Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=awardee_organization
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=awardee_organization", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=awardee_organization", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1420&sort=awardee_organization", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1418&sort=awardee_organization" }, "data": [ { "type": "Grant", "id": "15787", "attributes": { "award_id": "1R13HD118735-01", "title": "Health, Mortality, & Aging Among People with Criminal Legal System Contact in America", "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": 32868, "first_name": "RANDOLPH CHRISTOPHER", "last_name": "CAPPS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-01", "end_date": "2028-07-31", "award_amount": 10000, "principal_investigator": { "id": 32869, "first_name": "Sade", "last_name": "Lindsay", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32870, "first_name": "Bryan Lamont", "last_name": "Sykes", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2650, "ror": "", "name": "CORNELL UNIVERSITY", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "PROJECT SUMMARY/ABSTRACT: Criminal justice contact is an important contributor to population variation in health outcomes, including mortality, morbidity, and aging. During and following the COVID-19 pandemic, people in American jails, prisons, and detention centers experienced elevated health risks that exacerbated their already high morality and aging precariousness. This proposal requests support for a three-year conference program on, “Health, Mortality, and Aging Among People with Criminal Legal System Contact in America,” with each year focusing on critical questions about the health of incarcerated people that have emerged since the COVID-19 crisis. These interdisciplinary conferences will harness the research and expertise of established and emerging scholars conducting research at the intersections of health, mortality, and aging in economics, sociology, demography/population science, law, criminology, public health, medicine, and public policy. The proposed conference programs are innovative by including the participation of people affected by legal system involvement, as well as by investigating population heterogeneity to formulate and to advance a bold new research agenda that will benefit affected communities. The program has four specific aims: (1) to advance scientific knowledge and research recommendations to improve health across the life-course for people involved in the criminal legal system; (2) to amplify the voices of affected people, families, and communities; (3) to train the next generation of criminal legal scholars; and (4) to engage multiple audiences. Deliverables will include: (1) three special issues, each devoted to a unique conference theme over the three years (i.e., mortality, health, and aging), allowing for the discrete and unique treatment of each topic; and (2) research briefs that translate findings into potential interventions and recommendations that broadly engage the individuals, families, and communities subject to criminal justice contact, as well as other stakeholders (government officials, prison and court actors, non-profits, and other advocates). By discussing and documenting how life-course transitions that intersect with the criminal legal system matter for socio-economic, health, and well-being, this conference will engage and advance the conceptual and empirical dialogues that began with several National Academies of Sciences, Engineering, and Medicine workshops in 2013 and 2020, and, more recently, a half-day seminar in 2024. As leaders in the criminal legal field, the investigative team and Cornell University are uniquely positioned to host this program.", "keywords": [ "Acute", "Advocate", "Affect", "Aging", "American", "Area", "Attention", "COVID-19 pandemic", "Cessation of life", "Chronic", "Collaborations", "Collection", "Communities", "Community Health", "Consumption", "Correctional Institutions", "Criminal Justice", "Criminology", "Data", "Dedications", "Demography", "Development", "Economics", "Educational workshop", "Engineering", "Epidemiology", "Event", "Exclusion", "Exposure to", "Family", "Government Officials", "Health", "Imprisonment", "Individual", "Intervention", "Jail", "Journals", "Knowledge", "Laws", "Legal", "Legal system", "Life Cycle Stages", "Life Expectancy", "Lived experience", "Measures", "Medicine", "Mentors", "Methodology", "Methods", "Morality", "Morbidity - disease rate", "Outcome", "Participant", "Personal Satisfaction", "Persons", "Population", "Population Heterogeneity", "Population Sciences", "Positioning Attribute", "Prisons", "Public Health", "Public Policy", "Recommendation", "Reporting", "Request for Proposals", "Research", "Research Personnel", "Risk", "Scientific Advances and Accomplishments", "Scientific Inquiry", "Social Sciences", "Sociology", "Training", "Translating", "Translational Research", "United States National Academy of Sciences", "Universities", "Variant", "Voice", "career", "court", "detention center", "experience", "forging", "health difference", "improved", "improved outcome", "individualized medicine", "innovation", "labor market", "life span", "mortality", "next generation", "population health", "prison population", "programs", "public health relevance", "research and development", "socioeconomics", "symposium", "theories" ], "approved": true } }, { "type": "Grant", "id": "15788", "attributes": { "award_id": "1R16GM159146-01", "title": "Building Reliable Vision-Language Assistant for Dermatology AI through Modeling Uncertainties in Multimodal LLMs", "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": 32871, "first_name": "WUHONG", "last_name": "PEI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-12", "end_date": "2029-06-30", "award_amount": 175470, "principal_investigator": { "id": 32872, "first_name": "Zhiqiang", "last_name": "Tao", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2651, "ror": "", "name": "ROCHESTER INSTITUTE OF TECHNOLOGY", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Diagnosis delay is one of the key factors that lead to skin cancer death, especially for melanoma diagnosis during the COVID-19 pandemic. The long examination time and limited dermatological access have been the major roadblocks to the preventive treatment of skin cancers to lower the high mortality rate. Developing a clinical AI agent that can analyze digital skin images and provide timely, interactive text responses to patient symptoms and inquiries will significantly mitigate the nationwide dermatologist shortage, thereby improving the early diagnosis chance and teledermatology accessibility for melanoma as well as other skin diseases. Conventional dermatology AI methods mainly focus on medical image recognition to identify skin lesions and malignancies, falling short in visual-language assistance for remote healthcare services. A conversational diagnostic AI model, which is able to answer medical questions by sensing subtle visual patterns of skin disorders/cancers, is still in urgent need. The long-term goal of this research program is to develop a reliable large visual-language (VL) model that enables conversational Dermatology AI to facilitate early melanoma diagnosis and general skin care. The proposed research will generate accurate and interpretable clinical responses by finetuning Large Language Models – LLMs (e.g., the generative AI models deployed in ChatGPT) through answering questions and visual reasoning in multimodal contexts. Specifically, the project will realize three aims: 1) Build a new multimodal LLM specifically for dermatology to discern melanoma and other skin diseases and to automatically answer questions relevant to skin lesions. 2) Study uncertainties stemming from data bias and distribution shifts to enhance the reliability of LLM-powered AI diagnosis in multimodal contexts and teledermatology environments. 3) Determine the visual relevance in LLM decisions based on the rich public dermatological images with clinical text annotations. The proposed research will establish a new multimodal LLM that interweaves visual reasoning and uncertainties to advance Dermatology AI in broad VL assistance tasks, enabling automatic conversational diagnostic in teled- ermatology and providing new insights about how LLM understands skin lesions and dermatological knowledge. A pixelwise visual instruction tuning approach and a novel multi-level uncertainty quantification framework will be developed, providing technical foundations to benefit a wide range of LLM-based healthcare research. This project will be the first large visual-language research study that investigates LLM's intelligence and reliability in coping with multimodal dermatology contexts – visual skin lesions and text clinical annotations/dialogues. The success of this project will provide transformative AI techniques in assisting early melanoma diagnosis and remote skin care, leading to better teledermatology accessibility for patient treatments, reducing mortality from skin cancers through timely detection, and revolutionizing dermatological access in public healthcare systems.", "keywords": [ "Accounting", "Artificial Intelligence enhanced", "Behavior", "Benchmarking", "Benign", "COVID-19 pandemic", "Cellular Phone", "Cessation of life", "ChatGPT", "Clinical", "Cutaneous Melanoma", "Data", "Death Rate", "Dependence", "Dermatologic", "Dermatologist", "Dermatology", "Dermoscopy", "Detection", "Development", "Diagnosis", "Diagnostic", "Disease", "Early Diagnosis", "Environment", "Evaluation", "Foundations", "Generations", "Goals", "Grain", "Hallucinations", "Health Care", "Health Care Research", "Health Care Systems", "Image", "Instruction", "Intelligence", "Knowledge", "Language", "Learning", "Lesion", "Life", "Machine Learning", "Malignant Neoplasms", "Medical", "Medical Imaging", "Melanoma", "Methods", "Modeling", "Morphologic artifacts", "Noise", "Patients", "Pattern", "Preventive treatment", "Prognosis", "Protocols documentation", "Public Health", "Research", "Resolution", "Risk", "Series", "Skin", "Skin Cancer", "Skin Care", "Skin Imaging", "Skin Malignancy", "Symptoms", "Techniques", "Text", "Time", "Training", "Translations", "Uncertainty", "Underserved Population", "United States", "Vision", "Visual", "artificial intelligence model", "chatbot", "clinical imaging", "coping", "data acquisition", "design", "digital", "diverse data", "generative artificial intelligence", "health care service", "improved", "insight", "large language model", "lens", "mortality", "multimodality", "neglect", "novel", "programs", "remote health care", "research study", "response", "skin disorder", "skin lesion", "stem", "success", "teledermatology", "trustworthiness" ], "approved": true } }, { "type": "Grant", "id": "15790", "attributes": { "award_id": "1R21AI193738-01", "title": "Combined pathogen and host-based diagnostic to identify etiology of lower respiratory tract infection", "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": 32875, "first_name": "INKA I", "last_name": "SASTALLA", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-05", "end_date": "2027-07-31", "award_amount": 244044, "principal_investigator": { "id": 7629, "first_name": "GAYANI", "last_name": "TILLEKERATNE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Lower respiratory tract infection (LRTI) is the leading infectious cause of death globally. Despite its prevalence, the exact etiology of LRTI is unknown in the vast majority of cases. Even when identified, bacteria or viruses in nasopharyngeal (NP) or sputum samples may be colonizers in the upper tract rather than the cause of infection in the lower tract. Unclear LRTI etiology results in the overprescription of antibacterials, which in turn drives the global crisis in antibacterial resistance. Antibacterial overprescription and resistance are greater in low- or middle-income countries (LMICs), where basic diagnostic capacity is limited. Host-based diagnostics, which assess the host immune response to infection, have recently emerged as a complementary method to pathogen-based diagnostics for identifying the class of respiratory infection. Our team has developed host- based gene expression classifiers using peripheral blood samples to differentiate viral versus bacterial respiratory infection. The goal of the current application is to develop an integrated diagnostic that uses a single, non-invasive NP sample to detect both pathogen and host response to identify LRTI etiology. The following specific aims will be conducted at a collaborative research site in Sri Lanka: 1) Develop a novel NP- based gene expression classifier to identify viral versus non-viral LRTI, and 2) Design and validate an integrated pathogen and host gene expression test to identify viral versus non-viral LRTI using a quantitative real-time polymerase chain reaction (qRT-PCR) assay. For aim 1, we will use previously collected NP samples from clinically adjudicated viral and non-viral LRTI patients in Sri Lanka and conduct low-input RNA sequencing. Machine-learning approaches will identify host gene expression classifiers that discriminate viral versus non-viral LRTI. For aim 2, the genes identified in the NP-based classifier, as well as nucleic acid targets for two respiratory viruses that are frequently implicated in true infection as well as asymptomatic colonization (SARS-COV-2 and human rhinovirus [HRV]), will be migrated onto TaqMan Low-Density Array (TLDA) cards. A prospective cohort of patients will be enrolled in Sri Lanka, and etiological testing and clinical adjudications will be performed as the reference standard to identify viral (including SARS-CoV-2 and HRV) and non-viral LRTI. Using an optimally retrained and parsimonious viral versus non-viral classifier, we will perform a feasibility analysis of incorporating pathogen detection and host-response classifier. Among samples with TLDA-based pathogen detection for SARS-CoV-2 or HRV, performance of the host- response classifier to distinguish viral versus non-viral LRTI will be assessed. Successful completion of these aims will result in the development of a novel diagnostic that integrates host and pathogen detection using a single, non-invasive NP sample to identify the etiology of LRTI. Translation of this assay to a rapid platform will help shift the current diagnostic paradigm for LRTI.", "keywords": [ "2019-nCoV", "Anti-Bacterial Agents", "Bacteria", "Bacterial Drug Resistance", "Bacterial Infections", "Biological", "Biological Assay", "Blood Tests", "Blood specimen", "Bronchoscopy", "COVID-19 detection", "Cause of Death", "Clinical", "Country", "Data", "Development", "Diagnostic", "Diagnostic Procedure", "Enrollment", "Etiology", "Fever", "Future", "Gene Expression", "Gene Targeting", "Genes", "Goals", "Hour", "Human", "Immune response", "Income", "Infection", "Influenza", "Lower Respiratory Tract Infection", "Machine Learning", "Manuscripts", "Methods", "Nasopharynx", "Nucleic Acids", "Pathogen detection", "Patients", "Performance", "Pilot Projects", "Polymerase Chain Reaction", "Prevalence", "Procedures", "Prospective cohort", "Reference Standards", "Research", "Resistance", "Respiratory Signs and Symptoms", "Respiratory Tract Infections", "Respiratory syncytial virus", "Rhinovirus", "Sampling", "Site", "Sputum", "Sri Lanka", "Testing", "Time", "Training", "Translating", "Upper respiratory tract", "Viral", "Virus", "Work", "adjudication", "biobank", "density", "design", "differential expression", "experience", "migration", "novel", "novel diagnostics", "pathogen", "peripheral blood", "prospective", "respiratory virus", "transcriptome sequencing", "translation assay" ], "approved": true } }, { "type": "Grant", "id": "15821", "attributes": { "award_id": "1R01HL176569-01A1", "title": "Implementation partner-guided strategy to promote accuracy in ICU prognostication", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 44231, "first_name": "INGRID CECILIA", "last_name": "ESPINOZA GRANDON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": "2029-06-30", "award_amount": 738154, "principal_investigator": { "id": 44232, "first_name": "Deepshikha Charan", "last_name": "Ashana", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 21553, "first_name": "Christopher Ethan", "last_name": "Cox", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 44233, "first_name": "Chuan", "last_name": "Hong", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Accurately predicting whether patients will live or die is an essential task in critical care medicine. Yet such predictions are often incorrect based on specific patient characteristics. We and others found that Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), and other prognostic tools overestimate the risk of death for certain groups of patients. Because the incidence of serious cardiac or respiratory critical illnesses is so high, the consequences of this error are significant for clinical decision making, research, and health policy. Given that APACHE II alone has been included in >10,000 studies, an entire medical literature may imperfectly measure the effectiveness of ICU treatments. Furthermore, the use of SOFA in ICU triage policies, such as during the COVID-19 pandemic, may propagate variability in health outcomes by incorrectly withholding life-saving ICU treatments from some patients. There are two key knowledge gaps that limit a solution including a lack of understanding of how measurement errors arise in these prognostic tools and the absence of ICU prognostic tools that are free of such errors. Filling these gaps will promote high-quality ICU care and access to it, as well as ICU research. Our goal is to develop a novel ICU mortality risk prediction model with accurate performance across all patient groups. To mitigate measurement error, we will combine state-of-the-art methods with the necessary supervision and contextual grounding provided by multidisciplinary advisors (critical care, bioethics, data science). Utilizing data from >100,000 patients broadly representative of the US population from 23 ICUs at Duke and Medical University of South Carolina, this project has three aims: (1) Identify mechanisms of systematic measurement error in commonly used ICU mortality risk prediction models; (2) Develop an accurate in-hospital mortality prediction model for all groups of critically ill patients; and (3) Conduct a real-world feasibility study of the new model within our electronic health record. The expected outcomes of this study, which addresses NHLBI’s key objectives to reduce variability in health outcomes and improve care by leveraging the power of data science, are two-fold. First, to our knowledge, this will be the first study to identify mechanisms of measurement error in current ICU mortality risk prediction models and to include variables that reflect the living situation of patients in ICU risk prediction—innovations that will serve as a conceptual foundation for future work. Second, the new model will have an immediate and substantial public health impact by promoting quality and representativeness in clinical care, research, and policy. Our proposal is innovative because it includes novel applications of model variability constraints and metrics; it also includes multidisciplinary advisors to ensure person-centered model development and evaluation. This study is feasible because of our team’s expertise and our rich research and clinical environment—all critical to navigating the complex sociotechnical context in which the proposal exists.", "keywords": [ "APACHE II", "Acute Physiology and Chronic Health Evaluation", "Address", "Adult", "Algorithms", "American", "Bioethics", "Bioethics Consultants", "COVID-19 pandemic", "Cardiac", "Caring", "Cessation of life", "Characteristics", "Clinical", "Clinical Data", "Communities", "Complex", "Cox Proportional Hazards Models", "Critical Care", "Critical Illness", "Data", "Data Science", "Databases", "Decision Trees", "Devices", "Electronic Health Record", "Eligibility Determination", "Ensure", "Environment", "Evaluation", "Exclusion", "Feasibility Studies", "Foundations", "Future", "Goals", "Health", "Health Care", "Health Policy", "Hospital Mortality", "Incidence", "Intensive Care Units", "Knowledge", "Life", "Literature", "Lived experience", "Machine Learning", "Measurement", "Measures", "Medical", "Medicine", "Methodology", "Methods", "Modeling", "Monitor", "National Heart Lung and Blood Institute", "Natural Disasters", "Network-based", "Organ failure", "Outcome", "Outcome Study", "Oxygen", "Patient Care", "Patients", "Performance", "Persons", "Policies", "Population", "Process", "Public Health", "Research", "Resources", "Risk", "Risk Estimate", "Severity of illness", "South Carolina", "Statistical Models", "Structure", "Subgroup", "Supervision", "Survival Analysis", "Technology", "Testing", "Triage", "Universities", "Voice", "Work", "clinical care", "clinical decision-making", "dashboard", "data quality", "effectiveness measure", "end of life care", "forest", "hazard", "high dimensionality", "improved", "individual variation", "innovation", "machine learning method", "machine learning prediction", "member", "model development", "mortality", "mortality risk", "multidisciplinary", "neural network", "novel", "pandemic disease", "person centered", "predictive modeling", "prognostic tool", "prognostication", "respiratory", "risk prediction", "risk prediction model", "success", "usability" ], "approved": true } }, { "type": "Grant", "id": "15847", "attributes": { "award_id": "3U01AR071128-08S3", "title": "MoTrPAC DXA Quality Control", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 44264, "first_name": "EMILY FORAN", "last_name": "CARIFI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": "2026-08-31", "award_amount": 116380, "principal_investigator": { "id": 44265, "first_name": "Joseph A", "last_name": "Houmard", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 44266, "first_name": "WILLIAM E", "last_name": "KRAUS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "MoTrPAC DXA Supplement - Project Summary The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is designed to discover and characterize the range of molecular transducers underlying the effects of exercise in humans. MoTrPAC was launched in 2016 with six adult clinical centers and a pediatric center that have collaborated to generate extensive Manual of Operations to guide research protocols involving all aspects of the clinical operations (Phase I). Phase II began in the fall of 2019 with all human clinical centers showing excellent progress towards initial recruitment goals and implementation of the protocol. The initial goal set forth by NIH was to recruit 270 children (10-17 years of age) and 1980 sedentary adults (age 18 years or greater) randomized to endurance training (170 youth, 840 adults), resistance training (840 adults), or non-exercise controls (50 youth, 300 adults). An additional group of highly active endurance (50 youth, 150 adults) and resistance (150 adults) trained individuals serve as comparators, not participating in the MoTrPAC exercise training programs. The recruitment and enrollment approach are sex-balanced, with participants across a wide range of ages (10-17, 18-39, 40-59 and >60-year age groups) and of different races. Due to the COVID-19 pandemic, beginning in March 2020, MoTrPAC activities were suspended for over a year with continued constraints through 2022. Despite the numerous challenges encountered as a result of the pandemic, the human clinical centers have successfully enrolled and completed the adult and pediatric highly active cohorts, the pediatric cross-sectional cohort, and the previously sedentary adult and low-active pediatric randomized control trial portion of the study as of May 2025. As data quality control is underway, discrepancies in dual-energy X-ray absorptiometry (DXA) data have been detected across different machines. This led NIAMS to provide MoTrPAC with funding to conduct additional DXA scans among previously enrolled MoTrPAC participants to develop a correction equation accounting for differences in DXA machines, allowing all MoTrPAC DXA data to be included in the final dataset. Altogether, this will allow MoTrPAC to complete the intended goals as originally envisioned, will provide a more complete public database of the health benefits of exercise, and will provide insight into how physical activity mitigates disease risk.", "keywords": [ "17 year old", "18 year old", "Accounting", "Acute", "Address", "Adult", "Age", "Age Years", "Animals", "Award", "Bioinformatics core", "Biological Process", "Body Composition", "COVID-19 pandemic", "Calibration", "Charge", "Chemicals", "Child", "Childhood", "Chronic", "Clinical", "Clinical Research", "Collaborations", "Communities", "Data", "Data Set", "Development", "Disease Outcome", "Dual-Energy X-Ray Absorptiometry", "Enrollment", "Ensure", "Equation", "Exercise", "Fatty acid glycerol esters", "Funding", "Goals", "Health", "Health Benefit", "Human", "Individual", "Manuals", "Manufacturer", "Maps", "Measurement", "Methodology", "Minerals", "Molecular", "Multi-site clinical study", "National Health and Nutrition Examination Survey", "National Institute of Arthritis and Musculoskeletal and Skin Diseases", "Observational Study", "Participant", "Pathway interactions", "Phase", "Physical activity", "Physiological", "Procedures", "Process", "Protocols documentation", "Public Health", "Quality Control", "Race", "Randomized", "Randomized Controlled Trials", "Reporting", "Research", "Resistance", "Resources", "Scanning", "Site", "Standardization", "System", "Thinness", "Time", "Tissues", "Training", "Training Programs", "Transducers", "United States National Institutes of Health", "Weight", "Youth", "age group", "bone", "clinical center", "clinical research site", "cohort", "data quality", "design", "disorder risk", "endurance exercise", "exercise training", "falls", "improved", "insight", "instrument", "multiple omics", "operation", "pandemic disease", "phenotypic data", "pre-clinical", "public database", "recruit", "resistance exercise", "response", "sedentary", "sex" ], "approved": true } }, { "type": "Grant", "id": "15848", "attributes": { "award_id": "3U01AR071128-08S2", "title": "Molecular Transducers of Physical Activity and Health: NC Consortium Clinical Site", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 44264, "first_name": "EMILY FORAN", "last_name": "CARIFI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-01", "end_date": "2026-08-31", "award_amount": 345063, "principal_investigator": { "id": 44265, "first_name": "Joseph A", "last_name": "Houmard", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 44266, "first_name": "WILLIAM E", "last_name": "KRAUS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "MoTrPAC Project Summary The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is designed to discover and characterize the range of molecular transducers underlying the effects of exercise in humans. MoTrPAC was launched in 2016 with six adult clinical centers and a pediatric center that have collaborated to generate extensive Manual of Operations to guide research protocols involving all aspects of the clinical operations (Phase I). Phase II began in the fall of 2019 with all human clinical centers showing excellent progress towards initial recruitment goals and implementation of the protocol. The initial goal set forth by NIH was to recruit 270 children (10-17 years of age) and 1980 sedentary adults (age 18 years or greater) randomized to endurance training (170 youth, 840 adults), resistance training (840 adults), or non-exercise controls (50 youth, 300 adults). An additional group of highly active endurance (50 youth, 150 adults) and resistance (150 adults) trained individuals serve as comparators, not participating in the MoTrPAC exercise training programs. The recruitment and enrollment approach are sex-balanced, with participants across a wide range of ages (10-17, 18-39, 40-59 and >60-year age groups) and of different races. Due to the COVID-19 pandemic, beginning in March 2020, MoTrPAC activities were suspended for over a year with continued constraints through 2022. Despite the numerous challenges encountered as a result of the pandemic, the human clinical centers have successfully enrolled ~80% (adult) and 95% (pediatric) of the highly active cohort, ~93% of cross-sectional (pediatric), and ~60% (adult) and ~50% (pediatric) of the current target enrollment numbers in the randomized control trial portion of the study. This will enrich the participant cohorts that are critical to the understanding of exercise adaptations and heterogeneity across age, gender, and minority groups. Altogether, this supplement will allow MoTrPAC to complete the intended goals as originally envisioned, will provide a more complete public database of the health benefits of exercise, and will provide insight into how physical activity mitigates disease risk. The purpose of the supplement, which was previously discussed with Program Officer ,will be to assemble, curate andwill be to assemble, curate and harmonize the cardiopulmonary exercise testing (CPET) database.", "keywords": [ "17 year old", "18 year old", "Acute", "Adipose tissue", "Adult", "Age", "Age Years", "Ancillary Study", "Animals", "Attention", "Award", "Bioinformatics core", "Biological Process", "Blood", "COVID-19", "COVID-19 pandemic", "California", "Cardiopulmonary", "Categories", "Charge", "Chemicals", "Child", "Childhood", "Chronic", "Clinical", "Clinical Protocols", "Clinical Research", "Clinical Trials Data Monitoring Committees", "Collaborations", "Collection", "Communication", "Communities", "Data", "Databases", "Development", "Disease Outcome", "Enrollment", "Ensure", "Exercise", "Exercise Test", "Frequencies", "Funding", "Gender", "Goals", "Health", "Health Benefit", "Heterogeneity", "Human", "Individual", "Institutional Review Boards", "Intervention", "Manuals", "Manuscripts", "Maps", "Measurement", "Minority Groups", "Molecular", "Multi-site clinical study", "Muscle", "Observational Study", "Participant", "Pathway interactions", "Pediatric cohort", "Phase", "Phenotype", "Physical activity", "Physiological", "Procedures", "Protocols documentation", "Public Health", "Race", "Randomized", "Randomized Controlled Trials", "Recruitment Activity", "Research", "Research Personnel", "Resistance", "Resources", "Rest", "Site", "Suspensions", "Testing", "Time", "Tissues", "Training", "Training Programs", "Transducers", "United States National Institutes of Health", "Universities", "Visit", "Woman", "Youth", "age group", "clinical center", "clinical research site", "cohort", "design", "disorder risk", "endurance exercise", "ethnic minority", "exercise intervention", "exercise training", "falls", "improved", "innovation", "insight", "men", "multiple omics", "novel", "operation", "pandemic disease", "phenotypic data", "physical conditioning", "pre-clinical", "programs", "public database", "quality assurance", "racial minority", "recruit", "resistance exercise", "response", "sedentary", "sex", "working group" ], "approved": true } }, { "type": "Grant", "id": "15968", "attributes": { "award_id": "1R01AG092489-01A1", "title": "Effect of paid family care (vs aides) on Medicaid waiver participants w/ IDD across the lifespan", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Aging (NIA)" ], "program_reference_codes": [], "program_officials": [ { "id": 44276, "first_name": "PRISCILLA JOY", "last_name": "NOVAK", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-03-01", "end_date": "2031-02-28", "award_amount": 678231, "principal_investigator": { "id": 44421, "first_name": "Courtney Harold", "last_name": "Van Houtven", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Home and community-based services are the most common support for people with Intellectual and/or Developmental Disability (I/DD) who otherwise would require facility-level care. Many state Medicaid waivers allow participants to choose to hire family as direct support personnel to meet their care needs at home and in the community rather than a professional aide. Ability to hire family increased during the Covid-19 pandemic through state policy changes, and yet we do not know how involving paid family affects the ability for people with I/DD to remain at home. The objectives of this study are to use North Carolina (NC) as a case to elucidate the experiences of waiver participants with I/DD (Innovations Waiver participants). With no national data fields systematically identifying self-direction status or who is paid for personal care, foundational state-level work is required to understand people with I/DD’s experiences with self-direction. First, we will describe prevalence and dynamics of paid family care using Medicaid administrative data over the past 9+ years, including patterns by self-direction or not, by individual and geographic factors and by era (pre-post Covid-19 (Aim 1). Second, qualitative approaches will center the voices of people with I/DD and their families to obtain their perspectives on what is gained and what is lost from self-directed care including paid family care (Aim 2). Specifically, photo elicitation, case study, and focus group interviews will examine the lived experience of accessing and receiving care through the Innovations Waiver according to 1) individuals with I/DD, 2) their parent/partner/guardian/unpaid family caregiver, 3) their paid family caregivers, 4) their paid aide, and 5) Innovations Waiver experts. Third, we will estimate the comparative effectiveness of paid family care versus paid aide care only on person-centered outcomes (e.g., home time, preventive care) and on potential harms (potentially harmful medications, injurious falls, mistreatment) (Aim 3). We hypothesize that waiver participants with paid family care will have better person-centered outcomes and no increase in harms compared to those with paid aides alone. Effects of self- direction will also be explored. By using a convergent parallel mixed-methods process we will integrate results to paint a full picture of the comparative effectiveness of paid family care and self-direction from childhood to older adulthood, including identification of any harms. The results of this 5-year R01 study will be immediately applicable to state Medicaid office benefit design and inform strategies to optimize quality of care and life for people living with I/DD from childhood throughout the lifespan. Results from the North Carolina case will also position us to pursue a national study, given knowledge gained along with emerging efforts to identify “self- direction” in national CMS data sets. Examining paid family care and self-direction’s effects across the lifespan aligns with NIA’s strategic goal to improve the health, well-being, and independence of adults as they age.", "keywords": [ "Accident and Emergency department", "Acute", "Address", "Adult", "Affect", "Age", "Antipsychotic Agents", "COVID-19 pandemic", "Caring", "Case Study", "Characteristics", "Child", "Childhood", "Communities", "Control Groups", "Data", "Data Set", "Developmental Disabilities", "Disabilities experience", "Effectiveness", "Eligibility Determination", "Emergency Care", "Emergency department visit", "Ethnic Origin", "Exercise", "Family", "Family Caregiver", "Family member", "Focus Groups", "Geographic Factor", "Goals", "Group Interviews", "Health", "Health Care Facility", "Home", "Hospitalization", "Human Resources", "Incidence", "Individual", "Inpatients", "Intellectual functioning disability", "Intermediate Care Facilities", "Knowledge", "Life", "Lived experience", "Medicaid", "Methods", "North Carolina", "Outcome", "Paint", "Parents", "Participant", "Patient Preferences", "Pattern", "Perception", "Personal Satisfaction", "Persons", "Pharmaceutical Preparations", "Policies", "Policy Research", "Population", "Positioning Attribute", "Prevalence", "Preventive care", "Process", "Qualifying", "Qualitative Methods", "Quality of Care", "Quality of life", "Quasi-experiment", "Race", "Risk", "Self Care", "Self Direction", "Services", "Supported Employment", "Time", "Voice", "Work", "acute care", "beneficiary", "community based service", "comparative effectiveness", "cost", "design", "disabled", "experience", "fall injury", "flexibility", "health care quality", "improved", "innovation", "life span", "maltreatment", "older adult", "payment", "person centered", "post-COVID-19", "preference", "programs", "rurality", "treatment effect", "waiver" ], "approved": true } }, { "type": "Grant", "id": "16142", "attributes": { "award_id": "1UG3AT013109-01", "title": "1/2 Self-directed mobile mindfulness to address ICU survivors' psychological distress: the Lift RCT", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Complementary and Integrative Health (NCCIH)" ], "program_reference_codes": [], "program_officials": [ { "id": 44565, "first_name": "LANAY MARIE", "last_name": "MUDD", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-04-23", "end_date": "2027-03-31", "award_amount": 991295, "principal_investigator": { "id": 21553, "first_name": "Christopher Ethan", "last_name": "Cox", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Cardiorespiratory conditions such as the acute respiratory distress syndrome (ARDS), congestive heart failure, COVID pneumonia, and sepsis are among the most common causes of mortality and morbidity. They are also notable for high rates of persistent psychological distress symptoms including depression, anxiety, and PTSD that worsen quality of life and outcomes of the underlying conditions. Yet there are few effective strategies able to overcome barriers of limited access to mental health care. To address this gap, we developed Lift, a completely automated and self-directed mindfulness training intervention, from the ground up with patient input. First, Lift reduced depression symptoms and improved quality of life compared to an education program control in a multicenter pilot RCT (R34 AT008819) among those recently hospitalized with serious cardiorespiratory conditions. Next, a 247-person multicenter 2x2x2 factorial optimization trial (U01 AT00974) compared 8 intervention versions differing by program introduction (app vs. therapist), dose (standard vs. high), and approach to in-the-moment symptom management (app vs. therapist). This trial demonstrated that while all versions had a strong effect on depression, anxiety, and PTSD symptoms, the high dose, app-only version was optimized for effect, adherence, and retention. Given these promising findings, a formal test of the optimized Lift mobile mindfulness intervention’s efficacy is needed. Therefore, we propose a 4-site multicenter RCT with 6-month follow up among 450 cardiorespiratory failure survivors with elevated post-discharge symptoms of psychological distress. Our specific aims will: (1) Test Lift vs. an education program control delivered by similar platforms on symptoms of depression, anxiety, PTSD, and quality of life; (2a) Determine patient-level characteristics associated with a greater treatment response among a priori-defined subgroups using a heterogeneity of treatment effects analysis; (2b) Explore novel adherence metrics and outcomes; and (3) Ensure off-the-shelf intervention readiness with an exploratory mixed-methods hybrid type 1 implementation framework analysis that integrates quantitative trial implementation data with semi-structured trial participant interviews. Innovative and unique elements include a fully automated mobile health delivery system that personalizes content in response to changes in symptom trajectories, a focus on enrolling a population representative of the US, and strong community engagement with formalized roles. This project addresses national research priorities and could advance the field with a personalizable yet population-scalable therapy that has the potential to broadly improve mental health access.", "keywords": [ "Acute Respiratory Distress Syndrome", "Address", "Adherence", "Affect", "Age", "Anxiety", "Behavioral", "COVID-19", "COVID-19 pneumonia", "Characteristics", "Clinical", "Clinical Trials", "Collaborations", "Congestive Heart Failure", "Data", "Demographic Factors", "Disease", "Dose", "Education", "Educational Intervention", "Elements", "Enrollment", "Ensure", "Failure", "Financial Hardship", "Goals", "Health", "Heterogeneity", "Hospitalization", "Hybrids", "Intensive Care Units", "Intervention", "Lead", "Lifting", "Measures", "Mental Depression", "Mental Health", "Mental Health Services", "Methods", "Mindfulness Training", "Morbidity", "Mosses", "Multicenter Trials", "Outcome", "Participant", "Patients", "Persons", "Pneumonia", "Population", "Post-Traumatic Stress Disorders", "Quality of life", "Questionnaires", "Randomized", "Reach Effectiveness Adoption Implementation and Maintenance", "Readiness", "Recording of previous events", "Research", "Research Priority", "Role", "Sampling", "Self Direction", "Self Efficacy", "Sepsis", "Severity of illness", "Site", "Social support", "Socioeconomic Factors", "Socioeconomic Status", "Spanish/English", "Structure", "Subgroup", "Survivors", "Symptoms", "Testing", "Training", "Training Programs", "Trajectory", "Treatment Efficacy", "Work", "clinical implementation", "clinical trial participant", "cohort", "community engagement", "comparison control", "depressive symptoms", "design", "effectiveness/implementation design", "efficacy testing", "emotion regulation", "experience", "follow-up", "future implementation", "health care delivery", "implementation framework", "implementation intervention", "improved", "innovation", "mHealth", "mindfulness", "mindfulness intervention", "mindfulness-based stress reduction", "mobile application", "mortality", "multiphase optimization strategy", "novel", "participant interview", "physical symptom", "primary outcome", "programs", "psychological distress", "psychological symptom", "quality of life improvement", "randomized clinical trials", "response", "rurality", "screening", "sex", "skills", "success", "supportive environment", "symptom management", "therapy design", "treatment effect", "treatment response" ], "approved": true } }, { "type": "Grant", "id": "16151", "attributes": { "award_id": "5R01HL176569-02", "title": "Implementation partner-guided strategy to promote accuracy in ICU prognostication", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 44231, "first_name": "INGRID CECILIA", "last_name": "ESPINOZA GRANDON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-15", "end_date": "2029-06-30", "award_amount": 689860, "principal_investigator": { "id": 44232, "first_name": "Deepshikha Charan", "last_name": "Ashana", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 21553, "first_name": "Christopher Ethan", "last_name": "Cox", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 246, "ror": "https://ror.org/00py81415", "name": "Duke University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, { "id": 44233, "first_name": "Chuan", "last_name": "Hong", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Accurately predicting whether patients will live or die is an essential task in critical care medicine. Yet such predictions are often incorrect based on specific patient characteristics. We and others found that Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), and other prognostic tools overestimate the risk of death for certain groups of patients. Because the incidence of serious cardiac or respiratory critical illnesses is so high, the consequences of this error are significant for clinical decision making, research, and health policy. Given that APACHE II alone has been included in >10,000 studies, an entire medical literature may imperfectly measure the effectiveness of ICU treatments. Furthermore, the use of SOFA in ICU triage policies, such as during the COVID-19 pandemic, may propagate variability in health outcomes by incorrectly withholding life-saving ICU treatments from some patients. There are two key knowledge gaps that limit a solution including a lack of understanding of how measurement errors arise in these prognostic tools and the absence of ICU prognostic tools that are free of such errors. Filling these gaps will promote high-quality ICU care and access to it, as well as ICU research. Our goal is to develop a novel ICU mortality risk prediction model with accurate performance across all patient groups. To mitigate measurement error, we will combine state-of-the-art methods with the necessary supervision and contextual grounding provided by multidisciplinary advisors (critical care, bioethics, data science). Utilizing data from >100,000 patients broadly representative of the US population from 23 ICUs at Duke and Medical University of South Carolina, this project has three aims: (1) Identify mechanisms of systematic measurement error in commonly used ICU mortality risk prediction models; (2) Develop an accurate in-hospital mortality prediction model for all groups of critically ill patients; and (3) Conduct a real-world feasibility study of the new model within our electronic health record. The expected outcomes of this study, which addresses NHLBI’s key objectives to reduce variability in health outcomes and improve care by leveraging the power of data science, are two-fold. First, to our knowledge, this will be the first study to identify mechanisms of measurement error in current ICU mortality risk prediction models and to include variables that reflect the living situation of patients in ICU risk prediction—innovations that will serve as a conceptual foundation for future work. Second, the new model will have an immediate and substantial public health impact by promoting quality and representativeness in clinical care, research, and policy. Our proposal is innovative because it includes novel applications of model variability constraints and metrics; it also includes multidisciplinary advisors to ensure person-centered model development and evaluation. This study is feasible because of our team’s expertise and our rich research and clinical environment—all critical to navigating the complex sociotechnical context in which the proposal exists.", "keywords": [ "APACHE II", "Acute Physiology and Chronic Health Evaluation", "Address", "Adult", "Algorithms", "American", "Bioethics", "Bioethics Consultants", "COVID-19 pandemic", "Cardiac", "Caring", "Cessation of life", "Characteristics", "Clinical", "Clinical Data", "Communities", "Complex", "Cox Proportional Hazards Models", "Critical Care", "Critical Illness", "Data", "Data Science", "Databases", "Decision Trees", "Devices", "Electronic Health Record", "Eligibility Determination", "Ensure", "Environment", "Evaluation", "Exclusion", "Feasibility Studies", "Foundations", "Future", "Goals", "Health", "Health Care", "Health Policy", "Hospital Mortality", "Incidence", "Intensive Care Units", "Knowledge", "Life", "Literature", "Lived experience", "Machine Learning", "Measurement", "Measures", "Medical", "Medicine", "Methodology", "Methods", "Modeling", "National Heart Lung and Blood Institute", "Natural Disasters", "Network-based", "Organ failure", "Outcome", "Outcome Study", "Oxygen", "Patient Care", "Patients", "Performance", "Persons", "Policies", "Population", "Process", "Public Health", "Real-world monitoring", "Research", "Resources", "Risk", "Risk Estimate", "Severity of illness", "South Carolina", "Statistical Models", "Structure", "Subgroup", "Supervision", "Survival Analysis", "Technology", "Testing", "Triage", "Universities", "Voice", "Work", "clinical care", "clinical decision-making", "dashboard", "data quality", "effectiveness measure", "end of life care", "forest", "hazard", "high dimensionality", "improved", "individual variation", "innovation", "machine learning method", "machine learning prediction", "member", "model development", "mortality", "mortality risk", "multidisciplinary", "neural network", "novel", "pandemic disease", "person centered", "predictive modeling", "prognostic tool", "prognostication", "respiratory", "risk prediction", "risk prediction model", "success", "usability" ], "approved": true } }, { "type": "Grant", "id": "15793", "attributes": { "award_id": "1R03AI188484-01A1", "title": "Establishing Human 2D and 3D Testicular Models to Elucidate Monkeypox Virus Tropism and Pathogenic Mechanisms in the Testes", "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": 32841, "first_name": "JANE M", "last_name": "KNISELY", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-01", "end_date": "2027-07-31", "award_amount": 156500, "principal_investigator": { "id": 32880, "first_name": "SAGUNA", "last_name": "VERMA", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2653, "ror": "", "name": "UNIVERSITY OF HAWAII AT MANOA", "address": "", "city": "", "state": "HI", "zip": "", "country": "United States", "approved": true }, "abstract": "One of the most important lessons learned from the recent global outbreak of the monkeypox virus, now called Mpox virus (MPXV, clade II), is the recognition that males are at a much higher risk for infection and higher occurrence of genital rash. Transmission via sexual contact is one of the main routes of virus spread. However, whether MPXV constitutes a sexually transmitted infection and can infect the male reproductive tract is still being debated, thus affecting the strategies to minimize transmission risk. Confirmed reports of MPXV shedding in seminal fluid for long after the clearance of viremia suggest the ability of MPXV to establish a productive infection in genitourinary organs. Poxviruses can also cause testicular complications, including azoospermia and seminiferous tubule atrophy. More recently, MPXV antigens have been detected in the testis of nonhuman primates both during the acute and convalescent stages, and the presence of testicular inflammation and necrosis in these macaques collectively suggests testis-tropism of MPXV, like Zika and Ebola viruses. However, direct evidence of MPXV infection in human testis is currently lacking, including cell targets of the virus and downstream consequences. Relevant human in vitro models are needed to characterize MPXV testicular infection. Human testis immune homeostasis is tightly governed by an elaborate communication network between different cells including testosterone-producing Leydig cells (LC) and Sertoli cells (SC) that form the blood-testis barrier (BTB). We recently established a 3D human testicular organoid (HTO) system comprised of undifferentiated spermatogonia cells, SC, LC, and peritubular myoid cells that closely recapitulates the cell diversity and function of the human testis to study Zika virus and SARS-CoV-2 infection. We have also established 2D cultures of primary SC, LC, and mixed seminiferous tubule cells and an in vitro BTB model to delineate cell-specific responses to viruses. Therefore, the goal of this study is to utilize our 2D and 3D testicular culture systems as an effective in vitro surrogate to model testicular infection of MPXV and understand downstream consequences. In Aim 1, we will assess MPXV infection in the 2D and 3D HTOs, identify cell targets, and characterize key infection pathologies, including cytopathic effects, antiviral response, and effect on BTB integrity. Aim 2 will utilize single-cell RNA sequencing to determine relative infectivity in each cell type and key pathways, including antiviral and inflammatory response, cell death, and spermatogenesis. Collective data will provide much-needed evidence of the testis as one of the target organs of MPXV replication after it is cleared from blood and skin lesions and lay the foundation for future in vivo studies of transmission via the sexual route. The knowledge of whether MPXV is a sexually transmitted infection is critical in providing clinical management and transmission guidelines, especially in men who have sex with men (MSM), an underrepresented group in biomedical research.", "keywords": [ "2019-nCoV", "3-Dimensional", "Acute", "Affect", "Androgens", "Anti-viral Response", "Antigens", "Area", "Atrophic", "Basic Science", "Biological Assay", "Biomedical Research", "Biomimetics", "Blood", "Blood-Testis Barrier", "CASP3 gene", "Cell Death", "Cell Differentiation process", "Cell Survival", "Cells", "Clinical", "Clinical Management", "Communication", "Data", "Development", "Discipline of Nursing", "Disease", "Disease Outbreaks", "Dissociation", "Ebola virus", "Electrical Resistance", "Environment", "Exanthema", "Foundations", "Future", "Genitalia", "Genitourinary system", "Germ Cells", "Goals", "Guidelines", "Homeostasis", "Human", "Immune", "Impairment", "In Situ Nick-End Labeling", "In Vitro", "Individual", "Infection", "Inflammation", "Inflammatory Response", "Injury", "Interferons", "International", "Investigation", "Kinetics", "Knowledge", "Macaca", "Measures", "Modeling", "Monkeypox", "Monkeypox virus", "Necrosis", "Organ", "Organoids", "Pathogenesis", "Pathogenicity", "Pathology", "Pathway interactions", "Plaque Assay", "Poxviridae", "Productivity", "Public Health", "Reporting", "Research Personnel", "Risk", "Route", "SARS-CoV-2 infection", "Seminal fluid", "Seminiferous tubule structure", "Sexual Transmission", "Sexually Transmitted Diseases", "Skin", "Spermatogenesis", "Supporting Cell", "Surface", "Suspensions", "System", "Testing", "Testis", "Testosterone", "Time", "Tropism", "Tubular formation", "Underrepresented Populations", "Undifferentiated", "United States National Institutes of Health", "Viral", "Viral Pathogenesis", "Viremia", "Virus", "Virus Diseases", "Virus Replication", "ZIKA", "Zika Virus", "cell type", "effective therapy", "high risk", "high risk population", "human model", "in vitro Model", "in vivo", "infection risk", "insight", "leydig interstitial cell", "male", "men", "men who have sex with men", "migration", "multidisciplinary", "nonhuman primate", "novel", "novel therapeutics", "public health emergency", "receptor", "reproductive tract", "response", "sertoli cell", "single-cell RNA sequencing", "skin lesion", "spermatogonial stem cells", "therapeutic development", "three-dimensional modeling", "transmission process", "viral transmission", "virus tropism" ], "approved": true } } ], "meta": { "pagination": { "page": 1419, "pages": 1442, "count": 14420 } } }