Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=-program_officials
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-program_officials", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=-program_officials", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-program_officials", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-program_officials" }, "data": [ { "type": "Grant", "id": "16124", "attributes": { "award_id": "1K23DK147585-01", "title": "Harnessing Technology to Develop a Just-in-Time Adaptive Intervention to Promote Physical Activity in Adolescents with Type 2 Diabetes", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)" ], "program_reference_codes": [], "program_officials": [ { "id": 44603, "first_name": "NISHADI", "last_name": "RAJAPAKSE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-05-01", "end_date": "2031-03-31", "award_amount": 183540, "principal_investigator": { "id": 44604, "first_name": "Ana Maria", "last_name": "Gutierrez Colina", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3372, "ror": "", "name": "COLORADO STATE UNIVERSITY", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The incidence of youth-onset type 2 diabetes (T2D) is on the rise, with a 95% escalation rate since 2001, and a staggering 77% increase during the COVID-19 pandemic. Regular physical activity is a cornerstone of T2D treatment. Yet, physical activity engagement is alarmingly low among youth with T2D, and effective interventions are lacking. Dr. Gutierrez-Colina’s long-term career objective is to improve health outcomes for youth with T2D by developing state-of-the-art mobile health (mHealth) interventions that provide personalized support for T2D self-management. The goal of this K23 proposal is to develop a novel just-in-time adaptive intervention (JITAI) that integrates dissemination and implementation science with real-time assessments of physical activity barriers and facilitators to deliver tailored strategies for promoting physical activity in adolescents with T2D. In Aim 1, a sequential exploratory design will be used to identify time-varying factors (e.g., motivation, fatigue, self- regulation) that influence physical activity engagement in adolescents with T2D. Qualitative interviews with N=18 T2D stakeholders will be conducted to gather in-depth feedback about physical activity barriers and facilitators. Qualitative findings will be integrated into the development of a 2-week ecological momentary assessment protocol designed to examine daily temporal associations between real-world barriers/facilitators and physical activity in the daily lives of N=25 adolescents with T2D. In Aim 2, the intervention components of a physical activity JITAI (e.g., personalized text messages, tailoring variables) will be co-developed with an advisory board of adolescents and caregivers. Intervention development will draw from dissemination and implementation science frameworks, as well as the “Capability, Opportunity, and Motivation (COM-B) Model,” a well-established theory of health behavior change. The JITAI will target activity barriers/facilitators related to Capability, Opportunity, and Motivation and deliver tailored support at the right time, in the right dose, and only when needed. In Aim 3, a sequential factorial experimental design will be used to pilot a 28-day micro-randomized trial of the physical activity JITAI with N=30 adolescents. Study feasibility and acceptability will be evaluated through usability surveys and post-intervention end-user interviews. Findings will generate critical data to inform an R01 application focused on a full-scale micro-randomized trial to optimize the physical activity JITAI. The proposed K23 research and career development plan will be supported by an outstanding mentorship team and a rich research environment at Colorado State University and the University of Colorado/Children's Hospital Colorado. Completion of the K23 training goals in (1) qualitative/mixed methods, (2) JITAI intervention development with an emphasis on dissemination and implementation science, and (3) the design and evaluation of micro- randomized clinical trials will equip Dr. Gutierrez-Colina with essential expertise in digital interventions and the rigorous methods involved in their evaluation. This training will directly support her successful transition to an independent research career focused on advancing personalized digital interventions for youth with T2D.", "keywords": [ "Address", "Adherence", "Adolescent", "Adult", "Behavioral", "COVID-19 pandemic", "Caregivers", "Cellular Phone", "Childhood", "Clinical", "Colorado", "Data", "Data Analyses", "Deterioration", "Development", "Development Plans", "Diabetes Mellitus", "Dissemination and Implementation", "Dose", "Ecological momentary assessment", "Effectiveness", "Enrollment", "Environment", "Evaluation", "Experimental Designs", "Face", "Fatigue", "Feasibility Studies", "Feedback", "Funding", "Future", "Glycosylated hemoglobin A", "Goals", "Guidelines", "Health", "Health Benefit", "Health Technology", "Health behavior", "Health behavior change", "Incidence", "Individual", "Informal Social Control", "Intervention", "Interview", "Just-in-Time Adaptive Intervention", "Life", "Link", "Measures", "Mentored Patient-Oriented Research Career Development Award", "Mentors", "Mentorship", "Methodology", "Methods", "Modeling", "Motivation", "Nature", "Non-Insulin-Dependent Diabetes Mellitus", "Obesity", "Outcome", "Overweight", "Pediatric Hospitals", "Persons", "Pharmacotherapy", "Physical activity", "Physical assessment", "Positioning Attribute", "Protocols documentation", "Randomized", "Recommendation", "Research", "Resources", "Scientist", "Self Management", "Specific qualifier value", "Structure", "Surveys", "Technology", "Text Messaging", "Time", "Training", "Universities", "Work", "Youth", "acceptability and feasibility", "actigraphy", "adolescent health outcomes", "cardiometabolic risk", "cardiovascular health", "career", "career development", "course of disease", "design", "diabetes management", "diabetes self-management", "digital intervention", "digital technology", "digital tool", "dissemination science", "effective intervention", "exercise intervention", "glycemic control", "implementation framework", "implementation science", "improved", "interest", "lifestyle intervention", "mHealth", "meetings", "mhealth interventions", "novel", "post intervention", "precision medicine", "randomized trial", "randomized clinical trials", "research and development", "tailored text messaging", "theories", "therapy development", "usability" ], "approved": true } }, { "type": "Grant", "id": "16123", "attributes": { "award_id": "1R01HL184145-01", "title": "ENHANCE-O2: ENsuring High Accuracy in Non-invasive Clinical Evaluation of Oxygenation", "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": 44601, "first_name": "CATHERINE", "last_name": "BURKE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-05-01", "end_date": "2031-01-31", "award_amount": 753721, "principal_investigator": { "id": 44602, "first_name": "Ashraf", "last_name": "Fawzy", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2615, "ror": "", "name": "JOHNS HOPKINS UNIVERSITY", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Pulse oximeters are used extensively in medical settings to monitor acutely ill patients, but testing is only required on healthy volunteers in laboratory settings to attain Food and Drug Administration (FDA) clearance for clinical use. This project’s investigators have led the contemporary re-evaluation of pulse oximeter accuracy, showing that pulse oximeters overestimated true oxygen saturation three-fold more frequently among critically ill Black patients and that these inaccuracies resulted in delayed delivery of care and significantly higher rate of readmissions among patients with COVID-19. As a consequence, the FDA held two advisory committee meetings to re-evaluate regulatory requirements for pulse oximeters, culminating in updated draft guidance released in January 2025 requiring greater diversity of skin pigmentation among pulse oximeter testing subjects but maintains that laboratory testing in healthy volunteers is adequate, which continues to assume – without evidence – that pulse oximeters are accurate in acutely ill patients with common physiologic derangements that cannot be simulated in laboratories. Following up on compelling preliminary studies, this project investigates pulse oximeter accuracy and its impact on clinical outcomes among hospitalized patients with shock (organ hypoperfusion) and acute exacerbation of COPD (AECOPD) in whom severe hypercarbia (elevated blood CO2) is common. Shock will be accurately characterized based on vasopressor dose or low cardiac output in a very large retrospective cohort using a unique high-frequency repository of vital sign and waveform data to precisely match pairs of concurrent pulse oximeter readings (SpO2) with gold-standard arterial oxygen saturation (SaO2) and determine cardiac output by applying an algorithm from an FDA-cleared device to stored arterial catheter waveforms. Pulse oximeter accuracy in shock, its interaction with race, and impact on 3-month mortality and rehospitalization will be investigated. A prospective study will enroll 150 hospitalized patients with AECOPD to obtain arterial blood gases and test whether high levels of partial pressure of CO2 (pCO2) impact accuracy (SpO2- SaO2) of four FDA-cleared pulse oximeters, interact with objectively measured dark skin pigmentation to worsen accuracy, and if pulse oximeter error in AECOPD is associated with clinical outcomes such as hospital length of stay and 3-month rehospitalization for AECOPD. Additionally, we will evaluate and refine the performance of a prototype continuous non-invasive oxygen monitoring device in three sub-cohorts, each of 25 acutely ill patients with either shock, hypercarbia, or pure hypoxemia. This prototype measures partial pressure of diffused oxygen (PtcO2) while forgoing skin heating and harsh adhesives required of traditional PtcO2 devices and uses methods that are hoped to mitigate skin pigmentation-related inaccuracies of pulse oximetry. If our hypotheses that shock and hypercarbia significantly impact pulse oximeter performance are confirmed, clinician approaches to the management of patients will be directly impacted and the need to test existing and new devices in real patients rather than relying on healthy volunteers in laboratory settings will be confirmed.", "keywords": [ "Acute", "Adhesives", "Advisory Committees", "Algorithms", "Arteries", "Black race", "Blood", "Blood Tests", "Blood flow", "Blood gas", "Burn injury", "COVID-19 patient", "Carbon Dioxide", "Cardiac Output", "Caring", "Catheters", "Chronic", "Chronic Obstructive Pulmonary Disease", "Clinical", "Clinical Data", "Consensus", "Critical Illness", "Darkness", "Data", "Devices", "Diagnosis", "Diffusion", "Dose", "Eligibility Determination", "Enrollment", "Ensure", "Evaluation", "Exclusion", "Frequencies", "Funding", "Goals", "Heating", "Hispanic", "Hospitalization", "Hypercapnia", "Hypoxemia", "Impairment", "Intensive Care Units", "Investigation", "Laboratories", "Lead", "Length of Stay", "Life", "Low Cardiac Output", "Marketing", "Measurement", "Measures", "Medical", "Methods", "Monitor", "Organ", "Outcome", "Outpatients", "Oxygen", "Oxygen Therapy Care", "Partial Pressure", "Patients", "Performance", "Perfusion", "Peripheral", "Physiologic pulse", "Physiological", "Prospective Studies", "Proxy", "Pulse Oximetry", "Race", "Regulation", "Reporting", "Research Personnel", "Respiratory Disease", "Retrospective Studies", "Retrospective cohort", "Shock", "Skin", "Skin Pigmentation", "Spectrophotometry", "Testing", "Time", "Tissues", "Triage", "United States Food and Drug Administration", "United States National Institutes of Health", "Update", "Validation", "Vasoconstrictor Agents", "black patient", "care delivery", "clinically relevant", "clinically significant", "cohort", "continuous monitoring", "experience", "healthy volunteer", "hospital readmission", "hypoperfusion", "innovation", "light transmission", "meetings", "monitoring device", "mortality", "multidisciplinary", "new technology", "novel", "peripheral blood", "prospective", "prototype", "readmission rates", "repository", "research clinical testing", "sensor", "supplemental oxygen", "theories", "tool" ], "approved": true } }, { "type": "Grant", "id": "16120", "attributes": { "award_id": "2R01HL019278-50", "title": "FcγRIIa-induced Activation of Platelet αIIbβ3 in Thrombosis and Thromboinflammation", "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": 44598, "first_name": "ANDREI L", "last_name": "KINDZELSKI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-05-10", "end_date": "2030-02-28", "award_amount": 861102, "principal_investigator": { "id": 44599, "first_name": "Claudia Lorena", "last_name": "Buitrago", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 22562, "first_name": "Barry", "last_name": "Coller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 763, "ror": "https://ror.org/0420db125", "name": "Rockefeller University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 3476, "ror": "", "name": "ROCKEFELLER UNIVERSITY", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "There is increasing awareness of the role of thromboinflammation mediated by the activation of αIIbβ3 through the binding of immune complexes to FcγRIIa in the pathogenesis of a variety of serious disorders, among which are heparin-induced thrombocytopenia/thrombosis, viral infections including COVID-19, autoimmune disorders, and chronic inflammatory disorders, including cardiovascular disease. The primary mechanism proposed to underly the thrombotic phenomena is the binding of immune complexes to the FcγIIa receptor (FcγRIIa) on platelets, leading to activation of αIIbβ3 and platelet aggregation. Both infection and autoimmune diseases can generate immune complexes that interact with FcγRIIa. Activated αIIbβ3 interacts with soluble fibrinogen, leading to platelet aggregation, thrombus formation, and thrombin generation. We and others have found that highly-specific BTK inhibitors (BTKi) can inhibit FcγRIIa-mediated αIIbβ3 activation and confirmed that platelets of XLA patients, who lack BTK on a genetic basis but do not have a bleeding disorder, do not aggregate in response to engagement of FcγRIIa. Thus, the central hypothesis of this grant renewal is that the activation of αIIbβ3 by engagement of FcγRIIa plays an important role in the pathophysiology of many immune-mediated disorders. We propose to create better murine models of these disorders by using mice that have had their entire Fc receptor repertoire and their platelet factor 4 (PF4) genes humanized and then test in these models the safety and efficacy of already-approved BTKi and antiplatelet drugs, as well as new agents targeting FcγRIIa directly that are developed under this grant. Our goal is to improve our understanding of these disorders, improve their current therapy, and develop novel, orally active drugs that directly target FcγRIIa for chronic therapy. Specific aim 1: A) To better understand the pathogenesis of the FcγRIIa-mediated disorders of αIIbβ3 activation by developing optimal murine models in which the entire murine repertoire of Fc receptors has been humanized, along with PF4, and both the Arg131 and His131 variants of FcγRIIa are studied. B) To compare the effects of antiplatelet agents (aspirin, P2Y12 inhibitors, αIIbβ3 inhibitors), and inhibitors of immune complex binding to FcγRIIa or signaling through FcγRIIa, initially focusing on BTKi, on the pathogenesis of the disorders in the murine models. Specific aim 2. To develop a rapid functional diagnostic assay that measures the combined effects of platelet FcγRIIa number, FcγRIIa affinity for the Fc domain of immunoglobulins, and the downstream signaling pathways to platelet αIIbβ3 activation for screening compounds, individual risk assessment, and optimizing therapy by monitoring drug effects. Specific aim 3. To use both high-throughput and in silico screening, along with deep learning-based de novo design, to identify novel specific inhibitors of immune complex binding to FcγRIIa or signaling through FcγRIIa as potential human therapeutics.", "keywords": [ "Adhesions", "Affinity", "Agammaglobulinaemia tyrosine kinase", "Antibody Formation", "Antigen-Antibody Complex", "Antiplatelet Drugs", "Aspirin", "Autoimmune Diseases", "Awareness", "B-Lymphocytes", "Binding", "Binding Proteins", "Biology", "Blood Coagulation Disorders", "Blood Platelets", "COVID-19", "Cardiovascular Diseases", "Cell Physiology", "Chronic", "Clot retraction", "Coagulation Process", "Cryoelectron Microscopy", "Defect", "Deposition", "Disease", "Docking", "Drug Monitoring", "Drug Targeting", "FDA approved", "Family", "Fc Receptor", "Fc domain", "Fibrin", "Fibrinogen", "Functional disorder", "Generations", "Genes", "Genetic", "Goals", "Grant", "Hemostatic function", "Human", "Immobilization", "Immune", "Immunoglobulin Domain", "Individual", "Infection", "Inflammation", "Inflammatory", "Integrins", "Mass Spectrum Analysis", "Measures", "Mediating", "Methods", "Modeling", "Molecular", "Molecular Conformation", "Monitor", "Mus", "Myocardial Infarction", "Natural Immunity", "Neoplasm Metastasis", "New Agents", "Oral", "PF4 Gene", "Pathogenesis", "Pathway interactions", "Patients", "Pharmaceutical Chemistry", "Pharmaceutical Preparations", "Phase", "Platelet Activation", "Platelet aggregation", "Plavix", "Play", "Research", "Risk Assessment", "Role", "Safety", "Signal Pathway", "Signal Transduction", "Stroke", "Structure", "Testing", "Therapeutic", "Thrombin", "Thrombosis", "Thromboxanes", "Thrombus", "Tyrosine Kinase Inhibitor", "Variant", "Virus Diseases", "X-Ray Crystallography", "abciximab", "antagonist", "chronic inflammatory disease", "clopidogrel", "deep learning", "design", "diagnostic assay", "disease model", "heparin-induced thrombocytopenia", "high risk", "high throughput screening", "immunothrombosis", "improved", "in silico", "inhibitor", "interdisciplinary approach", "mortality", "mouse model", "novel", "novel therapeutics", "phase 3 study", "pre-clinical", "receptor", "response", "screening", "simulation", "targeted agent", "thromboinflammation", "thrombotic", "thrombotic complications", "treatment optimization", "von Willebrand Factor", "wound healing" ], "approved": true } }, { "type": "Grant", "id": "16118", "attributes": { "award_id": "1R21AI194202-01A1", "title": "Recovering CD8 T cell memory during chronic infection by JAK-inhibitors", "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": 44596, "first_name": "STACY E", "last_name": "FERGUSON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-05-22", "end_date": "2028-04-30", "award_amount": 234750, "principal_investigator": { "id": 44597, "first_name": "Mohamed", "last_name": "Abdel Hakeem", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3388, "ror": "", "name": "EMORY UNIVERSITY", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Vaccines are the most successful prophylactic measure against pathogens, and their protective capacity depends on the formation of long-lived immunological memory, e.g. memory T lymphocytes (TMEM). Coordination between B and T lymphocytes is required against intracellular pathogens to neutralize pathogens and eradicate infected cells, respectively, as recently highlighted with COVID19 vaccine responses. Individuals living with chronic infections were shown to exhibit inferior responses to subsequent infections and vaccination. Studies in animal models suggested the cause to be the inferior recall responses by the TMEM generated to acute stimuli in hosts with preexisting chronic infection (hereafter termed inflm-TMEM). However, epigenetic imprints of persistent inflammation on inflm-TMEM have not been examined, and interventions to restore efficient protective recall responses by inflm-TMEM are understudied. Previous studies and our preliminary data demonstrated arrested development of inflm-TMEM, especially the central memory subset (TCM). Previous transcriptional profiling and our chromatin-accessibility profiling revealed skewed transcriptional and epigenetic landscapes of antigen-specific inflm-TMEM compared to bona fide TMEM, with enrichment of signatures associated with inflammatory mediators including type I interferon (IFN-I) and interleukin-6 (IL-6), which signal downstream janus kinase 1/2 (JAK1/2). We hypothesize that JAK1/2 inhibition would recover normal TMEM differentiation in hosts with preexisting chronic infection. To investigate this, we will use the same mouse model utilized for our preliminary studies to examine the differentiation of antigen-specific T cells responding to acute infection in the presence of preexisting chronic infection with or without JAK1/2 inhibition (JAKi) treatment. We will dissect the profiles of inflm-TMEM using high- dimensional flowcytometry and combined scRNAseq/scATACseq (MultiOme) profiling. More importantly, we will investigate the functional impact of JAKi-treatment on enhanced pathogen clearance and protection upon rechallenge. Our team has expertise in immunology, mouse models, and MultiOme profiling tools. This puts us in a unique position to address our main objectives which are: (1) to examine the capacity of FDA-approved interventions (JAKi) to recover generation of efficient TMEM in hosts with preexisting chronic infection, and (2) to gain insights into the molecular mechanisms underlying the skewed differentiation of inflm-TMEM., and aspects recovered by JAKi treatment. These studies push the envelope of innovation where we will employ combined state-of-the-art high-dimension flowcytometry, functional assays, and MultiOme profiling, to decipher specific mechanisms associated with lower recall capacity of inflm-TMEM that is understudied to date. We will validate novel therapeutic approaches using FDA-approved medication(s) for enhancing TMEM formation in hosts with preexisting chronic infection. This is of high relevance for enhancing vaccine efficiency in people living with chronic viral infection, and can be extended to benefit other populations living with chronic diseases with underlying inflammation. Summary", "keywords": [ "ATAC-seq", "Acute", "Address", "Adoptive Transfer", "Animal Model", "Antigens", "Binding Sites", "Biological Assay", "CD8-Positive T-Lymphocytes", "CD8B1 gene", "COVID-19 vaccine", "Cell Separation", "Cells", "Chromatin", "Chronic", "Chronic Disease", "Clinical Research", "Cytotoxic T-Lymphocytes", "Data", "Development", "Differentiation Antigens", "Epigenetic Process", "Exhibits", "FDA approved", "FDA-approved drug", "Flow Cytometry", "Gene Expression Profiling", "Generations", "Genetic Transcription", "IRF1 gene", "Immunity", "Immunologic Memory", "Immunology", "Individual", "Infection", "Inferior", "Inflammation", "Inflammation Mediators", "Inflammatory", "Interferon Type I", "Interferons", "Interleukin-6", "Intervention", "JAK1 gene", "Knock-out", "Knowledge", "Listeria", "Listeria monocytogenes", "Lymphocyte", "Lymphocyte Subset", "Lymphocytic choriomeningitis virus", "Measures", "Memory", "Molecular", "Monitor", "Multiomic Data", "Mus", "Ovalbumin", "Persons", "Phenotype", "Population", "Positioning Attribute", "Production", "Signal Transduction", "Sorting", "Stimulus", "T memory cell", "T-Lymphocyte", "TNF gene", "Testing", "Therapeutic Intervention", "Vaccination", "Vaccines", "Virus Diseases", "acute infection", "antigen-specific T cells", "chronic infection", "chronic inflammation", "congenic", "cytokine", "epidemiology study", "high dimensionality", "imprint", "inhibitor", "innovation", "insight", "mouse model", "multiple omics", "novel therapeutic intervention", "pathogen", "prophylactic", "receptor", "response", "single cell ATAC-seq", "single-cell RNA sequencing", "tool", "transcription factor", "vaccine efficacy", "vaccine immunogenicity", "vaccine response" ], "approved": true } }, { "type": "Grant", "id": "16117", "attributes": { "award_id": "1R01HD120978-01", "title": "Deriving Real World Insights to Inform Trials for Pregnant Individuals Using Electronic Health Records and Machine Learning", "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": 44594, "first_name": "MEGAN S", "last_name": "MITCHELL", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-02", "end_date": "2031-05-31", "award_amount": 727070, "principal_investigator": { "id": 44595, "first_name": "Chengxi", "last_name": "Zang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3515, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Pregnant women have been routinely excluded from trials due to ethical and safety concerns. Nearly 95% of clinical trials submitted to the FDA excluded pregnant women. By contrast, almost all pregnant women take at least one medication in pregnancy, but trial evidence on the safety and efficacy of these medications is largely missing. Such evidence gaps present challenges for clinicians who advise treatment for pregnant women, and patients who are making decisions in the absence of data to inform safety and efficacy in pregnancy. 1) The COVID-19 pandemic further escalated these gaps for pregnant women. Acquiring SARS-CoV-2 infection during pregnancy is associated with an increased risk of mortality and obstetric complications. SARS-CoV-2 infection acquired in pregnancy is not only associated with worse perinatal outcomes but also exhibits risks of getting post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID. By contrast, all the initial COVID-19 vaccine trials and treatment studies, including Paxlovid, etc., excluded pregnant women, despite their higher risk of COVID-19 complications and risk of long-term complications. 2) Other examples are coexisting type 2 diabetes or living with obesity in pregnancy which are associated with increased risk of adverse pregnancy outcomes. Though glucagon-like peptide 1 (GLP-1) agonists and sodium-glucose co-transporter-2 (SGLT2) inhibitors are increasingly used in treating diabetes and obesity in the general population there is a paucity of data on safety in pregnancy limiting evidence-based decision making for this population, 3) Other examples include but are not limited to Selective Serotonin Reuptake Inhibitors (SSRIs) with perinatal mood and anxiety disorders affecting an estimated 10–20% of reproductive-aged females. Our proposal aims to bridge these critical evidence gaps for pregnant women with RWD and advanced machine learning methods, with applications including but not limited to generating real-world evidence on the safety and effectiveness of antivirals (e.g., Paxlovid), antidiabetic drugs (e.g., GLP-1, SGLT-2, metformin), and antidepressants (e.g., SSRIs) in pregnancy. To achieve our objectives, we will leverage the large-scale electronic health record (EHR) data from patients in New York City (~19 million) and Florida (~19 million). There are three specific aims for this study: (1) Develop NLP-assisted computable phenotyping algorithms and tools to extract pregnant cohorts, key patient characteristics, and outcomes relevant to pregnant trials, (2) Develop a federated target trial emulation framework for estimating the real-world safety and effectiveness outcomes of interventions from distributed CRNs, and (3) Develop an RWE-based trial design system consisting of causal AI and a prototype toolbox for assessing and optimizing eligibility criteria to inform pregnancy trials.", "keywords": [ "Acute", "Adoption", "Affect", "Alabama", "Algorithms", "Anti-diabetic Drugs", "Anti-viral Agents", "Anxiety Disorders", "Apgar Score", "Artificial Intelligence", "Birth", "Body mass index", "COVID-19", "COVID-19 complications", "COVID-19 pandemic", "COVID-19 risk", "COVID-19 vaccine", "Caring", "Characteristics", "Clinical", "Clinical Research", "Clinical Trials", "Collection", "Data", "Data Science", "Decision Making", "Diabetes Mellitus", "Discipline of obstetrics", "Effectiveness", "Electronic Health Record", "Eligibility Determination", "Ethics", "Exclusion", "Exhibits", "Female", "Florida", "Fright", "Future", "General Population", "Generations", "Gestational Age", "Glucagon like peptide-1 receptor agonists", "Goals", "Health", "Individual", "Infant", "Intervention", "Long COVID", "Machine Learning", "Metformin", "Methodology", "Methods", "Modeling", "Mood Disorders", "Mothers", "Natural Language Processing", "New York City", "Non-Insulin-Dependent Diabetes Mellitus", "Obesity", "Online Systems", "Outcome", "Patient-Focused Outcomes", "Patients", "Paxlovid", "Perinatal", "Pharmaceutical Preparations", "Policy Maker", "Population", "Population Characteristics", "Population Research", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Pregnancy", "Pregnant Women", "Process", "Prospective cohort", "Research", "Research Personnel", "Retrospective cohort", "Risk", "SARS-CoV-2 infection", "Safety", "Sample Size", "Selective Serotonin Reuptake Inhibitor", "Site", "Sodium glucose co-transporter 2 inhibitor", "System", "Target Populations", "Time", "Treatment Effectiveness", "Women's Health", "Work", "adverse pregnancy outcome", "anti-depressive agents", "causal inference", "chronic complications", "cohort", "computable phenotypes", "computerized tools", "data resource", "design", "effectiveness outcome", "electronic health record system", "evidence base", "experience", "fetal", "glucagon-like peptide 1", "high risk", "improved", "innovation", "insight", "interest", "machine learning method", "medication safety", "mortality risk", "novel", "obesity in pregnancy", "obstetrical complication", "patient oriented", "patient population", "patient safety", "perinatal outcomes", "phenotyping algorithm", "pregnant", "privacy preservation", "prototype", "real world data", "real world evidence", "reproductive age", "safety outcomes", "social health determinants" ], "approved": true } }, { "type": "Grant", "id": "16115", "attributes": { "award_id": "1R35GM164072-01", "title": "Generative AI-Powered Exploration of Protein Dynamics and Non-Deterministic Chemical Kinetics", "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": 44592, "first_name": "HAN", "last_name": "NGUYEN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2031-02-28", "award_amount": 447174, "principal_investigator": { "id": 44593, "first_name": "Peng", "last_name": "Tao", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3513, "ror": "", "name": "SOUTHERN METHODIST UNIVERSITY", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Overview. The PI’s lab focuses on uncovering the dynamic mechanisms underlying protein functions through advanced computational simulations and theoretical modeling, employing molecular mechanics (MM), quantum mechanics (QM), and their combined approaches (QM/MM). The group's research has concentrated on three major themes. Protein Allostery: Significant advances have been achieved in both methodological development and application studies of key allosteric proteins, including the Light-Oxygen-Voltage-sensing (LOV) domain and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Additionally, the group has created two user- friendly public computational servers to serve the broader research community. Evolution of Enzyme Catalysis: The PI’s lab has pioneered innovative theoretical frameworks that integrate machine learning techniques with catalytic reaction pathways derived from hybrid QM/MM calculations, enhancing our understanding of enzyme evolution and catalytic mechanisms. Fundamental Chemical Mechanism Studies: The PI has a longstanding interest in the mechanisms underlying chemical transitions, particularly focusing on their characterization as rare events. Through extensive computational investigations and innovative methodological advancements, the PI's laboratory has explored a broad spectrum of chemical processes in detail. Goals of Next Five Years. Over the next five years, our research will advance computational modeling across multiple cutting-edge domains, integrating artificial intelligence and advanced theoretical frameworks to address complex biochemical and photochemical phenomena. We aim to establish transformer-based conditional diffusion models to predict protein dynamics, creating an automated, scalable alternative to traditional molecular dynamics simulations. Concurrently, we will expand our generative AI approaches to dissect catalytic mechanisms of β-lactamase enzymes, systematically mapping the catalytic landscapes of serine-based and metallo-β-lactamases. By incorporating protein conformational dynamics into these catalytic models, we aim to elucidate enzyme regulation and evolution, enhancing understanding of antibiotic resistance mechanisms. Additionally, we will develop comprehensive stochastic differential equation (SDE) models using Itô calculus to characterize the stochastic molecular behaviors underlying mechanically activated photochemistry (MAPC). Integrating specialized models for chemiluminescence and mechanochemical activation into a unified MAPC framework, we aim to significantly enhance predictive simulation capabilities in this interdisciplinary area. Collectively, these efforts will produce robust theoretical and computational tools that deepen fundamental understanding and enable innovative applications across biophysics and biochemistry. Overall Vision. Understanding the dynamic behavior of proteins is central to decoding the mechanisms underpinning protein function, enzymatic catalysis, and structure-function relationships. It remains computationally prohibitive for simulating biophysical and biochemical processes at functionally relevant timescales, which often span seconds to minutes—far beyond the millisecond range achievable by current technologies. Chemical processes at the molecular level are inherently non-deterministic stochastic processes, governed by probabilistic events and random molecular fluctuations rather than fixed, reproducible trajectories. The PI has been developing a long-term vision to advance biomolecular modeling by shifting from traditional MD simulations and reaction pathway calculation for enzyme catalysis mechanism toward AI-driven, predictive models capable of generating protein dynamics, enzyme catalytic mechanisms, and stochastic biochemical behaviors. By combining generative AI, molecular simulations, and stochastic modeling, this research addresses critical limitations in scaling biomolecular dynamics studies. It promises transformative advances in protein engineering, drug discovery, and mechanistic enzymology, while providing computationally efficient tools for simulating molecular behavior at unprecedented scales.", "keywords": [ "2019-nCoV", "Acceleration", "Address", "Antibiotic Resistance", "Area", "Artificial Intelligence enhanced", "Behavior", "Biochemical", "Biochemical Process", "Biochemistry", "Biophysical Process", "Biophysics", "Calculi", "Catalysis", "Chemicals", "Communities", "Complex", "Computer Models", "Computer Simulation", "Development", "Differential Equation", "Diffusion", "Enzymatic Biochemistry", "Enzymes", "Event", "Evolution", "Goals", "Health", "Hybrids", "Investigation", "Laboratories", "Light", "Machine Learning", "Maps", "Mechanics", "Methodology", "Modeling", "Molecular", "Molecular Disease", "Oxygen", "Pathway interactions", "Photochemistry", "Process", "Protein Conformation", "Protein Dynamics", "Protein Engineering", "Proteins", "Public Health Applications Research", "Quantum Mechanics", "Reaction", "Regulation", "Reproducibility", "Research", "Serine", "Stochastic Processes", "Structure-Activity Relationship", "Techniques", "Technology", "Theoretical model", "Therapeutic", "Trajectory", "Translations", "Vision", "beta-Lactamase", "chemical kinetics", "computerized tools", "design", "drug discovery", "empowerment", "generative artificial intelligence", "innovation", "interest", "millisecond", "molecular dynamics", "molecular mechanics", "predictive modeling", "protein function", "protein structure", "resistance mechanism", "simulation", "tool", "user-friendly", "voltage" ], "approved": true } }, { "type": "Grant", "id": "16137", "attributes": { "award_id": "1R35GM164072-01", "title": "Generative AI-Powered Exploration of Protein Dynamics and Non-Deterministic Chemical Kinetics", "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": 44592, "first_name": "HAN", "last_name": "NGUYEN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2031-02-28", "award_amount": 447174, "principal_investigator": { "id": 44593, "first_name": "Peng", "last_name": "Tao", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3513, "ror": "", "name": "SOUTHERN METHODIST UNIVERSITY", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Overview. The PI’s lab focuses on uncovering the dynamic mechanisms underlying protein functions through advanced computational simulations and theoretical modeling, employing molecular mechanics (MM), quantum mechanics (QM), and their combined approaches (QM/MM). The group's research has concentrated on three major themes. Protein Allostery: Significant advances have been achieved in both methodological development and application studies of key allosteric proteins, including the Light-Oxygen-Voltage-sensing (LOV) domain and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Additionally, the group has created two user- friendly public computational servers to serve the broader research community. Evolution of Enzyme Catalysis: The PI’s lab has pioneered innovative theoretical frameworks that integrate machine learning techniques with catalytic reaction pathways derived from hybrid QM/MM calculations, enhancing our understanding of enzyme evolution and catalytic mechanisms. Fundamental Chemical Mechanism Studies: The PI has a longstanding interest in the mechanisms underlying chemical transitions, particularly focusing on their characterization as rare events. Through extensive computational investigations and innovative methodological advancements, the PI's laboratory has explored a broad spectrum of chemical processes in detail. Goals of Next Five Years. Over the next five years, our research will advance computational modeling across multiple cutting-edge domains, integrating artificial intelligence and advanced theoretical frameworks to address complex biochemical and photochemical phenomena. We aim to establish transformer-based conditional diffusion models to predict protein dynamics, creating an automated, scalable alternative to traditional molecular dynamics simulations. Concurrently, we will expand our generative AI approaches to dissect catalytic mechanisms of β-lactamase enzymes, systematically mapping the catalytic landscapes of serine-based and metallo-β-lactamases. By incorporating protein conformational dynamics into these catalytic models, we aim to elucidate enzyme regulation and evolution, enhancing understanding of antibiotic resistance mechanisms. Additionally, we will develop comprehensive stochastic differential equation (SDE) models using Itô calculus to characterize the stochastic molecular behaviors underlying mechanically activated photochemistry (MAPC). Integrating specialized models for chemiluminescence and mechanochemical activation into a unified MAPC framework, we aim to significantly enhance predictive simulation capabilities in this interdisciplinary area. Collectively, these efforts will produce robust theoretical and computational tools that deepen fundamental understanding and enable innovative applications across biophysics and biochemistry. Overall Vision. Understanding the dynamic behavior of proteins is central to decoding the mechanisms underpinning protein function, enzymatic catalysis, and structure-function relationships. It remains computationally prohibitive for simulating biophysical and biochemical processes at functionally relevant timescales, which often span seconds to minutes—far beyond the millisecond range achievable by current technologies. Chemical processes at the molecular level are inherently non-deterministic stochastic processes, governed by probabilistic events and random molecular fluctuations rather than fixed, reproducible trajectories. The PI has been developing a long-term vision to advance biomolecular modeling by shifting from traditional MD simulations and reaction pathway calculation for enzyme catalysis mechanism toward AI-driven, predictive models capable of generating protein dynamics, enzyme catalytic mechanisms, and stochastic biochemical behaviors. By combining generative AI, molecular simulations, and stochastic modeling, this research addresses critical limitations in scaling biomolecular dynamics studies. It promises transformative advances in protein engineering, drug discovery, and mechanistic enzymology, while providing computationally efficient tools for simulating molecular behavior at unprecedented scales.", "keywords": [ "2019-nCoV", "Acceleration", "Address", "Antibiotic Resistance", "Area", "Artificial Intelligence enhanced", "Behavior", "Biochemical", "Biochemical Process", "Biochemistry", "Biophysical Process", "Biophysics", "Calculi", "Catalysis", "Chemicals", "Communities", "Complex", "Computer Models", "Computer Simulation", "Development", "Differential Equation", "Diffusion", "Enzymatic Biochemistry", "Enzymes", "Event", "Evolution", "Goals", "Health", "Hybrids", "Investigation", "Laboratories", "Light", "Machine Learning", "Maps", "Mechanics", "Methodology", "Modeling", "Molecular", "Molecular Disease", "Oxygen", "Pathway interactions", "Photochemistry", "Process", "Protein Conformation", "Protein Dynamics", "Protein Engineering", "Proteins", "Public Health Applications Research", "Quantum Mechanics", "Reaction", "Regulation", "Reproducibility", "Research", "Serine", "Stochastic Processes", "Structure-Activity Relationship", "Techniques", "Technology", "Theoretical model", "Therapeutic", "Trajectory", "Translations", "Vision", "beta-Lactamase", "chemical kinetics", "computerized tools", "design", "drug discovery", "empowerment", "generative artificial intelligence", "innovation", "interest", "millisecond", "molecular dynamics", "molecular mechanics", "predictive modeling", "protein function", "protein structure", "resistance mechanism", "simulation", "tool", "user-friendly", "voltage" ], "approved": true } }, { "type": "Grant", "id": "16104", "attributes": { "award_id": "1K23AI199607-01", "title": "Epidemiological assessment of Strongyloides stercoralis in at-risk populations in Texas", "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": 44582, "first_name": "JOHN T", "last_name": "PESCE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-02", "end_date": "2031-05-31", "award_amount": 191219, "principal_investigator": { "id": 44583, "first_name": "Megan", "last_name": "Duffey", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3506, "ror": "", "name": "BAYLOR COLLEGE OF MEDICINE", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Strongyloidiasis (causative agent Strongyloides stercoralis) is a neglected tropical disease that is often asymptomatic but causes severe morbidity and mortality when the infection becomes disseminated in periods of host immunosuppression. Due to the auto-infective portion of the S. stercoralis lifecycle, infections can persist for decades, long after a person has left an endemic area. Although strongyloidiasis is not considered endemic across the United States, places in the southern US with subtropical climates, such as Texas, can support the S. stercoralis lifecycle; therefore, continued transmission can occur. Adequate screening and prompt treatment of asymptomatic chronic strongyloidiasis will prevent the morbidity that disseminated strongyloidiasis carries. Still, cases are likely missed due to a lack of symptoms and suboptimal screening. Wastewater-based epidemiology has recently been leveraged during the COVID-19 pandemic for disease surveillance independent of health- seeking behavior, making it a powerful tool to assess strongyloidiasis, which is often asymptomatic and likely underdiagnosed. The long-term objective of this project is to understand the epidemiology and risk factors of strongyloidiasis in the greater Houston metropolitan area so that public health efforts may be directed towards the timely detection and treatment of at-risk populations. We will achieve this objective by completing the following aims: 1) Determine chronic strongyloidiasis prevalence and associated risk factors in a population seeking healthcare at a safety-net hospital system in Houston, TX, and 2) Identify areas in the greater Houston area with S. stercoralis presence using wastewater-based epidemiology. The innovation of wastewater-based epidemiology in the novel area of strongyloidiasis will be bolstered by a rigorous epidemiological study and risk-factor analysis. Additionally, this project will be innovative in its combination of improved diagnostic testing compared to standard of care. In aim 1, patients seeking healthcare at two primary care clinics in a safety net hospital system in Houston, Texas, will be approached for enrollment. Participants will answer a risk factor questionnaire and undergo blood and stool testing for strongyloidiasis. A risk map will be generated from neighborhood-level variables. In aim 2, wastewater samples from 16 wastewater treatment plants in Houston will be tested for S. stercoralis, and a spatial analysis will be conducted to determine if strongyloidiasis positivity coincides with high-risk areas on the risk map. By completing this project, the candidate, Dr. Megan Duffey, will develop essential skills in advanced epidemiological study design, statistical modeling, geospatial analysis, and science communication and advocacy. With these skills, she will create an R01-funded laboratory studying emerging neglected tropical diseases across the southern US. This work will be completed in the supportive research environment at Texas Children’s Hospital Division of Pediatric Tropical Medicine and Baylor College of Medicine’s National School of Tropical Medicine under the mentorship of Dr. Sarah Gunter and Dr. Peter Hotez.", "keywords": [ "Address", "Advocacy", "Affect", "Agriculture", "Area", "Biological Assay", "Blood", "Blood specimen", "COVID-19 pandemic", "Characteristics", "Childhood", "Chronic", "Climate", "Communication", "Communities", "County", "Coupled", "Data", "Demographic Factors", "Detection", "Diagnosis", "Diagnostic", "Diagnostic tests", "Dideoxy Chain Termination DNA Sequencing", "Disease", "Disease Surveillance", "Enrollment", "Environment", "Environmental Risk Factor", "Epidemiologic Factors", "Epidemiologic Methods", "Epidemiologic Research Design", "Epidemiologic analysis", "Event", "Factor Analysis", "Feces", "Frequencies", "Funding", "Goals", "Growth", "Health Care", "Health system", "Home", "Hospitals", "Immunosuppression", "Immunosuppressive Agents", "Individual", "Infection", "Infrastructure", "Investigation", "Laboratory Study", "Larva", "Left", "Life Cycle Stages", "Low income", "Maps", "Medicine", "Mentorship", "Mexico", "Modeling", "Molecular", "Monitor", "Morbidity", "Nature", "Neighborhoods", "Occupations", "Parasitic nematode", "Participant", "Patients", "Pediatric Hospitals", "Penetration", "Persons", "Plants", "Population", "Populations at Risk", "Poverty", "Prevalence", "Public Health", "Questionnaires", "Recording of previous events", "Research", "Risk", "Risk Factors", "Schools", "Science", "Serology", "Seroprevalences", "Sewage", "Skin", "Soil", "Statistical Data Interpretation", "Statistical Models", "Strongyloides", "Strongyloides stercoralis", "Strongyloidiasis", "Symptoms", "System", "Techniques", "Testing", "Texas", "Time", "Tropical Disease", "Tropical Medicine", "United States", "Urban Developments", "Work", "built environment", "burden of illness", "chronic infection", "college", "deprivation", "disorder risk", "epidemiology study", "experience", "extreme weather", "geospatial analysis", "health seeking behavior", "high risk", "high risk population", "improved", "indexing", "innovation", "metropolitan", "mortality", "neglect", "neglected tropical diseases", "neighborhood characteristics", "novel", "prevent", "primary care clinic", "public health intervention", "recruit", "safety net", "screening", "screening guidelines", "skills", "socioeconomic disadvantage", "standard of care", "stool sample", "tool", "transmission process", "viral pandemic", "wastewater epidemiology", "wastewater samples", "wastewater screening", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "16099", "attributes": { "award_id": "1R01HL184418-01", "title": "The role of tPA in reducing Lp(a) assembly", "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": 44576, "first_name": "LIJUAN", "last_name": "LIU", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-07-01", "end_date": "2030-03-31", "award_amount": 812747, "principal_investigator": { "id": 44577, "first_name": "Gissette", "last_name": "Reyes-Soffer", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 44578, "first_name": "Ze", "last_name": "Zheng", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 3503, "ror": "", "name": "MEDICAL COLLEGE OF WISCONSIN", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Epidemiological, genome-wide association, and mendelian randomization studies have demonstrated that lipoprotein (a) is an independent causal risk factor for cardiovascular disease. High Lp(a) levels are associated with up to 8 times higher risk of myocardial infarction and ischemic stroke, which are thrombotic events that can occur in atherosclerotic vessels. Lp(a) consists of two main protein components that may both contribute to atherosclerosis and thrombosis: a full-length apolipoprotein B100 (apoB) moiety and an apolipoprotein (a) [apo(a)] moiety. The apoB moiety is pro-atherogenic because it is a low-density lipoprotein (LDL)-like particle with a scaffolding protein apoB. The apo(a) moiety is pro-thrombotic because it is a structural homolog to plasminogen and inhibits plasminogen activation which is the primary mechanism breaking down blood clots, a process called fibrinolysis. The plasma level of Lp(a) was thought to be mostly determined by the genetic variants of LPA gene, and therefore, its level theoretically should be stable, leading to the numerous clinical recommendations for measuring Lp(a) once in lifetime. However, recent evidence shows changes in Lp(a) levels in the same individual. These changes highlight the presence of regulatory mechanisms, beyond genetics, that regulate plasma Lp(a) levels. Fibrinolysis is initiated by a serine protease tissue-type plasminogen activator (tPA) by converting plasminogen to its active form, plasmin, which cleaves cross-linked fibrin clots. Recently we have made several observations that suggest a potential role of tPA in lowering Lp(a) levels. We have shown that: 1) hepatocytes synthesize tPA and hepatocyte-derived tPA contributes to circulating tPA concentration and fibrinolysis. 2) In patients with severe COVID-19, tPA activity decreases while Lp(a) increasing during hospitalization. Among those who survived, Lp(a) decreased and tPA enzymatic activity increased after recovering from COVID-19 symptoms. 3) The Kringle 2 domain of tPA directly binds apoB’s the N-terminus, where the Kringle IV types 5-8 domains of apo(a) also interacts with, suggesting a potential role of tPA in competing with apo(a) for its interaction with apoB. Our new preliminary data showed that 1) tPA forms complexes with apoB’s N-terminal fragments apoB9, apoB15, or apoB18 where apo(a) interacts with apoB through lysine-mediated mechanisms. 2) A lysine analog, tranexamic acid, partially blocks the interaction between tPA and apoB. 3) Silencing tPA in human primary hepatocytes increased Lp(a) in serum-free culture medium. 4) Silencing PAI1 in human primary hepatocytes increases Lp(a) levels. Based on these compelling data, in this proposal, we will test the central hypothesis that tPA inhibits Lp(a) assembly by blocking apoB binding to apo(a) using Lp(a) transgenic mice, human primary hepatocytes and human plasma samples in the following specific aims: Aim 1. To test the hypothesis that tPA blocks apoB-apo(a) interaction. Aim 2. To test the hypothesis that tPA reduces the available apoB and apo(a). Aim 3. To test the hypothesis that reducing Lp(a) by increasing hepatocyte tPA lowers atherosclerosis and improves fibrinolysis.", "keywords": [ "Affect", "Age", "Alteplase", "Antisense Oligonucleotides", "Apolipoproteins", "Aspartic Acid", "Atherogenicity", "Atherosclerosis", "Automobile Driving", "Autophagocytosis", "Autophagosome", "Binding", "Binding Sites", "Blood coagulation", "Body mass index", "COVID-19", "Cardiovascular Diseases", "Charge", "Circulation", "Clinical", "Coagulation Process", "Communities", "Complex", "Culture Media", "Cytolysis", "Data", "Endoplasmic Reticulum", "Epidemiology", "Event", "Fibrin", "Fibrinolysis", "Genes", "Genetic", "Golgi Apparatus", "Hepatocyte", "Homologous Gene", "Hospitalization", "Human", "In Vitro", "Individual", "Ischemic Stroke", "Kringles", "Length", "Lipoprotein (a)", "Low-Density Lipoproteins", "Lysine", "Lysosomes", "Measures", "Mediating", "Mendelian randomization", "Messenger RNA", "Myocardial Infarction", "Myocardial Ischemia", "N-terminal", "Patients", "Plasma", "Plasmin", "Plasminogen", "Plasminogen Activator", "Plasminogen Activator Inhibitor 1", "Process", "Proteins", "Reagent", "Recommendation", "Role", "Sampling", "Scaffolding Protein", "Serine Protease", "Serum", "Sorting", "Symptoms", "Testing", "Thrombosis", "Tranexamic Acid", "Transgenic Mice", "apolipoprotein Lp(a+)", "cardiovascular risk factor", "crosslink", "genetic variant", "genome wide association study", "high risk", "improved", "in vivo Model", "inhibitor", "lysine analog", "multicatalytic endopeptidase complex", "particle", "receptor", "severe COVID-19", "sortilin", "thrombotic" ], "approved": true } }, { "type": "Grant", "id": "16098", "attributes": { "award_id": "1K08AG098035-01", "title": "Identification of molecular-neuropathological signatures in AD, FTLD and Long COVID", "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": 44574, "first_name": "CHELSEA MARIE", "last_name": "HAAKENSON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-07-01", "end_date": "2031-04-30", "award_amount": 146750, "principal_investigator": { "id": 44575, "first_name": "Ting", "last_name": "Zhang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3502, "ror": "", "name": "UNIVERSITY OF CALIFORNIA LOS ANGELES", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Neurodegenerative diseases, including Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD), exhibit neuropathological features (e.g., amyloid and p-tau accumulation) that disproportionately affect certain brain regions and subsets of cells. This selective vulnerability to neuropathological features has been recognized for decades, yet its underlying molecular mechanisms remain unclear. Long COVID, a prevalent post-pandemic condition with largely unknown disease mechanisms, is reported to exhibit p-tau pathology, linking it to neurodegeneration. Current methods, including single-nucleus RNA sequencing, are unable to integrate spatial and pathological contexts with transcriptomic profiles, limiting direct molecular characterization of disease pathology at the single cell level. Here, I propose to leverage single-cell spatial multi-omics on postmortem brain tissues to in-situ capture molecular and cellular alterations that directly linked to selective vulnerability to neuropathological features across AD, FTLD, and Long COVID at single-cell resolution—insights previously unattainable. I will also identify potential pathological modulators using functional genomics in human induced pluripotent stem cell (iPSC)-derived neurons. In this proposal, Aim1 will characterize neuropathological and transcriptomic alterations in Long COVID and compare findings to AD and FTLD. Aim2 will leverage same- slide single-cell spatial multi-omics with neuropathology phenotyping (Xenium 5K + Phenocycler-Fusion) and define the molecular-neuropathological signatures specific to amyloid, neuronal p-tau, and glial p-tau pathology in AD, FTLD and Long COVID. Aim3 will employ CRISPRi/a screening in iPSC-derived neurons to identify neuronal p-tau modifiers from signatures uncovered in Aim2. By integrating single-cell spatial multi-omics with functional genomics, this project will bridge the gap between neuropathology and molecular profiling, provide novel insights into selective vulnerability to neuropathological features and identify pathological modulators. My long-term goal is to become a physician(neuropathologist)-scientist leading a NIH-funded research laboratory focused on neurodegenerative diseases and COVID-19. This five-year mentored career development plan will provide the necessary training for my transition to independence, emphasizing expertise in single-cell spatial transcriptomics and proteomics, bioinformatics, imaging analysis, iPSC technology and CRISPR-based functional genomics. Additionally, I will deepen my knowledge of neuropathology, molecular genetics, and clinical aspects of AD, FTLD, and Long COVID. I have assembled a multidisciplinary mentorship team of distinguished physician-scientists that includes Dr. Daniel Geschwind (primary mentor), Drs. Harry Vinters and Shino Magaki (co-mentors), and advisory members Drs. Inma Cobos, Vivek Swarup, and Edward Lee. With UCLA's cutting- edge facilities, exceptional research environment, and strong clinical resources, this award will prepare me to become a competitive neuropathologist-scientist, advancing our understanding of neurodegenerative diseases and COVID-19 while informing therapeutic strategies.", "keywords": [ "Address", "Affect", "Aging", "Alzheimer's Disease", "Alzheimer's disease brain", "Amyloid", "Astrocytes", "Autopsy", "Award", "Bioinformatics", "Brain", "Brain region", "COVID-19", "CRISPR interference", "Cell Communication", "Cell Death", "Cell model", "Cells", "Clinical", "Clustered Regularly Interspaced Short Palindromic Repeats", "Code", "Coiled Bodies", "Couples", "Data", "Dementia", "Development Plans", "Diagnosis", "Diffuse", "Disease", "Disease Progression", "Environment", "Etiology", "Exhibits", "Flow Cytometry", "Frontotemporal Lobar Degenerations", "Functional disorder", "Funding", "Genes", "Goals", "Image", "Immunofluorescence Immunologic", "In Situ", "Individual", "Induced pluripotent stem cell derived human neuron", "Induced pluripotent stem cell derived neurons", "Knowledge", "Laboratory Research", "Link", "Long COVID", "Mentors", "Mentorship", "Methods", "Molecular", "Molecular Genetics", "Molecular Profiling", "Nature", "Nerve Degeneration", "Neurodegenerative Disorders", "Neurofibrillary Tangles", "Neurons", "Onset of illness", "Pathologic", "Pathology", "Patients", "Phenotype", "Physicians", "Pick Disease of the Brain", "Pick body", "Progressive Supranuclear Palsy", "Proteins", "Proteomics", "Reporting", "Research", "Resolution", "Resources", "Scientist", "Senile Plaques", "Slide", "Stimulus", "Tauopathies", "Technology", "Therapeutic", "Tissues", "Training", "United States National Institutes of Health", "Work", "acute COVID-19", "amyloid pathology", "brain cell", "brain tissue", "career development", "cell type", "cohort", "four repeats tau", "functional genomics", "human induced pluripotent stem cells", "hyperphosphorylated tau", "induced pluripotent stem cell", "induced pluripotent stem cell technology", "insight", "member", "multidisciplinary", "neuropathology", "novel", "post-pandemic", "resilience", "response", "screening", "single nucleus RNA-sequencing", "spatial integration", "spatial omics", "spatial transcriptomics", "tau Proteins", "tau aggregation", "tau-1", "transcriptome sequencing", "transcriptomics" ], "approved": true } } ], "meta": { "pagination": { "page": 2, "pages": 1442, "count": 14420 } } }