Grant List
Represents Grant table in the DB
GET /v1/grants?sort=-funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1405&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-funder", "prev": null }, "data": [ { "type": "Grant", "id": "10601", "attributes": { "award_id": "1R21AI169139-01A1", "title": "Informatics Approach to Identification and Deep Phenotyping of PASC Cases", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 26420, "first_name": "MARY KATHERINE", "last_name": "Bradford", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-06", "end_date": "2024-08-31", "award_amount": 217865, "principal_investigator": { "id": 4919, "first_name": "Xiaoming", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 930, "ror": "", "name": "UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 930, "ror": "", "name": "UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "Increasingly there have been reports of persistent symptoms and multi-organ multi-system manifestations (e.g., pulmonary, cardiovascular, renal, and neurological) among individuals who were recovered from the acute phase of COVID-19, denoted as Post-Acute Sequela of SARS-CoV-2 infection (PASC). Given that 76.7 million people are known to have been infected in the US as of February of 2022, millions of people will potentially experience PASC. This projected disease burden will have a profound public health impact with respect to patients' clinical outcomes and US health systems during post-COVID-19 care. Timely identification of individuals with PASC from existing COVID-19 cohorts and newly identified COVID-19 patients is urgently needed for PASC clinics and longitudinal cohort studies on PASC. Building on biomedical informatics methodologies, we propose a high- throughput and semi-supervised Deep Phenotyping approach to identifying individuals with PASC and characterizing their phenotypes. Our approach is based on a Graph representational model constructed based on the South Carolina COVID-19 Cohort (S3C), funded by the National Institute of Allergy and Infectious Diseases (NIAID) (R01A127203-4S1). S3C (n=~1,400, 000 COVID-19 patients by the February of 2022) is a multi-modal data repository consisting of EHR, health systems data, community-based health services data, and claims data, with complete temporal trajectory of every datum at individual-level. Building on top of the Graph model, we will detect phenotypes of candidate PASC patients by using unsupervised clustering algorithms. We will then identify and validate clinically plausible PASC cases and corresponding phenotypes by incorporating clinical evaluation and supervised algorithms. This study will result in a high-throughput algorithm application for identifying and characterizing PASC cases from COVID-19 EHR cohorts. The resulted EHR and machine learning models are interpretable, generalizable, and will form a foundation for testing and implementing in state-wide and national post-COVID clinics/programs.", "keywords": [ "Acute", "Address", "Algorithms", "Back", "Biological Markers", "COVID-19", "COVID-19 patient", "Cardiovascular system", "Caring", "Chest Pain", "China", "Clinic", "Clinical", "Clinical Data", "Clinical Trials Design", "Collection", "Community Health", "Data", "Data Reporting", "Data Science", "Data Sources", "Disease", "Dyspnea", "Electronic Health Record", "Epidemiology", "Europe", "Event", "Fatigue", "Foundations", "Funding", "Gold", "Graph", "Guidelines", "Health", "Health Services", "Health Status", "Health system", "Heterogeneity", "Immune", "Individual", "Informatics", "Kidney", "Link", "Longevity", "Longitudinal cohort", "Longitudinal cohort study", "Lung", "Machine Learning", "Manuals", "Mediating", "Methodology", "Mining", "Modeling", "Morphology", "National Institute of Allergy and Infectious Disease", "Natural Language Processing", "Neurologic", "Observational Study", "Outcome", "Palpitations", "Patients", "Persons", "Phase", "Phenotype", "Physiological", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Public Health", "Reaction Time", "Records", "Recovery", "Reporting", "Research", "Risk Factors", "SARS-CoV-2 infection", "Semantics", "Social Behavior", "South Carolina", "Structure", "Supervision", "Symptoms", "Testing", "Therapeutic", "Time", "United States National Institutes of Health", "acute infection", "base", "biomedical informatics", "biomedical ontology", "burden of illness", "clinical care", "cohort", "data repository", "evidence base", "experience", "health record", "improved", "individual response", "machine learning method", "machine learning model", "multimodal data", "outcome prediction", "persistent symptom", "phenotyping algorithm", "post-COVID-19", "preventive intervention", "programs", "research clinical testing", "supervised learning", "symptom cluster", "trait", "treatment response", "unstructured data" ], "approved": true } }, { "type": "Grant", "id": "10625", "attributes": { "award_id": "1R01HD110844-01", "title": "\"Chanjo Kwa Wakati\" - Leveraging community health workers and a responsive digital health system to improve vaccination coverage and timeliness in rural settings", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 6227, "first_name": "Tracy", "last_name": "King", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-18", "end_date": "2027-08-31", "award_amount": 638030, "principal_investigator": { "id": 26669, "first_name": "Esther Stanslaus", "last_name": "Ngadaya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4149, "first_name": "Lavanya", "last_name": "Vasudevan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 26670, "first_name": "Jan", "last_name": "Ostermann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 930, "ror": "", "name": "UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "Ensuring equitable vaccinations is critical for protecting all children against preventable and potentially dangerous infections such as polio, diphtheria, and measles. Yet, numerous studies have highlighted low vaccination coverage and timeliness, particularly among children from resource-limited settings. For example, in Tanzania, only 68% of children receive all basic vaccines that are recommended in the first year of life. Reasons for vaccination inequities are multifaceted; they include low caregiver knowledge about vaccines, and challenges with health service delivery and access. Health service interruptions during the global COVID-19 pandemic have further restricted opportunities for caregiver education, impacted vaccine access, and exacerbated vaccination inequities. Approaches that optimally utilize limited health workforce capacity and rapidly evolving digital health capacity for remote healthcare in resource-limited settings hold great potential for mitigating childhood vaccination inequities. We recently completed (1) a Fogarty-funded study (R21TW010262) that demonstrated the feasibility and efficacy of mobile phone-based reminders and conditional financial incentives for improving the coverage and timeliness of childhood vaccinations, and (2) a community health worker (CHW) intervention that was shown to be acceptable for mitigating caregiver knowledge gaps about childhood vaccines. Building on this prior work and with support from Tanzania’s National Immunization and Vaccine Development program, we propose to evaluate an integrated, community-based, digital intervention for promoting equity in childhood vaccinations. The outreach and educational intervention, called ”Chanjo Kwa Wakati” (“timely vaccination”), is targeted toward recent mothers and comprises a combination of CHW outreach and low-cost digital strategies (autonomous mobile phone-based vaccination promotion messages, reminders, stockout notifications, and incentive offers for timely vaccinations). In Aim 1, we will evaluate the effectiveness of Chanjo Kwa Wakati in promoting the coverage and timeliness of childhood vaccinations in a Type I effectiveness implementation hybrid trial. The trial will involve the staggered implementation of the intervention across catchment areas of 40 rural health facilities in two predominantly rural regions of Tanzania with large numbers of un- or under-vaccinated children. Vaccination outcomes will be analyzed for children born to 1200 women participating in the trial. In Aim 2, we will evaluate implementation factors associated with variations in intervention effectiveness, analyze the cost effectiveness of the intervention, and develop an implementation blueprint to guide scale-up to other settings. In Aim 3, we will evaluate the feasibility and potential efficacy of a machine learning approach for proactively identifying children at risk of non- or delayed vaccinations and validate predictive models using vaccination data gathered in Aim 1. Study findings will inform future implementations and scale up of Chanjo Kwa Wakati, including potential interventions to improve vaccination equity for children living in rural, resource- limited, or underserved communities in the United States.", "keywords": [ "1 year old", "Address", "Appointment", "Area", "COVID-19 pandemic", "Car Phone", "Caregivers", "Catchment Area", "Child", "Childhood", "Collaborations", "Communities", "Community Health Aides", "Country", "Dangerousness", "Data", "Diphtheria", "Discipline of Nursing", "Disease", "Educational Intervention", "Effectiveness", "Effectiveness of Interventions", "Elements", "Enrollment", "Ensure", "Evaluation", "Funding", "Goals", "Health Personnel", "Health Professional", "Health Services", "Health Technology", "Health behavior", "Health care facility", "Health system", "Immunization", "Incentives", "Income", "Infection", "Interruption", "Intervention", "Knowledge", "Life", "Machine Learning", "Measles", "Modeling", "Morbidity - disease rate", "Mothers", "National Institute of Nursing Research", "Notification", "Nurse&apos", "s Role", "Participant", "Performance", "Poliomyelitis", "Prevention", "Program Development", "Randomized", "Reach Effectiveness Adoption Implementation and Maintenance", "Research", "Research Design", "Research Priority", "Residual state", "Resource-limited setting", "Resources", "Risk", "Rural", "Rural Health", "Rural Population", "Side", "Speed", "System", "Tanzania", "Time", "Training", "Translational Research", "Underserved Population", "United States", "United States National Institutes of Health", "Vaccinated", "Vaccination", "Vaccines", "Variant", "Woman", "Work", "acceptability and feasibility", "base", "caregiver education", "cost", "cost effective", "cost effectiveness", "digital", "digital health", "digital intervention", "economic incentive", "effectiveness evaluation", "effectiveness implementation study", "effectiveness outcome", "financial incentive", "future implementation", "health care delivery", "health equity promotion", "implementation determinants", "implementation evaluation", "implementation intervention", "implementation outcomes", "improved", "innovation", "machine learning model", "mortality", "outreach", "predictive modeling", "prevent", "remote health care", "research to practice", "rural area", "rural setting", "scale up", "supervised learning", "tool", "underserved community", "urban area", "urban disparity", "vaccination outcome", "vaccination strategy", "vaccine access", "vaccine development" ], "approved": true } }, { "type": "Grant", "id": "10649", "attributes": { "award_id": "1R01AI163665-01A1", "title": "Role of a Novel Interferon Responsive T Cell Subset in Allergy and Asthma", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 7248, "first_name": "Wendy F.", "last_name": "Davidson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-20", "end_date": "2027-07-31", "award_amount": 457500, "principal_investigator": { "id": 26707, "first_name": "Gregory", "last_name": "Seumois", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 777, "ror": "", "name": "LA JOLLA INSTITUTE FOR IMMUNOLOGY", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Disease associated with allergies such as asthma are a rising health problem with no current curative solutions. CD4+ helper T cells (TH) that respond to common allergens play an important role in driving airway inflammation in asthma. To better understand the diversity of T cell subsets in allergy and asthma, we analyzed the single-cell transcriptome of ~50,000 house dust mite (HDM) allergen-reactive TH cells from asthmatics and non- asthmatics, with and without HDM allergy. From our analysis, besides canonical clusters of cells such as TH2, TH17, and TH1, we identified a novel subset of allergen-reactive TH cells characterized by an IFN responsive gene signature that we called THIFNR cells (Seumois et al. Science Immunology, 2020). Proportions of THIFNR cells were significantly increased in nonallergic individuals compared to allergic patients, suggesting an allergen-specific host specific response even in non-allergic individuals. Moreover, the exclusive presence of the allergen-reactive TH2 cells in the allergic patients suggests a protective role (anti-TH2 response) of the THIFNR cells in the non-allergic patients with exposure to allergen. This potential protective role was reinforced by our in vitro studies showing that TNF-related apoptosis- inducing ligand (TRAIL) produced by THIFNR cells directly inhibits T cell activation triggered by TCR engagement. In follow-up studies, we found THIFNR cells among viral-reactive TH cells directed towards Flu or SARS-CoV2, suggesting a broader role of those cells in immune responses. Also, we found THIFNR cells as a stable TH subset in a large cohort of healthy individuals. Because of the recent discovery of THIFNR cells, very little is known about their origins, differentiation, phenotype, and function. We hypothesize that these THIFNR HDM-reactive T cells could play a role through TRAIL engagement in dampening TH2 inflammation in allergy and asthma. In Aim 1, we will utilize Interferon-stimulated response element (ISRE) reporter mice and T cell-specific interferon receptor 1 (IFNAR1) knockout mice to determine the importance of THIFNR cells in controlling allergic airway inflammation in asthma models. In Aim 2, we will perform single-cell ATAC-seq profiling to identify transcription factors that may be involved establishing and maintaining the epigenetic state of THIFNR cells. Finally, we will test functionally those TF by using shRNA knockdown experiments. Overall, studies in this program will improve our understanding of how THIFNR cells are generated in vivo and how they interact with other CD4+ T cells subsets like TH2 cells to curtail allergic airway inflammation in asthma models.", "keywords": [ "2019-nCoV", "ATAC-seq", "Address", "Adoptive Transfer", "Allergens", "Allergic", "Allergic Disease", "Allergic inflammation", "Allergic rhinitis", "Animal Model", "Asthma", "Automobile Driving", "Bioinformatics", "Blood", "Blood Circulation", "CD4 Positive T Lymphocytes", "COVID-19", "Cell Death", "Cell physiology", "Cells", "Characteristics", "Code", "Collaborations", "DNA Binding", "Data", "Development", "Disease", "Enhancers", "Epigenetic Process", "Exposure to", "Extrinsic asthma", "Follow-Up Studies", "Frequencies", "Genomics", "Grant", "Health", "Heterogeneity", "House Dust Mite Allergens", "Human", "Hypersensitivity", "IFNAR1 gene", "Immune", "Immune response", "Immunology", "In Vitro", "Individual", "Inflammation", "Influenza", "Interferon Receptor", "Interferons", "Knock-out", "Knockout Mice", "Lead", "Ligands", "Lung", "Maintenance", "Modeling", "Molecular", "Mus", "Pathogenicity", "Patients", "Persons", "Phenotype", "Play", "Population", "Process", "Program Development", "Property", "Proteins", "Pyroglyphidae", "Reporter", "Response Elements", "Role", "Science", "Signal Transduction", "Structure of parenchyma of lung", "T-Cell Activation", "T-Lymphocyte", "T-Lymphocyte Subsets", "T-cell diversity", "TNF-related apoptosis-inducing ligand", "TNFSF10 gene", "Testing", "Th2 Cells", "Therapeutic", "Tissue Sample", "Viral", "Work", "airway inflammation", "allergic airway inflammation", "allergic response", "asthma model", "asthmatic", "chromatin immunoprecipitation", "cohort", "dust mite allergy", "experimental study", "flu", "follow-up", "fungus", "gain of function", "genetic signature", "genome-wide", "genomic tools", "improved", "in vivo", "insight", "knock-down", "loss of function", "mouse model", "novel", "overexpression", "prevent", "programs", "receptor", "respiratory", "respiratory virus", "response", "single cell analysis", "small hairpin RNA", "tool", "transcription factor", "transcriptome", "transcriptomics" ], "approved": true } }, { "type": "Grant", "id": "15805", "attributes": { "award_id": "1K01DA062904-01", "title": "Clinician cannabis use-related preconceptions perpetuating low quality of prenatal care for women who use cannabis during pregnancy", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 32896, "first_name": "SARAH", "last_name": "VIDAL", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-07-15", "end_date": "2030-06-30", "award_amount": 196236, "principal_investigator": { "id": 32897, "first_name": "Rachel Carmen", "last_name": "Ceasar", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2622, "ror": "", "name": "UNIVERSITY OF SOUTHERN CALIFORNIA", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Cannabis is the most used illicit substance during pregnancy. Rates of self-medicating with cannabis escalated during the COVID-19 pandemic. The scientific objective of this proposal is to investigate the mechanisms contributing to preconceptions about those who use cannabis, especially during pregnancy. The central hypothesis is that preconceptions about those who use cannabis result in negative interactions between patients and clinicians that reduce the quality of healthcare and result in poor outcomes. This innovative project will be the first to: (a) leverage natural language processing/artificial intelligence (NLP/AI) techniques to investigate preconceptions about cannabis use in clinical notes, and (b) investigate associations between cannabis use and prenatal care quality. Research aims will: (Aim 1) Investigate preconceptions about those who use cannabis during pregnancy using a mixed methods approach that integrates NLP/AI and qualitative interviews; (Aim 2) Investigate associations between cannabis use and prenatal care quality among different population groups, such as differences in socioeconomic status and education levels; and (Aim 3) Develop, adapt, and test the feasibility and usability of a clinician training on quality health care practices for those who use cannabis during pregnancy using a multistage modified Delphi process, survey, and qualitative focus groups. This research is complemented by a training plan that builds upon Dr. Rachel Carmen Ceasar’s background in mixed qualitative-quantitative methods and substance use research. The training plan includes using NLP/AI approaches, advanced survey methods in reproductive epidemiology, and implementation science. Together, this research and training will prepare Dr. Ceasar to advance as an independent investigator conducting research on health and substance use among those who are pregnant across the lifespan. The proposed project will improve clinicians’ care of those who use cannabis during pregnancy, providing evidence to inform the development of interventions designed to reduce cannabis-use-related notions in prenatal care.", "keywords": [ "Adverse effects", "American College of Obstetricians and Gynecologists", "Artificial Intelligence", "Belief", "COVID-19 pandemic", "California", "Cannabis", "Caring", "Child Welfare", "Clinical", "Clinical Treatment", "Consensus", "Cross-Sectional Studies", "Data", "Detection", "Education", "Educational Status", "Family", "Focus Groups", "Fright", "Future", "Goals", "Guidelines", "Gynecologic", "Health", "Health Benefit", "Health Care", "Income", "Infant", "Interview", "Knowledge", "Language", "Legal", "Link", "Los Angeles", "Medical", "Medical center", "Mentored Research Scientist Development Award", "Mentors", "Methods", "Modeling", "Moods", "Mothers", "Natural Language Processing", "Nausea", "Outcome", "Output", "Pain", "Patient Outcomes Assessments", "Patients", "Persons", "Policies", "Policy Maker", "Population", "Population Group", "Pregnancy", "Pregnancy Outcome", "Pregnant Women", "Prenatal care", "Prevalence", "Process", "Quality of Care", "Questionnaires", "Recommendation", "Reporting", "Research", "Research Personnel", "Rice", "Risk", "Socioeconomic Status", "Supervision", "Survey Methodology", "Surveys", "Techniques", "Testing", "Time", "Training", "Woman", "authority", "cannabis cessation", "comparative", "efficacy evaluation", "evidence base", "experience", "feasibility testing", "follow-up", "health care delivery", "health care quality", "implementation science", "improved", "indexing", "innovation", "large language model", "life span", "low socioeconomic status", "marijuana use", "marijuana use in pregnancy", "neurodevelopment", "open source", "preconception", "prenatal", "provider behavior", "reproductive epidemiology", "substance use", "therapy design", "therapy development", "usability" ], "approved": true } }, { "type": "Grant", "id": "4937", "attributes": { "award_id": "1R21AI168799-01", "title": "Necroptosis in SARS-CoV-2 pathogenesis, evolution, and therapy", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 17784, "first_name": "Mary Chelsea", "last_name": "Lane", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2024-01-31", "award_amount": 280000, "principal_investigator": { "id": 17785, "first_name": "SIDDHARTH", "last_name": "BALACHANDRAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 750, "ror": "", "name": "RESEARCH INST OF FOX CHASE CAN CTR", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Our laboratory has recently implicated necroptosis as a pathogenic and targetable host pathway during pulmonary influenza A virus (IAV) infections. In this proposal, we seek to extend these findings to SARS-CoV-2 because we have strong reason to believe that SARS-CoV-2, like IAV, activates necroptosis. We have identified a mechanism by which SARS-CoV-2 may trigger necroptosis, and propose that such necroptosis underlies the alveolar cell death and inflammatory ‘cytokine storm’ observed in severe COVID-19 disease. Importantly, necroptosis can be targeted by dedicated RIPK3 kinase inhibitors, opening up a new and unanticipated therapeutic entry-point for COVID-19. Specifically, we have discovered that a SARS-CoV-2 nonstructural protein contains a functional RHIM motif that is essential for propagating necroptosis signaling. In all known cell types, necroptosis is initiated when the kinase RIPK3 engages in RHIM-RHIM interactions with other RHIM-containing proteins. For example, during IAV infection, the RHIM in RIPK3 interacts with the RHIM in the IAV sensor protein ZBP1 to trigger necroptosis. We thus hypothesized that the RHIM in the CoV-2 protein allows it to interact with RIPK3 to activate necroptosis. Indeed, we found that the SARS-CoV-2 protein engages RIPK3 and activates necroptosis in human cells. The precise mechanism responsible remains unknown. We have also found that all three pathogenic CoVs (SARS-CoV, MERS-CoV, and SARS-CoV-2) have a RHIM in this protein, whereas none of the human-adapted strains (HKU-1, CO43, NL63, and 229E) possess one. Finally, we have found that bats, the likely natural hosts of SARS-CoV-2 and other pathogenic CoVs, encode a variant of RIPK3 which contains a single amino acid change from non-bat RIPK3. This change significantly dampens necroptosis signaling, suggesting that the necroptosis machinery is defective or non-functional in bats. Based on these and other observations, we hypothesize that SARS-CoV-2 and allied pathogenic CoVs activate necroptosis in human pulmonary epithelia, via a RHIM-RHIM interaction involving the CoV-2 RHIM-containing protein and RIPK3, and that such necroptosis initiates and amplifies the lung injury and inflammation seen in severe cases of COVID- 19. We further propose that dampened necroptosis signaling in bats allows them to harbor pathogenic (to humans) CoVs without apparent hyper-inflammatory consequences. In this proposal, we will examine how SARS-CoV activates necroptosis in human cells, and if such necroptosis is a new therapeutic opportunity in vivo by evaluating FDA-approved and new, high potency RIPK3 inhibitors in a mouse model of SARS-CoV-2 infection. We have also developed a knock-in mouse harboring the bat RIPK3 polymorphism, and will test if SARS-CoV-2-initiated lung pathology is dampened in this mouse, compared to controls. The successful completion of these studies will provide pioneering insight into the mechanism and evolutionary biology of necroptosis signaling in SARS-CoV-2 pathogenesis and stand to have important ramifications for the treatment of severe COVID-19.", "keywords": [ "2019-nCoV", "Alveolar Cell", "Amino Acids", "Biology", "COVID-19", "COVID-19 test", "Cell Death", "Cells", "Chiroptera", "Disease", "Epithelial", "Epithelial Cells", "Evolution", "FDA approved", "Genetic Polymorphism", "Human", "Inflammatory", "Influenza A virus", "Knock-in Mouse", "Laboratories", "Laboratory Study", "Left", "Lung", "Middle East Respiratory Syndrome Coronavirus", "Mus", "Nonstructural Protein", "Pathogenesis", "Pathogenicity", "Pathology", "Pathway interactions", "Phosphotransferases", "Proteins", "Public Health", "Pulmonary Inflammation", "Pulmonary Pathology", "RIPK1 gene", "RIPK3 gene", "Role", "SARS coronavirus", "SARS-CoV-2 infection", "SARS-CoV-2 pathogenesis", "Series", "Severity of illness", "Signal Transduction", "Testing", "Therapeutic", "Variant", "Viral Pathogenesis", "Virulent", "Virus", "Virus Diseases", "base", "cell type", "cytokine release syndrome", "experimental study", "feasibility testing", "helicase", "in vivo", "inhibitor/antagonist", "insight", "kinase inhibitor", "lung injury", "mortality", "mouse model", "novel therapeutics", "pathogenic virus", "prevent", "sensor", "severe COVID-19" ], "approved": true } }, { "type": "Grant", "id": "10593", "attributes": { "award_id": "1R01HS028978-01", "title": "Learning from Hospital Preparedness during COVID: Chronically Under-Resourced Nurses and Patient Safety", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [ { "id": 25171, "first_name": "Monika", "last_name": "Haugstetter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-06", "end_date": "2025-08-31", "award_amount": 399292, "principal_investigator": { "id": 26621, "first_name": "Karen Blanchette", "last_name": "Lasater", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Learning from Hospital Preparedness during COVID: Chronically Under-Resourced Nurses and Patient Safety This study will evaluate how hospital nurses weathered the COVID-19 public health emergency, whether and to what extent hospital nurse resources (staffing, work environment, Magnet designation) buffered nurses from poor outcomes (such as burnout) during the pandemic and facilitated recovery 3 years after the onset of the COVID emergency, and the extent to which patient outcomes, safety, quality, and value of care indicators paralleled changes in nurse outcomes and hospital nurse resources over the study period. We will accomplish these objectives by leveraging already existing data from over 33,000 hospital nurses in 244 hospitals in New York and Illinois, [Wave 1 data collected just before COVID (Dec 2019-Feb 2020); Wave 2 collected 1 year after COVID onset] and by conducting primary data collection of repeat measures [Wave 3 to be collected 3 years after COVID onset (Oct 2022-Dec 2022)]. Each Wave includes repeated measures of nurse outcomes (e.g., burnout, job dissatisfaction, intent to leave job), hospital nurse resources (staffing, work environment, Magnet), measures of patient safety and quality of care, including items from the AHRQ Patient Safety Culture survey. These cross-sections of data will be linked with contemporaneous (1) patient-level data from CMS MedPAR Medicare to study risk-adjusted patient outcomes among patients hospitalized for common medical, surgical, and COVID diagnoses; (2) Hospital Compare data to evaluate hospital-level measures of patient satisfaction and healthcare value (Medicare spending per beneficiary), (3) American Hospital Association data for considering organizational features of hospitals, and (4) publicly available COVID hospitalization data to account for variation in COVID burden across hospitals. In combination, we will have 3 cross-sections of data from 244 hospitals (with fluctuating nurse and patient populations) just before, 1 year and 3 years after the onset of the COVID emergency. Our analytic approach uses multi-level nested (hierarchically-related) linear and logistic regression models (with interaction terms). The COVID emergency offers a unique opportunity to make a major advance in our scientific understanding of the potentially causal relationships between nurse outcomes and patient outcomes, which have until now largely only been rigorously evaluated in the cross- section. The tremendous shock imposed by the COVID emergency, combined with our propitiously timed data, enable us to evaluate how the pandemic impacted hospital nurses and what hospital factors contribute to a more favorable recovery in the years following the COVID emergency. Together, this evidence will inform high- impact actionable policy and organizational solutions for building and sustaining safe, high value healthcare systems that can endure future public health emergencies and thrive during ordinary times.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10609", "attributes": { "award_id": "1R01MD018206-01", "title": "CRISOL Mente: A Multilevel Community Intervention to Reduce Mental Health Disparities Among Latinos", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Minority Health and Health Disparities (NIMHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 6025, "first_name": "Crystal", "last_name": "Barksdale", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-19", "end_date": "2027-05-31", "award_amount": 1119618, "principal_investigator": { "id": 26650, "first_name": "Mariana", "last_name": "Lazo Elizondo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 23052, "first_name": "Ana P", "last_name": "Martinez-Donate", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 377, "ror": "https://ror.org/04bdffz58", "name": "Drexel University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Latinos in the U.S. experience significant disparities in access to mental health services due to lack of health insurance, cost of services, limited awareness of mental health resources, mental health stigma, and fear of deportation. Limited English proficiency coupled with an acute lack of bilingual and culturally competent providers further impede Latinos’ adequate access to quality mental health services. The COVID-19 pandemic has only amplified the need for mental health care and exacerbated mental health disparities for Latino communities, making it urgent to identify low-cost, effective strategies to reduce these gaps. This 5-year project seeks to develop and test a multi-level, community intervention to improve mental health outcomes and promote access to culturally appropriate mental health treatment for Latino communities in Philadelphia. CRISOL Mente will include components at various levels of the socio-ecological model: a clinic-based, stepped-care program relying on Latino lay health workers (LHW) for the delivery of mental health services (Aim 1), outreach and education activities to reduce mental health stigma in the community (Aim 2), and efforts to strengthen Latino-serving organizations’ capacity to address mental health and other syndemic conditions contributing to untreated mental health among Latinos (Aim 3). To improve mental health symptoms and engagement in care, we will recruit, train and supervise a cohort of Latino LHW who will be embedded into two Latino-serving clinics, extending the reach and effectiveness of the clinics’ mental health services. We will compare the impact of three different levels of LHW involvement: a) community outreach/navigation (i.e. screening and referral of community members); b) auxiliary care (i.e. screening, referral, and help overcoming barriers to better mental health); and c) task shifting (i.e. screening, referral, assistance, and supervised delivery of basic mental health treatment). The LHWs will also conduct outreach/education activities in the community (e.g. radio talks, info sessions, tables in community venues) to reduce mental health stigma. Our experienced and largely Latino community-academic research team will also engage in capacity building activities (i.e. monthly town halls, annual retreats, weekly newsletters, provision of trainings and technical support) with the Latino Health Collective, a coalition of Latino-serving organizations. Using mixed-methods and the RE-AIM framework, CRISOL Mente’s impact will be evaluated with clinical data, baseline and 6-month patient survey data (N=200 from each level of LHW involvement), and qualitative interviews with community members (N=30) referred to mental health services by the LHW (Aim 1); pre/post mental health stigma data from two respondent driven sampling (RDS) surveys of Latinos (N=400 each) conducted in 2022 (preliminary study) and in 2027 (Aim 2); community capacity indicators from three surveys of Latino-serving organizations conducted in 2019, 2021 (preliminary studies) and 2027, and key informant interviews (KII) with Latino-serving providers (N=30) in 2019 (preliminary study) and 2027 (Aim 3).", "keywords": [ "Acute", "Address", "Adherence", "Advocacy", "Awareness", "COVID-19 pandemic", "Caring", "Client", "Clinic", "Clinical Data", "Communication", "Communities", "Community Outreach", "Community Surveys", "Complex", "Coupled", "Data", "Development", "Discrimination", "Economics", "Education", "Education and Outreach", "Educational Background", "Effectiveness", "Evaluation", "Evidence based treatment", "Fright", "Health", "Health Insurance", "Health Resources", "Health Services Accessibility", "Healthcare", "Immigrant", "Intervention", "Interview", "Language", "Latina", "Latino", "Latino Population", "Legal", "Limited English Proficiency", "Linguistics", "Mental Depression", "Mental Health", "Mental Health Services", "Mental disorders", "Methods", "Modeling", "Newsletter", "Not Hispanic or Latino", "Occupations", "Outcome", "Patients", "Phase", "Philadelphia", "Population", "Poverty", "Provider", "Public Health", "Radio", "Reach Effectiveness Adoption Implementation and Maintenance", "Reduce health disparities", "Reporting", "Research", "Resource-limited setting", "Resources", "Respondent", "Rest", "Risk", "Sampling", "Services", "Site", "Social Conditions", "Social Work", "Stress", "Supervision", "Surveys", "Symptoms", "Syndrome", "Testing", "Time", "Training", "Woman", "barrier to care", "base", "bilingualism", "clinical anxiety", "cohort", "community engaged research", "community intervention", "community setting", "cost", "cost effective", "cultural competence", "design", "disparity reduction", "effectiveness testing", "experience", "follow-up", "health care availability", "health disparity", "improve minority health", "improved", "informant", "member", "men", "novel", "outreach", "peer", "post-COVID-19", "post-traumatic stress", "programs", "psychological distress", "racism", "recruit", "screening", "social stigma", "syndemic" ], "approved": true } }, { "type": "Grant", "id": "4929", "attributes": { "award_id": "3U24AI152172-03S1", "title": "A scalable platform for highly-multiplexed analysis of antibody reactivity from <1uL of blood", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 17768, "first_name": "John A.", "last_name": "Peyman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-10", "end_date": "2023-03-31", "award_amount": 61256, "principal_investigator": { "id": 17769, "first_name": "John", "last_name": "Altin", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 743, "ror": "", "name": "TRANSLATIONAL GENOMICS RESEARCH INST", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Antibodies are highly-specific, diverse and widely-assayed biomarkers used to determine recent or historical pathogen exposures, measure the protection conferred by a vaccine, understand the basis of autoimmune diseases or evaluate a host's immunological function. Traditional assays for antibodies focus on one or a small number of reactivities at a time, and so are incommensurate with the scale and diversity of an individual's antibody response. A tool to more holistically interrogate this diversity of reactivities using a small sample volume would enable a new generation of studies in systems immunology, disease association, and epidemiological surveillance. Here, we propose to optimize and significantly extend an approach we have developed for highly- multiplexed, reproducible and inexpensive assays that enable sensitive and high-resolution analysis of antibody reactivity across 100,000s of antigens from <1µL of blood. Our approach takes advantage of a rapid, fully-in- vitro method for generating 100,000s of DNA-barcoded peptides (`PepSeq') as probes for the highly-multiplexed interrogation of serum antibodies using DNA sequencing. As a proof-of-concept, we will be focusing here on an assay targeting all viruses known to infect humans (i.e., the human virome). The virome is an ideal use case for this technology, as viruses represent an incredibly diverse and ubiquitous challenge to the immune system, and because of their small genome sizes, the complete virome can be covered within a single library with minimal loss of diversity. Our preliminary data with this virome assay establishes the feasibility of this approach. Here, we will optimize the assay procedures for multiple sample types in order to increase sensitivity and specificity, while decreasing cost. We will also establish standardized protocols for isotype-specific profiling, adapt the technology to enable antigen-specific, single-cell characterization, and build a suite of open access data analysis and visualization tools to facilitate the use of this technology by the broader research community. Throughout this process, we will generate a panel of anti-virome antibody profiles, including a cohort profiled longitudinally – this data will be made available to the community through the ImmPort portal. If successful, this project will deliver: (i) an optimized assay SOP and library for comprehensive evaluation of pan-viral immunity using a small sample volume, (ii) a large set of publicly-available anti-virome immunity datasets, and (iii) a framework for multiplexed serological assay development that can be directly extended to other targets.", "keywords": [ "Allergens", "Amino Acids", "Antibodies", "Antibody Response", "Antigens", "Antiviral Agents", "Autoantigens", "Autoimmune Diseases", "B-Lymphocytes", "Bar Codes", "Basic Science", "Benchmarking", "Biological Assay", "Biological Markers", "Blood", "Blood specimen", "Cells", "Clinical Research", "Communities", "Custom", "DNA", "DNA sequencing", "Data", "Data Analyses", "Data Set", "Databases", "Disease", "Drops", "Epidemiologic Monitoring", "Evaluation", "Event", "Exposure to", "Frequencies", "Generations", "Genome", "Gold", "Human", "IgE", "Immune response", "Immune system", "Immunity", "Immunoglobulin A", "Immunoglobulin G", "Immunoglobulin M", "Immunologic Monitoring", "Immunology", "In Vitro", "Individual", "Length", "Libraries", "Link", "Measures", "Methods", "Monitor", "Mutagenesis", "Peptides", "Performance", "Plasma", "Population", "Procedures", "Process", "Production", "Proteins", "Protocols documentation", "Reproducibility", "Research", "Resolution", "Resources", "Sampling", "Sensitivity and Specificity", "Serology test", "Seroprevalences", "Serum", "Signal Transduction", "Specificity", "Spottings", "Standardization", "System", "Technology", "Testing", "Time", "Vaccines", "Variant", "Viral", "Viral Antibodies", "Virus", "Visualization software", "assay development", "cohort", "cost", "data access", "data visualization", "design", "human virome", "immune function", "longitudinal analysis", "open data", "pathogen", "pathogen exposure", "single cell sequencing", "success", "tool", "transcriptome", "transcriptome sequencing", "transcriptomics", "virome" ], "approved": true } }, { "type": "Grant", "id": "10617", "attributes": { "award_id": "1R35GM146861-01", "title": "Molecular Mechanisms of Programmed Necrosis Execution", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 24077, "first_name": "Baishali", "last_name": "Maskeri", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-20", "end_date": "2027-07-31", "award_amount": 410000, "principal_investigator": { "id": 26659, "first_name": "Ayaz", "last_name": "Najafov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1215, "ror": "", "name": "UT SOUTHWESTERN MEDICAL CENTER", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Necroptosis is a caspase-independent type of programmed necrosis. The activation of the necroptosis signaling cascade is implicated in the pathogenesis of various human diseases, including cancer, inflammatory bowel disease, liver injury, pancreatitis, neurodegenerative disorders, and a diverse range of viral, bacterial, and fungal infections, including SARS-CoV-2. The necroptosis signaling cascade is mediated by the sequential activation of RIPK1 and RIPK3 kinases downstream of pro-inflammatory ligands such as TNF or microbe-associated molecules. MLKL is a pseudokinase that tetramerizes upon phosphorylation by RIPK3 to form water-permeable pores that drive cell membrane rupture. This pore formation stage leads to the necrotic phenotype of necroptosis. It is also a critical point of cell fate determination, as necroptosis execution can be halted and reversed at the MLKL stage. The mechanisms regulating MLKL activation and execution of this type of programmed necrosis are poorly understood. Here, we will fill in the gaps of our understanding of the molecular mechanisms that regulate MLKL activation, tetramerization, and execution of necroptotic cell death via phosphorylation and ubiquitination. We aim to determine the mechanistic roles of the MLKL post-translational modification events in promoting or suppressing MLKL tetramerization and identify the enzymes regulating MLKL-driven necrotic cell death via these events. We also aim to determine which structural factors are required downstream of MLKL to execute the necroptotic cell death. Finally, to validate the roles of these enzymes and factors in mediating necroptosis in vivo, we will test how their genetic knockouts affect sensitivity to Vaccinia virus infection, contributing to the future development of strategies for enhancing host anti-viral response. Overall, this project will significantly expand our understanding of the cellular signaling mechanisms upstream and downstream of MLKL at the necroptosis execution stage and pave the way for future anti-microbial therapies, as well as treatments for diseases that involve necroptosis execution.", "keywords": [ "2019-nCoV", "Affect", "Antiviral Response", "Bacterial Infections", "Caspase", "Cell Death", "Cell membrane", "Cells", "Development", "Disease", "Enzymes", "Event", "Future", "Genetic", "Inflammatory", "Inflammatory Bowel Diseases", "Knock-out", "Ligands", "Malignant Neoplasms", "Mediating", "Microbe", "Molecular", "Mycoses", "Necrosis", "Neurodegenerative Disorders", "Pancreatitis", "Pathogenesis", "Permeability", "Phenotype", "Phosphorylation", "Phosphotransferases", "Post-Translational Protein Processing", "RIPK1 gene", "RIPK3 gene", "Regulation", "Role", "Rupture", "Signal Transduction", "TNF gene", "Testing", "Ubiquitination", "Vaccinia virus", "Virus Diseases", "Water", "Work", "antimicrobial", "human disease", "in vivo", "liver injury" ], "approved": true } }, { "type": "Grant", "id": "10641", "attributes": { "award_id": "1R01DA056407-01", "title": "Design and analysis advances to improve generalizability of clinical trials for treating opioid use disorder", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 22878, "first_name": "Sarah Q", "last_name": "Duffy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-15", "end_date": "2027-06-30", "award_amount": 770972, "principal_investigator": { "id": 26694, "first_name": "Kara Elizabeth", "last_name": "Rudolph", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26695, "first_name": "Elizabeth A.", "last_name": "Stuart", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 781, "ror": "", "name": "COLUMBIA UNIVERSITY HEALTH SCIENCES", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The opioid epidemic in the US is a public health emergency, exacerbated by the Covid-19 pandemic. Medi- cations for opioid use disorder (MOUD)-injection naltrexone, buprenorphine, and methadone-are the most effective tools for improving outcomes and preventing overdose among persons with OUD, but engagement in MOUD, especially long-term engagement typically required for a successful outcome, is unacceptably low. Long-term engagement rates tend to be even lower in real-world settings-what NIDA has termed the research-to-practice gap. This discrepancy between trial and real-world MOUD effectiveness could be par- tially attributable to differences between clinical trial versus real-world population characteristics (e.g., in terms of psychiatric and substance use comorbidities, previous treatment experience, immigration status, etc.) if treatment effects are modified (increased/decreased) by some of these characteristics that also relate to trial participation. Moreover, without knowing the relative effectiveness of MOUDs for certain real-world target pop- ulations, clinicians, researchers, and policymakers may be tasked with decision-making with biased evidence. Thus, there is a critical need to improve the generalizability of MOUD trials. Failing to meet this need would further ossify the research-to-practice gap, resulting in suboptimal treatment of OUD overall and within key subgroups. We propose to develop design and analytic approaches, what we call a generalizability through- line, to bridge MOUD trial evidence to real-world populations. The objectives of this project are: In Aim 1), to identify and characterize clinically meaningful, interpretable subgroups of persons seeking OUD treatment in US usual-care settings who are not represented or under-represented in MOUD trials based on multiple char- acteristics simultaneously. This will move us beyond existing approaches for assessing representation that have generally been limited to considering one individual-level characteristic at a time (e.g., race/ethnicity). We will apply the approach developed in the first part of Aim 1 to trial data (3 MOUD trials from NIDA CTN) and population data (California and New Jersey Medicaid claims) to characterize under-represented subgroups. In Aim 2), to generalize MOUD effectiveness to state-specific adult Medicaid populations, thereby estimating a realistic treatment goal if treatment retention supports, incentives, and dosing practices were improved to align with those in trials. Existing approaches for predicting generalized effects rely on extrapolation for non- and under-represented subgroups, which can result in biased and/or uninformative estimates. The approach developed in the first part of Aim 2 will make several improvements to limit extrapolation and increase effi- ciency. In Aim 3), to implement the methods developed for Aims 1 and 2 in user-friendly software to facilitate the easy adoption by applied trialists, researchers, and clinicians. The proposed research is expected to make a significant contribution to improving representation among trial participants and to understanding how and to whom trial findings generalize.", "keywords": [ "Accountability", "Address", "Adult", "Buprenorphine", "COVID-19 pandemic", "California", "Cessation of life", "Characteristics", "Clinical", "Clinical Trials", "Clinical Trials Network", "Data", "Decision Making", "Dose", "Effectiveness", "Ensure", "Foundations", "Future", "Goals", "Individual", "Left", "Medicaid", "Methadone", "Methods", "Morbidity - disease rate", "Naltrexone", "National Institute of Drug Abuse", "New Jersey", "Output", "Overdose", "Overdose reduction", "Participant", "Patients", "Persons", "Pharmaceutical Preparations", "Phase", "Policies", "Population", "Process", "Provider", "Race", "Recovery", "Software Tools", "Subgroup", "Target Populations", "Time", "Treatment Effectiveness", "Underrepresented Populations", "Vehicle crash", "base", "care seeking", "clinical trial participant", "comparative treatment", "data harmonization", "design", "evidence base", "improved", "improved outcome", "mortality", "opioid use disorder", "patient population", "prevent", "research to practice", "sociodemographics", "tool", "treatment as usual", "treatment effect" ], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1405, "count": 14046 } } }