On 2025-09-17 10:47:48, user Albert Kirshen, MD, FACP wrote:

Interesting article for palliative care or pain medicine. The authors could improve the generalizability of this reseache if<br /> 1) Specific information were provided about the cannabis used, i.e. % THC, %CB, form (smoked, oil, ingested), dosing<br /> 2) A clear description of the population evaluated, i.e. demographics, health condition(s), were provided<br /> 3) Prior history of cannabis use was noted<br /> to mention but a few.

On 2025-10-15 04:54:32, user CDSL JHSPH wrote:

Dear Author, This work presents an interesting analysis of the connection between the development of islet autoimmunity in early childhood and bile acid metabolism controlled by the gut microbiota. Understanding how early-life gut microbial and change in metabolism may affect type 1 diabetes risk is made possible by the long-term research design and the use of multi-omics methods, which combine metabolomics and metagenomics. Could gut microbiota regulation, such as with probiotics, prebiotics, or bile acid-targeted therapies, help normalize bile acid profiles and lower the risk of type 1 diabetes progression in children at risk, considering the noted changes in bile acid metabolism that occur before islet autoimmunity?

On 2025-10-18 15:17:01, 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 paper defines smoke-during-drought as a distinct exposure, building a single measurable target for preparedness from compound events.

National exposure counts computed are reproducible and dashboard-ready: ~3,630 county-months of drought, ~14,049 smoke-days, ~980 concurrent-days/year.

Results are policy-readable burdens for the contiguous U.S.: drought ~6,576 deaths/year (95% CI: 3,990-9,155), smoke ~10,465 (6,642-14,261), and concurrence +469 (256–682).

Reporting counts with CIs makes the outputs plug-and-play for briefs, staffing, and budgeting, and shows higher per-million impacts in high-SVI counties.

The concurrent added effect appears across causes (non-external, cardiovascular, respiratory, endocrine/metabolic, etc.), and for several categories it exceeds smoke-only. This supports joint operational triggers (when drought and smoke thresholds coincide) and targeted protections for vulnerable counties.

On 2025-10-20 13:59:35, user Dr Fiona Gullon Scott wrote:

This mirrors exactly the findings from work undertaken by researchers such as myself, Cathie Long, the ADASS team, Prof Luke Clements, Prof Andy Bilson, and more. I am delighted to see that this has been picked up by Simon, Carrie, and colleagues at Cambridge, because so far calls from those of us who have been challenging the issues around autistic mothers being over accused of FII or pulled into child-protection services have been fundamentally ignored. Perhaps the weight of Cambridge University will make a difference?

On 2025-10-20 15:14:25, user xPeer wrote:

Courtesy Peer Review Simulation from xPeerd :

Summary<br /> This manuscript examines the experiences of mothers of autistic children within UK child-protection services, with a particular focus on the prevalence and nature of social services' involvement and allegations of Fabricated or Induced Illness (FII). Using a survey of 242 mothers (diagnosed autistic, self-identified autistic, and non-autistic), the authors investigate whether mothers with autism face greater scrutiny or risk of having their children removed compared to others. The findings suggest high levels of investigation for all groups, but no significant differences between groups. However, a markedly elevated rate of FII allegations is identified among mothers of autistic children compared to general epidemiological estimates. The methodology integrates participatory approaches but is limited by its sample scope, lack of a typically developing comparison group, and exploratory design.

Potential Major Revisions

1. Methodological Scope and Representativeness
2. The sample lacks a control group of mothers with typically developing children: “we did not actively recruit mothers of typically developing children due to practical considerations…” (p. 18, Limitations). This hinders interpretation of whether findings are unique to mothers of autistic children or represent broader social service dynamics.

3. The design is exploratory, and as stated: “the questions in the survey were exploratory and therefore we did not enquire in detail about social service involvement” (p. 18, Limitations). More granular data (e.g., timelines, outcomes, types of interventions) would strengthen the work’s empirical claims.

4. Statistical Analysis and Power

5. Some subgroup analyses are based on small subsamples (e.g., N = 21 for autistic mothers called into a meeting), reducing statistical power (Table 3, p. 13).

6. The manuscript acknowledges no statistically significant differences between diagnostic groups in key outcomes such as child protection registration or FII allegations (e.g., “no statistical difference emerged”; p. 14), suggesting caution is required in interpreting implications for policy or discrimination.

7. Interpretive Overreach

8. The discussion interprets elevated rates of investigation as evidence of systemic discrimination, but alternative explanations (e.g., increased service contact for autistic children, reporting biases) are not fully interrogated: “our results suggest a significant increase in inquiries and registrations...compared to the general population” (p. 17).

9. The text could benefit from a more critical posture towards causal inference.

10. Ethical and Legal Framing

11. The work alludes to ethical and human rights implications but does not provide a detailed ethical analysis or legal context, which are crucial for claims concerning state intervention and discrimination (see discussion, pp. 17–19).

Potential Minor Revisions

• Typographical and Grammatical Errors
• Occasional word repetition and typographic slips (e.g., “we are separately reportingly the results here...”; p. 8).
• Consistent usage of terms (e.g., sometimes “non-autistic”, sometimes “nonautistic”).
• Formatting Issues
• The document is interspersed with license and preprint notices that disrupt readability.
• Table captions and labels (e.g., Table 3, Table 4) lack uniform placement and can be confusing in the PDF layout.
• Section headers could be standardized for clarity.
• References

• All references are recent and field-appropriate. No missing citations identified. All URLs and DOIs appear correct.

• AI Content Analysis

• The writing style, structure, and nuanced argumentation are consistent with human-authored academic research. Estimated AI-generated content: <5%. No sections strongly flagged as AI-generated; narrative voice and academic conventions are maintained throughout. No epistemic inconsistencies or abrupt shifts in style detected.

Recommendations

1. Include a Wider Comparison Group
2. For greater generalizability, future iterations should incorporate mothers of typically developing children. This would clarify whether the experiences described are unique to mothers of autistic children.
3. Deepen the Methodological Rigor
4. Enrich the survey to collect more detailed information on the nature, duration, and outcome of social services’ engagement.
5. Where possible, triangulate self-report data with administrative records or interviews with professionals (subject to ethical approval).
6. Clarify Causal Inferences
7. Approach claims about systemic discrimination with caution—consider and analytically address alternative explanations or confounds.
8. Expand the Legal and Ethical Analysis
9. A more thorough excursus on UK legal standards and the ethical principles governing child-protection interventions would enhance the policy relevance of the manuscript.
10. Address Subsample Limitations
11. Explicitly acknowledge and discuss the implications of small subsample sizes for statistical inference throughout the results sections.
12. Improve Readability and Consistency
13. Edit for grammar, typographic errors, and ensure formatting consistency between tables, figures, and narrative text.

On 2025-10-21 15:31:18, user David Bacsik wrote:

The final version of this manuscript is now available in the Journal of Clinical Microbiology: https://doi.org/10.1128/jcm.00415-25

On 2025-10-28 01:01:15, user Héctor wrote:

Are you taking into consideration the spinal schock?

On 2025-10-28 14:47:35, user W.R. wrote:

The paper has been published: https://doi.org/10.3390/medicina61111931

On 2025-11-11 03:13:33, 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:

Triangulating national surveys and HIV self testing kit-distribution data (2012–2024) from 27 African countries, the authors use a hierarchical Bayesian model to estimate adult HIV self-testing uptake by sex/age, map country & regional differences, and infer the share of distributed kits used and re-testing rates.

Uptake is rising: From <1% in 2012 to 6.8% in 2024 overall; men slightly higher than women, and 25–34 year-olds have the highest uptake.

Big regional & country gaps: Eastern/Southern Africa 10.2% vs Western/Central 2.0% in 2024; country peaks reach ~45% in Lesotho.

An estimated ~70% of distributed HIV self testing kits are actually used, and prior users are only slightly more likely to self-test again (RR ? 1.1).

In a serostatus-stratified meta-analysis, people living with HIV not on ART had lower odds of having ever used HIVST (OR 0.75; 95% CI 0.53–1.08), with heterogeneity across countries (e.g., higher odds in Kenya).

On 2025-11-11 14:32:38, 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:

NHANES baseline measures linked to Medicare/NDI to follow incident Alzheimer’s and dementia in >14,000 people, so the risk is tracked over time rather than a one-off snapshot.

The paper centers on cumulative lead (estimated patella/bone lead), which is the right biology for long-term neurotoxicity; the highest vs lowest quartile shows about a 3× higher Alzheimer’s risk (HR 2.96; 95% CI 1.37–6.39).

The same cumulative metric also predicts all-cause dementia, with the highest vs lowest quartile HR 2.15 (95% CI 1.33–3.46), a clean, brief-ready number for policy slides.

Methods-forward and reusable: survey-weighted Cox time-to-event models with up to 30 years of follow-up let the hazard accumulate with aging, exactly the structure we can port to other exposure->dementia questions.

We also like the operational clarity: blood lead showed no association, while cumulative (patella) lead did, pointing action toward lifetime load and legacy remediation rather than one-time screens.

On 2025-11-25 22:24:39, user Radim Skala wrote:

I appreciate the authors’ effort to compare different PIPAC nebulizers, but the manuscript contains major methodological and physical shortcomings that substantially distort the results. The measurements do not follow the established principles: pressure is not stabilized, nozzle orientation is not orthogonal, distance is inconsistent, and no calibrated instrumentation is used.

The Robert Bosch GLL/GCL device shown in the photos is a construction laser, not a scientific measurement tool. The spray images are presented at different scales—clearly visible from the rulers in the photographs—making direct comparison impossible. In addition, an incorrectly narrowed operating pressure range was used for nozzle C, fundamentally affecting its spray characteristics.

The corrosion test is non-clinical and selectively presented: only nozzle C is shown, while internal components of other nozzles (rubber seals, epoxy joints, moving pins, and machined metal surfaces) would exhibit comparable or greater corrosion after 12 days.

The manuscript also omits the well-known hot-spot risks associated with swirl/hollow-cone nozzles and fails to acknowledge the safety advantages of full-cone designs.

Finally, prior scientific collaboration between one of the authors and the manufacturer of Capnopen constitutes a potential conflict of interest that is not disclosed. These issues critically undermine the reliability and objectivity of the manuscript’s conclusions.

On 2025-12-01 15:00:29, user Mayank Sikarwar wrote:

Thank you for sharing this interesting work. Could you kindly provide the supplementary data associated with this study? It would greatly help in understanding and reproducing your findings.

On 2020-05-26 00:44:57, user Bill Jackson wrote:

It may be related to the amount of virus they faced when there were first infected. In theory one single virion can progress to an infection, but the infective cascade proceeds more slowly in that case and the innate immune system as well as the adaptive immune system are initiated as soon as the initlal infection begins by lysing cells - which may provide a progressively developing immunity that is able to suppress the virus before a fatal case develops. Casual air born infection has the potential to initiate the infection from this small initial exposure. On the other hand, a person, in a seniors home with the air full of particles could get a massive infection of many thousands of virus partcles and the virus cascade from this larger initial infection can increase the circulating load of virus in the blood to lead to the death of many more body cells. In a weaker, older person this might explain the high fatality rates in seniors or others who are in a weakened state.<br /> We do know that places with very high rates of testing, coupled with a high rate of infection tracking and isolation have been able to 'flatten the curve' as they say.<br /> We also know this virus is slower than flu virus in the progression from cell infection to cell death - which gives more time for the innate and adaptive immune systems to quell the infection.

On 2020-05-21 20:47:56, user Michael G Waldon wrote:

This paper represents a huge amount of effort and I am impressed with the apparent high quality of your work achieved in a short time. It will be instructive to now see if there is qualitative agreement between model predictions published here and future observations over coming weeks as restrictions are relaxed. <br /> Since I have been reading about the numerous and varied model predictions, I have been hoping to also see hindcasting and counterfactual simulations. These simulations provide information and guidance beyond that from simple predictions. I do ask that, in future reports, you include the added simulation in which actions are delayed beyond the factual case. This is important for a number of reasons. First, it gives balance to your analyses. Second, we need to know that the economic and social sacrifices that were made that we did (or did not) accomplished a purpose. In modeling I think it is important that we always estimate what has been achieved. <br /> If I were a peer reviewer, I would definitely support publication of this paper. Thank you.

On 2020-05-21 04:05:54, user David Ackerman wrote:

The counterfactual simulation data on early intervention as in 1-2 weeks earlier implementation of Social Distancing are quite telling yet I have to wonder if there is also data available on interventions even earlier. What if measures such as masks, distancing, sanitizing & hand washing had been ADVISED by CDC as early as 2/1/2020? I would be most interested at viewing those numerical hypotheses. Thanks again, and God bless.

On 2020-05-26 02:41:41, user Frank Bentrem wrote:

Nice study. I have also looked into the COVID-19 connection to genetic ancestry. See the preprint <br /> https://www.researchgate.ne...

On 2020-05-26 15:37:05, user Sinai Immunol Review Project wrote:

Main Findings <br /> The authors describe a small cohort of 27 COVID-19 patients treated with enoxaparin or heparin in escalating doses (corresponding to clinical severity) at Sirio-Libanes Hospital in Sao Paolo, Brazil. Importantly, no control patients are included in this study. Additionally, all patients received concomitant azithromycin and a subset received methylprednisolone therapy. Patients had mean WHO score of 4.0 ± 1.2 (corresponding to moderate clinical severity) upon admittance. PaO2/FIO2 was significantly increased from 254 (±90) to 325 (±80) after 72 hours after the initiation of heparin therapy. 56% of patients were discharged within 7.3 (±4.0) days. 50% of mechanically ventilated patients were extubated within 10.3 (±1.5) days. The study reported no fatal events or bleeding complications due to anticoagulation. The authors suggest that early heparin therapy significantly improves hypoxemia and may be beneficial in the management of such patients.

Limitations <br /> This is a small, single arm, retrospective study without controls and with concomitant confounding treatments. Therefore, no definitive conclusions can be made here.

Significance <br /> This article adds anecdotal evidence regarding coagulability in COVID-19 patients and points to the potential for anticoagulation in the right clinical study. Given the multiple limitation, evidence herein can only corroborate previous reports demonstrating associations between elevated D-dimer and disease severity [1-3]. Additionally, this study may add to the evidence regarding mortality benefits of heparin therapy in severe COVID-19 [2, 3].

Credit <br /> Reviewed 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.

Reference<br /> 1. Han H, Yang L, Liu R, et al. Prominent changes in blood coagulation of patients with SARS- CoV-2 infection. Clin Chem Lab Med 2020 doi: 10.1515/cclm-2020-0188 [published Online First: 2020/03/17]

On 2020-05-27 00:15:28, user Sam Wheeler wrote:

Interestingly, such changes were not obvious in the serum chlorine and potassium concentrations

On 2020-05-27 12:07:53, user disqus_BjRKMZenfK wrote:

How many samples were excluded as their ct value was > 35, which could indicate low viral load, but not able to be sequenced? Also, the timing of sample collection before, during, or after the peak of the infection could influence these results.

On 2020-06-03 10:57:43, user Sebastian Fiebiger // medizin+ wrote:

Interesting study that provides us with further indication of the transmission pathways of SARS-CoV-2 and their significance in the context of the pandemic.

There is currently no "Aha!" kind of study. But there are many small "building blocks" that provide an overall picture.

Thanks a lot to the team!

Warm regards from sunny Berlin,

Sebastian<br /> medizin.plus

On 2020-06-03 21:44:59, user Renee Arnold wrote:

Interesting study, but I have 2 comments: one is that I think the ICER (cost to avoid one death) is incorrect--I get $103,053,306. The other is a question--how did the authors derive that loss of one life = loss of 10 QALYS? Seems a rather simplistic transformation from cost avoidance to cost per QALY..

On 2020-06-19 08:35:06, user Takeshi Miki wrote:

I am not convinced by the authors' conclusion at all. Please check my comments from the point of mathematical biology in PubPeer. https://pubpeer.com/publica...

On 2020-05-06 01:14:20, user Cal Damage wrote:

I appreciate the work here.

I have an issue with the graphics. It takes the viewer a while to realize the sort in Tables S2, S3 & S4 is by population. An extra column, either of state's population or % of US Population, would help.

The actual order of the states in most of the 'Fig #' graphics is not apparent from the graphs. Not sure what it is, but at least it is consistent across the Figures that list states. <br /> And that includes the first Fig 4, but not the second Fig 4.

Thanks for doing the work. This is heady, scary stuff.

On 2020-05-06 14:20:24, user JasonP wrote:

Upon peer review, I would hope that they note that the study fails to identify the methodology used to confirm Coronavirus infection. It would be crucial, in my opinion, that the confirmation be done via virus isolation. The current practice of confirmation through Ab testing associated with symptoms alone is inadequate. This should merely be considered "front testing" and should lead to virus culture, harvest, filtration and viral load quantification for an accurate assessment of drug effectiveness. Just my opinion, but is the goal to defeat Covid or not? IgM antibodies can produce false negative and false positive results, and if the patient has produced sufficient IgG antibodies yet remains ill, other causes and/or contributing systems (multiple illnesses, drug interactions) should be investigated by the medical personnel before "confirming" Coronavirus. This information is excluded from the study.

On 2020-05-08 15:43:56, user Ryan Pavlik wrote:

This looks like a lot of work and some valuable data. The main question that came to mind when reading it was "What defined a 'pre-COVID' control?" There's one reference in the in-house ELISA procedure (in this version, in supplementary material) that a control was a "pre-July 2018 historical Negative Control serum from two donors", but there's no description in the main paper on this point. The reference cited when describing the controls was your earlier paper on Chagas disease testing, which I took to imply that the same control samples were used, but I think this might be better made explicit. Given what we're finding about earlier world-wide spread, it would be valuable to state the date that's being considered pre-COVID to avoid doubt.

(It's fascinating, although on reflection unsurprising, that the antibody tests appeared to have found some false-negatives according to PCR testing. I appreciated that result being highlighted.)

Thanks for all the work you're doing!

(PS: different-field academic here, immunology is definitely not my thing, so if this comment isn't applicable for some field-specific reason, I apologize for the distraction.)

On 2020-05-08 16:54:43, user Daniel Powell wrote:

I would guess that those with such severe vitamin D deficiencies at the point of this study don't get much sun under normal circumstances. The disconnect once one politicizes any finding is a false equivalancy. "Beach" does not equal "Vitamin D", or even sunshine. It's foggy as heck on the beach in front of my rental right now. And I get plenty of sun in my yard. I can also take vitimans daily if I am concerned about the levels.

On 2020-05-12 19:13:29, user ohbaby wrote:

This is old news. Plus this study doesn't do nearly enough to make the connection. Much more revealing is the vast majority of people entering the hospital are vitamin D deficient. It is even more prevalent for those with ARDS. Plus it was shown in studies, vitamin D has 3 specific mechanisms in combating this virus by reducing and modulating the cytokine storm,.. and upregulating peptides that disable the virion directly so it can't infect cells.

Comparing countries vitamin D status to COVID-19 deaths is woefully insufficient. Especially when vitamin D deficiency is a worldwide pandemic. I wrote two articles with the scientific evidence here...

https://www.dailykos.com/st...

https://www.dailykos.com/st...

On 2020-05-18 09:46:36, user 1ashu1 wrote:

There seems an error in the calculation presented in the abstract, authors should consider to correct this in the revision: "Combining COVID-19 patient data and prior work on Vit D and CRP levels, we show that the risk of severe COVID-19 cases among patients with severe Vit D deficiency is 17.3% while the equivalent figure for patients with normal Vit D levels is 14.6% (a reduction of 15.6%)."

On 2020-05-11 16:35:29, user Duncan Edwards wrote:

Dear authors,<br /> Thanks for a great paper that is really clinically useful, today, for GPs. Could you address a query about ethnicity as a risk factor for COVID-19 hospital death which I can't see addressed in the tweets? Social reasons are often a good hypothesis for why an ethnic group is linked to better/worse outcomes. Clearly you can't explore all such factors using routine data, such as increased levels of front line working among ethnic minorities. However, were you able to explore to what extent increased death rates in ethnic minorities could be due to their higher concentrations in areas of higher mortality, e.g. big cities/certain regions?<br /> Kind Regards,<br /> Dr Duncan Edwards<br /> GP, Cambs

On 2020-05-18 10:41:55, user Tony Gordon wrote:

There is a very similar BME excess of schizophrenia (due to greater noise exposure triggering auditory hallucinations?). Hence similar social correlates?

I suspect the BME excess may be due to inability or disinclination to social distancing (not mentioned in article?). I walk regularly between D Hill station and KCH and very few of the people there make any effort to keep two metres apart. Very many are BME, though of course I don't know if this is significant.

I am a healthy 77y old. I still have no idea of what my COVID risk is?

On 2020-05-19 04:00:43, user Sinai Immunol Review Project wrote:

Main Findings:<br /> During the unprecedented COVID-19 pandemic, identifying patients at high risk for mortality is critical so as to guide clinical decisions on early intervention and patient care. To identify factors associated with risk of death from COVID-19, the study developed a secure and pseudonymized analytics platform, OpenSAFELY, that links the UK National Health Service (NHS) patient electronic health records (EHR) with COVID-19 in-hospital death notifications. This platform enabled the rapid analysis of by far the largest cohort to date from any country, comprising 17,425,445 multi-ethnic adults and 5,683 COVID-19 deaths. The analyses were based on hazard ratio generated by cox-regression and were adjusted for demographics and co-morbidities.<br /> Increased risk of COVID-19 hospital death was associated with male gender, older age, certain clinical conditions (uncontrolled diabetes, severe asthma, other respiratory diseases, history of haematological malignancy or recent non-haematological cancer, obesity, cardiovascular disease, kidney, liver, neurological diseases, autoimmune conditions, organ transplant and splenectomy). Notably, the association of asthma with higher risk of COVID-19 related death is contradictory to previous findings of no increased risk of death or even protective association. This effect was even stronger with recent use of oral corticosteroids (i.e. more severe asthma). In addition, people of lower socio-economic background (i.e. deprivation) or black and Asian origin were identified at high risk. However, this association could not be entirely attributed to pre-existing health conditions or other risk factors, which warrants further exploration into drivers of these associations. The open source analytics code is available at OpenSAFELY.org.

Limitations:<br /> 1) There are few drawbacks in data source and collection. The study did not account for COVID-19 deaths in false-negative/ untested individuals, relied on EHR from specific software and dealt with incomplete EHR information. <br /> 2) Additional discussion regarding the reasons behind the associations would be insightful. Specifically, recent studies have shown that risk factors including asthma, hypertension, and diabetes impact the expression of ACE2 gene, which is the entry receptor for SARS-CoV-2. However, while asthma with type 2 inflammation has been associated with lower ACE2 expression and thereby potentially protective effect, this association has not been observed for nonatopic asthma in these studies. In the current study, asthma is only categorized in terms of severity (recent oral corticosteroid use vs not). Further categorization in terms of subtype would have been helpful.<br /> 3) Understanding the underlying causes of high risk in people of black and Asian origin is important for public health and mitigation of the spread. In this study, the most common assumptions of high burden of underlying comorbidities and lower socio-economic status are shown to contribute only partly to the risk. However, other factors, such as occupational exposure, neighborhood and household-density and possible influence of genetic or other biological factors still need to be explored. <br /> 4) The study suggests increased risk in former smokers and slight protective effect in current smokers. More in-depth analyses into whether the protective effect of current smoker status is an artifact of over-adjustment, selection protocol of healthy controls or a true correlation are needed. <br /> 5) It would be helpful to have the p-value along with the reported hazards ratio and 95% confidence intervals.

Significance: <br /> Overall, the OpenSAFELY platform allows secure and real-time analyses of clinical data stored in situ. As this global pandemic progresses, outcomes and data are expected to expand, revealing more insights to the effects of medical treatments and less common risk factors on COVID-19 infection, spread and death. This approach can help better identify additional factors that affect disease severity and immune response. Finally, this rapid and massive study was only possible because of the detailed longitudinal data already available through General Practitioners within the UK National Health Service (NHS), replication of which at a similar massive scale would be daunting within the highly fragmented healthcare system of the USA. While even within UK NHS many data integration issues remain, the findings from this study is a testament to the global model we need to follow to increase our power to rapidly answer crucial questions related to COVID-19 epidemiology. Such an approach will also open new avenues for increased understanding of other diseases.

Reviewed by Myvizhi Esai Selvan as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

On 2020-05-12 14:49:53, user Gruffmeister wrote:

Interesting article, however your data analysis does not support your conclusions.

The core problem is that you have made far too many assumptions in your data analysis, which has then led you to the conclusion that full lock-down had no effect when your data does not support this. The time variability between infection and death has no bearing on lock down effect - there are far too many variables that act as contributing factors to make this a valid measure.<br /> You show that social distancing (pre-lock down) in figure 4 and 5 are extrapolated to zero, with the assumption that the peak has already been hit and is short lived. There is no data to support this extrapolation - in actual fact pre-lock down in the four countries you mention that went into lock down, the infection and death rates were hitting a log exponential fit, not a linear regression fit which makes the extrapolation incorrect.<br /> Using the correctly fitting model, your data would show a much more aggressive case increase count pre-lock down measures.<br /> I would ask the question, what led you to conclude that this was a Gaussian model?<br /> There's also the consideration of when the measures were introduced. There doesn't seem to be any analysis of countries that did not go into lock down - when did they start their social distancing, and how compliant were the population to the social distancing requests? This has a huge bearing on the effectiveness of full lock down vs social distancing. I know for sure in the United Kingdom, that without aggressive lock down measures, vast numbers of people did not pay attention to any social distancing.

I'm not saying that lock down has or hasn't had minimal effect. Your data just does not support your conclusions.

On 2020-05-12 18:33:53, user Wouter wrote:

Readers should be aware of the considerable discrepancy between the simulated scenarios made using the model, and the actual spread of disease in Sweden after submission of the manuscript.<br /> Wouter van der Wijngaart, co-author

On 2020-05-13 03:11:18, user deirdre alexandra platt wrote:

Useful information thankyou. A query: on average, how long is the "longterm" exposure to airpollution? Are you looking at the previous 2 years, or 5 years, or more? How long can we endure breathing polluted air before we become vulnerable to a virus like COVID-19?

On 2020-05-13 22:04:02, user Steve Condie wrote:

The study concludes that the Infection Fatality Rate (IFR) in Santa Clara County is 0.17%. Currently New York State has a fatality rate from Covid-19 equal to 0.14% of the entire population of that state. (PFR) In New York serological studies have found less than 20% of the population has been infected. That puts a floor under the IFR of 0.7+% (People are still dying.) The authors brush that 400+% discrepency off as being due to overwhelmed hospitals (?)

New Jersey's PFR is over 0.1%. Five more states, the District of Columbia and many nations in Europe have PFR's over 0.04%. There is no reason to believe that any of those states or nations have infection rates higher than that of New York. That necessarily implies that the IFR in those states and countries is also far higher than that which the authors have calculated for Santa Clara County.

Is Santa Clara County a "special case?" Or is there a problem with the study?

On 2020-05-16 22:05:44, user Chris Bagshaw wrote:

Everyone seems to be quoting the death rates in various countries but my country is placing COVID19 on death certificates for people dying from other causes "following government guidelines" so how can anyone calculate the true infection / mortality rates when the actual numbers being quoted are fatally flawed by manipulation?

On 2020-04-19 00:10:56, user Joerg Stoye wrote:

Kudos to the authors for collecting very interesting data that arguably allow some cautiously optimistic conclusions. However, I am concerned that the statistical analysis might have been done in haste and that the rejection of 0% prevalence is an artifact of this. I would be more than happy to stand corrected! (Disclaimer: Jeffrey Spence posted very similar observations earlier. I post this anyway as I think I can marginally add value on exposition.)

The positive rate in the raw data is 50/3330=1.5%. The test’s false positive rate is estimated at 2/301=.5%, but with a 95% confidence interval extending upward to 1.9%. This means we cannot reject the hypothesis of zero prevalence. Note (this will become important): Because the binomial distribution is not well approximated by a normal here, the CI must be constructed as exact binomial, not by normal approximation. The authors do this and correctly report 1.9%. If they had mistakenly used a normal approximation combined with the sample variance, their CI would have extended only to 1.2% and zero prevalence would have been spuriously rejected.

Based on this, it is puzzling that the “headline” CI’s for prevalence do not include 0. Indeed, the authors state in their statistical appendix (bottom of page 2) that they need the positive rate to exceed 1-sensitivity. But then they move on to reject 0 anyway, in apparent violation of their own CI for sensitivity! What gives?

I conjecture that the problem is their subsequent use of the delta-method to analyze error propagation. This implicitly applies a normal approximation to all random variables under study. Indeed, the analysis culminates in providing standard errors, and these are only interpretable in the context of normal approximation. But recall that the normal approximation is inappropriate for the validation sample and furthermore that incorrectly using it would have yielded spurious rejection of zero prevalence. I conjecture that this is implicitly committed in the later part of the analysis. The earlier conclusion that 0 cannot be rejected seems appropriate to me.

Again, I’d be happy to stand corrected and also appreciate that the paper was put together under insane pressure.

On 2020-05-17 22:30:47, user Arvind Gulati wrote:

There is a new randomised controlled double blinded trial of 2000 participants starting sponsored by NED unfortunately they are not adding ZINC which seems to make the key difference. I don't know how to connect the study sponsors.

On 2020-05-18 13:48:34, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-18 21:51:22, user bigterguy wrote:

Um...<br /> ” We estimate that through the end of July, there will be 60,308 (34,063-140,381) deaths from COVID-19 in the USA ”<br /> 92,000 and counting on May 18. Likely close to the top of their projected range by end July. Is this even worth publishing?

On 2020-05-19 01:55:12, user Subhradip AIIMS New Delhi wrote:

Testis has a very poor expression of TMPRSS2 , a critical determinant for virus entry. I suppose the entire molecular machinery must be present to make possible the virus entry and replication. Drawing a conclusion based on just 1 -2 target proteins will be an over simplification.

On 2020-05-19 12:45:29, user Lynn Wright wrote:

Was the HCQ given alone or with Azithro as is being prescribed in doctor's offices and hospitals? If not, why?

On 2020-05-19 16:26:56, user Wizard of Oz wrote:

This study claims to compare the risk of dying of COVID19 to the risk of driving a car. It does so by assuming the former can be measured by the number of deaths that occured in a given timeframe divided by the population size in M. That is an utterly misleading metric. First of all, the data for COVID19 is incomplete (the pandemic is not over yet). Also the study does not take into account that up to 80% of the population can get infected if the virus is left unchecked, and that this has secondary effects causing many more die for lack of treatment. In conclussion the validity of this study's conclusions is highly doubtful.

On 2020-04-13 12:12:59, user Mark Upton wrote:

Dear Prof Ioannidis and colleagues

In other work, you have cautioned about possible risks to public health when responding to exaggerated claims about SARS-Cov-2. A flip-side is that there may be risks to occupational health from accurate manuscripts that, never-the-less, may be taken out of context by policy makers and employers keen to reboot economies and businesses after perceived peaks in the local epidemic curves of Covid-19.

I worry that one of the main messages conveyed in the abstract of your manuscript, i.e., that the absolute risk of death from Covid-19 among individuals aged <65 years is low, and consistent with risks encountered on the roads, may be mis-applied in occupational settings as the “lockdown” is relaxed. Workers aged 40-64 years, an age-group in which your manuscript recognises that most of the deaths under age 65 occur, may be exposed to higher risks of Covid-19 related death in occupational settings with increased social mixing, compared to the rather theoretical diluted risks estimated across the entire population.

I entirely understand the reason why your manuscript uses a binary age classification at 65, and note that the first paragraph of the discussion section of the manuscript mentions the concentration of deaths at ages 40-64. However, this is omitted from the abstract, no doubt for reasons of word count, and so may be overlooked by time-pressed readers keen to learn lessons for policy and employment practice. Also, it would be helpful to make the rather obvious point that numbers dying equals fatality rate x numbers infected, and that whilst numbers infected may be low across when averaged across entire populations, infections cluster, and this includes occupational settings.

So, good paper, but please consider some caution in how your message is conveyed. I speak as someone with relatives, and patients among the key workers soon to be re-mobilised.

Kind regards<br /> Mark Upton

On 2020-05-19 18:29:40, user Guy Gadboit wrote:

The paper correctly points out that extensive nosocomial infections will result in an overestimate of IFR based on seroprevalence, since seroprevalence is measured in the general population outside the hospital. But we we quantify this by comparing estimated IFRs with known CFRs for different age groups? If we find that the CFR for the over 70s is only 8x for confirmed cases, but that based on seroprevalence, the IFR looks like it's 30x higher for the over 70s, then that would imply a much greater risk of becoming infected inside the hospital (where the average age of the patients will be high) rather than outside it. The hospitals in these regions should know the CFRs or have accurate estimates of them.

On 2020-05-19 18:37:43, user Plonit Almonit wrote:

If the calculations use the official number of laboratory confirmed deaths, they could be low. Excess mortality data show there is/was some serious undercounting of fatalities at least in some places (UK, Wuhan, Bergamo, Spain, New York). These will either be additional cases (many go uncounted despite clinical diagnosis) or additional mortality because of strained medical services. And one of the major arguments for the mitigation measures was to avoid higher mortality via lack of sufficient medical capacity.

On 2020-05-22 18:36:01, user Frank Taeger wrote:

Why would you basically choose the LOWEST of all possivle IFRs from every single study. Even the Gangelt study. The authors of the study itself have already done most of the corrections for you. If you actually did the corrections for external validation of specificity, the Gangelt study for example ranks MUCH higher, almost double the value you have counted there. Not even the authors use such a low estimate of IFR. And later on, another person in that area died, so that is not even correct anymore.

On 2020-05-23 02:15:34, user Dick Meehan wrote:

Suppose at the end of it all, when apparent excess deaths are seen in some degree to be accelerated deaths of the moribund, not always unwelcome especially from the perspective of the elders, the greater damage is determined to be lasting health effects and shortened life of the younger recovered? Seems to me that death counts even of the best quality may be a poor measure of the disease outcome, just as ventilator availability was once imagined to be the critical factor.

On 2020-05-23 02:16:10, user Eduardo Spitzer wrote:

Dear Dr.Wyllie

Kudos for such a great and elegant work.<br /> I think this article will set a foundation for future actions con Sars-Cov-2 detection, and particular in low/ difficult resources settings...<br /> I live in Argentina and we have a serious problem importing swabs from abroad and there is a huge surge in prices....and this makes national/private screening programs work in sub-optimal conditions.

I would like to know about running a direct RT-qPCR from saliva and avoid RNA extraction methods (Spin columns are also very difficult to obtain from Trusted EU/USA suppliers)??? This would be the perfect equation for increasing surveillance....

Again, Congratulations!!

Best<br /> Eduardo

On 2020-05-23 13:15:36, user Peter Frost wrote:

Why is Basu (2020) included in this meta-study? That study is not comparable to the others, since IFR is calculated only in relation to symptomatic cases. That kind of methodology leads to unrealistically high IFRs. For example, Basu (2020) calculates an IFR of 3.6% for King County WA. If we include asymptomatic cases, the real figure would be about a third lower.

On 2020-05-24 02:16:20, user Ken wrote:

Given the overlap between the 95% confidence intervals for Intubated and Non Intubated it would be worthwhile to have a p value to see if there actually was a difference.

On 2020-05-26 06:03:55, user Hooman Noorchashm wrote:

Cyclosporine's generic status and global scalability as a potential effective therapeutic to block progression of COVID-19 disease is one of its most attractive attributes: https://medium.com/@noorcha...

On 2020-05-29 06:56:04, user Luis Ayerbe wrote:

Probably the largest Covid database in the world. Hydroxihycholoroquine given to 87% of patients. Data available on mortality. Wouldn't you consider building a model to see if HCQ improves survival at all?

On 2020-05-30 11:48:52, user LizJMD wrote:

It would be of interest to know more of the baseline characteristics of the patients. I wonder if sHLH is related to baseline characteristics like younger age (viz the speculation on relation to pediatric multisystem inflammatory syndrome), obesiity, or other known risk factors for poor outcome.

A letter in JAMA detailed autopsy findings in 10 German patients (Schaller et al), ages 64 to 90, in which no vascular thrombosis was seen, just "pure" histologic findings of ARDS. "Only" 2/10 had morbid obesity.

I look forward to more revelations from the remaining subjects in your 67 patient cohort (seems to me only 25 analyzed) including distribution of ACE 2 receptor with respect to pathology and SARS CoV 2 infection.

Liz Jenny MD Jacobi Medical Center, Bronx NY

On 2020-05-30 19:58:47, user wbgrant wrote:

I did additional correlation analyses including life expectancy in 2018, 25OHD conentrations, and cardiovascular disease incidence rates (for males). Indeed, life expectany has a much higher correlation with COVID-19 case and death rates. A problem with the 25OHD concentration data used is that they are probably not recent and not representative of those most likely to develop COVID-19 infection. Two observational studies reported inverse correlations between 25OHD and severity of COVID-19 infection, and the mechanisms of how vitamin D reduces risk of respiratory tracth infections are well known

On 2020-05-31 13:21:46, user Pete Jones wrote:

Nice. Although I don't think the SVOP guys would be happy to not to be mentioned ( https://tvst.arvojournals.o... )

I take your point about calibration being key ("discrete eye movement perimetry is heavily reliant on optimal instrument calibration: if the average calibration error exceeds the ROI radius of each target, it is very well possible to have completely invalid maps."), and funnily enough this was precisely the problem when they tried SVOP at moorfields ( <br /> https://pubmed.ncbi.nlm.nih... ).

It isn't true, however, that calibration need be 'optimal'. And it isn't necessarily the case that one needs to use rigid/binary ROIs (I originally used a probabilistic [Maximum Likelihood] estimator, and still do for the calibration stage. It caused more problems than it solved for the main test stage though, where a simple ROI is often sufficient). But yeah, calibration is certainly a huge and constant challenge.

On 2020-06-01 17:03:22, user David Curtis wrote:

Thank you for this. I have the following comments.

The report should give a fuller account of the studies of the exome-sequenced Swedish schizophrenia case-control cohort (Curtis et al., 2018; Genovese et al., 2016). There should be an explicit comparison with these to see which findings of the earlier studies are strengthened and which are weakened. Our study showed that the signal was not confined to singleton variants but that rare, damaging non-singleton variants were also enriched in cases. It showed that variants impacting the function of the NMDA receptor were associated with increased schizophrenia risk and this hypothesis should be explicitly tested.

This sentence is confusing because it appears that standard errors or confidence intervals are being presented: “However, cases had significantly more novel exome-wide variants, exclusively limited to singletons (17.76±6.24 vs 15.44±6.42, p=6.13x10^-10; Table 1)”. When presented this way, it appears that the distributions largely overlap and it is not obvious why there is a significant difference between them.

It would be better to write something like: “However, cases had significantly more novel exome-wide variants, exclusively limited to singletons (mean (SD): 17.76 (6.24) vs 15.44 (6.42), 95% CI for mean difference [1.59, 3.05], p=6.13x10^-10; Table 1).” Or the standard deviations could just be omitted and left in Table 1.

I do not understand why the supplementary material does not provide a list of case-only and control-only genes, along with their subject counts.

“The foregoing analyses examined singleton URVs only, which have been the primary focus of exome studies in schizophrenia to date.” This is not correct. Our exome study of schizophrenia focused on non-singleton damaging rare variants.

I have a problem with focusing on singleton variants in a population with strong founder effects. Surely, the whole point is that some variants will be present at increased frequency? (While others will be absent.) So why focus on singletons, which by definition cannot be at increased frequency? Here is a key sentence from the introduction: “Importantly, a recent large-scale (n>5,000) sequencing study of AJ individuals demonstrated that this enrichment is widespread across the exome, with approximately one-third of all protein-coding alleles demonstrating frequencies in AJ that were an order of magnitude greater than the maximum frequency in any well-characterized outbred population.” The value of a population with strong founder effects is that some variants will be present at unusually high frequency. Concentrating only on singleton variants discards this advantage. It is not obvious to me that pathogenic singleton variants should be any commoner in AJ cases than in cases drawn from other populations. It would be helpful to address this issue more explicitly.

Curtis, D., Coelewij, L., Liu, S.-H., Humphrey, J., Mott, R. (2018) Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology. Behav. Genet. 43, 198–208.<br /> Genovese, G., Fromer, M., Stahl, E.A., Ruderfer, D.M., Chambert, K., Landén, M., Moran, J.L., Purcell, S.M., Sklar, P., Sullivan, P.F., Hultman, C.M., McCarroll, S.A. (2016) Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat. Neurosci. 19, 1433–1441.

On 2020-06-03 00:30:13, user Delemir Delev wrote:

WONFO rapid test is not the optimal choice for a seroprevalence study !

On 2020-06-03 06:36:37, user John Lambiase wrote:

So, out of curiosity, you mentioned Vitamin D as not being a risk factor but you did not show the data. What levels did you deem sufficient vs deficient? Many studies out there are providing their data and show contrast to your findings.

On 2020-06-04 16:36:04, user Rosemary TATE wrote:

Unfortunately the complicated modelling approach and poorly labelled graphs makes this potentially interesting article difficult to understand. There is no mention of limitations in the discussion section (see STROBE guidelines). Two obvious ones are 1. The progress of covid is different in different countries, so some countries will have lower cases just because the pandemic started later there 2. The data is very variable between countries and is dependent on how well they record covid deaths, so may not be reliable.

On 2020-06-04 18:12:50, user Fahd Al-Mulla wrote:

I am concerned about this study as a scientist. Am I right by saying that they compared DNA from hospitalized Covid-19 patients to data obtained from databank?! if so how do we know that some people in the control group would not have been hospitalized if they were infected? Am I right? if so this is a major problem in this study.

On 2020-06-05 22:06:31, user K Jane wrote:

I think everyone should replace the word antigen with .... Protein or pathogen.?

On 2020-06-09 16:50:01, user Arturo Tozzi cns wrote:

In this marvelous manuscript, the Authors state that SARS-Cov-2 did not undergo phenotypic modifications. <br /> It is not entirely true... there is an underrated viral component that did undergo phenotypic modifications. See this comment:

https://www.bmj.com/content...

On 2020-06-05 17:17:20, user Helmuth Haslacher wrote:

There is an error in the abstract, as manufacturer names have been confused. A revision has already been submitted. Many apologizes. The sentence should read as follows:

However, at low seroprevalences, the minor differences in specificityresulted in profound discrepancies of positive predictability at 1%seroprevalence: 52.3% (36.2-67.9), 77.6% (52.8-91.5), and 32.6% (23.6-43.1) for Abbott, Roche, and DiaSorin, respectively.

On 2020-06-07 01:37:32, user vinu arumugham wrote:

Oral famotidine/cetirizine, mast cell stabilizers, etc. avoid risk of de novo autoimmunity associated with biologics. They also have a broader effect instead of targeting IL-6 alone.<br /> Immunological mechanisms explaining the role of IgE, mast cells, histamine, elevating ferritin, IL-6, D-dimer, VEGF levels in COVID-19 and dengue, potential treatments such as mast cell stabilizers, antihistamines, Vitamin C, hydroxychloroquine, ivermectin and azithromycin<br /> https://doi.org/10.5281/zen...

COVID-19: Famotidine, Histamine, Mast Cells, and Mechanisms<br /> www.researchsquare.com/arti...

Vaccines and Biologics injury table based on mechanistic evidence – Feb 2020 Covering over 125 conditions<br /> https://doi.org/10.5281/zen...

On 2020-06-10 13:15:34, user Dallas Weaver wrote:

Interesting paper but the results on masks was very surprising and possibly wrong.

If we look at health care workers (HCW) the personal protective equipment (PPE) consists of masks and outer garments along with shoe covers. For most activities and interactions with infected people more extreme PPE isn't required.

If we look at the 2 million known (tested) cases in the US and note that about 200,000 of those people ended up in the health care system and also note than only about 10,000 HCW have became infected when in direct high-intensity contact with those 200,000 cases entering the system, the effective reproduction of the virus in the culture of PPE utilization is R= <0.05. As all infections including early infections of HCW and infections outside of the health case system are included, it is clear that PPE works very effectively.

With all external garments and masks being sanitized with respect to this virus by heating to greater than 60ºC for 30 minutes, a citizens version of PPE will work the same as it does for HCW. Home ovens will do the job against this virus allow re-use of PPE. No damage on N95 masks up to 100ºC.

If masks don't work like this analysis claims, why does the WHO, CDC, FDA and the rest of the people who claim N-95 masks from industry (90% of the market for masks in normal times) must be diverted to HCW, increasing the health risk for industrial workers? Your results seem to say masks don't work for civilians but do for HCW which seem to be magical thinking.

On 2020-06-11 04:56:01, user Sinai Immunol Review Project wrote:

Main findings<br /> Despite similarities in presentation at onset, differences in the underlying immunopathology of SARS-CoV-2 infections and other respiratory infections, like influenza, remain largely unknown. In this pre-print, Mudd et al. performed single-cell RNA sequencing (scRNAseq) of PBMCs from COVID-19 patients and influenza patients, in order to delineate potential key differences between the aforementioned respiratory infections. Analyses were performed using a cohort of 79 COVID-19 patients (n=79; 35 of whom developed acute respiratory failure), 26 influenza patients (n=26; 7 of whom developed acute respiratory failure), and 15 healthy controls (n=15

First, plasma cytokine levels were evaluated in the 79 COVID-19 patients, 26 influenza patients, and 8 of the healthy controls. Cytokine analyses identified a reduced production of GM-CSF, IFN-?. and IL-9 but a significant elevation of IL-6 and IL-8 across all COVID-19 patients, compared to influenza patients. In fact, certain chemokines and others were more up-regulated in influenza patients, as opposed to COVID-19 patients. The authors subsequently performed a computational assessment of whether certain groups (or modules) of cytokines were predictive of one of two infections.

Interestingly, two modules, containing G-CSF, IFN-?, IL-2R, IL-6, IL-8, and MCP-1 (among several others), were inversely correlated with an increased likelihood of being SARS-CoV-2-positive. The authors observe that while higher generalized inflammation is characteristic of influenza patients, COVID-19 patients exhibit a marked elevation of a distinct subset of cytokines. Using intubation status and expiration as end-markers of disease severity, the authors found that IL1-RA and IL-6 were associated with COVID-19 disease severity and predictive of poor outcome, both with and without comparison to influenza patients. Collectively, these results suggest that only a selection of inflammatory cytokines are predictive of disease severity, while cytokine storm syndrome is not necessarily descriptive of all COVID-19 patients; these characterizations distinguish COVID-19 immunopathology from that of influenza.

PBMCs had been collected from 79 COVID-19 patients (n=79; 35 of whom developed acute respiratory failure), 26 influenza patients (n=26; 7 of whom developed acute respiratory failure), and 15 healthy controls (n=15). A comparison of the peripheral immune landscape identified several primary differences. Though both groups of patients exhibited pan-lymphopenia, generally, COVID-19 patients had more antibody-secreting plasmablasts and activated CD4+ T cells than influenza patients or controls. However, COVID-19 patients showed significantly reduced numbers of circulating monocytes, in line with previous reports that inflammatory monocytes are recruited to the lung and reduced in the periphery in COVID-19 patients. Notably, both these peripheral monocytes and CD4+ T cells in COVID-19 patients showed reduced HLA-DR expression, indicative of reduced activation.

A closer interrogation of potential immuno-regulatory cell types (as a compensatory response to the hyper-inflammation observed in COVID-19 patients) via scRNAseq (of 3 COVID-19 patients [n=3], 3 influenza patients [n=3], and 1 healthy control) revealed a significantly suppressed type I interferon (IFN) response among B cells, CD8+ T cells, regulatory T cells, plasmacytoid dendritic cells (pDCs), and especially among monocytes. In contrast, his pathway and its associated downstream cascades were enriched in influenza patients. Notably, pathways enriched in COVID-19 patients were glucocorticoid and metabolic stress pathways across multiple cell types, but most significantly in monocytes.

Limitations<br /> Technical<br /> The limited patient sample size of the scRNAseq analysis should be noted.

Biological<br /> Without additional clinical information, it is difficult to know whether relative time-points (at which blood samples were collected and cytokine analyses were performed) may have been different, so an analysis of patients at different stages of their disease course may be a confounding factor. Indeed, the authors make some reference to this potential limitation, in addition to age, in their linear regression models.

In addition, the authors use HLA-DR expression to evaluate myeloid cell activation; other markers should be used to validate the observation of reduced HLA-DR expression. This reduced activation phenotype, in combination with the fewer number of monocytes in the periphery and down-regulated IFN response, provides the basis for the authors' conclusion that an overall suppressed monocyte response underscores COVID-19 immunopathology, when compared to the immune profile of influenza patients. However, it is important to consider the recruitment of the inflammatory subset of monocytes to the lung or other extrapulmonary organs as a reason for the reduced number in the vasculature.

Significance<br /> Through a much needed comparison, Mudd et al. provide a closer look at the cellular differences between the immune response to COVID-19 and influenza. Using scRNAseq, the authors identify notable changes in monocyte transcriptional activity and number and in cytokine profiles that suggest potential associations with disease severity of COVID-19, but not influenza. In particular, the identification of a glucocorticoid response in monocytes is worth further investigation, given previous claims towards the use of immunosuppressive agents to treat COVID-19.

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.

On 2020-06-11 16:22:18, user Keith Gentile wrote:

Is this the study used by the CDC to estimate the covid IFR?

On 2020-06-13 23:46:41, user Norbert Bujtas wrote:

We have the missing component: a water soluble iodine complex! Edible and pure - patented. <br /> We have been waiting for the in vitro test fro 10 weeks but this research shows that we are on the right track.

On 2020-06-14 14:46:08, user wbgrant wrote:

It appears that the dose of 1000 IU/d vitamin D is insufficient to raise 25(OH)D concentration significantly. See<br /> Heaney R et al. Human Serum 25-hydroxycholecalciferol Response to Extended Oral Dosing With Cholecalciferol. Am J Clin Nutr. 2003 Jan;77(1):204-10. doi: 10.1093/ajcn/77.1.204.<br /> and<br /> Grant WB, Baggerly CA, Lahore H. Reply: “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...

On 2020-06-16 00:52:47, user Wen Minneng wrote:

A question is really difficult to answer: Will the COVID-19 pandemic show a upward or a downward trend in the southern hemisphere in the coming winter?

On 2020-06-17 07:34:31, user Nicolas Gambardella wrote:

The final paper is here: https://www.sciencedirect.c...

It would be good form to update the preprint.

On 2020-06-17 14:38:08, user Paul Gordon wrote:

A great piece of work! Could you please add the list of all 452 Scottish viral genomes use in the analysis? Only 18 are listed in the supplemental data. Thank you!

On 2020-06-18 12:11:32, user Marcus wrote:

Hi, I had the error using my data:<br /> Maximum number of function evaluations exceeded: increase options.MaxFunctionEvaluations.

Can you help me?

On 2020-06-18 18:59:17, user mike wrote:

I read a earlier MSM version of this report last week. Somehow the firm conclusion was that none of the recorded infections studied were come by by touching " infected " surfaces. I can't, even reading between the lines, find that conclusion in the pdf.

On 2020-06-18 21:46:00, user Paul Gordon wrote:

Very interesting work, thanks for posting. I'm wondering if you can explain slight discrepancies in the # viral genomes sequenced. The text says 49, while there appear to be 48 red dots in the figure, and 47 'CZB' genomes matching the collection dates (March 25-28, May 6-7) in GISAID. Maybe an overlaid (non-offset) red dot? Two genomes refused by GISAID due to issues, or alternate names perhaps? Thanks for any insight you can provide.

On 2020-06-19 03:12:29, user AMM wrote:

1. I would like to know if serological tests were performed on the 118 healthcare workers who did have COVID, and what that data shows.

2. The methods state that 2 separate tests for IgG/M were used: MCLIA and colloidal gold. Were both used on all subjects, did some subjects get just one or the other? This is important because they have different sensitivities and specificities.

3. The paper states that the LAST TEST RESULT for each person was used (for RT-PCR and IgG/M). For the hospitalized COVID patients, did they ever have a positive test? If so, when?

4. You assume that all COVID+ patients must have developed the antibodies, but 10% lost them. You base this from a paper that said 100% of patients developed IgG by day 17-19. However, the population tested in that paper has much milder cases than in your population. It would not be wise to assume that every patient develops antibodies from a limited population study.

5. It could easily be that the 10% of hospitalized COVID patients who tested negative for IgG/M just represent the false negatives. The colloidal gold test you used only has a sensitivity of 88.6%. So a 10% false negative would be expected from that test.

6. You cited another research article that tested for IgG in 1021 people returning to work. This was also in Wuhan. They found 10% of their population tested positive for antibodies, while your general population group showed 4.6% positive. I believe this is more reason to perform your own sensitivity tests of the kit you used.

7. One observation not mentioned about your data is that it shows 4% of healthcare workers (who had much more exposure to COVID) tested positive for IgG, compared to 4.6% of non-healthcare workers. One would expect their numbers to be higher, not lower, than non-healthcare workers.

I do not believe there is sufficient scientific evidence here to support the claim that “after SARS-CoV-2 infection, people are unlikely to produce long-lasting protective antibodies against this virus.”

On 2020-06-19 07:54:01, user Dr. Sebastian Boegel wrote:

Thank you very much for this huge community effort and the very nice results. Congrats to the team. This is a very important study and in analogy to what have been proposed in cancer a while ago: https://www.ncbi.nlm.nih.go...

I have a couple of questions:<br /> 1.) I am not sure, if I understand that right: the clusters are derived from patients with immunemodulating treatment, such as glucocortocoids, MMF, etc.. In order to make sure that the defined clusters reflect the underlying disease and not the medication, you applied the same model to newly diagnosed patients, of which only a minority received prior treatment. And what you find is roughly the same proportions of diseases in each module. Is that right? If not, my question is: could you describe clearer why you think that these groups reflect the disease itself and not the treatment.

2.) If 1.) is correct, than i am wondering, that untreated and treated patients cluster in the same way as I would except that immunemodulating treatment affects gene expression of many, esp. immune related genes, systemically, such that the blood transcriptome ist totally different. How do you explain that?

3.) In the last sentence of the discussion, you wrote that this study will be usefule for a personalized medicine. From a clinical point of view, can you describe how this will help (maybe some examples, what does that study mean for a clinician? and for a diagnostics company?)

4.) This is a multi center study. How did you normalize the sequencing data, such that the data doesnt cluster according to site? Did you check that? See also TCGA or GTEX.

5.) How and when is it possible to access the raw data? Will RNA-Seq fastqs also shared? And are clinical information for each patient available?

Thanks again for this very informative and well structured study. I acknowledge the hard work. This will be )once published peer reviewed) a seminal study in this field.

Sebastian

On 2020-07-12 14:50:03, user Sabbir Ferdoush wrote:

RNA-extraction free SARS-CoV-2 RT-qPCR .... Do you know which are the manufacturer.

On 2020-06-21 12:42:23, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-20 13:11:39, user Raquel Rabionet wrote:

Hi, nice work.

it's interesting that there is such a clear difference in methylation of a single CpG between the V30M carriers and those of other variants in TTR gene. Have you considered that this V30M variant that is so highly associated to the cg13139646 site (located in TTR exonic region) is really close to this site? Based on your table, the cg site is at chr18:29172936, while the V30M variant is at chr18:27172937, just one base pair away. I wonder if there might be some kind of interference with the detection?

If so, maybe you could confirm the methylation at this site using other methods?

On 2020-04-03 21:24:44, user Sinai Immunol Review Project wrote:

SUMMARY: The authors used bioinformatics tools to identify features of ACE2 expression in the lungs of different patent groups: healthy, smokers, patients with chronic airway disease (i.e., COPD) or asthma. They used gene expression data publicly available from GEO that included lung tissues, bronchoalveolar lavage, bronchial epithelial cells, small airway epithelial cells, or SARS-Cov infected cells.

The authors found no significant differences in ACE2 expression in lung tissues of Healthy, COPD, and Asthma groups (p=0.85); or in BAL of Healthy and COPD (p=0.48); or in epithelial brushings of Healthy and Mild/Moderate/Severe Asthma (p=0.99). ACE2 was higher in the small airway epithelium of long-term smokers vs non-smokers (p<0.001). Consistently, there was a trend of higher ACE2 expression in the bronchial airway epithelial cells 24h post-acute smoking exposure (p=0.073). Increasing ACE2 expression at 24h and 48h compared to 12h post SARS-Cov infection (p=0.026; n=3 at each time point) was also detected.

15 lung samples’ data from healthy participants were separated into high and low ACE2 expression groups. “High” ACE2 expression was associated with the following GO pathways: innate and adaptive immune responses, B cell mediated immunity, cytokine secretion, and IL-1, IL-10, IL-6, IL-8 cytokines. The authors speculate that a high basal ACE2 expression will increase susceptibility to SARS-CoV infection.

In 3 samples SARS-Cov infection was associated with IL-1, IL-10 and IL-6 cytokine production (GO pathways) at 24h. And later, at 48h, with T-cell activation and T-cell cytokine production. It is unclear whether those changes were statistically significant.

The authors describe a time course quantification of immune infiltrates in epithelial cells infected with SARS-Cov infection. They state that in healthy donors ACE2 expression did not correlate with the immune cell infiltration. However, in SARS-Cov samples, at 48h they found that ACE2 correlated with neutrophils, NK-, Th17-, Th2-, Th1- cells, and DCs. Again, while authors claim significance, the corresponding correlation coefficients and p-values are not presented in the text or figures. In addition, the source of the data for this analysis is not clear.

Using network analysis, proteins SRC, FN1, MAPK3, LYN, MBP, NLRC4, NLRP1 and PRKCD were found to be central (Hub proteins) in the regulating network of cytokine secretion after coronavirus infection. Authors conclude this indicates that these molecules were critically important in ACE2-induced inflammatory response. Additionally, authors speculate that the increased expression of ACE2 affected RPS3 and SRC, which were the two hub genes involved in viral replication and inflammatory response.

LIMITATIONS: The methods section is very limited and does not describe any of the statistical analyses; and description of the construction of the regulatory protein networks is also limited. For the findings in Figures 2 authors claim significance, which is not supported by p-values or coefficients. For the sample selection, would be useful if sample sizes and some of the patients’ demographics (e.g. age) were described. <br /> For the analysis of high vs low ACE2 expression in healthy subjects, it is not clear what was the cut off for ‘high’ expression and how it was determined. Additionally, further laboratory studies are warranted to confirm that high ACE2 gene expression would have high correlation with the amount of ACE2 protein on cell surface. For the GO pathway analysis significance was set at p<0.05, but not adjusted for multiple comparisons. <br /> There were no samples with SARS-CoV-2 infection. While SARS-Cov and SARS-CoV-2 both use ACE2 to enter the host cells, the analysis only included data on SARS-Cov and any conclusions about SARS-CoV2 are limited.

Upon checking GSE accession numbers of the datasets references, two might not be cited correctly: GSE37758 (“A spergillus niger: Control (fructose) vs. steam-exploded sugarcane induction (SEB)” was used in this paper as “lung tissue” data) and GSE14700 (“Steroid Pretreatment of Organ Donors to Prevent Postischemic Renal Allograft Failure: A Randomized, Controlled Trial” – was used as SARS-Cov infection data).

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.

On 2020-04-04 01:47:57, user Criticalheritage wrote:

From Tokyo Japan. Do you think low number of death rate in Japan is related to the BCG ? If so it is a good news to save the world. BCG production should be started immediately and given to the babies and possible patients in North America and Europe.

On 2020-03-29 23:18:26, user MVianna wrote:

This is quite interesting. I was wondering, however, if you have taken into consideration the onset date of the infection in each country. Because when you consider the number of deaths per mi habitant you may be comparing data from countries with different stages of the contamination. It seems that lower income countries are going to be affected later in time and this could significantly impact the effect you observed in Figure 1.

On 2020-04-04 18:15:08, user clem mcdonald Jr wrote:

If you or anyone has infection rates broken down by people in inner cabines only ventilated by the ships airconditioner verus those who had an open balconey-- where we could presume most of those people spent much time, we could ice the question as to whether the airducts were spreading the disease. It is well known that the airconsitioning systems have meager filtering ( only blocking large particles > 100 microns. (In contrast airplanes have HEPA filters ( which stop 3 micron and smaller particles). influenza eppidemics also have occured on shipd.

With detailed information about he kind of cabine (interior os seaside) we could know whether the airvcondiationing was the cupret. If it was would expect that the infection rate would be higher among inner cabin passengers.

Thanks<br /> h

On 2020-04-04 20:34:04, user Sinai Immunol Review Project wrote:

Title: First Clinical Study Using HCV Protease Inhibitor Danoprevir to Treat Naïve and Experienced COVID-19 Patients

Keywords: Clinical study – HCV protease inhibitor – Danoprevir – Ritonavir – Covid19 treatment

Main findings:<br /> The authors treated 11 Covid-19 patients with Danoprevir, a commercialized HCV protease inhibitor [1], boosted by ritonavir [2], a CYP3A4 inhibitor (which enhances the plasma concentration and bioavailabilty of Danoprevir). Two patients had never received anti-viral therapy before (=naïve), whereas nine patients were on Lopinavir/Ritonavir treatment before switching to Danoprevir/Ritonavir (=experienced). The age ranged from 18 to 66yo.<br /> Naïve patients that received Danoprevir/Ritonavir treatment had a decreased hospitalization time. Patients treated with Lopinavir/Ritonavir did not have a negative PCR test, while after switching to Danoprevir/Ritonavir treatment, the first negative PCR test occurred at a median of two days.

Limitations:<br /> The results of the study are very hard to interpret as there is no control group not receiving Danoprevir/Ritonavir treatment. This was especially true in naïve patients who seemed to have more mild symptoms before the start of the study and were younger (18 and 44yo) compared to the experienced patients (18 to 66yo). The possibility that the patients would have recovered without Danoprevir/Ritonavir treatment cannot be excluded.

Relevance:<br /> The authors of the study treated patients with Danoprevir, with the rational to that this is an approved and well tolerated drug for HCV patients [2], and that it could also target the protease from SARS-CoV-2 (essential for viral replication and transcription). Indeed, homology modelling data indicated that HCV protease inhibitors have the highest binding affinity to Sars-Cov2 protease among other approved drugs [3]. <br /> While this study shows that the combination of Danoprevir and Ritonavir might be beneficial for Covid-19 patients, additional clinical trials with more patients and with better methodology (randomization and control group) are needed to make further conclusions.

1. Seiwert SD, Andrews SW, Jiang Y, et al. Preclinical Characteristics of the Hepatitis C Virus NS3/4A Protease Inhibitor ITMN-191 (R7227). Antimicrob Agents Chemother. 2008;52(12):4432-4441. doi:10.1128/AAC.00699-08
2. Xu X, Feng B, Guan Y, et al. Efficacy and Safety of All-oral, 12-week Ravidasvir Plus Ritonavir-boosted Danoprevir and Ribavirin in Treatment-naïve Noncirrhotic HCV Genotype 1 Patients: Results from a Phase 2/3 Clinical Trial in China. J Clin Transl Hepatol. 2019;7(3):213-220. doi:10.14218/JCTH.2019.00033
3. Nguyen DD, Gao K, Chen J, Wang R, Wei G-W. Potentially highly potent drugs for 2019-nCoV. bioRxiv. February 2020:2020.02.05.936013. doi:10.1101/2020.02.05.936013

Review by Emma Risson part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-04-04 22:53:46, user Dick Stern wrote:

I believe these forecasts are terribly deceiving! The death tolls the projections in Florida for example seem incredibly underreported. Even in the best case of all assumptions Florida could have death tolls of over 100,000. They have not accepted social distancing, are not staying home,: they are not flattening the curve. With the number of seniors over 65 at approximately 4,465,169 this is a disaster waiting to happen.<br /> The death toll for people over 65 exposed to the Coronavirus Sars 2 is around 8%. That means if half the population of the elderly must be exposed to Covid-19 before herd immunity starts to kick in. <br /> Then the death toll in just this age group could be 176,000 or more. Alternatively, a very conservative death rate is 1% of the general population of over 21 million would lead you to believe that there will be 210,000 deaths!<br /> I also don't see a cumulative nature of ventilator usage. A patient must stay on a ventilator for several days. It can be as long as 15 days. How come the ventilation equipment usage curve is growing slowly. In fact you will not kick someone off ventilators until after the 5 to 15 days of usage. The growth of the ventilators required should be be closer to a logarithmic growth curve and then flatting at the limit of number of ventilation tubes available.

On 2020-04-01 12:23:21, user Semper Explorans wrote:

Where is the exact methodology for the statistical model? What are the assumptions? What specific numbers and percentage rates for Ro, herd immunity are used? How is the data collected? While I do not doubt the exact gravity of the situation, and we must prepare for the worst, as both a doctor in the frontlines and as a researcher trained in evidence-based research, I need to establish whether a model is sound before I believe any of its conclusions. Incorrect data and/or incorrect assumptions or methodology leads to inaccurate numbers and conclusions.

On 2020-04-02 02:14:17, user Van Hovenga wrote:

Correct me if I'm wrong but this seems to be a very crude model. Even if the data, assumptions, and methodology is sound, all of the models parameters are assumed to be static. Also, they fit data that is currently exponential to a sigmoid function to generate predictions. Even though there has been some observed location specific inflection points, I feel that there still has not been enough data collected past these points to make reliable predictions based off of curve fitting alone. It seems to me that the data and information about the virus is far to sparse currently to generate accurate statistical predictions. I really wish more light was being shed on the stochastic models that have been recently developed that account for the dynamics of the disease spread.

On 2020-04-02 18:46:04, user Kristopher Purens wrote:

Does anyone have their previous data downloads? They do not appear to be sharing old predictions. Validating their previous models against real data would be very valuable to understand the strengths and weaknesses of their model and so it is baffling that is not being provided clearly.

On 2020-04-05 18:23:54, user Sinai Immunol Review Project wrote:

Summary: Authors evaluate clinical correlates of 10 patients (6 male and 4 female) hospitalized for severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). All patients required oxygen support and received broad spectrum antibiotics and 6 patients received anti-viral drugs. Additionally, 40% of patients were co-infected with influenza A. All 10 patients developed lymphopenia, two of which developed progressive lymphopenia (PLD) and died. Peripheral blood (PB) lymphocytes were analyzed – low CD4 and CD8 counts were noted in most patients, though CD4:CD8 ratio remained normal.

Critical analysis: The authors evaluated a small cohort of severe SARS-CoV-2 cases and found an association between T cell lymphopenia and adverse outcomes. However, this is an extremely small and diverse cohort (40% of patients were co-infected with influenza A). These findings need to be validated in a larger cohort. Additionally, the value of this data would be greatly increased by adding individual data points for each patient as well as by adding error bars to each of the figures.

Significance: This study provides a collection of clinical data and tracks evolution of T lymphocyte in 10 patients hospitalized for SARS-CoV-2, of which 4 patients were co-infected with influenza A.

Review by Katherine E. Lindblad as part of a project of students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai.

On 2020-04-05 22:02:35, user WhiskersInCalif wrote:

Some are seeing math that adds up to 111% in total?<br /> That might be addressed in the final peer reviewed note or a link to <br /> help media answer this type of question.

The issue of long term lung damage is important for COVID-19.<br /> Keep on with the hard work.

On 2020-04-07 20:57:35, user Xavier de Roquemaurel wrote:

The BCG strain does have an impact on the efficiency of the immunity. Could you run the study one step further and identify if the Tokyo-172 strain (or another strain) has a higher response against Covid-19?<br /> Data from Taiwan, Japan and Malaisia seem to say so. It needs now a scientific evaluation. Thanks. X

On 2020-04-08 16:52:12, user Winton Gibbons wrote:

The data appear to be from middle of March. How does the analysis change, at least at a gross level, given what has happened over the intervening 3 weeks and counting?

It seems that perhaps even the lowest prevalence case is likely to be an overestimate, perhaps due to a) both local and statewide interventions, b) the specific, regional population structure and demographics, and c) additional hidden variables related to disease transmission.

On 2020-04-09 19:39:41, user Harold Smith wrote:

What dosages of the drugs were used (on a body weight basis)? How did the patients' QT intervals correlate with potassium levels?

On 2020-04-10 04:47:04, user SAJIV RAJASEKARAN wrote:

Fumigation of all the Covid19 positive isolation rooms atleast once in two days might prove successful... Importance of fumigation has been documented?

On 2020-03-28 16:52:02, user Ben Auxier wrote:

I have sent the following questions to the authors by email:

=============================================================

Hello Dr. Santarpia,

I just finished reading your preprint, and I was wondering if you could clarify the following:

1. Most of the samples had RNA copy numbers of 0.1-0.5 /uL. If I am <br /> performing the back caclulations properly, this means the ct value was <br /> between 37 and 41. What was the ct value of the negative controls, or <br /> did the never reach detection threshold?

2. I cannot find any information regarding negative control samples. I see<br /> that you used no template controls, but I do not see for example a swab<br /> of the inside of a sterile container inside the hospital room to <br /> control for contamination during sampling itself and subsequent sample <br /> processing.

3. I do not know if there is an error in the calculations for your table <br /> (labelled as Figure 2), but almost all of your values have SD that <br /> overlap zero. Additionally I notice that your Figure 1A axis cuts off at<br /> zero, which fails to show the SD values overlapping zero. While I agree<br /> there will not be negative copies of virus in your sample, I think <br /> these SD values show something important about your measurement accuracy<br /> and precision.

I have posted these as a comment on the MedRxiv article itself if you would rather respond there.

-Thanks for your time

Ben

On 2020-04-10 17:40:13, user Sinai Immunol Review Project wrote:

Key findings<br /> The authors investigated the use of a commercially available form of heparin, low molecular weight heparin (LMWH), as a therapeutic drug for patients with COVID-19. Previous studies showed that in addition to its anticoagulant properties, LMWH exerts anti-inflammatory effects by reducing the release of IL-6 and counteracting IL-6.

This was a retrospective single-center study conducted in Wuhan, China. Forty-two (42) hospitalized patients with coronavirus pneumonia were included, of which 21 underwent LMWH treatment (heparin group) and 21 did not (control). The general characteristics of the two groups of patients were statistically comparable. Both control and LMWH had the same hospitalization time and there were no critical cases in either group.

This study found that treatment with LMWH significantly reduced IL-6 levels in patients in the heparin group compared to the control group. However, LMWH treatment did not have an effect on the levels of other inflammatory factors: CRP, IL-2, IL-4, IL-10, TNF-?, and IFN-?. Compared with the control group, patients in the heparin group had a significantly increased percentage of lymphocytes after treatment, further suggesting that LMWH treatment has anti-inflammatory effects and can reduce the lymphopenia associated with COVID-19.

Consistent with other studies in COVID-19 patients, they found that LMWH treatment can improve hypercoagulability. D-dimer and FDP levels (biomarkers of coagulation) in the heparin group significantly decreased from baseline after treatment, whereas there was no significant change in levels for the control group. Of note however, patients in the heparin group had a significantly higher level of D-dimer and FDP at baseline compared to the control group.

Importance<br /> Many studies have shown that severely ill COVID-19 patients have significantly higher levels of IL-6 compared to patients with mild cases and it has been proposed that progression to severe disease may be caused by lymphopenia and cytokine storms. The anti-inflammatory effects of heparin may help prevent or reverse a cytokine storm caused by the virus and thus delay COVID-19 progression and improve overall condition in patients. The pleiotropic effects of heparin may have a greater therapeutic effect than compounds that are directed against a single target, such as an anti-IL-6 therapy. This is because COVID-19 patients commonly have complications such as coagulopathy and endothelial dysfunction leading to cardiac pathology that may be mitigated by heparin treatment (Li J, et.al; Wojnicz et.al).

Limitations<br /> This study is limited by a small sample size (n=44) and a single-center design. Double-blinded, randomized, placebo controlled clinical trials of LMWH treatment are needed to understand the possible benefit of the treatment. Additionally, this study was unable to control for the dose and days of treatment of LMWH. Identifying the correct dose and timing of LMWH is a matter of immediate interest. Of note, patients in the heparin group received two types of LMWH, enoxaparin sodium or nadroparin calcium, which have been reported to have differing anticoagulant activity. The use of different LMWHs in the heparin group warrants further explanation.

Another caveat of this study is that the levels of D-dimer and fibrinogen degradation products were significantly higher at baseline for patients in the heparin group compared to those in the control group. Therefore, it is difficult to decipher whether some of the positive effects of heparin treatment were due to its anti-coagulation effects or direct anti-inflammatory effects. Future studies are that delineate the anti-inflammatory functions of heparin independently of its anticoagulant properties in cases of COVID-19 would be useful.

Lastly, this study did not discuss any side-effects of heparin, such as the risk of bleeding. Moreover, coagulation can help to compartmentalize pathogens and reduce their invasion, therefore anticoagulant treatments like heparin may have risks and it remains to be determined which patients would benefit from this therapy.

References:<br /> Li J, Li Y, Yang B, Wang H, Li L. Low-molecular-weight heparin treatment for acute lung injury/acute respiratory distress syndrome: a meta-analysis of randomized controlled trials. Int J Clin Exp Med 2018;11(2):414-422

Wojnicz R, Nowak J, Szygula-Jurkiewicz B, Wilczek K, Lekston A, Trzeciak P, et al. Adjunctive therapy with low-molecular-weight heparin in patients with chronic heart failure secondary to dilated cardiomyopathy: oneyear follow-up results of the randomized trial. Am Heart J. 2006;152(4):713.e1-7

Review by Jamie Redes as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-04-15 19:06:22, user Greg Lambert wrote:

For ultraviolet disinfection the study uses a UVC lamp (260-285nm) but measures the power output with a UVAB meter (which measures 280-400nm) hence the N95s are being exposed to a much, much higher UV power level than is stated in the paper.

On 2020-04-16 14:40:22, user David Steadson wrote:

The paper reports "Observed cumulative death counts are illustrated as circles (O) and amount to 62 by the 25th of March, 2020"

As of April 15 2020, the Swedish Public Health Authority is reporting cumulative deaths of 106 as of that date.

On 2020-03-21 08:30:14, user Yuxin Wu wrote:

This study assumes that:

Treatment has minor influence on outcome The provided healthcare in countries is comparable. For developed countries such as Italy and South Korea, it is assumed that the population has similar access to treatment. The death rates reported by age group are thus applicable in all countries.

and then it applies the death rate in one country (South Korea) to other countries. However, the assumption is terribly wrong.

As a matter of fact, the population in different countries/regions of countries have very different access to treatment. In particular, population in areas like Hubei, China and Italy do not have access to as much medical resources as other places since the medical system is overwhelmed.

One evidence. This is what a Lancet report from Italy says about Italy:

Intensive care specialists are already considering denying life-saving care to the sickest and giving priority to those patients most likely to survive when deciding who to provide ventilation to.

Another evidence. Taking today's number, China has a fatality rate of 3139/67800=4.6% in Hubei, but a fatality rate of 122/13639=0.9% outside Hubei.

This false assumption leads to a drastic overestimation of actual cases, in areas that suffer the outbreak the most, namely Iran, China and Italy, shown by Table 2 in this preprint.

On 2020-03-25 05:16:38, user Sinai Immunol Review Project wrote:

Summary: Based on a retrospective study of 162 COVID patients from a local hospital in Wuhan, China, the authors show an inverse correlation between lymphocyte % (LYM%) of patients and their disease severity. The authors have also tracked LYM% of 70 cases (15 deaths; 15 severe; 40 moderate) throughout the disease progression with fatal cases showing no recovery of lymphocytes ( <5%) even after 17-19 days post-onset. The temporal data of LYM % in COVID patients was used to construct a Time-Lymphocyte% model which is used to categorize and predict patients’ disease severity and progression. The model was validated using 92 hospitalized cases and kappa statistic test was used to assess agreement between predicted disease severity and the assigned clinical severity (k = 0.49).

Limitations: Time-Lymphocyte % Model (TLM) that authors have proposed as a predictive model for clinical severity is very simple in its construction and derives from correlative data of 162 patients. In order for the model to be of use, it needs validation using a far more robust data set and possibly a mechanistic study on how COVID leads to lymphopenia in the first place. In addition, it should be noted that no statistical test assessing significance of LYM % values between disease severities was performed.

Significance of the finding: This article is of limited significance as it simply reports similar descriptions of COVID patients made in previous literature that severe cases are characterized by lymphopenia.

On 2020-03-25 14:25:55, user Sinai Immunol Review Project wrote:

Summary<br /> The use of heat inactivation to neutralize pathogens in serum samples collected from suspected COVID-19 patients reduces the sensitivity of a fluorescent immunochromatographic assay to detect anti-SARS-CoV-2 IgM and IgG.

Major findings<br /> Coronaviruses can be killed by heat inactivation, and this is an important safety precaution in laboratory manipulation of clinical samples. However, the effect of this step on downstream SARS-CoV-2-specific serum antibody assays has not been examined. The authors tested the effect of heat inactivation (56 deg C for 30 minutes) versus no heat inactivation on a fluorescence immunochromatography assay. Heat inactivation reduced all IgM measurements by an average of 54% and most IgG measurements (22/36 samples, average reduction of 50%), consistent with the lower thermal stability of IgM than that of IgG. Heat inactivation caused a subset of IgM but not IgG readings to fall below a specified positivity threshold.

Limitations<br /> Limitations included the use of only one type of assay for testing heat inactivated vs non-inactivated sera, and the use of the same baseline for heat inactivated and non-inactivated sera. The results indicate that heat inactivation affects the quantification of SARS-CoV-2-antibody response, specially IgM, but still allows to distinguish positive specific IgG. Therefore, the effect of heat inactivation should be studied when designing assays that quantitatively associate immunoglobulin levels (especially IgM) to immune state.

Review by Andrew M. Leader as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-03-25 15:27:05, user Sinai Immunol Review Project wrote:

While RT-PCR is being used currently to routinely diagnose infection with SARS-CoV-2, there are significant limitations to the use of a nucleic acid test that lead to a high false-negative rate. This article describes ELISAs that can measure IgM and IgG antibodies against the N protein of SARS-CoV-2 to test samples from 238 patients (153 positive by RT-PCR and 85 negative by RT-PCR) at different times after symptom onset. The positivity rate of the IgM and/or IgG ELISAs was greater than that of the RT-PCR (81.5% compared to 64.3%) with similar positive rates in the confirmed and suspected cases (83% and 78.8%, respectively), suggesting that many of the suspected but RT-PCR-negative cases were also infected. The authors also found that the ELISAs have higher positive rates later after symptom onset while RT-PCR is more effective as a diagnostic test early during the infection.

The authors make a strong case for using a combination of ELISA and RT-PCR for diagnosis of infection with SARS-CoV-2, especially considering the dynamics of positivity rates of RT-PCR and ELISA. Fewer false-negative diagnoses would improve infection control and patient management.

On 2020-03-25 17:03:41, user Sinai Immunol Review Project wrote:

Summary: Most common chronic conditions among 25 patients that died from COVID-19 related respiratory failure were hypertension (64%) and diabetes (40%). Disease progression was marked by progressive organ failure, starting first with lung dysfunction, then heart (e.g. increased cTnI and pro-BNP), followed by kidney (e.g. increased BUN, Cr), and liver (e.g. ALT, AST). 72% of patients had neutrophilia and 88% also had lymphopenia. General markers of inflammation were also increased (e.g. PCT, D-Dimer, CRP, LDH, and SAA).

Limitations: The limitations of this study include small sample size and lack of measurements for some tests for several patients. This study would also have been stronger with comparison of the same measurements to patients suffering from less severe disease to further validate and correlate proposed biomarkers with disease severity.

Importance: This study identifies chronic conditions (i.e. hypertension and diabetes) that strongly correlates with disease severity. In addition to general markers of inflammation, the authors also identify concomitant neutrophilia and lymphopenia among their cohort of patients. This is a potentially interesting immunological finding because we would typically expect increased lymphocytes during a viral infection. Neutrophilia may also be contributing to cytokine storm. In addition, PCT was elevated in 90.5% of patients, suggesting a role for sepsis or secondary bacterial infection in COVID-19 related respiratory failure.

On 2020-03-26 03:58:20, user Eric Kennedy Blatt wrote:

I need help! I am not a scientist or doctor. I have organized a small army of sewers and sourced enough material for up to 15,000 masks, with more coming. I have sources for decommissioned military uniform fabric. Most of what I have now is cotton with polyester, but a lot is 100% cotton - enough for another 2,500 masks. I need to handle safely among the volunteer army, shippers and end-users. Can anyone help with guidance on how long the coronavirus lives on these materials? Any ideas on a sanitizing process or product that can be used instead of wrapping and isolating the fabric for a period of time?

On 2020-03-27 17:09:13, user Peterson Biodiversity Lab wrote:

So one prediction from the models presented by Miguel Bastos Araújo and Babak Naimi was that of low or no local transmission of COVID-19 in humid tropical countries. They stated, "Much of the tropics have low levels of climate suitability for spread of SARS-CoV-2 Coronavirus owing to their high temperatures and precipitation... human populations will likely be spared from outbreaks arising from local transmissions..." Two weeks or so of further data say that that prediction is not robust--rather, it is proving quite wrong. See attached image... source: https://coronavirus.jhu.edu... https://uploads.disquscdn.c...

On 2020-03-29 05:04:16, user Reza Azad wrote:

I am an STANFORD graduate system engineer. Where is the equations for variables and parameters? I like to see equations as they are the KERNEL of any such model and should be rigorous and flawless. reza.azad@alumni.stanford.edu

On 2020-03-29 12:41:54, user Boris wrote:

Great work! But I'd like to look at this at a different angle. Is anyone trying to analyse statistics based on the households (a new household - a new case)? I think this statistics would give much more information about the efficiency of #stayhome-type quarantine measures. Also the "statistic's response" to a new measures will be way faster than the response based on individual case statistics. I'd even introduce an R0_h to understand how effectively COVID-19 jumps to new households at different level of quarantine measures. Is anyone doing such studies?

On 2020-03-30 22:23:50, user Sinai Immunol Review Project wrote:

Study Description

This is a randomized clinical trial of hydroxychloroquine (HCQ) efficacy in the treatment of COVID-19. From February 4 – February 28, 2020 142 COVID-19 positive patients were admitted to Renmin Hospital of Wuhan University. 62 patients met inclusion criteria and were enrolled in a double blind, randomized control trial, with 31 patients in each arm.

Inclusion criteria:<br /> 1. Age >= 18 years<br /> 2. Positive diagnosis COVID-19 by detection of SARS-CoV-2 by RT-PCR<br /> 3. Diagnosis of pneumonia on chest CT <br /> 4. Mild respiratory illness, defined by SaO2/SPO2 ratio > 93% or PaO2/FIO2 ratio > 300 mmHg in hospital room conditions (Note: relevant clinical references described below.)

a. Hypoxia is defined as an SpO2 of 85-94%; severe hypoxia < 85%. <br /> b. The PaO2/FIO2 (ratio of arterial oxygen tension to fraction of inspired oxygen) is used to classify the severity of acute respiratory distress syndrome (ARDS). Mild ARDS has a PaO2/FIO2 of 200-300 mmHg, moderate is 100-200, and severe < 100.

1. Willing to receive a random assignment to any designated treatment group; not participating in another study at the same time

Exclusion criteria: <br /> 1. Severe or critical respiratory illness (not explicitly defined, presumed to be respiratory function worse than outlined in inclusion criteria); or participation in trial does not meet patient’s maximum benefit or safe follow up criteria<br /> 2. Retinopathy or other retinal diseases<br /> 3. Conduction block or other arrhythmias<br /> 4. Severe liver disease, defined by Child-Pugh score >= C or AST > twice the upper limit<br /> 5. Pregnant or breastfeeding<br /> 6. Severe renal failure, defined by eGFR <= 30 mL/min/1.73m2, or on dialysis<br /> 7. Potential transfer to another hospital within 72h of enrollment<br /> 8. Received any trial treatment for COVID-19 within 30 days before the current study

All patients received the standard of care: oxygen therapy, antiviral agents, antibacterial agents, and immunoglobulin, with or without corticosteroids. Patients in the HCQ treatment group received additional oral HCQ 400 mg/day, given as 200 mg 2x/day. HCQ was administered from days 1-5 of the trial. The primary endpoint was 5 days post enrollment or a severe adverse reaction to HCQ. The primary outcome evaluated was time to clinical recovery (TTCR), defined as return to normal body temperature and cough cessation for > 72h. Chest CT were imaged on days 0 and 6 of the trial for both groups; body temperature and patient reports of cough were collected 3x/day from day 0 – 6. The mean age and sex distribution between the HCQ and control arms were comparable.

Findings

There were 2 patients showing mild secondary effects of HCQ treatment. More importantly, while 4 patients in the control group progressed to severe disease, none progressed in the treatment group.<br /> TTCR was significantly decreased in the HCQ treatment arm; recovery from fever was shortened by one day (3.2 days control vs. 2.2 days HCQ, p = 0.0008); time to cessation of cough was similarly reduced (3.1 days control vs. 2.0 days HCQ, p = 0.0016).<br /> Overall, it appears that HCQ treatment of patients with mild COVID-19 has a modest effect on clinical recovery (symptom relief on average 1 day earlier) but may be more potent in reducing the progression from mild to severe disease.

Study Limitations

This study is limited in its inclusion of only patients with mild disease, and exclusion of those on any treatment other than the standard of care. It would also have been important to include the laboratory values of positive RT-PCR detection of SARS-CoV-2 to compare the baseline and evolution of the patients’ viral load.

Significance

Despite its limitations, the study design has good rigor as a double blind RCT and consistent symptom checks on each day of the trail. Now that the FDA has approved HCQ for treatment of COVID-19 in the USA, this study supports the efficacy of HCQ use early in treatment of patients showing mild symptoms, to improve time to clinical recovery, and possibly reduce disease progression. However, most of the current applications of HCQ have been in patients with severe disease and for compassionate use, which are out of the scope of the findings presented in this trial. Several additional clinical trials to examine hydroxychloroquine are now undergoing; their results will be critical to further validate these findings.

Reviewed by Rachel Levantovsky as a part of a project by students, postdocs and faculty in the Immunology Institute at the Icahn school of Medicine at Mount Sinai.

On 2020-03-31 10:25:53, user ortograf wrote:

difficult comparision of the control and treated groups at inclusion in the absence of PCR results.<br /> See <br /> https://annals.org/aim/full...<br /> for comments on influence of this parameter in the evolution of patients in a previously published study.

On 2020-04-01 16:34:11, user Sinai Immunol Review Project wrote:

Study Description <br /> This is a randomized clinical trial of hydroxychloroquine (HCQ) efficacy in <br /> the treatment of COVID-19. From February 4 – February 28, 2020 142 <br /> COVID-19 positive patients were admitted to Renmin Hospital of Wuhan <br /> University. 62 patients met inclusion criteria and were enrolled in a <br /> double blind, randomized control trial, with 31 patients in each arm.

Inclusion criteria:<br /> 1. Age >= 18 years<br /> 2. Positive diagnosis COVID-19 by detection of SARS-CoV-2 by RT-PCR<br /> 3. Diagnosis of pneumonia on chest CT <br /> 4. Mild respiratory illness, defined by SaO2/SPO2 ratio > 93% or <br /> PaO2/FIO2 ratio > 300 mmHg in hospital room conditions (Note: <br /> relevant clinical references described below.)<br /> a. Hypoxia is defined as an SpO2 of 85-94%; severe hypoxia < 85%. <br /> b. The PaO2/FIO2 (ratio of arterial oxygen tension to fraction of inspired<br /> oxygen) is used to classify the severity of acute respiratory distress <br /> syndrome (ARDS). Mild ARDS has a PaO2/FIO2 of 200-300 mmHg, moderate is <br /> 100-200, and severe < 100.<br /> 5. Willing to receive a random assignment to any designated treatment group; not participating in another study at the same time

Exclusion criteria: <br /> 1. Severe or critical respiratory illness (not explicitly defined, <br /> presumed to be respiratory function worse than outlined in inclusion <br /> criteria); or participation in trial does not meet patient’s maximum <br /> benefit or safe follow up criteria<br /> 2. Retinopathy or other retinal diseases<br /> 3. Conduction block or other arrhythmias<br /> 4. Severe liver disease, defined by Child-Pugh score >= C or AST > twice the upper limit<br /> 5. Pregnant or breastfeeding<br /> 6. Severe renal failure, defined by eGFR <= 30 mL/min/1.73m2, or on dialysis<br /> 7. Potential transfer to another hospital within 72h of enrollment<br /> 8. Received any trial treatment for COVID-19 within 30 days before the current study

All patients received the standard of care: oxygen therapy, antiviral <br /> agents, antibacterial agents, and immunoglobulin, with or without <br /> corticosteroids. Patients in the HCQ treatment group received additional<br /> oral HCQ 400 mg/day, given as 200 mg 2x/day. HCQ was administered from <br /> days 1-5 of the trial. The primary endpoint was 5 days post enrollment <br /> or a severe adverse reaction to HCQ. The primary outcome evaluated was <br /> time to clinical recovery (TTCR), defined as return to normal body <br /> temperature and cough cessation for > 72h. Chest CT were imaged on <br /> days 0 and 6 of the trial for both groups; body temperature and patient <br /> reports of cough were collected 3x/day from day 0 – 6. The mean age and <br /> sex distribution between the HCQ and control arms were comparable.

Findings<br /> There were 2 patients showing mild secondary effects of HCQ treatment. More <br /> importantly, while 4 patients in the control group progressed to severe <br /> disease, none progressed in the treatment group.<br /> TTCR was significantly decreased in the HCQ treatment arm; recovery from fever <br /> was shortened by one day (3.2 days control vs. 2.2 days HCQ, p = <br /> 0.0008); time to cessation of cough was similarly reduced (3.1 days <br /> control vs. 2.0 days HCQ, p = 0.0016).<br /> Overall, it appears that HCQ treatment of patients with mild COVID-19 has a modest effect on clinical recovery (symptom relief on average 1 day earlier) but may be more <br /> potent in reducing the progression from mild to severe disease.

Study Limitations <br /> This study is limited in its inclusion of only patients with mild disease, <br /> and exclusion of those on any treatment other than the standard of care.<br /> It would also have been important to include the laboratory values of <br /> positive RT-PCR detection of SARS-CoV-2 to compare the baseline and <br /> evolution of the patients’ viral load.

Significance<br /> Despite its limitations, the study design has good rigor as a double blind RCT <br /> and consistent symptom checks on each day of the trail. Now that the FDA<br /> has approved HCQ for treatment of COVID-19 in the USA, this study <br /> supports the efficacy of HCQ use early in treatment of patients showing <br /> mild symptoms, to improve time to clinical recovery, and possibly reduce<br /> disease progression. However, most of the current applications of HCQ <br /> have been in patients with severe disease and for compassionate use, <br /> which are out of the scope of the findings presented in this trial. <br /> Several additional clinical trials to examine hydroxychloroquine are now<br /> undergoing; their results will be critical to further validate these <br /> findings.

Reviewed by Rachel Levantovsky as a part of a project<br /> by students, postdocs and faculty in the Immunology Institute at the <br /> Icahn school of Medicine at Mount Sinai.

On 2020-04-02 23:06:44, user Zvi Herzig wrote:

The intro to this paper states:<br /> "through a follow-up survey, we found that none of our 80 SLE patients who took long-term oral HCQ had been confirmed to have SARS-CoV-2 infection or appeared to have related symptoms."

On the other hand, COVID-19 Global Rheumatology Alliance reports that "25% of patients who developed a COVID-19 were on HCQ at the time of diagnosis".

https://twitter.com/rheum_c...

On 2020-03-31 07:38:25, user Candido Hernandez Lopez wrote:

The 15 co-infected patients were treated for the chronic hepatitis B? this is an important aspect to clarify

On 2020-03-31 16:51:49, user Parmjeet Randhawa wrote:

I think the subject of this<br /> manuscript deserves further discussion as it has obvious implications for organ<br /> transplant. I would like to make the following points: <br /> Overall, the evidence<br /> presented certainly raises concern about the ability of COVID-19 to infect the kidney.<br /> However, the implications of this finding are such that more rigorous immunohistochemistry<br /> controls are very desirable. Normal kidney and renal sections form unrelated<br /> autopsies showed no antibody staining, but it would be important to rule out non-specific<br /> staining in damaged tissue. I have personally seen such non-specific staining<br /> with C5b-9 antibodies. Therefore, negative controls should include tissues with<br /> acute tubular necrosis and interstitial nephritis not related to COVID-19

Electron microscopy can<br /> certainly be done on paraffin embedded tissue, since viruses survive formalin fixation<br /> quite well. That would make the case watertight and provide really convincing<br /> evidence that COVID-19 can infect tubular epithelium.<br /> To further define the<br /> spectrum of renal injury in COVID-19 infection, the cases with proteuria reported<br /> in this draft manuscript should undergo systematic urine sediment examination<br /> with special reference to presence of inflammatory debris, casts, viral cytopathic<br /> effect and ultrastructural evidence of viral particles.<br /> Even if renal infection is further<br /> confirmed, one should keep in mind that the kidney pathology presented may only<br /> occur in a minority of patients with very severe infection. Renal dysfunction<br /> in the setting of viral pneumonia can be due to pre-renal factors, medications,<br /> cardiac-renal syndrome, and other comorbidities. The risk of transmission of COVID-19<br /> from an unrecognized infected donor to a recipient may be much lower via kidney<br /> compared to lung transplantation.<br /> Further research is needed to quantify that risk in numeric terms and come up<br /> with more precise risk estimates of COVID-19 transmission via organ transplantation.<br /> Parmjeet Randhawa,<br /> Professor of Pathology,<br /> Division of Transplantation Pathology, <br /> University of Pittsburgh and The Thomas E Starzl<br /> Transplantation Institute, <br /> Pittsburgh, PA, USA

On 2020-04-01 13:46:59, user Sinai Immunol Review Project wrote:

Main Findings: This is a simple study reporting clinical characteristics of patients who did not survive COVID-19. All patients (mean age=69.22 years) had acute respiratory distress syndrome (ARDS) and their median time from onset to ARDS was 11 days. The median time from onset to death was 17 days. Most patients were older male (70% male) with co-morbidities and only 11 % were smokers. 75% patients showed bilateral pneumonia. Many patients had chronic diseases, including hypertension (58.33%). cardiovascular disease (22.22%) and diabetes (19.44%). Typical clinical feature measured in these patients includes lymphopenia and elevated markers of inflammation.

Limitations: As noted by the authors, the conclusions of this study are very limited because this is single-centered study focusing on a small cohort of patients who did not survive. Many clinical parameters observed by the authors (such as increase levels of serum CRP, PCT, IL-6) have also been described in other COVID19 patients who survived the infection

Relevance: This study is essentially descriptive and may be useful for clinical teams monitoring COVID19 patients.

Comment by Zafar

On 2020-04-02 23:28:48, user Sinai Immunol Review Project wrote:

Review of paper titled:

Virus shedding patterns in nasopharyngeal and fecal specimens of1COVID-19 patients

Zhang et al. 2020

Keywords: Viral shedding, testing specimens.

Summary

The authors tested feces, urine, plasma, nasal/throat swabs (respiratory samples)<br /> for SARS-CoV-2 virus load analysis by qPCR several days after illness onset<br /> (DAO).

• All plasma samples were negative (n=56).

• Two urine samples were positive from patients that had severe disease and were positive at least 16-21 DAO (n=56).

• 10 of 12 cases (83.3%) tested positive for fecalsamples.

• 14 of 21 cases (66.7%) were positive for respiratory samples.

• Respiratory samples were positive for 21 DAO, nevertheless fecal samples were still positive.

• Median duration of virus shedding was 10.0 days in respiratory samples

• For fecal samples median shedding was 22.0 days for the feces.

• Viral titers of respiratory samples peaked at six to nine DAO. and at 14-18 DAO for fecal samples, and the highest virus titers at the peak was significantly higher for feces (105.8 copies/ml, mean 5623 copies/ml) than of respiratory samples (106.363 copies/ml, mean 2535 copies/ml).

Importance of findings and caveats

Notably, these data reflect viral RNA and there is mention of infectious particles.<br /> Without these data, it is difficult to ascertain the significance of this paper. While these data demonstrate that the duration of fecal viral RNA shedding is significantly prolonged when compared to viral RNA shedding from the respiratory tract, it is critical for the field to ascertain if this represents infectious virions or not. If viable virus were to be isolated from<br /> the GI tract, that would indeed be very concerning for potential feco-oral transmission of SARS-COV2. As such, this paper does not establish this important fact.

Review by Jovani Catalan-Dibene and validated by Saurabh Mehandru as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-08-05 16:31:21, user Rhysling545 wrote:

The peer-reviewed version of this article is now published in Contact in Context (http://contactincontext.org) as Population Data-Driven Formulation of a COVID-19 Therapeutic (https://docs.google.com/document/d/1bE_tHO0Qg6r7SkOGzHygbYtydDHTBdmL4PBP3ZL_2Kk/edit#)

On 2020-08-06 21:57:31, user James Wyatt wrote:

It would seem that the same techniques could be used to predict the time when a vaccine will appear.

On 2020-08-08 05:01:13, user Callum J.C Parr wrote:

So after the initial seed from China there was a second from Europe and “other countries” (whatever that means) and the slow move to control immigration allowed this. So what do you think about the current growth in cases seen in japan has pretty much blocked entry from any country?

On 2020-08-10 11:48:29, user memini wrote:

The assumption of constant case ascertainment (Line 474) in these countries from March through June is a critical one. It is probably most accurate in Portugal where health systems were least overwhelmed and which has the most testing per covid-19 death. Portugal's dashboard reports <100 tests processed per day to >10,000 tests per day over this period, so it is unlikely that the assumption of constant case ascertainment is sound in Portugal or in the other three countries.

The authors could show whether similar parameters fit a more realistic time series of infections (estimated from deaths). Alternatively, simulated infection trajectories could be filtered by a reasonable estimate of how case ascertainment varied over time before estimating parameters.

Also, Fig S1 shows gamma distributions with CV=0.5, 1, 2; but best fits give CV = 2, 3 or 4 for these countries (Table S1). The relative susceptibility of the median person for those distributions is 0.17, .01, and 0.0001. Given the narrow confidence intervals in Table S1, authors could hypothesize how susceptibility in Portugal could be so much more variable than England, for instance.

On 2020-08-14 20:52:35, user Pavel Valerjevich Voronov wrote:

For those, who started to incorrectly interpret this study. It seems that it doesn't show how severe illness is for Rh- based on data analyzed. As my assumption stands based on other two related clinical studies - Rh-, especially O- and A- have usually mild symptoms and recover easily. BUT this oneactually shows interesting fact - because for O- and A- sickness is light they MIGHT transmit the disease not knowing anything about it or recover silently without any side effects. That what I see as a reason why covid spread is higher in countries with more Rh- population. If you badly sick - most likely you're at home or in the hospital, isolated. Thus, not spreading the "bug".

On 2020-08-17 22:58:52, user Irene Tosetti wrote:

This is the French RT-Lamp saliva test that I've mentioned, Easycov, validated by Uni of Montpellier https://www.skillcell-alcen...

On 2020-08-18 03:52:43, user Harald Dienes wrote:

Have you considered that temperature and humidity indoors may vary considerably from outdoor conditions? This may be particularly significant during lockdowns when people spend more time indoors and in warmer climates where people escape the heat through air conditioning, which produces lower absolute humidity. Low absolute humidity also prevails in winter: https://journals.plos.org/p...

On 2020-08-18 08:04:09, user Christian Fritze wrote:

Compare the probability of infection in line 262 with the statement starting line 336. So then we are to conclude that traveling on a 2 hr flight gives us a similar probability of infection as the average American does spending 2 hrs on other average activities. At the present time, the pandemic is not under control in the US, so we would want to be participating in activities that have far lesser risk of infection than average US behaviors presently have. Thus flying is not a responsible choice.

On 2020-08-18 09:41:35, user Subhajit Biswas wrote:

We are pleased to inform our readers that our discovery that Dengue sera can cross-react in SARS CoV-2 antibody tests has been further investigated and validated by a group of scientists from Israel.

They have more extensively probed and confirmed the cross-reactivity between Dengue antibodies and SARS CoV-2 antigen(s) and vice versa via lateral flow-based rapid tests and ELISA tests in a larger number of patient samples. Like ours, they have also used Dengue sera collected before September 2019, predating the outbreak of SARS CoV-2 in China.

Title of the publication: Potential antigenic cross-reactivity between SARS-CoV-2 and Dengue viruses

Journal: Clinical Infectious Diseases (Oxford Academic)

Link: https://doi.org/10.1093/cid...

The data reported in the aforesaid paper further supports our original idea of antigenic similarity between Dengue virus and SARS CoV-2 that forms the basis of the observed cross-reactivity between the two viruses.

On 2020-08-20 15:14:43, user Carlo Ferrigno wrote:

How many of the infected had an asymptomatic, mild, severe ,or critical outcome. What is their age and clinical status? It would also be important to know as well. Congratulations for this study.

On 2020-08-25 03:39:38, user T Wiz wrote:

This is as close to a controlled experiment as you can get. Few comments, first I did not see a discussion of the symptoms or seriousness of the 3 crewpersons who tested positive for the nucleoprotein antibody but did not have neutralizing antibodies, as to whether or not they upon infection were symptomatic and how serious a COVID19 case they had and whether they had any indication ie. symptoms of a previous mild COVID infection. Second, there was no discussion of the 18 that did not get infected as defined by RT-PCR and serological testing, as to how they were able to avoid infection and did they have other neutralizing antibodies that were cross reactive. Third, as to the three that tested before embarking to have neutralizing antibodies, how serious was their COVID cases, and whether they had symptoms. Last, can they run on the samples kept, tests to determine if there are Tc cells for SARS-CoV-2 as to the uninfected and 3 with neutralizing antibodies.

On 2020-08-21 05:23:58, user Lucy Telfar Barnard wrote:

Hi there, this is a really great review. Just one problem: the reference numbers in the paper don't seem to match the references at the end. Pretty sure all the references to paper 41 comparing tests of cloth and surgical masks aren't talking about Leete's 1919 Lancet paper?

On 2020-08-24 09:08:33, user David H wrote:

Folks - you recruited 35,322 individuals and you conclude that "This information may be informative for the treatment of COVID-19 and design of randomized clinical trials involving convalescent plasma". You could have got this information from a pilot study that was a fraction of the size AND confirmed the findings in a rigorous randomized trial and still not have needed such a large total sample size. In the Recovery Trial run by Oxford University investigators randomized the first patient to hydroxychloroquine within days of trial planning. Maybe convalescent plasma works but after all this effort we should have a clear-cut answer. Uncertainty leads to poor care.

On 2020-08-29 15:35:25, user Enginer01 wrote:

In evaluating these tests, keep in mind that the main effect of HCQ is to allow zinc into the cells, where it is rapidly consumed preventing replication of the virus. Normal zinc intake, easily met from healthy vegetable intake, is 10 mg/d. HQC tests on SARS-CoV-2 indicate an initial daily does of 200 mg/d followed by 50 mg/day. Note that excessive zinc intake can cause copper anemia.

On 2020-09-02 14:24:31, user Maha Abou El-Naga wrote:

Good work , it explained the situation there clearly .. well done????

On 2020-09-04 21:26:30, user Samir Arbache wrote:

Dear authors. Interesting essay, I am very interested in this subject. I made these injections at a specific point in the dermis of my forearm for 20 days (once a day), the skin came to ulcerate. After healing, I made a manual touch on the area but I didn't feel that the skin was hardened, so I avoided the biopsy. My question is: after the treatment of the rats, did you feel touching if the skin was hardened as morphea?

On 2020-09-06 08:17:57, user Marc Girardot wrote:

Agree with Helene Banoun<br /> Given the importance of cellular immunity in the case of a virus, this study does not prove its conclusion. "..., this study cannot conclude that there is no cross-immunity with HcoVs since it only measures humoral immunity (and for some antibodies only).".

Effective Resident Effector Memory T-cells from other HcoVs have been proven to exist, and in the case of Covid-19, vaccine induced reaction produced. In larger number than regular traditional Memory Effector T-cells in the blood, with a large peptidic repertoire (70% genes in common with other HcoVs), it is quite probable and scientifically logical that they provide a powerful sentinel immunological protection directly in the tissue, even before the virus reaches the blood stream.

Tissue-resident memory CD8 T-cell responses elicited by a single injection of a multi-target COVID-19 vaccine https://www.biorxiv.org/con...

On 2020-09-07 14:44:00, user Thomas P. Dooley wrote:

The article has been published in Pulmonary Pharmacology & Therapeutics:

https://doi.org/10.1016/j.p...

On 2020-09-08 05:45:42, user Asher Zeiger wrote:

First of all, since you apparently don't know what what "peer-reviewed" means or why it is significant - Peer-reviewed is the bottom line standard for knowing if a scientific study can be taken seriously. It doesn't look at the resuklts of the study, it makes sure that the methods used to conduct the study were scientifically grounded and not flawed.

For several decades now, scientific studies have not been cited or used to guide clinical practice without being peer-reviewed.

Second, what does the study say about long-term health effects of people who get Covid, but don't die?

Before we downplay the pandemic, it's a good idea to look at it with an open mind. Maybe - just MAYBE - the epidimiologists and immunologists who keep telling us how awful it is know a tad more than the average Joe opining about it on social media....

On 2020-09-11 02:07:36, user kdrl nakle wrote:

There was a RCT study concluding exactly the opposite. Who to believe now?

On 2020-09-12 03:26:51, user SW wrote:

I'm confused by this comment: "In a clinical study, quantitative viral culture was ~25-fold lower<br /> than viral RNA measurement by PCR." I've looked at the study to which you link, but I don't see anything about 25, and I don't really see *quantitative* viral culture -- isn't it just about whether a viral culture ends up positive or negative, without quantifying it? This factor of 25 seems to work well, so I'd love to understand it. Thanks.

On 2020-09-13 17:03:48, user kdrl nakle wrote:

If you are comparing two groups without control group then you cannot say that both groups have improved outcomes. You can only compare the two. By the way, both samples are rather small, in particular the sample with Tocilizumab.