likely to develop more severe symptoms to this disease. So, it is the elderly with Covid-19, suffering from symptoms, that were being tested early on, more frequently than younger people, who were more likely to have mild or no symptoms. As testing has become more prevalent and contact tracing has begun, we are testing more people with mild or no symptoms. So more young people appear in the statistics. Surely that is the most likely explanation for the shift in frequency between age groups.

    1. On 2020-05-29 06:53:23, user Chris Valle-Riestra wrote:

      This is a great contribution to our knowledge of the epidemic. It's not an ideal way of determining the IFR, obviously, and the underlying serological studies had their shortcomings, but it's a well-reasoned effort to draw conclusions based upon the best available data. From what I've been able to learn, previous highly-publicized estimates of IFR by public health authorities have mostly been based on very thin data or been no better than educated guesses.

      Critiques just point up the great need for large scale rigorously-designed programs to gather far more data empirically. If that data leads to considerably different conclusions, so be it, but right now we don't have it.

    2. On 2020-05-21 23:33:25, user Jack A Syage wrote:

      Very interesting analysis, but I have a counter argument to this. Most of these studies were conducted before the death rate peak. Deaths represent infections from about 2.5 weeks before whereas antibody measurements are current. So cases have grown by multiples by then. As a check I see the following trend in Table 3: the earliest dates show the lowest IFR's (since growing cases run way ahead of deaths) and latest dates show the highest IFR's (as cases are subsiding and catching up to deaths). So I plotted this and there is a distinct upward dependence for IFR vs. date with a Pearson coeff of 0.61 (pretty strong) and a 2-tailed, paired t-value of a staggering p = 0.00003.

      I suspect continued antibody tests for populations well past the death rate peak will start to converge on a higher value of IFR, e.g., about 1%.

      I have been doing modeling and interested in views: please check out:

      https://www.medrxiv.org/con...

      and

      syage-covid19-assessment.com

      @jacksyage<br /> https://twitter.com/jacksyage<br /> https://twitter.com/medrxiv...

    1. On 2020-05-29 18:32:49, user Sinai Immunol Review Project wrote:

      Main Findings<br /> The authors analyzed and compared the stability of viable SARS-COV-2 and SARS-CoV-1 inoculums in five environmental conditions (aerosol, copper, cardboard, steel, and plastic) by using Bayesian regression model. It was reported that SARS-COV-2 was still detected in aerosols at 3 hours, with an exponential reduction in infectious titer that was similarly observed for SARS-CoV-1. The study also concluded that both SARS-COV-2 and SARS-CoV-1 are more stable on stainless steel and plastic than cardboard and copper. Viable SARS-CoV-2 was detected up to 72 hours on stainless steel and plastic. On copper and cardboard, SARS-COV-2 was viable up to 4 hours and 24 hours, respectively, compared to SARS-CoV-1 which could be detected up to 8 hours on both material types. The half-lives between both viruses are similar, except for on cardboard.

      Limitation of the study<br /> The strain used in the study was SARS-COV-2 nCoV-WA1-2020 (MN985325.1) from the first case of 2019 novel coronavirus in the US. However, mutation throughout the course of the pandemic is inevitable and may cause unpredictable consequences on its transmissibility and disease severity. Thus, follow-up on samples from various patients in different geographic and temporal time points should be conducted.

      Significance<br /> The results support that modes of SARS-COV-2 transmission can be attributed to both aerosol and fomites, due to extended viability for hours in aerosol and up to 72 hours on stainless steel surfaces. The types of plastic, cardboard, copper, and stainless materials were selected to reflect typical hospital and household situations. It is important to compare with the SARS-CoV-1 as similarities between the two suggests methods of mitigating the pandemic by abrogating transmission both in the community and hospital.

      Review by Joan Shang as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of medicine, Mount Sinai.

    1. On 2020-05-30 02:04:01, user jeff wrote:

      Has anyone correlated the asymptomatic people to those on a low dose aspirin regiment? Are these people who have contracted and recuperated on blood thinners and low dose aspirin? Is this virus really a virus and not a bacterium? These autopsies site thrombosis! Is anyone looking into this any further?

    1. On 2020-05-30 09:02:50, user Alberto 97 wrote:

      These data should be completed and submitted to a peer reviewed journal in the field, otherwise results reported in the Table cannot be trusted as experimentally sound, even without a thorough description of the methods used in the paper. Did you address the hypothesis to expand your evidence to be reported in a full publication in a specialized journal?

      Prof A. Manzini (Roma III)

    1. On 2020-06-01 12:41:21, user Ron Conte wrote:

      SARS-CoV-1 (causes SARS) is more similar to SARS-CoV-2 (causes Covid-19) than these cold coronaviruses used in the study. SARS antibodies last 2 to 3 years ("Duration of Antibody Responses after Severe Acute<br /> Respiratory Syndrome", Emerging Infections Diseases, 13:10, 2007), and "Memory T cell responses targeting the SARS coronavirus persist up to 11 years post-infection" (dx.doi.org/10.1016/j.vaccin... "dx.doi.org/10.1016/j.vaccine.2016.02.063)").

    1. On 2020-06-01 19:37:39, user Marcelo Fernandes wrote:

      The prediction model has several problems, and there are several wrong assumptions. At the moment, the number of cases and depths in Brazil is growing very fast. The results of this paper created a false feeling about Pandemic in Brazil.

    1. On 2020-06-04 15:25:54, user Andy Loveman wrote:

      what other factors were considered: prevalence of O-type, A-type in the population at large; and underlying health factors compared in both groups?

    2. On 2020-06-08 16:44:40, user Georg Mumelter wrote:

      Thank you! Would it be possible and interesting to further analyze the risk difference by patient age and maybe gender - is the difference especially prevalent in younger or older age, male female? Should be a farily quick and easy analysis (cluster or regression) and plot.

    3. On 2020-06-12 19:34:13, user Amr Sawalha, MD wrote:

      Nice work. The lack of association in the HLA region is very interesting given the perceived exaggerated immune-mediated response in patients with severe COVID-19. Genetic studies looking at patients with confirmed cytokine storm will be of interest in this regard, and of course a closer look at the epigenetics of immune-response genes will be of interest.

    1. On 2020-06-05 10:23:43, user Alberto M. Borobia wrote:

      This manuscript has been published in "Journal of Clinical Medicine" https://www.mdpi.com/2077-0...

      Borobia, A.M.; Carcas, A.J.; Arnalich, F.; Álvarez-Sala, R.; Monserrat-Villatoro, J.; Quintana, M.; Figueira, J.C.; Torres Santos-Olmo, R.M.; García-Rodríguez, J.; Martín-Vega, A.; Buño, A.; Ramírez, E.; Martínez-Alés, G.; García-Arenzana, N.; Núñez, M.C.; Martí-de-Gracia, M.; Moreno Ramos, F.; Reinoso-Barbero, F.; Martin-Quiros, A.; Rivera Núñez, A.; Mingorance, J.; Carpio Segura, C.J.; Prieto Arribas, D.; Rey Cuevas, E.; Prados Sánchez, C.; Rios, J.J.; Hernán, M.A.; Frías, J.; Arribas, J.R.; on behalf of the COVID@HULP Working Group. A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe. J. Clin. Med. 2020, 9, 1733.

    1. On 2020-06-05 23:05:25, user Amy E. Herr wrote:

      *WARNING to READER*: Essential technical information is missing from this PDF which prohibits accurate interpretation and repeatability of the results.There is (1) insufficient evidence substantiating successful 'decontamination', (2) insufficient information on UV-C source and detector, and (3) insufficient information on UV-C dosing. We urge the authors to add these critical details which are represent the bare-minimum for accurate reporting and reproducibility, as further described below:

      1. Claims of “decontamination” do not align with FDA EUA guidance/terminology. FDA guidance on requesting EUAs for respirator decontamination systems define “decontamination” and “bioburden reduction” in terms of specific log-reduction values for specific classes of microorganisms. Because 6-log or 3-log reduction was not always observed or possible to be measured in this study, and no non-enveloped viruses or bacteria were tested, the results do not fall within the FDA definitions for decontamination and bioburden reduction. We suggest adjusting terminology to align with FDA EUA guidance.

      2. Critical information on UV-C source and detector is not provided. Make, model, wavelength emission spectrum, type of UV-C source (e.g., low pressure mercury lamp, LED, etc.), and dimensions of any UV-C bulbs should be reported for the source; make, model, and wavelengths detected are key parameters to report for any radiometer/dosimeter. Because UV-C decontamination equipment is not standardized and measured UV-C dose depends critically on the details of the UV-C source and detector (e.g., whether emitted and detected wavelengths match), reporting these details is critical for accuracy and reproducibility.

      3. Missing details on UV-C dose distribution across the N95. For example, where was the N95 placed within the UVGI device, relative to the UV-C source? Was the ~10% dose permeation observed across all locations on all N95 models? Providing details on characterization of UV-C dose distribution across the N95 is requisite for readers to understand whether a ‘worst-case’ scenario is being modeled.

      We thank the authors for their important research efforts on N95 decontamination during this COVID-19 pandemic & look forward to an updated/revised PDF posting.

    1. On 2020-06-06 13:55:03, user Jürgen Heuser wrote:

      Thx very much for this very helpful work!!

      I'm afraid I do not understand the term <br /> "Comorbidities marked by * are defined by hospital discharge diagnoses in combination with drug redemptions (i.e. filled prescription within 6 months prior to the test date. Of note, there is a lag of 15 days on prescription data)" <br /> when applied to diagnoses like alcohol abuse, overweight or dementia. What kind of medication prescribed would qualify a patient into those categories?

      Best <br /> Jürgen Heuser

    1. On 2020-06-06 15:50:28, user Alberto M. Borobia wrote:

      Dear Authors, congratulations for your publication. Your reference Borobia et al. is now published in JCM.

      Borobia, A.M.; Carcas, A.J.; Arnalich, F.; Álvarez-Sala, R.; Monserrat-Villatoro, J.; Quintana, M.; Figueira, J.C.; Torres Santos-Olmo, R.M.; García-Rodríguez, J.; Martín-Vega, A.; Buño, A.; Ramírez, E.; Martínez-Alés, G.; García-Arenzana, N.; Núñez, M.C.; Martí-de-Gracia, M.; Moreno Ramos, F.; Reinoso-Barbero, F.; Martin-Quiros, A.; Rivera Núñez, A.; Mingorance, J.; Carpio Segura, C.J.; Prieto Arribas, D.; Rey Cuevas, E.; Prados Sánchez, C.; Rios, J.J.; Hernán, M.A.; Frías, J.; Arribas, J.R.; on behalf of the COVID@HULP Working Group. A Cohort of Patients with COVID-19 in a Major Teaching Hospital in Europe. J. Clin. Med. 2020, 9, 1733.

      Best regards,

    1. On 2020-06-07 11:11:53, user peter kilmarx wrote:

      Great work! Why not use both in a pool of two specimens? You missed 8 positives with NP only and 3 positives with saliva only.

    1. On 2020-06-30 12:50:48, user Dude Dujmovic wrote:

      "Secondary cases"? I think you need to precisely define what do you mean by that. The whole paper is extremely vague in what the numbers are about.

    1. On 2020-06-09 20:59:34, user Brenner Silva wrote:

      Comment:<br /> Well explained and valid analysis.<br /> Suggestions: <br /> line 203. please indicate the formula variables in the text.<br /> Possible corrections:<br /> line 147. please name the app as in "we used the COVID-19 app to"<br /> line 210. "where each is"<br /> line 213. "is defined by the"<br /> line 325. "and future work to better understand"

    1. On 2020-06-10 01:57:51, user Sinai Immunol Review Project wrote:

      Main findings<br /> To improve understanding of the cellular changes in the T and B cell compartments of COVID-19 patients, both during and after disease, Fan et al. analyzed lymphocytes isolated from the PBMCs of 4 severe COVID-19 patients (n=4), 6 COVID-19 recovered patients (n=6), and 3 healthy controls (n=3). Of note, 3 recovered patients' samples were collected 7 days after a negative SARS-CoV-2 test (recovery-early stage; RE) and absence of clinical symptoms, whereas the other 3 samples were collected 20 days after these criteria (recovery-late stage; RL). The authors used single-cell RNA sequencing and single-cell V(D)J sequencing to perform their analysis.

      The authors identified 9 classes of T cells, which included 4 sub-classes of CD4+ T cells and 5 sub-classes of CD8+ T cells. Not surprisingly, across severe COVID-19 patients, the proportion of T cells was reduced, compared to healthy controls. However, differential gene expression analysis revealed that T cells from severe COVID-19 patients highly expressed inflammatory markers, including IFNG and GZMA. Interestingly, when compared to these patients with active disease, RE samples showed significant enrichment of ICOS+ TH2-like follicular helper T cells (TFH), whereas RL samples showed a reportedly significant enrichment of a cluster identified as TH1 cells, though this result should be revisited for review (See biological limitations). These cell types were, in fact, reduced in severe COVID-19 patients. Generally, these T cells from recovering patients continued to indicate persistent activation and counter-regulation, based on expression of TCR activation-associated genes, including RNF125 and PELI1. Subsequent trajectory analyses of transcriptional dynamics indicated transition of effector CD8+ T cells to central memory T cells in RL patients. Ligand-receptor analysis revealed potential interactions between TH1 cells and CD14+ monocytes in severe COVID-19 patients. Finally, TCR sequencing identified several VJ combinations in high frequencies in severe COVID-19 patients, but not others.

      Within the B cell compartments across patients, the authors identified 9 clusters of naive B cells, 2 clusters of memory B cells, 2 clusters of plasma B cells, and a cluster of plasmablasts. Of these clusters, one, in particular, expressed genes characteristic of FCRL5+ atypical memory B cells, which have been described to be induced by viral infections. Interestingly, ligand-receptor analyses of the clusters in each group of patient samples identified different degrees of TFH cell and B cell interactions, suggesting different stages of T cell help for B cell activation. Subsequent BCR characterizations revealed the presence of homogenous monoclonal and heterogeneous clonally expanded B cell populations; the latter population exhibited an enrichment of B cell activation genes. The authors, then, compare across patients to evaluate T and B cell clonality based on V(D)J recombination analyses of RE and RL patient samples (See technical limitations).

      Interestingly, cytokine expression analysis revealed IL-6 expression by B cells. In contrast, B cells expressed IL12A in RE patients, while effector memory CD8, proliferative CD8, and CD4 T cells and plasma B cells highly expressed IL16 in RL patients. The authors report additional cytokine (and cellular) characteristics that distinguish severe COVID-19 patients and recovering patients.

      Limitations<br /> Technical<br /> A primary technical limitation is the sample size of this study for each group. There is little clinical information about the patients and no details about disease severity in patients recruited after viral clearance. For example, age and CMV status have a huge impact on the TCR repertoire, therefore clinical data on the different groups should be presented. Moreover, without additional information on the clinical management of the severe COVID-19 patients and what therapies were given to the recovering COVID-19 patients, it is difficult to compare the cellular changes in the immune landscapes of the COVID-19 patients across samples. Longitudinal analysis would have been more informative especially with regards to repertoire analysis and how expanded clones during active infections might differentiate into particular phenotypes after viral clearance.CD8 expression should have been included in the violin plots, as it is usually more robust and reliable than CD4 expression.

      Biological<br /> An immediate concern is whether the authors mis-characterized cluster 13 as a TH1 cell cluster. The cluster exhibits a low expression of CD3G and CD4. It’s neighboring clusters within the hierarchy belong to monocyte groups, so it is unexpected that a T cell subtype would be belong to their branch of the hierarchy tree. Consider also cluster 38, which shows more robust expression of CD3G and NKG7 and is arranged with the B cell group.

      In addition, the authors did not highlight or discuss expression of co-inhibitory receptors that could elucidate the heterogeneity of T cell differentiation during COVID-19. As a result, it is difficult to truly assess the activation status of the CD8+ cytotoxic T cells and whether there are features of T cell exhaustion.

      Finally, the distinction between naïve and some subsets of memory T cells by scRNA analysis can be challenging. It would be important for the authors to explore whether cluster 26, classified as a naïve CD8 T cell cluster predominant in RL group could be actually memory cells. It would have been important to show clonal diversity of the different clusters.

      Significance<br /> In summary, Fan et al. provide a comparative analysis of lymphocyte changes between PBMCs of patients with ongoing COVID-19 progression and of patients recovering from the disease. Using a combination of single-cell RNA sequencing and V(D)J recombination sequencing, the authors describe specific changes in T and B cell subpopulations over the course of early and late-stage recovery.

      This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-06-10 09:22:39, user alanarchibald wrote:

      Am I correct in understanding that your definition of (European) travel in the context of this study is limited to travel by persons who are normally resident in the UK and that travel by visitors to Scotland/UK is not addressed, presumably as you did not have access to the necessary samples and medical records.

    1. On 2020-06-15 01:10:48, user Serge wrote:

      There are many inaccuracies in the report that may significantly affect the conclusions.<br /> 1. Diamond Princess analysis: the mortality data (in single digits) is not sufficient for a confident estimate of the mortality per jurisdiction (for some nations there was only a single case). Moreover, most countries started universal BCG vaccination around 1950s plus the effect of WWII would likely compromise any earlier program to a significant extent. That means that regardless of the country of origin, large part of over 70 population would not be protected and thus shouldn't be considered in verification of the hypothesis.<br /> 2. Certainly there can be no expectation that the protection effect would extend equally into a very advanced age, 60 years and longer after vaccination.<br /> 3. What is meant by the statement "BCG was provided mostly in Europe"? This is plain incorrect, please check "BCG World Atlas".<br /> 4. Country analysis: was the population taken into account? It is not clear from the description of diagrams. I would advise to attempt to calculate mortality per capita, from the most current data and compare it between jurisdictions at a similar period of exposure. Note that all countries with the highest M.p.c. adjusted for the time of exposure, never had a BCG program (or equivalent as in Spain where it was provided for 18 years out of 70) there's simply not a single exception.

    1. On 2020-06-15 21:36:07, user Marm Kilpatrick wrote:

      Fantastic (but worrisome) work! <br /> Would it be possible to give the full details of the regression of infectious viral load via culturing (PFU/ml) vs RNA via qPCR? This relationship is robust and could be used as the basis for inferring infectious viral load from qPCR, but doing so in a way that explicitly incorporates uncertainty would require more details of the regression than you currently report. Specifically, if you could report the slope, intercept and residual standard error and sample size for this regression that would enable others to make maximal use of your results. Even better would be to make the individual data points from graph available and then the data could be used directly.<br /> Thank you very much for this important work!<br /> marm

    1. On 2020-06-17 13:21:18, user Jumana Haji wrote:

      Amazing experience working with this group to sort through guidelines and evaluate them for completeness while also developing a tool for future guidelines. The tool is ideal when keeping healthcare worker safety and wellbeing perspective as priorities.

    1. On 2020-06-17 20:11:26, user LB wrote:

      Zotero (a popular citation manager) says that this article has been retracted. If this is not the case, please ask Retraction Watch to correct the error.

    1. On 2020-06-18 01:00:02, user Alex Backer wrote:

      See https://ssrn.com/abstract=3... for a global study that shows case and death counts had significantly lower growth rates at higher temperatures (>14 °C) when aligned for stage in the epidemic. We then show irradiance and in particular solar elevation angle in combination with cloudopacity explain COVID-19 morbidity and mortality growth better than temperature: a reduction of mean solar elevation of 9 degrees led on average to a 2500% increase in COVID-19 case growth over the following two weeks. COVID-19 exploded during the darkest January in Wuhan in over a decade. Our results suggest transmission models should incorporate solar elevation and that the impact of UV irradiance on individual morbidity and mortality should be tested. We discuss implications for the best locations and optimal behaviors for high-risk individuals to weather the pandemic. --Alex Bäcker, Ph.D.

    1. On 2020-06-18 22:54:00, user RockyNBullwinkle wrote:

      would be nice to see just one large prospective randomized double blind study for hospitalized patients, one study for patients that don't meet criteria of hospitalization, and one for prevention. Zinc 50 mg daily and HCQ 200 mg twice daily.

    1. On 2020-06-19 18:24:34, user ChrisdeZilcho wrote:

      Apparently a new study from same team shows CoV2-positive samples from savage water stored in Dec last year. Would be interesting to see the phylogenetic sequence analysis. Did virus fizzle out in Dec/Jan or was there a "quiet" transmission activity? Have there been many independent intros into Italy? Looking forward to reading the publication.

    1. On 2020-06-19 22:15:27, user Michelle Kimple wrote:

      Have you thought of performing analyses of your data by city/county size and/or population density? In the abstract you state "We did not find an association between county level prevalence of COVID-19 cases and face covering use" but when I limited the data to only counties with the 5 most populous cities, there appears to be a strong correlation. I just tweeted my analysis of your data (the county populations may not be what you used, but the city and county population ranks are correct): https://twitter.com/KimpleL...

    1. On 2020-06-21 20:05:22, user Jørgen K. Kanters wrote:

      Please note that by some (yet) unknown reason one of the authors Claus Graff is omitted from the MedRxiv page, but correctly included in the pdf file. We will submit a revision tomorrov to correct it

    1. On 2020-03-26 13:52:15, user Sinai Immunol Review Project wrote:

      SUMMARY: This study aimed to find prognostic biomarkers of COVID-19 pneumonia severity. Sixty-one (61) patients with COVID-19 treated in January at a hospital in Beijing, China were included. On average, patients were seen within 5 days from illness onset. Samples were collected on admission; and then patients were monitored for the development of severe illness with a median follow-up of 10 days].

      Patients were grouped as “mild” (N=44) or “moderate/severe” (N=17) according to symptoms on admission and compared for different clinical/laboratory features. “Moderate/severe” patients were significantly older (median of 56 years old, compared to 41 years old). Whereas comorbidies rates were largely similar between the groups, except for hypertension, which was more frequent in the severe group (p= 0.056). ‘Severe’ patients had higher counts of neutrophils, and serum glucose levels; but lower lymphocyte counts, sodium and serum chlorine levels. The ratio of neutrophils to lymphocytes (NLR) was also higher for the ‘severe’ group. ‘Severe’ patients had a higher rate of bacterial infections (and antibiotic treatment) and received more intensive respiratory support and treatment.

      26 clinical/laboratory variables were used to select NLR and age as the best predictors of the severe disease. Predictive cutoffs for a severe illness as NLR >= 3.13 or age >= 50 years.

      Identification of early biomarkers is important for making clinical decisions, but large sample size and validation cohorts are necessary to confirm findings. It is worth noting that patients classified as “mild” showed pneumonia by imaging and fever, and in accordance with current classifications this would be consistent with “moderate” cases. Hence it would be more appropriate to refer to the groups as “moderate” vs “severe/critical”. Furthermore, there are several limitations that could impact the interpretation of the results: e.g. classification of patients was based on symptoms presented on admission and not based on disease progression, small sample size, especially the number of ‘severe’ cases (with no deaths among these patients). Given the small sample size, the proposed NLR and age cut offs might not hold for a slightly different set of patients. For example, in a study of >400 patients, ‘non-severe’ and ‘severe’ NLR were 3.2 and 5.5, respectively 1.

      References:<br /> 1. Chuan Qin, MD, PhD, Luoqi Zhou, MD, Ziwei Hu, MD, Shuoqi Zhang, MD, PhD, Sheng Yang, MD, Yu Tao, MD, PhD, Cuihong Xie, MD, PhD, Ke Ma, MD, PhD, Ke Shang, MD, PhD, Wei Wang, MD, PhD, Dai-Shi Tian, MD, PhD, Dysregulation of immune response in patients with COVID-19 in Wuhan, China, Clinical Infectious Diseases, , ciaa248, https://doi.org/10.1093/cid...

      This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-05-22 16:23:27, user Jim Parfitt wrote:

      It is difficult to find any discussion on this issue. I am a person who has taken NO systemic antibiotics for over 40 years. And I basically never get sick. I have been wondering how many of the severe cases of Covid 19 are in people who regularly take systemic antibiotics, and so have messed up gut flora. This is what i suspect. I would like to read the whole study; if that is possible.

    1. On 2020-05-23 16:44:37, user Rosemary TATE wrote:

      Thank-you for this well-written and interesting paper. It's very puzzling s that ethnicity was not a factor for hospital mortality (either unadjusted or adjusted rr's). Statistics reported here in the UK suggest that ethnic minorities are at far higher risk. This recent preprint on US deaths suggests the same.(https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.05.21.20109116v1.full.pdf)")

      Do you have an explanation for these disparities. Could it be that non-whites are less likely to go to hospital in the US? Or is there another reason?

    1. On 2020-05-24 08:36:41, user Lauren wrote:

      Accidental death rates for my age group of 30 to 39 are roughly 1 in 1000 (white female) roughly the same as COVID. I get what others are saying but this specifically addresses percent of death compared to other fatality statistics. I do think though that thr 50 or 60 age range is more likely to die of COVID IF almost everyone were to be exposed, hopefully we will not see that. This however does not include impact of COVID on black and Hispanic populations which are much higher.

    1. On 2020-05-24 17:09:55, user Gary Kast wrote:

      Assuming that 1/3 of covid patients were taking the ace-I or arbs unless that is the percentage of the entire elderly pop taking them,( or at least the percentage of bp patients ) I would think that fact indicates a too high association of the meds and covid....I think general numbers should be discussed to support the conclusion. I see the math but question the assumptions and therefore conclusions without that additional number. If high bp is a comorbidity and patients takingbeta blockers and calcium channel and diuretics (b c d) are likewise or even higher numbers represented then clearly the type of bp meds is not too concerning. But if angiotensin drugs are only 20% of total bp meds consumed but 1/3 of patients in hospitals ...uh oh . Clearly not everyone who carries the virus ends up a patient .

    1. 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.

    1. On 2020-05-27 01:37:02, user Keith wrote:

      Very exciting new and a likely game changer for dentists/ENTs or anyone who manipulates the mucosa of a potentially covid + patient

    1. On 2025-04-10 17:31:51, user SMR Hashemian wrote:

      At the peak of the COVID-19 crisis, when the world was gripped by fear and despair, Iran was not only battling a deadly virus but also grappling with brutal and inhumane sanctions. Economic sanctions severely restricted Iran's access to medicine, medical equipment, and vaccines, creating one of the biggest obstacles in the fight against this crisis. Yet, despite these unprecedented pressures, Iran did not surrender and, through relentless efforts, found ways to overcome these limitations.<br /> The Iranian government made every effort to bypass the sanctions through international negotiations and the creation of alternative financial channels to import the necessary medicines and equipment. These efforts, though fraught with difficulties, demonstrated Iran's resolve to save lives. Even as many countries refused to assist Iran, the nation relied on domestic capabilities and national solidarity to find solutions to the crisis.<br /> Amidst these challenges, Iran's healthcare workers stood on the front lines like unsung soldiers, making unparalleled sacrifices. Doctors, nurses, and all healthcare workers in hospitals not only played a critical role in saving countless lives but also faced significant personal risks, with many losing their lives in the process. These dedicated professionals demonstrated extraordinary commitment and selflessness, setting an example of resilience and dedication in the face of a global health crisis.<br /> But it was not just the healthcare workers who fought in this battle. Iran's scientific community also stepped up with full force. Iranian scientists and researchers, despite cruel sanctions and countless limitations, never stopped striving. They not only succeeded in producing domestic vaccines like Noora and SpikoGen, but also published numerous articles in prestigious international journals, showcasing Iran's role in advancing global science. These efforts are a testament to the fact that Iran, even under the toughest conditions, can rely on science and knowledge.<br /> The Iranian government, despite all limitations, spared no effort in controlling this crisis. From the very beginning, extensive education on health protocols was launched through the media. The public was continuously informed about health recommendations such as mask-wearing, social distancing, and hand hygiene. Even during Nowruz, one of the most important cultural events in Iran, the government encouraged people to reduce travel and celebrate at home. School and university closures, the shift to remote learning, and the reduction of workplace presence through teleworking all demonstrated the government's resolve to control the spread of the virus.<br /> These efforts, though accompanied by challenges, reflect Iran's national determination to confront this global crisis. Iran, despite all limitations, proved that it could stand firm against the toughest conditions by relying on science, sacrifice, and national solidarity. The accusations raised in this article are not only unfair but also overlook the relentless efforts of a nation. Iran fought with all its might to save lives, and that is something to be proud of.

      Seyed MohammadReza Hashemian<br /> Professor of Critical Care Medicine

    1. On 2020-04-20 15:36:38, user Philip Davies wrote:

      Interesting study, thank you.

      This is another study that attempts to ascertain if oral HCQ tablets can be of clinical use in patients more than one week into symptomatic disease, hospitalized with bilateral pneumonia and with evidence of established inflammatory reaction (cytokine storm). That's a big ask for any oral medication.

      The study is again small (both arms have less than 100 patients). The most significant outcome measured (death) is realized in very small numbers (3 and 4). The confidence levels are extremely wide.

      The are several problems with this study. There are marked differences in the two populations. The study honestly attempts to accommodate these confounding factors using a propensity score method (IPTW). Normally this method is valuable but here I can’t see that it has been well applied.

      It pays to look at the raw data. There is a significant difference (between the two arms) in the initial intensity of disease.

      At baseline (admission), HCQ arm comprises 78.3% men (>20% more of these higher risk patients than control arm with 64.9%); HCQ arm has 21.9% patients with more severe disease in the form of CT showing >50% lung affected). This is >80% more than in control arm (12.1%). HCQ arm has 90.5% patients with CRP > 40mg/l (CRP is a good indicator of impending/current severity). This is 10% higher than control arm (81.9%). HCQ arm had median O2 flow on admission = 3 litres/minute (50% higher than control arm at 2 litres / minute).

      So, at baseline, the HCQ arm had significantly more patients with severe disease than control arm. The O2 flow is actually more significant than first sight would suggest. 2 l/m is always the first step in O2 therapy. The data shows us that most patients in the control arm could hold their sats on this first step therapy. This also means they may have been OK on just 1 l/m. We don't know. But we do know that most patients in the HQN could not hold their sats at that first step and needed an increase (3 l/m ... so that's 50-300% more O2 than control arm).

      Admittedly there were other confounding factors which compromised the control arm more than HCQ arm (some chronic disease elements). But it's clear to me that disease severity was markedly more established in the HCQ arm.

      Another factor to note: the HCQ treatment was not initiated at the moment those baseline values were obtained (on admission). The HCQ was initiated within 48 hours. So let’s look again at the timelines. The median duration of symptoms at admission shows that the HCQ arm comprised patients who were further into worsening illness: they were admitted on D8 compared to control, D7. They may not have had HCQ initiated until D10.

      Then we look at outcomes: the raw data shows that the disadvantaged HCQ arm actually does better in the two most important outcomes, death and ICU admission. The HCQ delivers 12% less death and ICU admissions than the control arm. Admittedly the numbers are small so the confidence levels are very wide.

      So what does that tell us? The answer is not much. But even accepting the poorly aligned baseline for disease severity, the outcomes with their wide 95% confidence levels do deliver a mildly promising indication on the 'swingometer'. They point more towards benefit than harm when using HCQ in this advanced disease state.

      As a final comment on significant side effects (increased QT interval) from the use of HCQ. Once again, this trial used a particularly high dose of HCQ (600mg/day...right at ceiling dose for rheumatological use and much higher than the total antimalarial treatment dose). They also added azithromycin (another QT lengthening drug) to 20% of the HCQ patients. It’s not surprising at all to find such QT lengthening in a sick, more elderly population taking these medications in particularly high doses).

      Further trials should utilize conservative doses of CQ/HCQ which have been proven safe in many millions of patients.

      We don't yet know how this will pan out. We urgently need proper evidence. Statistically robust studies into prophylaxis and early intervention are likely to deliver the most interesting results.

      Dr Philip Davies<br /> Aldershot Centre For Health<br /> http://thevirus.uk

    1. On 2020-04-06 18:50:52, user Sinai Immunol Review Project wrote:

      Main Findings: Currently, the diagnosis of SARS-CoV-2 infection entirely depends on the detection of viral RNA using polymerase chain reaction (PCR) assays. False negative results are common, particularly when the samples are collected from upper respiratory. Serological detection may be useful as an additional testing strategy. In this study the authors reported that a typical acute antibody response was induced during the SARS-CoV-2 infection, which was discuss earlier1. The seroconversion rate for Ab, IgM and IgG in COVID-19 patients was 98.8% (79/80), 93.8% (75/80) and 93.8% (75/80), respectively. The first detectible serology marker was total antibody followed by IgM and IgG, with a median seroconversion time of 15, 18 and 20 days-post exposure (d.p.e) or 9, 10- and 12-days post-onset (d.p.o). Seroconversion was first detected at day 7d.p.e in 98.9% of the patients. Interestingly they found that viral load declined as antibody levels increased. This was in contrast to a previous study1, showing that increased antibody titers did not always correlate with RNA clearance (low number of patient sample).

      Limitations: Current knowledge of the antibody response to SAR-CoV-2 infection and its mechanism is not yet well elucidated. Similar to the RNA test, the absence of antibody titers in the early stage of illness could not exclude the possibility of infection. A diagnostic test, which is the aim of the authors, would not be useful at the early time points of infection but it could be used to screen asymptomatic patients or patients with mild disease at later times after exposure.

      Relevance: Understanding the antibody responses against SARS-CoV2 is useful in the development of a serological test for the diagnosis of COVID-19. This manuscript discussed acute antibody responses which can be deducted in plasma for diagnostic as well as prognostic purposes. Thus, patient-derived plasma with known antibody titers may be used therapeutically for treating COVID-19 patients with severe illness.

      Reference:

      1. Antibody responses to SARS-CoV-2 in patients of novel coronavirus disease 2019

      doi: https://doi.org/10.1101/202...

    1. On 2020-04-23 17:27:44, user Sinai Immunol Review Project wrote:

      Presence of SARS-CoV-2 reactive T cells in COVID-19 patients and healthy donors

      Braun J et al.; medRxiv 2020.04.17.20061440; https://doi.org/10.1101/202...

      Keywords

      • SARS-CoV-2 specific CD4 T cells

      • Human endemic coronaviruses

      • COVID-19

      Main findings

      In this preprint, Braun et al. report quantification of virus-specific CD4 T cells in 18 patients with mild, severe and critical COVID-19, including 10 patients admitted to ICU. Performing in vitro stimulation of PBMCs with two sets of overlapping SARS-CoV-2 peptide pools – the S I pool spanning the N-terminal region (aa 1-643) of the S protein, including 21 predicted SARS-CoV-1 MHC-II epitopes, and the C-terminal S II pool (aa 633-1273) containing 13 predicted SARS-CoV-1 MHC-II epitopes – the authors detected S-protein-specific CD4 T cells in up to 83% of COVID-19 patients based on intracellular 4-1BB (CD137) and CD40L (CD154) induction. Notably, peptide pool S II shares higher homology with human endemic coronaviruses (hCoVs) 229E, NL63, OC43, and HKU1 that may cause the common cold, but it does not include the SARS-CoV-2 receptor-binding domain (RBD), which has been identified as a critical target of neutralizing antibodies in both SARS-CoV-1 and SARS-CoV-2. S I-reactive CD4 T cells were found in 12 out of 18 (67%) patients, whereas CD4 T cells against S II were detected in 15 patients (83%). Intriguingly, S-specific CD4 T cells could also be found in 34% (n=23) of 68 SARS-CoV-2 seronegative donors, referred to as reactive healthy donors (RHD), with a preference for S II over S I epitopes. Only 6 of 23 RHDs also had detectable frequencies of S I-specific CD4 T cells, overall suggesting S II-reactive CD4 T cells had likely developed in response to prior infections with hCoVs. Of 18 out of 68 total healthy donors tested, all were found to have anti-hCoV antibodies, although this was independent of concomitant anti-S II CD4 T cell frequencies detected. This finding mirrors observations of declining numbers of specific CD4 T cells, but persistent humoral memory after certain vaccinations such as against yellow fever. The authors further speculate that these pre-existing virus-specific T cells against hCoVs might be one of the reasons why children and younger patients, usually considered to have a higher incidence of hCoV infections per year, are seemingly better protected against SARS-CoV-2. Unlike specific CD4 T cells found in RHDs, most S-specific CD4 T cells in COVID-19 patients displayed a phenotype of recent in vivo activation with co-expression of HLA-DR and CD38, as well as variable expression of Ki-67. In addition, a substantial fraction of peripherally found HLA-DR+/CD38+ bulk CD4 T cells was found to be refractory to peptide stimulation, potentially indicating cellular exhaustion.

      Limitations

      This is one of the first preprints reporting the detection of virus-specific CD4 T cells in COVID-19 (also cf. Dong et al., https://www.medrxiv.org/con... Weiskopf et al., https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.11.20062349v1.article-info)"). While it generally adds to our current knowledge about the potential role of T cells in response to SARS-CoV-2, a few limitations, some of which are discussed by the authors themselves, should be addressed. Findings in this study pertain to a relatively small cohort of patients of variable clinical disease. To corroborate the observations made here, larger studies including both more healthy donors and more patients of all clinical stages are needed to better assess the function of virus-specific CD4 T cells in COVID-19. Specifically, the presence of pre-existing, potentially hCoV-cross-reactive CD4 T cells in healthy donors needs to be explored in the context of COVID-19 immunopathogenesis. While the authors suggest a potentially protective role based on higher incidence of hCoV infection in children and younger patients, and therefore a presumably larger pool of pre-existing virus-specific memory T cells, the opposite could also be the case given cumulatively increased number of hCoV infections in older patients. In this context, it would therefore have been interesting to also measure anti-hCoV antibodies in COVID-19 patients. Furthermore, this study did not quantify virus-specific CD8 T cells. Based on observations in SARS-CoV-1, virus-specific memory CD8 T cells are more likely to persist long-term and confer protection than CD4 T cells, which were detected only at lower frequencies six years post recovery from SARS-CoV-1 (cf. Li CK et al., Journal of immunology 181, 5490-5500.) Morover, no other specifities such as against the N or M epitopes were evaluated. Robust generation of virus-specific T cells against the N protein was shown to be induced by SARS-CoV-2 in another pre-print by Dong et al. (Dong et al., https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.03.17.20036640v1)"), while Weiskopf et al. recently reported preference of both CD8 and CD4 T cells for S epitopes https://www.medrxiv.org/con... "https://www.medrxiv.org/content/10.1101/2020.04.11.20062349v1.article-info)"). Moreover, the authors seem to suggest that some of the virus-specific CD4 T cells detected could be potentially cross-reactive to predicted SARS-CoV-1 epitopes present in the peptide pools used. Indeed, this has been recently established for several SARS-CoV-2 binding antibodies, while it was found not to be the case for RBD-targeting neutralizing antibodies (cf. Wu et al., https://www.medrxiv.org/con... Ju et al., https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.03.21.990770v2)"). A similar observation has not been made for T cells so far and should be evaluated. Finally, since reactive healthy donors were only tested for anti-S1 IgG, however not for other more ubiquitous binding antibodies, e.g. against M, and only a fraction of these donors was additionally confirmed to be negative by PCR, there is, though unlikely, the possibility that some of the seronegative reactive donors had been previously exposed to SARS-CoV-2.

      Significance

      Quantification of virus-specific T cells in peripheral blood is a useful tool to determine the cellular immune response to SARS-CoV-2 both in acute disease and even more so post recovery. Ideally, once immunogenic T cell epitopes are better characterized, tetramer assays will allow for faster and more efficient detection of their frequencies. Moreover, assessing the potential role of pre-existing virus-specific CD4 T cells in healthy donors in the context of COVID-19 pathogenesis will be of particular importance. The observations made here are also highly relevant for the design and development of potential vaccines and should therefore be further explored in ongoing research on potential coronavirus therapies and prevention strategies.

      This review was undertaken by V. van der Heide as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-06-24 11:34:40, user Renzo Huber wrote:

      This is a nice review that might also be valuable to the field of layer-fMRI. <br /> I think the manuscript might benefit from an additional brief discussion of the related laminar connectivity findings from non-invasive human fMRI studies:

      -> layer-dependent connectivity in Fig. 6 and 7 of the following study: <br /> Huber L, Handwerker DA, Jangraw DC, et al. High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron. 2017;96(6):1253-1263.e7. doi:10.1016/j.neuron.2017.11.005

      -> layer-dependent connectivity with gppi in this study: <br /> Sharoh D, Mourik T van, Bains LJ, et al. Laminar Specific fMRI Reveals Directed Interactions in Distributed Networks During Language Processing. PNAS. 2019:1907858116. doi:10.1101/585844

      -> layer-dependent hierarchical connectivity discussed in this study: <br /> 1. Huber L, Finn ES, Chai Y, et al. Layer-dependent functional connectivity methods. Prog Neurobiol. 2020:in print. doi:j.pneurobio.2020.101835

    1. On 2020-06-05 17:35:30, user wbgrant wrote:

      Dark-skinned people living in Spain are at an increased risk of COVID-19 due to lower vitamin D production from solar UVB. This effect probalby explains the finding for Sub-Saharan Africa and the Caribbean. Not sure about Latin America, where rates are very high in several countries. See:<br /> Grant WB, Lahore H, McDonnell SL, Baggerly CA, French CB, Aliano JA, Bhattoa HP. Evidence that vitamin D supplementation could reduce risk of influenza and COVID-19 infections and deaths. Nutrients 2020, 12, 988. https://www.mdpi.com/2072-6...<br /> and references thereto at scholar.google.com<br /> as well as this response<br /> Grant WB, Baggerly CA, Lahore H. Response to Comments Regarding “Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths”. Nutrients 2020, 12(6), 1620; https://doi.org/10.3390/nu1...

    1. On 2020-06-06 01:33:13, user David Hood wrote:

      I think the "39.5% of cases seeking medical consultation in primary care settings" may be overly conservative in the model for a parameter representing getting medical advice, as it is based of influenza in the 2018 'flu season (a fairly typical year). We know from the ESR influenza surveillance site that healthline historically (I don't know the period for what they determine historical) get around 40000 Influenza like illness calls a year, and for the period from the week of 14/2 to 29/5 there are historically around 10000 ILI calls. In 2020, for the period from the week of 14/2 to 29/5, there were around 26000 ILI calls. Even allowing for false positive worries from anxious people boosting call numbers, it suggests that people seeking official advice about ILI is dramatically higher in 2020 (which I also acknowledge is not the same as visiting a primary care location about an ILI, which is the 39.5% figure, but the official advice was to ring Healthline, who were presumably advising testing/ isolation/ primary health as appropriate)

    1. On 2021-06-06 09:28:02, user Ulltand wrote:

      4 days after one dose. What does it say? We know from other studies that 21 days after one dose protection is about 90 %. 14 days is a to short period.

    1. 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)

    1. On 2020-06-30 15:59:24, user Dr. Hans-Joachim Kremer wrote:

      Very good trial.<br /> It is interesting that the analysis by days since symptoms onset (<=7 vs. >7) appeared to be as discriminative as the main analysis or the subgroup analysis by respiratory support. Then, the onset of symptoms was strongly correlated with type of respiratory support. Hence, it would be interesting which of both (days since symptoms onset or respiratory support) was more discriminative, i.e. the independent predictor of efficacy of dexamethasone.

    1. On 2020-07-02 13:57:50, user Dr. Amy wrote:

      It would be useful to see how obesity and A+ blood type change the HLH genetic expression. This paper has extremely useful clues toward targets to reduce severity.

    1. On 2020-06-11 13:53:48, user peter tofts wrote:

      please include: 1.) what type of corticosteroid was used (meythyleprednisolone) 2.) the dose (?1mg/kg or other v pulsed) duration etc... 3.) timing: the authors mention timing around 7 days from onset of symptoms- also Delay respect to Sx 13+/- 4.2 so I suppose maybe 6 days +/- 4 into their hospitalization? interesting paper thankyou

    1. On 2020-07-03 19:26:50, user Jun Wan wrote:

      Dr. Anthony Fauci warned this Tuesday (https://www.youtube.com/wat... "https://www.youtube.com/watch?v=m5l5UGS9ngc)") that a new strain of the coronavirus was found to be dominant around the world which was published the past Sunday by the Cell (https://www.cell.com/action... "https://www.cell.com/action/showPdf?pii=S0092-8674%2820%2930820-5)"). The new strain G614 they referred is the exactly same as what this paper identified associated with the mutation A23403G (group A). In addition to the mutation on Spike (614), another mutation C14408T on ORF1ab (P4715 changed to L4715) was also reported by this paper which co-occurred with the mutation on Spike (614). Both can become the features of these strains. Indeed, the paper combined both together to name them as strain GL (G614+L4715) or DP (D614+P4715). Their results suggest "that the GL strain of SARS-CoV-2 might become much more stable and prevailing than DP identified from Wuhan-Hu-1 after 6-month evolution and transmission." Actually, when you read the paper carefully, you may find more novel interesting findings discussed in their work.

    1. On 2020-07-07 20:00:16, user Ron Conte wrote:

      The article above assumes that ivermectin works as an inhibitor, and therefore compares approved dose to IC50. But the results of clinical studies (Chowdhury et al. ResearchGate; Rajter et al. medRxiv) suggest that ivermectin works in some other way, i.e. not as an inhibitor that would depend upon concentration.

    1. On 2020-07-11 14:17:56, user DMelanogaster wrote:

      I understand that this study was done just to explore safety, not efficacy, but doesn't the finding that mortality rates were not decreased in those infused with the antibodies in such a large sample indicate that the antibody treatment was not at all useful for severely ill patients?

    1. On 2020-05-01 23:43:12, user Sinai Immunol Review Project wrote:

      Title A single-cell atlas of the peripheral immune response to severe COVID-19<br /> Wilk, A.J. et al. MedRxiv ; doi:10.1101/2020.04.17.20069930

      Keywords<br /> scRNAseq; Interferon-Stimulating Genes (ISGs); Activated granulocytes

      Main Findings<br /> The authors performed single-cell RNA-sequencing (scRNAseq) on peripheral blood from 6 healthy donors and 7 patients, including 4 ventilated and 3 non-ventilated patients. 5 of the patients received Remdesivir.

      scRNAseq data reveal 30 gene clusters, distributed among granulocytes, lymphocytes (NK, B, T cells), myeloid cells (dendritic cells DCs, monocytes), platelets and red blood cells. Ventilated patients specifically display cells containing neutrophil granule proteins that appear closer to B cells than to neutrophils in dimensionality reduction analyses. The authors named these cells “Activated Granulocytes” and suggest them to be class-switched B cells that have lost the expression of CD27, CD38 and BCMA and acquired neutrophil-associated genes, based on RNA velocity studies.

      SARS-CoV2 infection leads to decreased frequencies of myeloid cells, including plasmacytoid DCs and CD16+ monocytes. CD14+ monocyte frequencies are unchanged in the patients, though their transcriptome reveals an increased activated profile and a downregulation of HLAE, HLAF and class II HLA genes. NK cell transcriptomic signature suggests lower CD56bright and CD56dim NK cell frequencies in COVID-19 patients. NK cells from patients have increased immune checkpoint (Lag-3, Tim-3) and activation marker transcripts and decreased maturation and cytotoxicity transcripts (CD16, Ksp-37, granulysin). Granulysin transcripts are also decreased in CD8 T cells, yet immune checkpoint transcripts remain unchanged in both CD8 and CD4 T cells upon SARS-CoV2 infection. The frequencies of memory and naïve CD4 and CD8 T cell subsets seem unchanged upon disease, though gdT cell proportions are decreased. SARS-CoV2 infection also induces expansion of IgA and IgG plasmablasts that do not share Ig V genes.

      Interferon-signaling genes (ISGs) are upregulated in the monocyte, the NK and the T cell compartment in a donor-dependent manner. ISG transcripts in the monocytes tend to increase with the age, while decreasing with the time to onset disease. No significant cytokine transcripts are expressed by the circulating monocytes and IFNG, TNF, CCL3, CCL4 transcript levels remain unchanged in NK and T cells upon infection.

      Limitations<br /> The sample size of the patients is limited (n=7) and gender-biased, as all of them are men.<br /> The activating and resting signatures in monocytes should be further detailed. The authors did not detect IL1B transcripts in monocytes from the patients, though preliminary studies suggest increased frequencies of CD14+ IL1B+ monocytes in the blood of convalescent COVID-19 patients[1].<br /> Decreased NK cells, B cells, DCs, CD16+ monocytes and gdT cells observed in peripheral blood might not only reflect a direct SARS-CoV2-induced impairment, but also the migration of these cells to the infected lung, in line with preliminary data suggesting unchanged NK cell frequencies in the patient lungs[2].<br /> The authors identified platelets in their cluster analyses. Recent reports of pulmonary complications secondary to COVID-19 describe thrombus formation that is probably due, in part, to platelet activation[3, 4]. A targeted characterization of the platelet transcriptome may thus benefit an increased understanding of this phenomenon.<br /> The transcriptome of the Activated Granulocytes should be further detailed. As discussed by the authors, IL24 and EGF might be involved in the generation of the Activated Granulocytes, though these cytokines are poorly represented in the blood of the patients. The generation of these cells should therefore be further investigated in future studies.

      Significance<br /> The authors show a SARS-CoV2-induced NK cell dysregulation, in accordance with previous studies[5]. Alongside the upregulation of ISGs in NK cells, these findings suggest an impaired capacity of the NK cells to respond to activating signals in COVID-19 patients. The unchanged expression of immune checkpoints on CD4 and CD8 T cells suggest distinct SARS-CoV2 dysregulation pathways in the NK and the T cell compartments. In particular, the downregulation of transcripts encoding for class II HLA but not for the HLA-A, -B, -C molecules in monocytes suggest an impaired antigen presentation capacity to CD4 T cells, which should be further investigated.<br /> The authors provide preliminary results suggesting an age-related activation of the monocytes in the COVID-19 patients. Future studies will be needed to evaluate if the age impacts the involvement of the monocytes in the cytokine storm observed in COVID-19 patients.

      References<br /> 1. Wen, W., et al., Immune Cell Profiling of COVID-19 Patients in the Recovery Stage by Single-Cell Sequencing. MedRxiv, 2020.<br /> 2. Liao, M., et al., The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing. MedRxiv, 2020.<br /> 3. Giannis, D., I.A. Ziogas, and P. Gianni, Coagulation disorders in coronavirus infected patients: COVID-19, SARS-CoV-1, MERS-CoV and lessons from the past. J Clin Virol, 2020. 127: p. 104362.<br /> 4. Dolhnikoff, M., et al., Pathological evidence of pulmonary thrombotic phenomena in severe COVID-19. J Thromb Haemost, 2020.<br /> 5. Zheng, M., et al., Functional exhaustion of antiviral lymphocytes in COVID-19 patients. Cell Mol Immunol, 2020.

      Credit<br /> Reviewed by Bérengère Salomé and Zafar Mahmood as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

    1. On 2020-05-04 18:15:10, user Dr SK Gupta wrote:

      High Dose Chloroquine with Poor patient selection are the culprits- not the drug <br /> Investigators were over enthusiastic in using a higher dose of chloroquine in elderly patients. In China National Health and Care Commission officially included the Chloroquine as medical agent on 19 Feb 2020 to be used in corona virus treatment plan. The dose of 500mg of chloroquine twice a day was decided following in vitro studies EC50 values, PBPK modeling and mice RLTEC data projected on Human beings (1). <br /> The initial recommended dose of 500 mg of chloroquine phosphate salt twice per day can quickly approach danger thresholds with sustained use at the maximum course of 10 days (Total chloroquine base 6gm). The lethal dose of chloroquine base in adults is about 5g. In China, On Feb 26, 2020, the treatment guidelines were revised, shortening the maximum course to 7 days to keep the total dose of chloroquine base 4.2 gm much lower than toxic dose (2). <br /> Elderly population is particularly prone to chloroquine toxicity especially at high doses. It is unfortunate in present study, that a base line ECG was not done to measure the QTc interval because the drug should be avoided if the QTc was more than 500ms especially in patients with severe disease prone to develop myocarditis due to primary disease Covid-19 per se(3). On the contrary we find that the higher dose regimen included Older age population with mean [SD] age, 54.7 [13.7] years vs 47.4 [13.3] years with more heart disease (5 of 28 [17.9%] vs 0) as compared to lower dose regimen. We in India having hige experience of using the drug would refrain from using such high doses.<br /> Gao et al reported results from more than 100 patients demonstrated that chloroquine phosphate is superior to the control treatment: in inhibiting the exacerbation of pneumonia, improving lung imaging findings, promoting a virus-negative conversion, shortening the disease course. Severe adverse reactions to chloroquine phosphate were not noted in the aforementioned patients (4). <br /> Poor patient selection and use of toxic doses of chloroquine seems to have brought disrepute to a promising drug. More studies are required before condemning the drug in present indication of Covid 19<br /> References:<br /> 1. Wang M, Cao R, Zhang L, et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res 2020; 30:269–71<br /> 2. COVID-19: a recommendation to examine the effect of hydroxychloroquine in preventing infection and progression Dan Zhou, Sheng-Ming Dai and Qiang Tong J Antimicrob Chemother doi:10.1093/jac/dkaa114

      1. Cardiovascular risks of hydroxychloroquine in treatment and prophylaxis of COVID-19 patients: A scientific statement from the Indian Heart Rhythm Society<br /> Aditya Kapoor, Ulhas Pandurangi,Vanita Arora, Anoop Gupta, Aparna Jaswal et al. Indian Pacing and Electrophysiology Journal, https://doi.org/10.1016/j.i...

      2. Gao J, Tian Z, Yang X. Breakthrough: chloroquine phosphate has shown apparent efficacy in treatment of COVID-19 associated pneumonia in clinical studies. Bioscience Trends 2020; 14:72–3

    1. On 2020-04-17 23:52:12, user Daniel H Vlad, PhD wrote:

      Dear Author, <br /> I appreciate your efforts to shed light on this very important topic but I believe the article has a major bias, which is indeed mentioned in the article.<br /> "Other biases, such as ... bias favoring those with prior COVID-like illnesses seeking antibody confirmation are also possible. The overall effect of such biases is hard to ascertain. "

      I think this is a very serious bias and let me explain why. It's very likely that your Facebook ads attracted a fair number of participants that were concerned they may have been infected with Covid-19. People who had experienced Covid-like symptoms in the recent past were more likely to pay attention to your Facebook ad and were also more likely to enroll in your study. Therefore your sample is not random.<br /> Let's make some reasonable assumptions. Let's assume that 10% of your sample, or 333 participants had Covid-19 like symptoms in the past and were seeking antibody confirmation. I believe this percentage is very reasonable. <br /> According to California statistics, approximately 25% of people tested for Covid-19 test positive. It could be very reasonable to assume that 15% of these 333 participants seeking antibody confirmation had been indeed infected. So that will equal to 50 positive participants, or the entire number of antibody test positive participants in your sample.<br /> The example above proves that this bias is a valid concern. Your entire list of 50 antibody positive participants could have been participants seeking antibody confirmation.

      There are several ways to remove the bias:<br /> a. You mention in the article that you asked survey participants if they had prior clinical symptoms. You should try to exclude all participants that had symptoms (fever, chest pressure) similar to Covid-19 this year. This will indeed exclude all people that were ill, not only those who replied to the add to seek antibody confirmation. However, it will give you insight into the percentage of silent Covid-19 carriers, which is a question as important as the question you are trying to answer, and in a way equivalent. And the results will be unbiased. <br /> b. Attempt to adjust for this bias. Calculate the percentage of participants in your sample who experienced Covid-19 symptoms and compare this with reasonable epidemiological data. Calculate a weight and apply it to your sample in addition to your zip-sex-race weights.

      Regards, Daniel H. Vlad, PhD.

    1. On 2020-05-05 18:21:51, user Valerie Natale wrote:

      I looked at supplementary figure 1 and I'm not convinced that anyone should be jumping to use the N protein in a diagnostic assay.

      It looks like the ELISA for the N protein had MORE false negatives than the ELISA for the S protein (10 vs. 8).

      Also, the negatives in the N assay in both systems were all over the place, with at least 1 or 2 giving false positives. The legend doesn't explain what all those lines in the figures are, but the N protein ELISA is in no way as tidy as the N protein LIPS assay.

      Why is there no ELISA for ORF8?

      Finally, the sample size (15 patients and was very small. They should have done this work on 50+ patients and the same number of controls. Results using small sample sizes can look sooo good, until you pile more data in, and suddenly...it gets messy.

    1. On 2019-10-10 12:11:25, user GuyguyKabundi Tshima wrote:

      EPIDEMIOLOGICAL SITUATION

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT OCTOBER 06, 2019<br /> Monday, October 07, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,205, of which 3,091 are confirmed and 114 are probable. In total, there were 2,142 deaths (2028 confirmed and 114 probable) and 1006 people healed.<br /> 363 suspected cases under investigation;<br /> 1 new confirmed case at CTE in North Kivu at Oicha;<br /> No new confirmed deaths<br /> 2 people healed from Butembo CTE;<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

      7 people healed from Ebola Virus Disease released Monday at Komanda CTE<br /> - A total of 7 people cured of Ebola Virus Disease were released on Monday October 7th at the Ebola Treatment Center (ETC) in Komanda. ;<br /> - This is 4 people from Mambasa and 3 cases from Komanda Health Zone to whom discharge certificates were given by the director of this Ebola Treatment Center<br /> - This certificate of discharge bears as inscription: "On the date of issue of this document the bearer of this certificate does not present any risk of contaminating other people, because his test was negative for the Ebola virus disease. He / she is thus DECLARE GUERI (E) . His current state of health is not a danger to the community. That is why he / she can return to his household and his professional environment to continue the daily activities. The family, the community and the authorities are asked to welcome him to promote his social integration ".

      VACCINATION

      • Continuation of vaccination around the confirmed case of 04 October 2019 in the Tenambo Health Area in Oicha, North Kivu;
      • Continuation of the vaccination of newly recruited front-line staff at the General Reference Hospitals of Katwa and Kyondo in North Kivu;
      • Since vaccination began on 8 August 2018, 234,693 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • Since the beginning of the epidemic, the total number of travelers checked (temperature increase) at the sanitary control points is 103,167,809 ;
      • To date, a total of 111 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:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. 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 toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions 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.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    2. On 2019-10-17 18:36:39, user GuyguyKabundi Tshima wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AS OF OCTOBER 15, 2019

      Wednesday, October 16, 2019<br /> Since the beginning of the epidemic, the cumulative number of cases is 3,227, of which 3,113 are confirmed and 114 are probable. In total, there were 2,154 deaths (2040 confirmed and 114 probable) and 1038 people healed.<br /> 530 suspected cases under investigation;<br /> 3 new confirmed cases, including:<br /> No cases in North Kivu;<br /> 3 in Ituri in Mandima;<br /> 1 new confirmed death, of which:<br /> 1 community death in Ituri in Mandima;<br /> No confirmed deaths;<br /> 2 people healed from the 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

      The state of play of the response at the center of an interview in Goma between the Technical Secretary of the CMRE and the United Nations Emergency Coordinator for Ebola<br /> - The Technical Secretary of the Multisectoral Committee on Epidemic Response to Ebola Virus Disease (ST / CMRE), Prof. Jean Jacques Muyembe Tamfum, granted a hearing on Wednesday, October 16, 2019 in Goma to the United Nations Emergency Coordinator for Ebola;<br /> - During their meeting, the two personalities discussed the state of play of the response to the 10th Ebola Virus Disease outbreak and the security situation in the areas affected by this epidemic;<br /> - It should be noted that the 10th Ebola epidemic has been taking place in the Democratic Republic of the Congo in areas of armed conflict, particularly in the provinces of North Kivu and Ituri, for more than a year;<br /> - Some time before this meeting, the technical secretary of the Multisectoral Committee for the Response to the Ebola Virus Disease Epidemic (ST / CMRE), Prof. Muyembe Tamfum, who is currently staying in Goma, North Kivu to inquire about the evolution of the response, chaired the morning meeting of the general coordination of the Ebola response to the epidemic.

      Pygmies at Mahombo camp in Mambasa territory in Ituri pledge to fight Ebola Virus Disease

      • The pygmies residing in Mahombo camp located more than 30 minutes walk from the main road from the village Nyangwe in the territory of Mambasa in ITURI, pledged Tuesday, October 15, 2019 to fight against Ebola by raising alerts with teams of the response;<br /> This commitment is the result of awareness raising by the Community Risk and Commitment (CREC) teams for 79 pygmies about the generalities of the Ebola virus disease, its methods of prevention and contamination;<br /> Pygmies have, for this purpose, asked for hand washing kits to break the chain of transmission of the Ebola virus in their respective communities.

      VACCINATION

      • Since vaccination began on 8 August 2018, 238,700 people have been vaccinated;
      • The only vaccine to be used in this outbreak is the rVSV-ZEBOV vaccine, manufactured by the pharmaceutical group Merck, following approval by the Ethics Committee in its decision of 20 May 2018.

      MONITORING AT ENTRY POINTS

      • The Governor of North Kivu, Carly Nzanzu Kasivita accompanied by a strong delegation, visited Maboya Control Points (PoCs) in Kalunguta and Kangote in Butembo in North Kivu Province;
      • The providers of the Mususa Point of Control (PoC) in Butembo, North Kivu, in collaboration with the Ndondo Primary School and the Kyambogho School Complex, participated in a mass sensitization session (travelers and riverside population) under the theme " All Eliminate Ebola Virus Disease "on International Handwashing Day;
      • Since the beginning of the epidemic, the total number of travelers checked (temperature rise) at the sanitary control points is 106.625.956 ;
      • To date, a total of 111 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:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. 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 toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions 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.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    3. 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:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. 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 toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions 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.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.
    4. On 2019-11-30 16:39:58, user Guyguy wrote:

      EVOLUTION OF THE EPIDEMIC IN THE PROVINCES OF NORTH KIVU AND ITURI AT NOVEMBER 26, 2019<br /> Wednesday, November 27, 2019<br /> • Since the beginning of the epidemic, the cumulative number of cases is 3,304, of which 3,186 are confirmed and 118 are probable. In total, there were 2,199 deaths (2081 confirmed and 118 probable) and 1077 people cured.<br /> • 366 suspected cases under investigation;<br /> • No new confirmed cases;<br /> • No new deaths among confirmed cases;<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

      Closure of training of Ebola Rapid Response Teams in Goma

      • The Ebola response coordinator for the Ebola response to operations, Dr. Luigino Mikulu, closed on Wednesday 27 November 2019 the training of Rapid Response Teams (RRTs), composed of units of the Armed Forces. (FARDC) and the Congolese National Police (PNC), on the Ebola virus disease that took place in Goma, capital of North Kivu Province from 22 to 26 November 2019;<br /> • For Dr. Luigino, this team is the first in the Rapid Response Teams to be composed of elements from other sectors, such as those of the Ministries of Defense and Security and the Ministry of the Interior;<br /> • This training aligns with the vision of the Technical Secretariat of the Multisectoral Ebola Virus Disease Response Committee (ST / CMRE), through the overall coordination of the response, to expand its mixed and multidisciplinary teams available and able to intervene 24 hours a day, 7 days a week and everywhere, where they will be deployed, not only for the response to this epidemic to Ebola Virus Disease, but also for other epidemics;<br /> • This training was a pride for WHO to accompany the Ministry of Health in order to capitalize the capacity building of FARDC and PNC units in public health;<br /> • The participants, in turn, reassured the overall coordination of the response, the Ministry of Health and all those who contributed to the delivery of this training, particularly to WHO and all facilitators, to be faithful disciples in the field by putting into practice all the notions learned during these sessions;<br /> • At the end of this training, the thirty participants, including the facilitators, received a participation certificate.

      VACCINATION

      • Despite the tense situation of the city of Beni, a vaccination ring was opened around the confirmed case of 24 October 2019 in the Kanzulinzuli Health Area of the General Reference Hospital;<br /> • 724 people were vaccinated, until Tuesday, November 26, 2019, with the 2nd Ad26.ZEBOV / MVA-BN-Filo vaccine (Johnson & Johnson) in the two health zones of Karisimbi in Goma;<br /> • Since the start of vaccination on August 8, 2018 with the rVSV-ZEBOV vaccine, 255,247 people have been vaccinated;<br /> • Approved October 22, 2019 by the Ethics Committee of the School of Public Health of the University of Kinshasa and October 23, 2019 by the National Ethics Committee, the second vaccine, called Ad26.ZEBOV / MVA-BN -Filo, is produced by Janssen Pharmaceuticals for Johnson & Johnson;<br /> • This new vaccine is in addition to the first, the rVSV-ZEBOV, vaccine used until then (since August 08, 2018) in this epidemic manufactured by the pharmaceutical group Merck, after approval of the Ethics Committee on May 20, 2018. has recently been pre-qualified for registration.

      MONITORING AT ENTRY POINTS

      • Sanitary control activities are disrupted in the towns of Beni and Butembo in North Kivu province following demonstrations by the population which decries killings of civilians;<br /> • Since the beginning of the epidemic, the total number of travelers checked (temperature measurement ) at the sanitary control points is 121,159,810 ;<br /> • To date, a total of 109 entry points (PoE) and sanitary control points (PoCs) have been set up in the provinces of North Kivu and Ituri to protect the country's major cities and prevent the spread of the epidemic in neighboring countries.

      As a reminder, the recommendations of the MULTISECTORAL COMMITTEE OF THE RESPONSE TO EBOLA VIRUS DISEASE are as follows:

      1. Follow basic hygiene practices, including regular hand washing with soap and water or ashes;
      2. 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 toll-free number;
      3. If you are identified as a contact of an Ebola patient, agree to be vaccinated and followed for 21 days;
      4. If a person dies because of Ebola, follow the instructions 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.
      5. For all health professionals, observe the hygiene measures in the health centers and declare any person with symptoms of # Ebola (fever, diarrhea, vomiting, fatigue, anorexia, bleeding).<br /> If all citizens respect these health measures recommended by the Secretariat, it is possible to quickly end this 10th epidemic.