Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.

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.

A protective role for B-1 cells and oxidation-specific epitope IgM in lung fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a morbid fibrotic lung disease with limited treatment options. The pathophysiology of IPF remains poorly understood, and elucidation of the cellular and molecular mechanisms of IPF pathogenesis is key to the development of new therapeutics. B-1 cells are an innate B cell population which play an important role linking innate and adaptive immunity. B-1 cells spontaneously secrete natural IgM and prevent inflammation in several disease states. One class of these IgM recognize oxidation-specific epitopes (OSE), which have been shown to be generated in lung injury and to promote fibrosis. A main B-1 cell reservoir is the pleural space, adjacent to the typical distribution of fibrosis in IPF. In this study, we demonstrate that B-1 cells are recruited to the lung during injury where they secrete IgM to OSE (IgM OSE ). We also show that the pleural B-1 cell reservoir responds to lung injury through regulation of the chemokine receptor CXCR4. Mechanistically we show that the transcription factor Id3 is a novel negative regulator of CXCR4 expression. Using mice with B-cell specific Id3 deficiency, a model of increased B-1b cells, we demonstrate decreased bleomycin-induced fibrosis compared to littermate controls. Furthermore, we show that mice deficient in secretory IgM ( sIgM -/- ) have higher mortality in response to bleomycin-induced lung injury, which is partially mitigated through airway delivery of the IgM OSE E06. Additionally, we provide insight into potential mechanisms of IgM in attenuation of fibrosis through RNA sequencing and pathway analysis, highlighting complement activation and extracellular matrix deposition as key differentially regulated pathways.

Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19.

The variable etiology of persistent breathlessness after COVID-19 have confounded efforts to decipher the immunopathology of lung sequelae. Here, we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate lung disease, but attenuated T-cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators, and autoantibodies distinguished each restrictive phenotype, and differed from those of patients without significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.

Local, Quantitative Morphometry of Fibroproliferative Lung Injury Using Laminin.

Investigations into the mechanisms of injury and repair in fibroproliferative disease require consideration of the spatial heterogeneity inherent in the disease. Most scoring of fibrotic remodeling in preclinical animal models relies on the modified Ashcroft score, which is an ordinal rubric of macroscopic resolution. The obvious limitations of manual histopathologic scoring have generated an unmet need for unbiased, repeatable scoring of fibroproliferative burden in tissue. Using computer vision approaches on immunofluorescence imaging of the extracellular matrix component laminin, we generated a robust and repeatable quantitative remodeling scorer. In the bleomycin lung injury model, the quantitative remodeling scorer shows significant agreement with the modified Ashcroft scale. This antibody-based approach is easily integrated into larger multiplex immunofluorescence experiments, which we demonstrate by testing the spatial apposition of tertiary lymphoid structures to fibroproliferative tissue, a poorly characterized phenomenon observed in both human interstitial lung diseases and preclinical models of lung fibrosis. The tool reported in this article is available as a stand-alone application that is usable without programming knowledge.

Conjugation of IL-33 to Microporous Annealed Particle Scaffolds Enhances Type 2-Like Immune Responses In Vitro and In Vivo.

The inflammatory foreign body response (FBR) is the main driver of biomaterial implant failure. Current strategies to mitigate the onset of a FBR include modification of the implant surface, release of anti-inflammatory drugs, and cell-scale implant porosity. The microporous annealed particle (MAP) scaffold platform is an injectable, porous biomaterial composed of individual microgels, which are annealed in situ to provide a structurally stable scaffold with cell-scale microporosity. MAP scaffold does not induce a discernible foreign body response in vivo and, therefore, can be used a "blank canvas" for biomaterial-mediated immunomodulation. Damage associated molecular patterns (DAMPs), such as IL-33, are potent regulators of type 2 immunity that play an important role in tissue repair. In this manuscript, IL-33 is conjugated to the microgel building-blocks of MAP scaffold to generate a bioactive material (IL33-MAP) capable of stimulating macrophages in vitro via a ST-2 receptor dependent pathway and modulating immune cell recruitment to the implant site in vivo, which indicates an upregulation of a type 2-like immune response and downregulation of a type 1-like immune response.

Analysis of National Institutes of Health Funding for the COVID-19 Pandemic.

Background: Evaluating the National Institute's Health's (NIH's) response to the coronavirus disease 2019 (COVID-19) pandemic via grants and clinical trials is crucial to determining the impact they had on aiding US citizens. We determined how the NIH's funding for COVID-19 research was disbursed and used by various institutions across the United States. Methods: We queried NIH RePORTER and isolated COVID-19-related grants from January 2020 to December 2021. We analyzed grant type, geographical location, and awardee institution. Manuscripts published from these grants were quantitatively analyzed. COVID-19 clinical trials were mapped and distances from counties to clinical trial sites were calculated using ArcGis. Results: A total of 2401 COVID-19 NIH grants resulted in 14 654 manuscripts from $4.2 billion and generated more than 150 000 citations. R01s make up 32% of grants (763/2401) and 8% of funding ($329 million). UM1 grants account for the majority of funding (30.8%; $1.3 Billion). Five states received 50.6% of funding: North Carolina, Washington, New York, California, and Massachusetts. Finally, of the 1806 clinical trials across 1266 sites in the United States, the majority were in metropolitan areas in close proximity to areas of high COVID-19 disease burden. Conclusions and Relevance: Evaluating the outcome of the NIH's response to the COVID-19 pandemic is of interest to the general public. The present study finds that the NIH disbursed more than $4 billion in funding to large consortiums and clinical trials to develop diagnostics, therapeutics, and vaccines. Approximately 8% of funding was used for R01 grants. Clinical trial sites were generally located in areas of high COVID-19 burden.

Pulmonary Function and Survival 1 Year After Dupilumab Treatment of Acute Moderate to Severe Coronavirus Disease 2019: A Follow-up Study From a Phase 2a Trial.

Background: We previously conducted a phase 2a randomized placebo-controlled trial of 40 subjects to assess the efficacy and safety of dupilumab use in people hospitalized with coronavirus disease 2019 (COVID-19) (NCT04920916). Based on our preclinical data suggesting that downstream pulmonary dysfunction with COVID-19 induced type 2 inflammation, we contacted patients from our phase 2a study at 1 year for assessment of post-COVID-19 conditions. Methods: Subjects at 1 year after treatment underwent pulmonary function tests, high-resolution computed tomographic imaging, symptom questionnaires, neurocognitive assessments, and serum immune biomarker analysis, with subject survival also monitored. The primary outcome was the proportion of abnormal diffusion capacity for carbon monoxide (DLCO) or 6-minute walk test (6MWT) at the 1-year visit. Results: Of those survivors who consented to 1-year visits (n = 16), subjects who had originally received dupilumab were less likely than those who received placebo to have an abnormal DLCO or 6MWT (Fisher exact P = .011; adjusted P = .058). As a secondary endpoint, we saw that 16% of subjects in the dupilumab group died by 1 year compared to 38% in the placebo group, though this was not statistically significant (log-rank P = .12). We did not find significant differences in neurocognitive testing, symptoms, or chest computed tomography between treatment groups but observed a larger reduction in eotaxin levels in those who received dupilumab. Conclusions: In this observational study, subjects who received dupilumab during acute COVID-19 hospitalization were less likely to have a reduced DLCO or 6MWT, with a nonsignificant trend toward reduced mortality at 1 year compared to placebo.

Viral and Host Factors Are Associated With Mortality in Hospitalized Patients With COVID-19.

BACKGROUND: Persistent mortality in adults hospitalized due to acute COVID-19 justifies pursuit of disease mechanisms and potential therapies. The aim was to evaluate which virus and host response factors were associated with mortality risk among participants in Therapeutics for Inpatients with COVID-19 (TICO/ACTIV-3) trials. METHODS: A secondary analysis of 2625 adults hospitalized for acute SARS-CoV-2 infection randomized to 1 of 5 antiviral products or matched placebo in 114 centers on 4 continents. Uniform, site-level collection of participant baseline clinical variables was performed. Research laboratories assayed baseline upper respiratory swabs for SARS-CoV-2 viral RNA and plasma for anti-SARS-CoV-2 antibodies, SARS-CoV-2 nucleocapsid antigen (viral Ag), and interleukin-6 (IL-6). Associations between factors and time to mortality by 90 days were assessed using univariate and multivariable Cox proportional hazards models. RESULTS: Viral Ag ≥4500 ng/L (vs <200 ng/L; adjusted hazard ratio [aHR], 2.07; 1.29-3.34), viral RNA (<35 000 copies/mL [aHR, 2.42; 1.09-5.34], ≥35 000 copies/mL [aHR, 2.84; 1.29-6.28], vs below detection), respiratory support (<4 L O2 [aHR, 1.84; 1.06-3.22]; ≥4 L O2 [aHR, 4.41; 2.63-7.39], or noninvasive ventilation/high-flow nasal cannula [aHR, 11.30; 6.46-19.75] vs no oxygen), renal impairment (aHR, 1.77; 1.29-2.42), and IL-6 >5.8 ng/L (aHR, 2.54 [1.74-3.70] vs ≤5.8 ng/L) were significantly associated with mortality risk in final adjusted analyses. Viral Ag, viral RNA, and IL-6 were not measured in real-time. CONCLUSIONS: Baseline virus-specific, clinical, and biological variables are strongly associated with mortality risk within 90 days, revealing potential pathogen and host-response therapeutic targets for acute COVID-19 disease.