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View Code? Open in Web Editor NEWA collaborative review of the emerging COVID-19 literature. Join the chat here:
Home Page: https://gitter.im/covid19-review/community
License: Other
A collaborative review of the emerging COVID-19 literature. Join the chat here:
Home Page: https://gitter.im/covid19-review/community
License: Other
Hello interested contributors! Welcome to the covid19-review
project. Our goal here is to provide an up-to-date perspective on the current peer reviewed and preprinted literature around diagnostics and therapeutics relevant to COVID-19. There's more in our README, which you may have already seen: https://github.com/greenelab/covid19-review#sars-cov-2-and-covid-19-an-evolving-review-of-diagnostics-and-therapeutics
As a first step, let's get to know each other. Please answer these questions!
Title: JUST A TEST virology IN VITRO
JUST A TEST
Edited to help testing. Another edit to test. Yet another test
Title: Nutraceuticals have potential for boosting the type 1 interferon response
to RNA viruses including influenza and coronavirus
Link: https://www.sciencedirect.com/science/article/pii/S0033062020300372?via%3Dihub
DOI: https://doi.org/10.1016/j.pcad.2020.02.007
This review paper discusses the potential use of nutraceuticals to prevent and lessen the response to infection by RNA viruses such as influenza and coronavirus. In particular, they highlight the utility of nutraceuticals capable of amplifying TLR7 and mitochondrial antiviral-signaling protein (MAVs) to induce type 1 interferon production.
Several types of nutraceuticals are considered, including lipoic acid, ferulic acid, and vitamin supplements among others. However, the most promising seem to be phycocyanobilin (PCB - spirulina), N-acetylcysteine, and glucosamine.
This review highlights potential nutraceuticals and supplements already on the market and makes the case that some nutraceuticals, namely spirulina (PCB), phase 2 inducers, high-dose glucosamine, and N-acetylcysteine might aid in the prevention or modulation of RNA virus infections via evoking type 1 interferon production. Overall there is the suggestion that these supplements may benefit the consumer. However, it is a review and there was little clinical evidence to support the claims.
Context
As we begin the review, we need a place for contributors to track which papers have been read as well as a place to record summaries of the articles. This will help us to:
Problem
We need to make some sort of template to encourage the submission of papers as issues in a standardized format that will allow for filtering, searching, maintenance, etc. It would also be good to recommend contributors include certain pieces of information in their summaries.
Ideas
Request thoughts from
@cgreene
Please paste a link to the paper (preferably DOI) or citation information here:
Is this paper primarily relevant to Background, Diagnostics, or Therapeutics? (OK if more than one)
Please list some keywords (3-10) that help identify the relevance of this paper to COVID-19
Please leave a comment with your summary below.
Suggested questions to address in summary:
@LucyMcGowan and @SiminaB had been discussing the value of having a checklist where someone submitting a paper summary can confirm which sections they filled out vs skipped.
@LucyMcGowan implemented a prototype here
My only concern is related to implementation, specifically formatting the check boxes correct so that they will work when copy and pasted. I believe the issue templates open in plain text (so the check boxes look like - [ ]
; however, once the issue is submitted, the check boxes will be visible as interactive boxes:
If I copy the "test box" lines above from Preview mode (translated markdown), here's how they look:
test box 1
test box 2
But if we use the code formatting like I did for the inline example ( `- [ ] `) then they won't work when copied from plain text!
This seems kind of trivial but I'm not sure how to work around it from a usability standpoint, since in many cases, the paper submitter is likely to copy the check boxes out of the plain text template (so they will work), but all subsequent commenters will copy them out of the issue and lose the formatting.
Title: A new coronavirus associated with human respiratory disease in China
Link: https://www.nature.com/articles/s41586-020-2008-3
Citation: [@doi:10.1038/s41586-020-2008-3]
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
A few groups are discussing and reviewing published manuscripts and preprints in various forums. These could be candidate contributors to contact:
Feel free to edit this post if you want to make one list.
Are you looking for a way to contribute? Great, we're happy to have you!
We'll comment below with the most up-to-date information about where help is needed.
As a note, here is some information about how to get started using GitHub
Title: COVID-19 Treatment and Vaccine Tracker
Link: https://milkeninstitute.org/sites/default/files/2020-03/Covid19%20Tracker%20NEW3-24-20-REVISED.pdf
DOI: N/A
This is not a full paper but rather a spreadsheet from the Milken Institute listing COVID-19 treatments and vaccines in development. It provides links to original sources.
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
For example, is it an interventional or an observational study?
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
For example: Is the outcome assessed by a clinician or is it self-reported?
Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)?
What method is used to measure the outcome?
How long after a treatment is the outcome measured?
For example, are there individuals lost to follow up?
For example, a lack of masking in randomized clinical trials.
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Should this review paper also explicitly address public health policy / epidemiology? Is there a critical mass of folks who can help manage contributions in this area?
Some specific subtopics that a section like this could address are listed in the comment below.
I think people may be more overwhelmed than anything by the volume of papers that are available, but I wanted to start a record of collections of relevant papers in case anyone is looking for a place to start:
Please feel free to comment below with additional links for good ways to find papers, or to open a new discussion on any interesting paper you find using by starting a new Issue and filling in one of the new paper templates (either just the top part, or the whole thing!) (Just make sure you search first in case someone already opened one!)
Title: The proximal origin of SARS-CoV-2
https://www.nature.com/articles/s41591-020-0820-9
10.1038/s41591-020-0820-9
Background
origin, evolution, genomic features
The authors describe the genomic features of SARS-CoV-2, in particular the receptor binding domain and the polybasic cleavage site. They relate these to other known coronaviruses such as SARS-CoV, and using these comparisons provide evidence for and against several putative origins of SARS-CoV-2.
Comparative genomics
SARS-CoV-2 in relation to other coronaviruses
It's highly likely that SARS-CoV-2 evolved naturally.
SARS-CoV-2 is unlikely to have a laboratory origin (either designed, genetically altered, or escaped). Instead it's highly likely to have evolved naturally in an animal host, however current evidence cannot discriminate an origin of the current form before or after zoonotic transfer to humans. It's genomic features highly resemble known coronaviruses present in bats (overall sequence similarity) and pangolins (receptor binding site).
No
This repo requires conflict of interest disclosure (COI) to be added to the metadata file for a new author entry but the template format is not specified in the linked file.
Additionally, the contribution section does not make note of the fact that it should be one of the fourteen CredIT taxonomy roles found here: https://casrai.org/credit/
Possible solutions include pointing the template format at something other than the rootstock default since this is repo specific or including extra directions and links in the main README.md file. Alternatively, a CONTRIBUTING.md file could describe this process in greater detail (and be linked to from the main page). More info on CONTRIBUTING files and there use in software projects on GitHub can be found here: https://help.github.com/en/github/building-a-strong-community/setting-guidelines-for-repository-contributors . I would be happy to help author any/all of these documents or changes.
Thanks again for putting this all together and welcoming so many people!
Many open source software projects have a dedicated chatroom to discuss their project, welcome newcomers, and provide rapid feedback. Might be something to consider in this context as well as a place for people to quickly ask questions, get help, and contribute without directly engaging with GitHub.
Some Examples:
Pros:
Some potential downsides:
I am happy to elaborate on the pros/cons of each and/or set up the service if that'd be helpful, just let me know!
Title: A pneumonia outbreak associated with a new coronavirus of probable bat origin
Link: https://www.nature.com/articles/s41586-020-2012-7
Citation: [@doi:10.1038/s41586-020-2012-7]
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
Suggested by @vagarwal87
Title: The landscape of lung bronchoalveolar immune cells in COVID-19 revealed by single-cell RNA sequencing
Link: https://www.medrxiv.org/content/10.1101/2020.02.23.20026690v1
Citation: @doi:10.1101/2020.02.23.20026690
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
In #12 , @cgreene pointed out:
Reagents are going to become an even larger challenge than they are now. Advances that require fewer reagents (particularly those in short supply) might be good to talk about. In any case, the PPE situation might be the thing that eventually limits testing.
My take is also that at some point the spread is so much that testing for the sake of telling people they are infected and to stay isolated is no longer practical, so the focus shifts to testing healthcare workers or those for whom a diagnosis would change their course of treatment. In this setting faster tests may become even more important.
Some updates have been added in #20
We would love to hear more thoughts on this topic from people in fields like epidemiology and possibly public health. Comment below if you have opinions!
Title: Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1
Link: https://www.nejm.org/doi/full/10.1056/NEJMc2004973
Citation: @doi:10.1056/NEJMc2004973
The authors tested the stability of SARS-CoV-1 and SARS-CoV-2 on different surfaces.
Viral stability in aerosol and on different environmental surfaces.
Virus surface deposition, and re-hydration/recovery using DMEM
Bayesean regression
SARS-CoV-2 and SARS-CoV-1
Virus stability on different surfaces is similar between SARS-CoV-2 and SARS-CoV-1 in the experimental conditions.
SARS-CoV-2 remains viable in aerosols for at least 3 hours with a median half-life between 1.1 and 1.2 hours
SARS-CoV-2 is more stable on plastic and stainless steel than on copper and cardboard.
The paper provides background information potentially relevant to transmission, epidemiology and prevention.
These results were obtained in experimental conditions; it is unclear how they translate to real-world environmental conditions, particularly in relation to viral load necessary for infection.
@mprobson suggested offline that feature branching has worked well on some of his large-scale collaborations.
Would you be OK with adopting feature branching as our standardized workflow? (When possible, obviously!)
I usually only get to work on my PRs piecemeal during the day, and end up having to deal with merge issues by the time I try to push. It sounds like rebasing each merge might help make this go a little more smoothly.
Currently only the four of us have write access so it shouldn't affect many people if we switch.
Title(s):
https://www.nature.com/articles/nrmicro2147
https://www.sciencedirect.com/science/article/pii/B9780123858856000092?via%3Dihub
https://www.nature.com/articles/s41579-018-0118-9
https://doi.org/10.1038/nrmicro2147
https://doi.org/10.1016/B978-0-12-385885-6.00009-2
https://doi.org/10.1038/s41579-018-0118-9
Background
history, pathogenesis of related viruses
Coronaviruses generally - SARS-CoV, MERS-CoV, SADS-CoV, etc.
Current knowledge (Dec 2018) on the origin and evolution of SARS-CoV and MERS-CoV and their receptor usage.
The diversity and potential of spillover of bat-borne coronaviruses, as evidenced by the recent spillover of swine acute diarrhoea syndrome coronavirus (SADS-CoV) to pigs.
Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
Title: Please edit the title to add the name of the paper after the colon
A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
Link: https://www.biorxiv.org/content/10.1101/2020.03.22.002386v1
Citation: doi:10.1101/2020.03.22.002386
The authors cloned, tagged and expressed 26 of the 29 viral proteins in human cells
Using affinity-purification mass spectrometry the authors have identified the 332 high confidence SARS-CoV-2-human protein-protein interactions between the pathogen and human proteins
66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs are identified
SARS-CoV-2 interacts with multiple innate immune pathways
Cellular proteins implicated in innate immune signaling that are targeted by several SARS-CoV-2 viral proteins
Large scale interactome analysis
Whether there are high confidence interactions between the SARS-CoV-2 and human proteins, if yes, are those potential drug targets?
HEK293T cells
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
For example, is it an interventional or an observational study?
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
For example: Is the outcome assessed by a clinician or is it self-reported?
Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)?
What method is used to measure the outcome?
How long after a treatment is the outcome measured?
For example, are there individuals lost to follow up?
For example, a lack of masking in randomized clinical trials.
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Title: Hydroxychloroquine and azithromycin as a treatment of COVID-19: results of an open-label non-randomized clinical trial
Link: https://www.sciencedirect.com/science/article/pii/S0924857920300996
Citation: doi:10.1016/j.ijantimicag.2020.105949
The study investigated the impact of hydroxychloroquine combined with azithromycin on viral loads in SARS-CoV-2-infected patients.
Does hydroxychloroquine and azithromycin effect SARS-CoV-2-infected patients?
2 drugs (hydroxychloroquine and azithromycin) were being considered
In vitro and in vivo positive effects of Chloroquine have been reported.
Hydroxychloroquine has been demonstrated to have an anti-SARS-CoV activity in vitro and is safer than chloroquine.
Hospitalized patients with confirmed COVID-19 that were older than 12 years and PCR documented SARS-CoV-2 carriage in nasopharyngeal sample at admission whatever their clinical status.
Human
20 treated
16 controls
Marseille, France
12 years old
Mean age of patients was 45.1
15 patients were male
hydroxychloroquine patients were older than controls
in patient (hospitalized)
12 years old
PCR documented SARS-CoV-2 carriage in nasopharyngeal sample at admission
Patients were excluded if they had a known allergy to hydroxychloroquine or chloroquine or had another known contraindication to treatment with the study drug, including retinopathy, G6PD deficiency and QT prolongation. Breastfeeding and pregnant patients were excluded based on their declaration and pregnancy test results when required.
oral hydroxychloroquine sulfate 200 mg, three times per day during ten days
Six patients received azithromycin (500mg on day1
followed by 250mg per day, the next four days) to prevent bacterial super-infection under daily electrocardiogram control
Interventional
Patients had to consent to receive hydroxychloroquine, those that did not were included in the control group.
No
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
non-randomized clinical trial
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
PCR positive nasopharyngeal samples
At day 3,4,5, and 6 post-inclusion treated patients had increased virological clearance between patients and controls.
At day 6 post-inclusion, 70% hydroxychloroquine treated patients had increased virological clearance, compared to 12.5% in the control group.
At day 6 post-inclusion, 100% of patients treated with hydroxychloroquine and azithromycin increased virological clearance.
The outcome measurement was PCR positive nasopharyngeal samples. The authors said that clinical follow-up and occurrence of side-effects will be
described in a further paper at the end of the trial.
For example: Is the outcome assessed by a clinician or is it self-reported?
Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)?
What method is used to measure the outcome?
How long after a treatment is the outcome measured?
For example, are there individuals lost to follow up?
Not a randomized clinical trial.
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Title: Treatment of 5 Critically Ill Patients With COVID-19 With Convalescent Plasma
Link: https://jamanetwork.com/journals/jama/fullarticle/2763983
Citation: @doi:10.1001/jama.2020.4783
In this uncontrolled case series of 5 critically ill patients with COVID-19 and acute respiratory distress syndrome (ARDS), administration of convalescent plasma containing neutralizing antibody was followed by an improvement in clinical status.
Can plasma transfusion improve clinical status
Antiviral treatment + Plasma transfusion
Can plasma containing neutralizing antibodies improve critically ill patients disease status
Human study:5 Patients that are sick and did not responded to antivirals received plasma from 5 patients that have been confirmed sick but recovered
Only one treatment group:
China
36-65 years, 3 men, 2 woman,
5 critically ill patients, required ventilation, did not respond to antivirals
Case series of 5 critically ill patients with laboratory-confirmed COVID-19 and acute respiratory distress syndrome (ARDS) who met the following criteria: severe pneumonia with rapid progression and continuously high viral load despite antiviral treatment; Pao2/Fio2 <300; and mechanical ventilation
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
Not stated
All 5 people got treatment (uncontrolled)
For example, is it an interventional or an observational study?
Interventional
No
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
Age range and gender distribution is fair
Changes of body temperature, Sequential Organ Failure Assessment (SOFA) score (range 0-24, with higher scores indicating more severe illness), Pao2/Fio2, viral load, serum antibody titer, routine blood biochemical index, ARDS, and ventilatory and extracorporeal membrane oxygenation (ECMO) supports before and after convalescent plasma transfusion.
For example, are there individuals lost to follow up?
Since every patient got treatment there is of course bias towards before vs after plasma transfusion
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
Pre vs post infusion
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
No statistics
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
It is interventional but they do not have a control group therefore we can not conclude that the transfusion did anything or if it was just time.
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
Those patient also got steroids and antivirals
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
There is a lot of variability here: The donors for the plasma, recipients etc.
1-2 sentences on concluding remarks, including a summary of strengths, weaknesses, limitations.
While this is an interesting study, it is also the epitome of a poorly controlled study. It is impossible to say if the treatment caused the beneficial effects or if it was the other treatments or just time. But the results are pretty striking (for me not being a clinician). The reports concerning the benefit of using neutralizing antibodies remain controversial at this point. I just wish someone would conduct a proper clinical trial on this topic as there seems potential but we really do not understand what is going on.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Title: A serological assay to detect SARS-CoV-2 seroconversion in humans
Link: https://www.medrxiv.org/content/10.1101/2020.03.17.20037713v1
DOI: https://doi.org/10.1101/2020.03.17.20037713
The study describes a method for the detection of seroconversion upon SARS-CoV-2 infection based on the reactivity of the immunogenic spike protein (S protein) of the virus. The ELISA based assay is both sensitive and specific and capable for detecting COVID19 seroconverters, through plasma/serum samples, as early as 3 days post symptom onset.
It is a serological assay capable of detecting the generation of SARS-CoV-2 antibodies after initial viral infection. Detection of seroconversion is important for three main reasons: it allows for the study of the immune response to SARS-CoV-2 in a dynamic fashion, it allows to determine which individuals have already been exposed to the virus and have mounted an immune response (and therefore are non-contagious or they could serve as donors for the generation of convalescent serum therapeutics), and it allows to determine the precise rate of infection in a given region, providing better estimates of the true infection fatality rate.
Human subjects
Human serum samples
59 banked human serum samples with previous exposure to viral infection (to generate an immune background as a control for the assay)
4 plasma/serum samples from three COVID-19 patients
Control serum samples were acquired from individuals living in the US prior to the dissemination of COVID-19 within the US
COVID-19 samples were provided by the University of Melbourne and the University of Helsinki (no further information is provided from these subjects)
For the control subjects: Ages ranged from 20-60+. Self-reported ethnicities include: Caucasian, Asian, African American, and Hispanic.
Previous viral exposure of the control subjects include: hantavirus, dengue virus, coronavirus NL63, and others.
COVID-19 Samples: Samples were obtained 2 and 6 days post symptom onset (for subject 3), 4 days post symptom onset (for subject 2), and 20 days post symptom onset (for subject 1)
Examples of assignment could include: Recruited individuals have initially undergone neither the new nor the reference test; individuals tested as positive or negative by the reference test undergo the new test; individuals who have undertaken the new test are assessed by the standard test.
Depending on how individuals are chosen, the test may be biasing towards more sick or less sick individuals or very clear-cut positive/negative cases.
Any factors that would influence this bias should be included here.
Describe technical details of assays used, when measurements were taken and by whom, etc. for both the new and standard tests.
For example: Do some participants undergo just one test (the new or the reference test)?
Are there individuals with inconclusive results?
Note that the PPV and NPV represent "post-test" probabilities of disease and are generally more meaningful than sensitivity and specificity.
Sometimes the post-test odds will be given instead.
For example, if the prevalence is lower, then the PPV will also be lower, but the NPV will be higher.
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Title: Please edit the title to add the name of the paper after the colon
Link:
https://doi.org/10.1016/S0140-6736(20)30251-8
Citation: [@doi:10/ggjr43]
Title: Potential roles of social distancing in mitigating the spread of coronavirus disease 2019 (COVID-19) in South Korea
Link: https://github.com/parksw3/Korea-analysis/blob/master/v1/korea.pdf
Data & code link: https://github.com/parksw3/Korea-analysis
Citation: tag:Park2020_distancing
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
What did they analyze?
What methods did they use?
Does this paper study COVID-19, SARS-CoV-2, or a related disease and/or virus?
What is the main finding (or a few main takeaways)?
What does this paper tell us about the background and/or diagnostics/therapeutics for COVID-19 / SARS-CoV-2?
Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
Good supplemental data to #69 on dynamics of infection.
Title: Repurposing host-based therapeutics to control coronavirus and influenza virus
Link: https://www.sciencedirect.com/science/article/pii/S1359644618303805
DOI: 10.1016/j.drudis.2019.01.018
This is more of a speculative piece that discusses a few potential avenues that one might consider. It is a review paper, so it's more of a "potential mechanisms" description than a specific drug, though they do mention some (I found it via a tweet from @mattmight about one of these).
This is a review, not a research paper.
This is a review, not a research paper.
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This is a review paper, so it is primarily speculation around potential mechanisms.
Title: A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19
Link: https://www.nejm.org/doi/full/10.1056/NEJMoa2001282
Citation: doi:10.1056/NEJMoa2001282
The study is trial of a combination therapy for COVID-19. The intervention did not succeed.
Efficacy of a combination of antiviral drugs in COVID-19.
A combination of Ritonavir-Lopinavir.
The efficacy of Ritonavir-Lopinavir in COVID-19 over placebo.
Human study.
199 patients in total.
Wuhan, China.
median age: 58 years
Patients admitted for COVID-19 symptoms.
For example, is it an interventional or an observational study?
Interventional.
yes.
Of the 357 patients, 158 were excluded and 199 were included. Among the 99 assigned to treatment group, three died after admission and two could not take the treatment.
The outcome is determined by a clinician.
Five patients were dropped during the study.
I think that accounting for a placebo arm (standard-care) corrects for several bias.
Kaplan-Meier plot, hazard ratio.
Yes.
No adjustment was considered because no important between-group
differences in demographic characteristics, baseline laboratory test results, distribution of ordinal
scale scores, or NEWS2 scores at enrollment
odds ratio.
Treatment with lopinavir–ritonavir was not associated with a
difference from standard care in the time to clinical improvement (hazard ratio for
clinical improvement, 1.24; 95% confidence interval [CI], 0.90 to 1.72). Mortality
at 28 days was similar in the lopinavir–ritonavir group and the standard-care group
(19.2% vs. 25.0%; difference, −5.8 percentage points; 95% CI, −17.3 to 5.7).
Yes.
Patients with such characteristics were excluded from the study.
No.
No.
-I think it is hard to tell if the study extrapolates to other countries with n=199, with all the patients from Wuhan, China.
I think it is a remarkable study, with sound design and flawless execution and design. It helps narrow down the search for therapy.
Check off the components as they are completed. If the component is not applicable, check the box as well.
@cgreene since we don't have AppVeyor notifications linking directly to artifacts and GitHub Actions comments are still being explored (manubot/rootstock#322), we should stick your screenshots and instructions above somewhere permanent. They'll be very helpful for pull request reviewers.
Do we have / want a Wiki for this?
Originally posted by @agitter and @mprobson in #23 (comment)
Here I upload a document with several points of discussion, including the most recent literature on the most hopeful experimental drugs (I have found 4 of them), as well as diagnostic tests, based on antibody detection, recently developed. I also added some thoughts concerning the Italian Covid-19 situation.
Please let me know your feedback.
Best,
Ada
20200326_Corona virus review.pdf
Originally posted by @aadattoli in #17 (comment)
Title: Is There a Role for Tissue Plasminogen Activator (tPA) as a Novel Treatment for Refractory COVID-19 Associated Acute Respiratory Distress Syndrome (ARDS)?
Citation: doi:10.1097/TA.0000000000002694
A big cause of the deaths from the coronavirus is due to ARDS (acute respiratory distress syndrome). Past work has noted that ARDS causes deposition of fibrin (protein involved in blood clotting along with platelets) in airspaces and lung parenchyma along with fibrin platelet microthrombi deposited in the pulmonary vasculature. In other words, blood clots are deposited into the lungs, causing damage.
tPA is a protein involved in breaking down blood clots. The protein can be manufactured using recombinant techniques and is widely used as a treatment for strokes (where blood clots obstruct blood flow to the brain). Given the current drastic shortage of ventilators with the ongoing coronavirus crisis, the authors posit that using tPA as a stopgap treatment for ARDS may have useful effect. The use of agents such as tPA is typically considered higher risk, but may be necessary given the current crisis.
The paper suggests that using experience from strokes, intravenous administration of tPA may be useful. They suggest that unlike strokes, where a brief dose is used, for coronanavirus, a better treatment may be to have a first dose of 25 mg tPA administered over 2 hours, followed by another 25 mg tPA dose over the subsequent 22 hours. They suggest patients with severe ARDS where ventilators are not available may be good candidates.
Can tPA be used as an emergency stopgap treatment for severe cases where ventilators aren't available
tPA, administered intravenously
Can tPA be used as a stopgap treatment for ARDS in the absence of a ventilator?
N/A. This is a proposal paper, no data yet.
N/A. Human studies would be needed to validate
N/A
N/A
N/A
N/A
N/A
Given the risks of tPA, the authors recommend using this potential treatment only for patients with severe ARDS where P/F < 50.
N/A
N/A. This is a position paper
N/A
This is a position paper, no results yet.
N/A
N/A
N/A
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
Recommended by @vagarwal87
Title: Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)
Link: https://science.sciencemag.org/content/early/2020/03/24/science.abb3221
Citation: [@doi:10.1126/science.abb3221]
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
Title: Early dynamics of transmission and control of COVID-19: a mathematical modelling study
Link: https://www.sciencedirect.com/science/article/pii/S1473309920301444?via%3Dihub
Link to code: https://github.com/adamkucharski/2020-ncov/
DOI: 10.1016/S1473-3099(20)30144-4
Background
What did they analyze?
What methods did they use?
Does this paper study COVID-19, SARS-CoV-2, or a related disease and/or virus?
What is the main finding (or a few main takeaways)?
What does this paper tell us about the background and/or diagnostics/therapeutics for COVID-19 / SARS-CoV-2?
Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
Title: A human monoclonal antibody blocking SARS-CoV-2 infection
Neutralizing antibodies,
SARS-CoV-2 glycoproteins,
Receptor-binding-interference,
Antibody binding to SARS-CoV-2 surface glycoprotein
Inhibition of infection by SARS-CoV-2 using the neutralizing antibody
Binding affinities for various spike protein domains
What methods did they use?
Immunofluorescence,
Flow-cytometry,
Neutralization assays,
ELIZA,
Hybridoma antibody generation,
Does this paper study COVID-19, SARS-CoV-2, or a related disease and/or virus?
Yes
What is the main finding (or a few main takeaways)?
Generation of a humanized monoclonal antibody against SARS-CoV-2
Mechanism of action unknown but distinct from ACE2 receptor binding interference
The antibody may be useful for diagnostics (antigen detection) and therapeutics
What does this paper tell us about the background and/or diagnostics/therapeutics for COVID-19 / SARS-CoV-2?
No approved targeted therapy available to date,
Background about the emergence of the disease,
Structural biology of the spike proteins,
Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
Only in vitro, no in vivo validation
Neutralizing concentration in vitro is relatively high
Title: Pangolin homology associated with 2019-nCoV
Link: https://www.biorxiv.org/content/10.1101/2020.02.19.950253v1
Citation: [@doi:10.1101/2020.02.19.950253]
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
Is your feature request related to a problem? Please describe.
Given how rapidly information is being disseminated on general public-facing platforms and this pandemic being a global community concern, I think this review should have some aspects of general public-facing information. Reading through the albeit limited discussion thus far, I do not think such public-facing elements are in the plan.
Describe the solution you'd like
The review certainly cannot be pulled out of academic and toward losing the nuance of the issues at hand so I propose a "checkpointing" solution for any public or media-crew readers. I foresee this being sort of "bottom line" elements in highlighted boxes (supported in Manubot?), as well as asides for addressing any public misinformation. We cannot allow this review to be pulled so far toward obscure academic hair splitting that such a publicly accessible resource which will evolve as we learn more is impenetrable to a non-academic reader.
Describe alternatives you've considered
Mentioned above I see the solution as highlighted boxes in the manuscript, but could also foresee a section of public questions with brief answers starting the review. For example:
This is intentionally structured as a clear question (1), clear answer (2), why the question is on peoples' minds (3), then what the literature tells us as academics (4).
I would not consider a separate document as there is not way to ensure the two are consistent and current. One document with public and academic elements is the solution, in my opinion. How these elements are presented and laid out on the page is the concern.
Additional context
My motivation for this feature request is that this review will be easily accessible to all and can be leveraged to combat misinformation if only we allow room for doing so.
Hi all,
So excited that you are starting this initiative and very grateful to be involved! I was involved in a systematic review a couple years ago. Based on that experience, I have some thoughts/questions outlined below.
Centralized database. In order to synthesize the data we garner from the literature, wouldn't it be helpful to have information on each paper stored in a data format that is able to be searched and sorted? Is it possible to make a collaborative excel sheet on GitHub? That way we could list the paper information, name of the individual reviewing, whether it contains information relevant to diagnosis/therapeutics/other, the area of study (virology vs. epidemiology etc.), the summary of findings, and whatever other information we deem important. We would then have a centralized database that can be sorted and searched for relevant information when writing up the paper.
Inclusion and Exclusion Criteria. The typical systematic review process (from my experience) starts with an initial scan of titles and abstracts based on pre-specified inclusion and exclusion criteria. Those deemed relevant in that first pass are then subjected to a full-text scan based on another set of pre-specified inclusion and exclusion criteria. The resulting set of relevant papers are then subjected to data extraction. I think it might be important to get area experts involved in designing these inclusion and exclusion criteria. The literature at hand encompasses so many different topics that we would need help identifying those relevant to therapeutics and diagnostics (for example, do we want to include papers that estimate the reproduction number? or model the global spread of the pandemic? or are these questions beyond the scope of therapeutics/diagnostics?) Once we have these inclusion/exclusion criteria in place, we could crowd-source for help in the first and second pass scans of the literature.
In my experience, these scans of the literature are always done by hand, usually by two independent reviewers who talk through any discrepancies. When I was involved in this process, we used a software called EROS (but the link no longer seems to work...so it may not exist anymore?). Perhaps it would be possible to do this with machine learning? The main roadblock I can foresee is that abstracts and full-texts will include references to previous literature, so it would be difficult for a non-human to identify what the primary data from each paper is.
Risk of Bias. Typical systematic reviews also include a Risk of Bias assessment, in which each paper included in the review is assessed based on a pre-specified set of criteria (ex. Is there a control group? Were researchers blinded to experimental conditions?). Performing such a review informs how well we can trust the results of each study. The relevant criteria for performing a Risk of Bias assessment will vary depending on the type of study, however. For example, I can imagine that the sources of bias for a study coming from a virology lab performing a randomized control trial with cell lines will be vastly different from an epidemiological study attempting to estimate the reproduction number. Could we recruit experts in each area to design risk of bias assessment tools so we can understand the quality of data we collect?
New Articles How would we ensure we are including all new publications? In systematic reviews, you develop a search strategy using a combination of keywords and synonyms, and then input that search strategy into several different databases (such a PubMed, Web of Science, etc.). For most databases you can sign up to receive alerts when new articles are published that meet your search criteria. Would we set something like this up?
Thinking about all of this, I wonder if getting a reference librarian involved would be a good idea. I have heard of a terrific librarian from the Biomedical library at UPenn who is the go-to expert on systematic reviews. Perhaps she would be willing to be involved!
There seem to be several people in the introductions thread (#17 ) who are interested in zoonotic diseases, a topic that is currently missing from the introduction section outline.
I wanted to encourage a discussion of what you (and anyone else who is interested!) think are the most important elements of this topic to include in the introduction to help guide an understanding of the virus towards an understanding of possible diagnostics/therapeutics.
Please feel free either to discuss here or to propose modifications to the outline document -- here are some directions.
Also, feel more than welcome to start adding sentences/paragraphs if you feel inspired to write!
Finally, let me know if there are other fields to tag to help bring other relevant experts into this conversation (I'm putting virology for now)
People who stated interest in this topic:
@rmrussell @Huiyanangelchow
Due to the complexity of manubot, right now there are a lot of files and folders in the repository, and many of these file extensions might be new for folks. I think we might be running into some confusion about which are up for grabs in terms of editing. I'm wondering if some sort of TOC could help direct people to which ones we want edits on!
Happy for comments, or else I will keep thinking about it and hopefully submit a PR later today.
Would anyone be willing to do some research on the support / questions surrounding treatment with hydroxychloroquine? @rishirajgoel had mentioned that we don't seem to have anyone looking at it yet, and obviously it's a very popular topic right now.
If you find any interesting papers and can record them (with the DOI and some background info) as a New Paper (Therapeutic) in the Issues, that would be great! Even better if you want to start to fill out the questions in the Template describing what the studies do/find.
Additionally, it would be great to start putting together a rough draft or an outline for that section of the treatments analysis, if anyone is interested.
@cgreene found this resource that might be helpful: http://www.uphs.upenn.edu/antibiotics/COVID19.html
Please use this space to discuss and collaborate!
Title: Potent human neutralizing antibodies elicited by SARS-CoV-2 infection
Link: https://www.biorxiv.org/content/10.1101/2020.03.21.990770v2
Citation: @doi:10.1101/2020.03.21.990770v2
If you would like to submit a summary of the paper, please copy and paste the following into a comment.
Title: Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases
Link: https://pubs.acs.org/doi/10.1021/acscentsci.0c00272
Citation: doi:10.1021/acscentsci.0c00272
This review uses the CAS content collection to systematically search for treatments for related viruses, many of which are being investigated for repurposing against SARS-CoV-2. One benefit for our review could be statistics about the abundance of different classes of treatments.
Many types of treatments and vaccines are reviewed
Review article
N/A
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
For example, is it an interventional or an observational study?
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
For example: Is the outcome assessed by a clinician or is it self-reported?
Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)?
What method is used to measure the outcome?
How long after a treatment is the outcome measured?
For example, are there individuals lost to follow up?
For example, a lack of masking in randomized clinical trials.
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
In filing #56 I noticed a few issues with the templates that made things a bit more confusing. I'll attempt to take those on now.
Title: A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
Link: https://www.biorxiv.org/content/10.1101/2020.03.22.002386v2
DOI: https://doi.org/10.1101/2020.03.22.002386
Therapeutics
What did they analyze?
What methods did they use?
Does this paper study COVID-19, SARS-CoV-2, or a related disease and/or virus?
What is the main finding (or a few main takeaways)?
What does this paper tell us about the background and/or diagnostics/therapeutics for COVID-19 / SARS-CoV-2?
Do you have any concerns about methodology or the interpretation of these results beyond this analysis?
N/A
Twitter thread with other info: https://twitter.com/fraser_lab/status/1241929245812592641
Currently, clicking on the gear next to "Reviewers" or "Assignees" on a PR only allows for the selection of users from within certain groups (my guess is manubot and GreeneLab, possibly AlexsLemonadeStand as well).
This is also the case for tagging users within comments on PRs or in issues they didn't initiate. Users also can't add reviewers etc. to their own PRs. They can comment on issues. (Thanks to @mprobson for testing).
Looking at the settings, it seems like read permissions should be public, which to me implies that anyone can comment, but that doesn't seem to be the case. I explicitly gave @mprobson read permissions and then I could tag him here and assign him to the issue.
Is there a way to open this up so that contributors in general can comment on/be tagged in PRs? Or do we just need to add them individually?
For people interested in joining the UPenn COVID-19 journal club via bluejeans. Hosted by the Wherry lab. https://bluejeans.com/2359300514?src=join_info
This Thursday (March 26 at 12pm EST)
Title: Please edit the title to add the name of the paper after the colon
Link: https://www.biorxiv.org/content/10.1101/2020.02.10.942748v2.full.pdf+html
Citation: @doi:10.1101/2020.02.10.942748
Background
There have been some exciting new suggestions for avenues we can explore in the section on therapeutics! (Thanks, @RLordan and @nilswellhausen !)
Initially we had just thought about:
But now we have content about nutraceuticals and clonal antibodies as well.
Does anyone have ideas about how to most effectively group these into categories? Is there a hierarchical structure at all, where some of them are more or less similar to others, or do we have 5 distinct categories as the text reflects now?
DOI: 10.1038/nrmicro.2016.81
@rmrussell (who initially added this citation in #27 )
Reviewers, please copy and paste the section below into a comment and fill it in.
Title: Anti–spike IgG causes severe acute lung injury by skewing macrophage responses during acute SARS-CoV infection
Link: https://doi.org/10.1172/jci.insight.123158
Citation: doi:10.1172/jci.insight.123158
Adding this paper that @nilswellhausen mentioned in #97 (comment) See also:
For example, do the investigators exclude patients with diagnosed neoplasms or patients over/under a certain age?
For example, is it an interventional or an observational study?
A study can be interventional but not randomized (e.g., a phase I or II clinical trial is interventional but often not randomized).
This includes possible treatment stratification (e.g., within litters for animal studies, within hospitals for human studies), possible confounding variables (e.g., having a large age range of individuals), possible risks of bias and how they are addressed (e.g., is there masking in a clinical trial? how are individuals chosen in an observational study?).
For example: Is the outcome assessed by a clinician or is it self-reported?
Is the outcome based on viral load or a functional measurement (e.g., respiratory function, discharge from hospital)?
What method is used to measure the outcome?
How long after a treatment is the outcome measured?
For example, are there individuals lost to follow up?
For example, a lack of masking in randomized clinical trials.
For example, logistic regression, nonparametric methods.
For example, are clustered data treated independently or are clusters adjusted for, such as different hospitals or litters?
For example, adjustment for age, sex, or comorbidities.
For example, is it an estimated odds ratio, a median difference in detected cases, etc?
For example, confidence intervals, p-values, and/or Bayes factors.
For example, with a well-performed animal study or randomized trial it is often possible to infer causality.
If is an observational study, does it match up with some of the Bradford Hill criteria? https://www.edwardtufte.com/tufte/hill https://en.wikipedia.org/wiki/Bradford_Hill_criteria
For example, is the treatment known to cause liver damage or to not be prescribed for individuals with certain comorbities?
For example, do individuals with a specific baseline seem to do particularly well? Be particularly cautious with respect to multiple testing here.
Is the model system appropriate? Is there evidence from past use that it's highly-relevant to therapeutic design in this context?
1-2 sentences on concluding remarks, including summary of strengths, weaknesses, limitations.
Check off the components as they are completed. If the component is not applicable, check the box as well.
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