Sonnefreeman3229

To provide Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guidance for the consideration of study limitations (risk of bias) due to missing participant outcome data for time-to-event outcomes in intervention studies.

We developed this guidance through an iterative process that included membership consultation, feedback, presentation, and iterative discussion at meetings of the GRADE working group.

The GRADE working group has published guidance on how to account for missing participant outcome data in binary and continuous outcomes. When analyzing time-to-event outcomes (e.g., overall survival and time-to-treatment failure) data of participants for whom the outcome of interest (e.g., death and relapse) has not been observed are dealt with through censoring. To do so, standard methods require that censored individuals are representative for those remaining in the study. Two types of censoring can be distinguished, end of study censoring and censoring because of missing data, cnce can be expressed in the study limitations (risk of bias) domain of the GRADE approach.

Concern for risk of bias resulting from censoring of participants for whom follow-up data are missing in the underlying studies of a body of evidence can be expressed in the study limitations (risk of bias) domain of the GRADE approach.

To assess the feasibility of a modified workflow that uses machine learning and crowdsourcing to identify studies for potential inclusion in a systematic review.

This was a substudy to a larger randomized study; the main study sought to assess the performance of single screening search results versus dual screening. This substudy assessed the performance in identifying relevant randomized controlled trials (RCTs) for a published Cochrane review of a modified version of Cochrane's Screen4Me workflow which uses crowdsourcing and machine learning. We included participants who had signed up for the main study but who were not eligible to be randomized to the two main arms of that study. The records were put through the modified workflow where a machine learning classifier divided the data set into "Not RCTs" and "Possible RCTs." The records deemed "Possible RCTs" were then loaded into a task created on the Cochrane Crowd platform, and participants classified those records as either "Potentially relevant" or "Not relevant" to the review. Using a prespecified agreement algorithm, we calculated the performance of the crowd in correctly identifying the studies that were included in the review (sensitivity) and correctly rejecting those that were not included (specificity).

The RCT machine learning classifier did not reject any of the included studies. In terms of the crowd, 112 participants were included in this substudy. Of these, 81 completed the training module and went on to screen records in the live task. Applying the Cochrane Crowd agreement algorithm, the crowd achieved 100% sensitivity and 80.71% specificity.

Using a crowd to screen search results for systematic reviews can be an accurate method as long as the agreement algorithm in place is robust.

Open Science Framework https//osf.io/3jyqt.

Open Science Framework https//osf.io/3jyqt.Coronavirus-triggered pulmonary and systemic disease, i.e. systemic inflammatory response to virally triggered lung injury, named COVID-19, and ongoing discussions on refining immunomodulation in COVID-19 without COX2 inhibition prompted us to search the related literature to show a potential target (COX2) and a weapon (celecoxib). The concept of selectively targeting COX2 and closely related cascades might be worth trying in the treatment of COVID-19 given the substantial amount of data showing that COX2, p38 MAPK, IL-1b, IL-6 and TGF-β play pivotal roles in coronavirus-related cell death, cytokine storm and pulmonary interstitial fibrosis. Considering the lack of definitive treatment and importance of immunomodulation in COVID-19, COX2 inhibition might be a valuable adjunct to still-evolving treatment strategies. Celecoxib has properties that should be evaluated in randomized controlled studies and is also available for off-label use.

The global push for the use of hydroxychloroquine (HCQ) and chloroquine (CQ) against COVID-19 has resulted in an ongoing discussion about the effectivity and toxicity of these drugs. Recent studies report no effect of (H)CQ on 28-day mortality. We investigated the effect of HCQ and CQ in hospitalized patients on the non-ICU COVID-ward.

A nationwide, observational cohort study was performed in The Netherlands. Hospitals were given the opportunity to decide independently on the use of three different COVID-19 treatment strategies HCQ, CQ, or no treatment. We compared the outcomes between these groups. The primary outcomes were 1) death on the COVID-19 ward, and 2) transfer to the intensive care unit (ICU).

The analysis included 1064 patients from 14 hospitals 566 patients received treatment with either HCQ (n = 189) or CQ (n = 377), and 498 patients received no treatment. In a multivariate propensity-matched weighted competing regression analysis, there was no significant effect of (H)CQ on mortality on tom the regular ward to the ICU. Recent prospective studies have reported on 28-day, all-cause mortality only; therefore, additional prospective data on the early effects of HCQ in preventing transfer to the ICU are still needed.

We critically evaluated the quality of evidence and quality of harm reporting in clinical trials that evaluated the effectiveness of hydroxychloroquine (HCQ) or chloroquine (CQ) for the treatment of coronavirus disease 2019 (COVID-19).

Scientific databases were systematically searched to identify relevant trials of HCQ/CQ for the treatment of COVID-19 published up to 10 September 2020. The Cochrane risk-of-bias tools for randomized trials and non-randomized trials of interventions were used to assess risk of bias in the included studies. A 10-item Consolidated Standards of Reporting Trials (CONSORT) harm extension was used to assess quality of harm reporting in the included trials.

Sixteen trials, including fourteen randomized trials and two non-randomized trials, met the inclusion criteria. The results from the included trials were conflicting and lacked effect estimates adjusted for baseline disease severity or comorbidities in many cases, and most of the trials recruited a fairly small cohort of patiof a properly designed and reported clinical trial cannot be overemphasized amid the COVID-19 pandemic, and its dismissal could lead to poorer clinical and policy decisions, resulting in wastage of already stretched invaluable health care resources.An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19.

Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and re-searched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports.

After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a w.82%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents a completely unbiased point estimate. It is likely that, due to age and perhaps underlying comorbidities in the population, different places will experience different IFRs due to the disease. Given issues with mortality recording, it is also likely that this represents an underestimate of the true IFR figure. More research looking at age-stratified IFR is urgently needed to inform policymaking on this front.The chromatin modulator Set5 plays important regulatory roles in both cell growth and stress responses of Saccharomyces cerevisiae. However, its function in filamentous fungi remains poorly understood. Here, we report the pathogenicity-related gene CgSET5 discovered in a T-DNA insertional mutant M285 of Colletotrichum gloeosporioides. Bioinformatic analysis revealed that CgSET5 encodes a SET domain-containing protein that is a homolog of the budding yeast S. cerevisiae Set5. CgSET5 is important for hyphae growth and conidiation and is necessary for appressorium formation and pathogenicity. CgSet5 regulates appressorium formation in a mitogen-activated protein kinase-independent manner. Inactivation of CgSET5 resulted in a significant reduction in chitin content within the cell wall, indicating CgSet5 plays a vital role in cell wall integrity. CgSet5 is involved in peroxisome biogenesis. We identified CgSet5 as the histone H4 methyltransferase, which methylates the critical H4 lysine residues 5 and 8 in C. gloeosporioides. We carried out a yeast two-hybrid screen to find CgSet5 interacting partners. We found CgSet5 putatively interacts with an inorganic pyrophosphatase named CgPpa1, which co-localized in the cytoplasm with CgSet5. Finally, CgPpa1 was found to strongly interact with CgSet5 in vivo during appressorium formation by bimolecular fluorescence complementation assays. These data corroborate a complex control function of CgSet5 acting as a core pathogenic regulator, which connects cell wall integrity and peroxisome biogenesis in C. gloeosporioides.Biomedical literature contains unstructured, rich information regarding proteins, ligands, diseases as well as biological pathways in which they are involved. Systematically analyzing such textual corpus has the potential for biomedical discovery of new protein-protein interactions and hidden drug indications. For this purpose, we have investigated a methodology that is based on a well-established text mining tool, Word2Vec, for the analysis of PubMed full text articles to derive word embeddings, and the use of a simple semantic similarity comparison either by itself or in conjunction with k-Nearest Neighbor (kNN) technique for the prediction of new relationships. To test this methodology, three lines of retrospective analyses of a dataset with known P53-interacting proteins have been conducted. First, we demonstrated that Word2Vec semantic similarity can infer functional relatedness among all kinases known to interact with P53. Second, in a series of time-split experiments, we demonstrated that both a simple similarity comparison and kNN models built with papers published up to a certain year were able to discover P53 interactors described in later publications. Third, in a different scenario of time-split experiments, we examined the predictions of P53-interacting proteins based on the kNN models built on data prior to a certain split year for different time ranges past that year, and found that the cumulative number of correct predictions was indeed increasing with time. We conclude that text mining of research papers in the PubMed literature based on Word2Vec analysis followed by a simple similarity comparison or kNN modeling affords excellent predictions of protein-protein interactions between P53 and kinases, and should have wide applications in translational biomedical studies such as repurposing of existing drugs, drug-drug interaction, and elucidation of mechanisms of action for drugs.