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Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seen multiple anti-SARS-CoV-2 antibodies being generated globally. It is difficult, however, to assemble a useful compendium of these biological properties if they are derived from experimental measurements performed at different sites under different experimental conditions. The Coronavirus Immunotherapeutic Consortium (COVIC) circumvents these issues by experimentally testing blinded antibodies side by side for several functional activities. To collect these data in a consistent fashion and make it publicly available, we established the COVIC database (COVIC-DB, https://covicdb.lji.org/). This database enables systematic analysis and interpretation of this large-scale dataset by providing a comprehensive view of various features such as affinity, neutralization, in vivo protection and effector functions for each antibody. Interactive graphs enable direct comparisons of antibodies based on select functional properties. We demonstrate how the COVIC-DB can be utilized to examine relationships among antibody features, thereby guiding the design of therapeutic antibody cocktails. Database URLhttps://covicdb.lji.org/ [ABSTRACT FROM AUTHOR]

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL : http://philm2web.live [ABSTRACT FROM AUTHOR]

Objectives To ascertain if the use of hydroxychloroquine(HCQ)/cloroquine(CLQ) and other conventional DMARDs (cDMARDs) and rheumatic diseases per se may be associated with COVID-19-related risk of hospitalization and mortality. Methods This case–control study nested within a cohort of cDMARD users was conducted in the Lombardy, Veneto, Tuscany and Lazio regions and Reggio Emilia province. Claims databases were linked to COVID-19 surveillance registries. The risk of COVID-19-related outcomes was estimated using a multivariate conditional logistic regression analysis comparing HCQ/CLQ vs MTX, vs other cDMARDs and vs non-use of these drugs. The presence of rheumatic diseases vs their absence in a non-nested population was investigated. Results A total of 1275 patients hospitalized due to COVID-19 were matched to 12 734 controls. Compared with recent use of MTX, no association between HCQ/CLQ monotherapy and COVID-19 hospitalization [odds ratio (OR) 0.83 (95% CI 0.69, 1.00)] or mortality [OR 1.19 (95% CI 0.85, 1.67)] was observed. A lower risk was found when comparing HCQ/CLQ use with the concomitant use of other cDMARDs and glucocorticoids. HCQ/CLQ was not associated with COVID-19 hospitalization as compared with non-use. An increased risk for recent use of either MTX monotherapy [OR 1.19 (95% CI 1.05, 1.34)] or other cDMARDs [OR 1.21 (95% CI 1.08, 1.36)] vs non-use was found. Rheumatic diseases were not associated with COVID-19-related outcomes. Conclusion HCQ/CLQ use in rheumatic patients was not associated with a protective effect against COVID-19-related outcomes. The use of other cDMARDs was associated with an increased risk when compared with non-use and, if concomitantly used with glucocorticoids, also vs HCQ/CLQ, probably due to immunosuppressive action. [ABSTRACT FROM AUTHOR]

Objective To better define the clinical distinctions between the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related paediatric inflammatory multisystem syndrome (PIMS) and Kawasaki disease (KD). Methods We compared three groups of patients: group 1, cases from our national historic KD database (KD-HIS), before the SARS-CoV-2 pandemic; group 2, patients with KD admitted to an intensive care unit (KD-ICU) from both our original cohort and the literature, before the SARS-CoV-2 pandemic; and group 3, patients with PIMS from the literature. Results KD-HIS included 425 patients [male:female ratio 1.3, mean age 2.8 years (s. d. 2.4)], KD-ICU 176 patients [male:female ratio 1.3, mean age 3.5 years (s. d. 3.1)] and PIMS 404 patients [male:female ratio 1.4, mean age 8.8 years (s. d. 3.7)]. As compared with KD-HIS patients, KD-ICU and PIMS patients had a higher proportion of cardiac failure, digestive and neurological signs. KD-ICU and PIMS patients also had a lower frequency of typical KD-mucocutaneous signs, lower platelet count, higher CRP and lower sodium level. As compared with KD-HIS and KD-ICU patients, PIMS patients were older and more frequently had myocarditis; they also had fewer coronary abnormalities and lower sodium levels. Unresponsiveness to IVIG was more frequent in KD-ICU than KD-HIS and PIMS patients. Conclusion On clinical grounds, KD-HIS, KD-ICU and PIMS might belong to a common spectrum of non-specific pathogen-triggered hyperinflammatory states. The causes of increasing inflammation severity within the three entities and the different effects on the heart remain to be determined. [ABSTRACT FROM AUTHOR]

Background The coincidence of Black Lives Matter (BLM) protests with the COVID-19 pandemic in the USA has raised concerns about the safety of mass gatherings for political causes. This study examines two databases to probe any correlation between protests and increases of COVID-19 case rates afterward. Methods A BLM protest aggregator and a county-level COVID-19 database were crosswalked, matching the city that the protest occurred in with the county and its case rates at 0, 1, 2 and 3 weeks after the index protest, and was compared with a control county in the same state with the nearest match of population size and case rate at Week 0. Results In the 22 days after the killing of George Floyd, there were 326 counties participating in 868 protests, attended by an estimated 757 077 protestors. The median case rate at Week 3 was 0.0049 in protest counties versus 0.0041 in control counties, which was found to be statistically significant. Regression analysis found that each individual protestor contributed to the case rate by 7.65 × 10−9, which was not statistically significant. Conclusion Although the increase was statistically significant, it was very small in magnitude and likely due to limitations of significantly different population sizes in comparators. [ABSTRACT FROM AUTHOR]