Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&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=1397&sort=program_officials", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=program_officials", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=program_officials" }, "data": [ { "type": "Grant", "id": "15582", "attributes": { "award_id": "3R21AI175883-03S1", "title": "What Precursors Become Lung-Resident CD4 Memory that Protect Against Respiratory Infections or Cause Lung Pathology?", "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": 31609, "first_name": "Hariharan", "last_name": "Subramanian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-11-22", "end_date": "2025-10-31", "award_amount": 92708, "principal_investigator": { "id": 29185, "first_name": "Priyadharshini", "last_name": "Devarajan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1415, "ror": "", "name": "STATE UNIVERSITY NEW YORK STONY BROOK", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "What Precursors Become Lung-Resident CD4 Memory that Protect Against Respiratory Infections or Cause Lung Pathology? Respiratory viruses such as SARS-CoV1, Influenza and recently SARS-CoV2 (COVID-19) have caused the major pandemics in the 21st century and influenza causes high levels of death from yearly circulating outbreaks. T cells can target internal viral proteins, that mutate less frequently. Thus, T cell memory induced by previous vaccination or infection can still be effective against emerging mutant viral strains. Tissue resident memory (TRM) cells, that develop in the lung are at the first line of defense of our adaptive immune response against respiratory infections because of their location. However, lung CD8 TRM, which are most- studied, are short- lived. The few studies that have examined lung CD4 TRM suggest that they may decay less rapidly. Relatively little is known about lung CD4 TRM longevity and mechanisms of function, though they are known to protect against many respiratory infections such Influenza, Sendai, B.pertussis, pneumococcal pneumonia and tuberculosis infections. Moreover, it is unclear which CD4 effectors precursors become lung CD4 TRM. If CD4 lung TRM are longer-lived, they might compensate over the long-term for the rapid decline in CD8 lung TRM, thus making them good vaccine targets to provide strong more durable immunity. A majority of the CD4 and CD8 T cells in human lung express TRM features, so it is vital to understand their impact when they are reactivated during an immune response, both their positive effect on protection against pathogens and negative effects on lung function and tissue damage. In many respiratory infections such as influenza and COVID-19 there is also potential for severe lung damage leading to poor prognosis. We show that cytotoxic CD4 T cells, that are resident effectors in the lung and that contribute to damage, can be precursors of lung CD4 TRM. Thus, an understanding of how CD4 TRM can both protect and cause lung pathology on reactivation, especially if they are maintained long-term, is vital. Here, the research proposed will identify the precursors of CD4 lung TRM from CD4 lung effectors, and better define their protective and pathogenic potentials. It will phenotypically and molecularly characterize the CD4 TRM formed from subsets of lung CD4 effectors. It will study their longevity and their maintenance via mechanisms such as homeostatic proliferation and recruitment from circulation. Finally, it will study in detail their functional mechanisms of eliciting protection vs those causing lung immunopathology by direct cytolysis, inflammation andhelper function. Understanding mechanisms/conditions driving protection and pathology by CD4 TRM will enable design of interventions like vaccines and immunotherapies, that favor the development of protection while minimizing pathology. Identifying precursor CD4 effectors that give rise to protective CD4 TRM will also allow to finetune vaccine approaches that drive generation of those CD4 effector subsets. In future studies, the knowledge gained here, will allow identification of transcriptional networks that regulate the development of CD4 TRM from CD4 effectors and naïve CD4.", "keywords": [ "2019-nCoV", "Automobile Driving", "Bordetella pertussis", "CD4 Positive T Lymphocytes", "CD8-Positive T-Lymphocytes", "CD8B1 gene", "COVID-19", "Cells", "Cessation of life", "Circulation", "Compensation", "Cytolysis", "Development", "Disease Outbreaks", "Future", "Generations", "Genetic Transcription", "Grant", "Human", "Immune", "Immune response", "Immunity", "Immunotherapy", "Infection", "Inflammation", "Influenza", "Knowledge", "Location", "Longevity", "Lung", "Maintenance", "Memory", "Molecular", "Morbidity - disease rate", "Mutate", "Pathogenicity", "Pathology", "Pathway interactions", "Phenotype", "Pneumococcal Pneumonia", "Prognosis", "Proliferating", "Pulmonary Pathology", "Research", "Respiratory Tract Infections", "SARS coronavirus", "Severe Acute Respiratory Syndrome", "Structure of parenchyma of lung", "T memory cell", "T-Lymphocyte", "Tissues", "Tuberculosis", "Vaccination", "Vaccines", "Viral", "Viral Proteins", "adaptive immune response", "cell type", "cytotoxic", "immunopathology", "influenza outbreak", "insight", "lung injury", "mortality", "mutant", "pandemic disease", "pandemic influenza", "pathogen", "pulmonary function", "recruit", "respiratory virus", "therapy design", "vaccine development" ], "approved": true } }, { "type": "Grant", "id": "15014", "attributes": { "award_id": "5R35GM150778-02", "title": "Understanding the antiviral roles of acellular RNA quality control pathway", "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": 31610, "first_name": "Dimitrios Nikolaos", "last_name": "Vatakis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-15", "end_date": "2028-05-31", "award_amount": 411250, "principal_investigator": { "id": 7527, "first_name": "Anna-Lena", "last_name": "Steckelberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 781, "ror": "", "name": "COLUMBIA UNIVERSITY HEALTH SCIENCES", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Eukaryotic cells use multi-layered strategies to ensure the fidelity of gene expression. One of the best-studied RNA quality control pathways is Nonsense-mediated mRNA decay (NMD), a ribosome-associated surveillance machinery that recognizes and degrades cellular transcripts with premature termination codons (PTC). Initially identified as a mechanism to rid the cell of faulty mRNAs, it is now known that NMD also regulates the expression of 5-10% of cellular transcripts with important functions in cell differentiation, homeostasis, cellular stress responses, and more. In addition, recent studies have shown that many proteins of the NMD pathway function in the cell-autonomous defense against RNA viruses, including human-pathogenic alphaviruses, coronaviruses and flaviviruses. As obligate intracellular parasites, these viruses interface closely with cellular mRNA processing pathways, and we can gain new insight to NMD functions by understanding how the machinery is repurposed to fight viral infection. We currently have a very incomplete picture of NMD functions during viral infection. Many viruses lack PTCs or other NMD-inducing features, and it is not known whether NMD proteins recognize viral RNA in manners similar to canonical NMD, or through new and unusual interactions or mechanisms. Moreover, NMD is often globally downregulated during virus infection, suggesting that viruses have mechanisms to rewire the antiviral NMD network. Using the flavivirus Zika virus (ZIKV) as a model RNA virus, we aim to understand the molecular interactions between the NMD machinery and viral infection. We will dissect the NMD-ZIKV protein-protein and protein-RNA interaction network with a multi-disciplinary approach that combines virology with RNA biochemistry and high-throughput assays to link molecular interactions to cellular phenotypes. Specific questions addressed within this study are: 1) How do NMD proteins interact to fight viral infection? 2) Are there specific features in the viral RNA that trigger NMD, and if so, how are they recognized by the NMD machinery? 3) How do viral proteins inhibit NMD? And 4) Are antiviral NMD functions conserved in the important mosquito host of flaviviruses? Collectively, our study will provide new insight into an important cell-autonomous antiviral defense network. In addition, by studying the unusual ways in which NMD is regulated during virus infection, we hope to discover new cellular functions of the NMD machinery itself.", "keywords": [ "Address", "Alpha Virus", "Binding", "Cell Differentiation process", "Cell Physiology", "Cells", "Cellular Stress", "Coronavirus", "Culicidae", "Ensure", "Eukaryotic Cell", "Flavivirus", "Gene Expression", "Gene Expression Regulation", "Goals", "Homeostasis", "Human", "Infection", "Link", "Mediating", "Messenger RNA", "Modeling", "Pathogenicity", "Pathway interactions", "Phenotype", "Proteins", "Quality Control", "RNA", "RNA Biochemistry", "RNA Viruses", "RNA-Protein Interaction", "Ribosomes", "Role", "Terminator Codon", "Transcript", "Viral", "Viral Proteins", "Virus", "Virus Diseases", "Zika Virus", "fighting", "high throughput screening", "insight", "interdisciplinary approach", "mRNA Decay", "obligate intracellular parasite", "premature", "viral RNA", "virology" ], "approved": true } }, { "type": "Grant", "id": "15018", "attributes": { "award_id": "5R34MH130639-02", "title": "Expanding minority youth access to evidence-based care: A pilot effectiveness trial of a digital mental health intervention", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Mental Health (NIMH)" ], "program_reference_codes": [], "program_officials": [ { "id": 31611, "first_name": "Marcy Ellen", "last_name": "Burstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-01", "end_date": "2026-06-30", "award_amount": 249162, "principal_investigator": { "id": 27619, "first_name": "ERUM", "last_name": "NADEEM", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 27620, "first_name": "Anna Robinson", "last_name": "Van Meter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 832, "ror": "", "name": "NEW YORK UNIVERSITY SCHOOL OF MEDICINE", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The prevalence of anxiety and depression are high among adolescents. COVID-19-related stressors have increases rates of internalizing disorders and risk for suicide, especially among ethnic minority adolescents who have been disproportionately affected by the pandemic. Although evidence-based interventions are effective, the vast majority ethnic minority adolescents do not access any MH care. School-based MH services can address common barriers to care such as cost and transportation. However, even with access to school- based MH services, capacity can be highly limited in schools, and some adolescents may be reticent to seek services at school due to concerns about privacy, judgment, and academic disruption. Innovative approaches to provide accessible, low-cost, evidence-based MH care to minority youth are urgently needed. Technology creates an opportunity to treat youth in need of MH services and could expand the reach of school health services. Despite the potential impact of digital care, to date there are no studies examining whether digital mental health interventions improve service access for vulnerable youth served by school-based mental health services. Consistent with NIMH Notice of Information MH-18-031 designating digital health technology as a priority, this application proposes to determine the feasibility and acceptability of delivering SilverCloud, a clinician-guided, empirically-supported, app-based, CBT program to vulnerable adolescents through school- based health centers (SBHCs). In contrast to most MH apps, SilverCloud has demonstrated strong engagement and medium-to-large effect sizes. Its features, including personalized feedback, stories from relatable peers, and routine outcome assessment, directly promote engagement. This would be the first trial to evaluate SilverCloud as a school-based intervention and refine it to meet the needs of low income, ethnic/racial minority youth. We propose a four-phase study conducted at two diverse, public schools in Brooklyn. In Phase I, participants who endorse internalizing symptoms (n=20) during routine MH screening at the SBHC will participate in an open trial of SilverCloud. In Phase II, open trial participants will provide feedback that will be used to refine the SilverCloud program. In Phase III participants will be screened (PHQ-9/GAD-7). Those who endorse symptoms will be randomized (N=100) to SilverCloud or treatment as usual (TAU). Outcomes will be evaluated each semester. In Phase IV, participant and stakeholder feedback will be collected systematically to further inform program adaptations and implementation strategy refinement for a large effectiveness trial. Primary aims are to establish the feasibility, acceptability, and preliminary effectiveness of SilverCloud among minority youth served through SBHCs and to examine whether SilverCloud changes engagement (i.e., initiation, dose) and clinical (i.e., emotion regulation, negative cognitions, coping skills, behavioral activation) targets, and whether changes are associated with clinical benefit (i.e., reduction in depression and anxiety).", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15265", "attributes": { "award_id": "1R01MH139134-01", "title": "SCH: Personalized AI-Driven Models for Supporting User Engagement and Adherence in Health Interventions: Validation in Cognitive Behavioral Therapy for Anxiety", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Mental Health (NIMH)" ], "program_reference_codes": [], "program_officials": [ { "id": 31611, "first_name": "Marcy Ellen", "last_name": "Burstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-01", "end_date": "2028-07-31", "award_amount": 299999, "principal_investigator": { "id": 31853, "first_name": "Maja J", "last_name": "Mataric", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Untreated anxiety undermines long-term physical and emotional wellbeing, especially among college students, with rates worsening since the onset of the COVID-19 pandemic. Cognitive Behavioral Therapy (CBT) is the leading evidence-based intervention for anxiety, but many students fail to complete exercises between CBT sessions, reducing its effectiveness. Socially assistive robots (SARs) help promote adherence to home-based practice in the context of elder care, social skill learning, and physical therapy, but it is unknown how SARs can enhance CBT. The specific objective of this research is to develop personalized CBT SARs that can support CBT compliance for college students with anxiety. To meet the goals of the proposed work, we will conduct eight collaborative design sessions and three user studies and data collections and evaluations: Specifically, studies will determine how SAR personalization based on implicit and explicit feedback can help promote greater CBT compliance and anxiety reduction outcomes for students. Specific Aim 1 will develop machine learning models to personalize a CBT SAR with implicit personalization–using only visual and auditory cues and no user input. Specific Aim 2 will develop machine learning models to enhance SAR engagement based on explicit user feedback–using direct input from the user to change the SAR behaviors. Specific Aim 3a will test the efficacy of personalized CBT SARs on key outcomes of a 6-week CBT for anxiety intervention: robot-student alliance, CBT engagement, CBT adherence, and anxiety symptom reduction. In Study 3a, n=60 students with anxiety will be randomly assigned to either a CBT SAR that performs implicit personalization (n=30) or a CBT SAR with no personalization (control, n=30). In Aim 3b, a separate sample of n=60 students will be randomly assigned to either complete a 6-week CBT SAR intervention that performs explicit personalization (n=30) or a CBT SAR with no personalization (control, n=30). We predict that implicit and explicit CBT SAR personalization will enhance pre- versus post-intervention SAR-user alliance, engagement in CBT, and lower anxiety outcomes over the course of a 6-week daily CBT home-based intervention for anxiety compared to the non-personalized control CBT SAR. RELEVANCE (See instructions): The proposed research is relevant to public health, as it will assess whether personalized SARs impact engagement and outcomes in CBT exercises for anxiety, which is key to developing effective, scalable treatments for mood disorders such as anxiety. This research aligns with the NIMH mission of leveraging novel methods to intuitively and intelligently collect, sense, connect, analyze and interpret data from individuals, devices and systems to enable discovery and optimize health.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15628", "attributes": { "award_id": "1R34MH134930-01A1", "title": "Building Healthy Eating and Self-Esteem Together for University Students (BEST-U): A Pilot Randomized Controlled Trial of an mHealth Intervention for Binge-Spectrum Disorders", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Mental Health (NIMH)" ], "program_reference_codes": [], "program_officials": [ { "id": 31611, "first_name": "Marcy Ellen", "last_name": "Burstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": "2027-12-31", "award_amount": 249521, "principal_investigator": { "id": 32128, "first_name": "Kara Alise", "last_name": "Christensen Pacella", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32129, "first_name": "Kelsie Terese", "last_name": "Forbush", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1496, "ror": "", "name": "UNIVERSITY OF KANSAS LAWRENCE", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "Eating disorders (EDs) are a critical concern on college campuses. Moreover, since the COVID-19 pandemic, ED prevalence has increased by 62% in university women and 140% in university men. Resources are inadequate to meet demand, leading to delays in students’ access to treatment. Untreated (or poorly treated) EDs result in greater healthcare utilization and costs to students, as well as lower academic achievement and increased psychiatric disability and mortality, suggesting a critical need for quality ED treatment on university campuses and to rethink treatment delivery. One way to address this gap in care delivery is to improve treatment accessibility and scalability, such as dissemination via mobile apps. Guided self-help Cognitive- Behavior Therapy (CBT-gsh) is a cost-effective option that can be delivered by non-traditional service providers, such as nurses and physicians. Our scientific premise is that the mHealth CBT-gsh app, Building Healthy Eating and Self-Esteem Together for University Students (BEST-U), will lead to reductions in binge eating (primary outcome) through reductions in dietary restraint and weight/shape concerns (target mechanisms). Our pilot data showed strong support for our premise, specifically the need for brief, targeted mHealth interventions in students and the ability of the program to significantly reduce binge eating and impairment and increase wellbeing, with high user acceptability and low drop-out rates. However, prior to implementing BEST-U at other universities, we need to test the intervention in a real-world setting with the end goal of disseminating at scale. Our objectives are to: 1) conduct an effectiveness test of BEST-U compared to a similar dose of present-centered therapy (PCT) in students with non-low weight binge-spectrum EDs and 2) test target mechanisms that lead to changes in binge eating. To accomplish our objectives, we will test the following specific aims: 1) conduct an RCT of BEST-U (N=47) compared to a similar dose of PCT (N=47) in students with non-low weight binge-spectrum EDs; 2) test target mechanisms that lead to changes in binge eating and other ED symptoms; and 3) characterize barriers and facilitators to implementation across two campuses. Our exploratory aim will test food reinforcement and food-choice impulsivity as potential target mechanisms or response moderators of rapid response in binge eating. Given the rapidly rising rates of EDs and the lack of existing treatment resources, the proposed study is innovative and significant because it will provide a scalable treatment to fill gaps in care to promote student wellness and educational attainment. Our pilot data showed initial efficacy for BEST-U, yet the proposed study is necessary to validate the treatment in a student health setting prior to large-scale dissemination. Furthermore, given that few studies have identified underlying mechanisms that explain how CBT-gsh works and for whom, this study may lead to improved ability to tailor or modify existing CBT-gsh (e.g., personalized medicine approaches) or lead to novel intervention development for students who are unlikely to respond rapidly (or at all) to first-line CBT interventions for EDs.", "keywords": [ "Academic achievement", "Address", "Administrator", "Binge Eating", "COVID-19 pandemic", "Caring", "Clinical", "Clinical Services", "Cognitive Therapy", "Community Practice", "Data", "Disease", "Dose", "Dropout", "Eating", "Eating Disorders", "Education", "Effectiveness", "Effectiveness of Interventions", "Elements", "Emotions", "Ensure", "Focus Groups", "Food", "Frequencies", "Goals", "Health", "Health Care", "Health Personnel", "Health Services Accessibility", "Healthy Eating", "Impairment", "Impulsivity", "Intervention", "Lead", "Mental Health", "Mission", "Mobile Health Application", "Monitor", "National Institute of Mental Health", "Nurses", "Outcome", "Pattern", "Personal Satisfaction", "Physicians", "Population", "Postdoctoral Fellow", "Prevalence", "Prevention", "Professional counselor", "Psychological reinforcement", "Public Health", "Randomized", "Randomized Controlled Trials", "Reporting", "Research", "Resources", "Risk", "Shapes", "Student Health Services", "Students", "Symptoms", "Testing", "Theoretical Domains framework", "Training", "Treatment outcome", "United States National Institutes of Health", "Universities", "Weight", "Woman", "Work", "care delivery", "college", "cost", "cost effective", "dietary", "doctoral student", "effectiveness testing", "evidence base", "excessive exercise", "follow-up", "health administration", "health care service utilization", "health care settings", "implementation facilitators", "implementation framework", "implementation intervention", "implementation strategy", "improved", "innovation", "mHealth", "men", "mhealth interventions", "mobile application", "mortality", "multi-site trial", "novel", "personalized medicine", "practice setting", "primary outcome", "programs", "psychiatric disability", "psychologic", "purge", "response", "restraint", "secondary outcome", "self esteem", "self help", "service providers", "therapy development", "treatment arm", "university student" ], "approved": true } }, { "type": "Grant", "id": "15043", "attributes": { "award_id": "5F31LM014282-02", "title": "Addressing Algorithmic Unreliability and Dataset Shift in EHR-based Risk Prediction Models", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 31613, "first_name": "Goutham", "last_name": "Reddy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-06-01", "end_date": "2026-05-31", "award_amount": 48974, "principal_investigator": { "id": 27807, "first_name": "Likhitha", "last_name": "Kolla", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Predictive analytic algorithms built on electronic health record (EHR) inputs, such as patient characteristics, administrative codes, and lab values, are increasingly used in health care settings to direct resources to high- risk patients. Data play an indispensable role in the development and deployment of effective predictive models. The greatest, yet understudied, challenge in the maintenance of these tools arises from a data-related concern, namely dataset shift, in which training data distribution differs from the population on which the algorithm is deployed, leading to model deterioration and inaccurate risk predictions. Dataset shift is a pervasive cause of algorithmic unreliability in EHR-based models due to inevitable changes in physician behaviors and health system operations that alter (1) the input distribution (covariate drift); and (2) changes in the relationship between predictors and outcome (concept drift). Sudden changes in healthcare utilization during the COVID-19 pandemic may have impacted the data generation process and the performance of clinical predictive models. Our preliminary study showed that decreased collection of patient labs during the COVID-19 quarantine period led to sparse data generation for important predictors of a single-institution EHR-based mortality risk prediction algorithm, underpredicting risk for patients with advanced cancers. Despite the increasing use of predictive tools in high stakes clinical applications; and growing recognition of dataset shift, we lack a framework for reasoning shift and its effects on care delivery; and for proactively addressing shift to maintain performance over time. In Aim 1, we propose to extend prior works on shift to a nationally deployed risk prediction algorithm, the VA Care Assessment Need (CAN) model, used on millions of VA beneficiaries each year. The VA CAN model predicts the likelihood of hospitalization within 90 days or 1 year after a primary care encounter to identify high-risk patients who would benefit from additional outpatient interventions. We also investigate covariate and concept drift as two possible mechanisms for COVID-19 associated dataset shift. In Aim 2, we apply an interrupted time series design to study the association between sudden shift at the onset of the pandemic on case-management decisions. Current solutions to address dataset shift have primarily been reactive (i.e. model retraining with recent data), however, fail to be robust in new testing environments. In Aim 3, we consider revision of the VA CAN model via machine learning and inclusion of variables that reflect potential drivers of shift. This project is innovative as it is the first to leverage a rigorous statistical framework to study extent and mechanisms of shift and develop proactive guidelines for model maintenance. The training plan is rigorous for Ms. Kolla, an MD-PhD student in biostatistics. She is strongly supported by her department and institution as well as her two high- qualified sponsors: Dr. Jinbo Chen, an expert in EHR-based risk prediction modeling, and Dr. Ravi Parikh, an expert in implementation of predictive analytics. The proposed research and career development plan will be an essential step towards Ms. Kolla’s development as an interdisciplinary and independent physician-scientist.", "keywords": [ "Accident and Emergency department", "Address", "Advanced Malignant Neoplasm", "Algorithms", "Biometry", "COVID-19", "COVID-19 pandemic", "Caring", "Case Management", "Cessation of life", "Characteristics", "Clinical", "Code", "Collection", "Compensation", "Data", "Data Set", "Data Sources", "Deterioration", "Development", "Development Plans", "Early Intervention", "Electronic Health Record", "Environment", "Future", "Generations", "Guidelines", "Health", "Health system", "Healthcare", "Hospital Costs", "Hospitalization", "Impairment", "Incentives", "Inpatients", "Institution", "Interruption", "Intervention", "Knowledge", "Laboratories", "Machine Learning", "Maintenance", "Methods", "Mission", "Modeling", "Needs Assessment", "Onset of illness", "Outcome", "Outpatients", "Patient Care", "Patients", "Performance", "Physicians", "Play", "Policies", "Policy Maker", "Population", "Predictive Analytics", "Primary Care", "Process", "Public Health", "Qualifying", "Quarantine", "Refit", "Reporting", "Research", "Research Personnel", "Resource Allocation", "Resources", "Risk Adjustment", "Role", "Scientist", "Sepsis", "Series", "System", "Testing", "Time", "Training", "Triage", "United States National Institutes of Health", "Validation", "Veterans Health Administration", "Work", "analytical tool", "beneficiary", "care delivery", "career development", "clinical application", "clinical decision-making", "clinical predictive model", "demographics", "design", "doctoral student", "evidence base", "health care disparity", "health care service utilization", "health care settings", "health service use", "high risk", "improved", "innovation", "model design", "model development", "mortality risk", "operation", "outpatient programs", "pandemic disease", "pandemic impact", "prediction algorithm", "predictive modeling", "predictive tools", "prevent", "provider behavior", "research and development", "risk prediction", "risk prediction model", "tool" ], "approved": true } }, { "type": "Grant", "id": "15217", "attributes": { "award_id": "1R21LM014569-01", "title": "AI-MedWise: Developing and validating an artificial intelligence solution for effective video-based monitoring of medication adherence", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 31613, "first_name": "Goutham", "last_name": "Reddy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": "2026-07-31", "award_amount": 203850, "principal_investigator": { "id": 24127, "first_name": "Juliet Nabbuye", "last_name": "Sekandi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 888, "ror": "https://ror.org/00te3t702", "name": "University of Georgia", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 31799, "first_name": "Jin", "last_name": "Sun", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 888, "ror": "https://ror.org/00te3t702", "name": "University of Georgia", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Poor adherence to oral medications is highly prevalent in 40-50% of patients with chronic diseases worldwide. Close monitoring of medication adherence is a critical part of successful routine patient care and drug efficacy trials but traditional observation methods of medication monitoring have had limited reliability and scalability. Thus, there is an urgent need for effective methods of remote patient monitoring to ensure proper medication adherence. Moreover, this need for remote monitoring and healthcare delivery was accentuated during the COVID-19 pandemic. Our proposal seeks to advance the field of medication adherence monitoring and support. A promising novel technology-based intervention, video directly observed treatment (VDOT), has been shown to improve medication adherence monitoring and support in patients with TB and HIV. VDOT enables patients to use a smartphone app to record and submit videos of daily medication intake. The health workers are able to access and manually review the videos remotely to confirm adherence and take follow up actions when doses are missed. Although VDOT is highly acceptable, effective and cost-saving, its large-scale adoption is limited by the requirement for the manual task of reviewing daily medication videos. Harnessing the potential of artificial intelligence (AI), we propose to create an innovative machine learning (ML) model that enhances the efficiency of the VDOT by eliminating the need for manual review of the medication intake videos by the health workers. We seek to build on our promising results from a ‘proof of concept’ model that was based on a small sample of video images from TB medication. We will develop a robust and more effective model that focuses on fine-grained medication ingestion behaviors by patients, not captured in previous ML models. We have access to ~20,000 medication intake videos collected from our recently completed VDOT study of consenting patients with TB in Uganda. This large video image dataset will facilitate the training of a robust machine learning model for proper detection of medication adherence. Specific Aim 1: To construct a large, high-quality dataset of video images for training AI models for recognition of fine- grained patient actions using existing TB medication videos and open source human activities videos. Specific Aim 2: To develop, train and validate a novel machine learning model for robust recognition of fine-grained patient behavior. The results of this study will provide a robust AI-based model that will be further validated in routine clinical settings for optimum performance. We expect the new model to serve as tool that will accelerate the adoption of VDOT and be adapted for various chronic diseases.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15632", "attributes": { "award_id": "1R21LM014699-01", "title": "Novel designs for multi-arm multi-dose multi-stage platform trials", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 31613, "first_name": "Goutham", "last_name": "Reddy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": "2026-12-31", "award_amount": 183938, "principal_investigator": { "id": 24739, "first_name": "Ruitao", "last_name": "Lin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1420, "ror": "", "name": "UNIVERSITY OF TX MD ANDERSON CAN CTR", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1420, "ror": "", "name": "UNIVERSITY OF TX MD ANDERSON CAN CTR", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The drug development process is an intricate and resource-intensive endeavor, particularly in the context of addressing complex diseases with unmet medical needs, such as cancer, serious rheumatic diseases, acute ischemic stroke, among others. There has been a surge in the development of new therapeutic agents and combination treatment strategies, driven by rapid advancements in biological knowledge. However, the conventional approach to clinical trials, where treatments are evaluated one at a time in a sequential manner, is fraught with several shortcomings, especially in the era of precision (personalized) medicine. This \\one-treatment-at-a-time\" paradigm in drug development is associated with notably low success rates. It not only consumes valuable time and resources between separate trials but also extends the overall drug development timeline. The discrete nature of these phases impedes the ecient exchange of information across di erent stages, potentially leading to a loss of overall eciency. Furthermore, the evaluation of multiple treatments individually presents challenges in accurately estimating and interpreting the relative treatment e ects of each drug due to the presence of \\treatment{trial\" confounding. Viewing the drug development process as a whole system, the multi-arm multi-stage platform trial provides an e ective way to eciently evaluate modern treatments. Platform trial can eciently evaluates treatment by quickly advancing promising ones and discarding ine ective or overly toxic ones. This reduces the time it takes to identify e ective treatments for patients in unmet medical need. Platform trials can easily add new treatment arms, enabling continuous adaptation and optimization. Additionally, they control family-wise false positives and increase eciency with methods like multiple testing procedures and adaptive randomization to allocate more patients to better treatment arms, among other prominent bene ts. During recent years, several well-known platform trials have been conducted to advance drug development. These trials include I-SPY2 for breast cancer, REMAP-CAP for pneumonia, and DIAN-TU for Alzheimer's disease, among others. Additionally, during the COVID-19 pandemic, more than 50 COVID-19 platform trials were registered globally between 2020 and 2021. In response to the growing prevalence of platform trials, this research aims to propose robust Bayesian adaptive designs and methods to address the practical challenges that arise in real-world platform trials. These challenges include optimizing sequential monitoring of multiple treatments and/or multiple endpoints, eciently establishing proof-of-concept and dose selection in multi-arm multi-dose platform trials, managing incompatibility issues due to non-concurrent controls, and addressing late-onset outcomes, among others. Each proposed method will be tailored to tackle a combination of these challenges in speci c platform trial settings. For each design, user-friendly software will be developed, which will include programs for trial simulation to establish design operating characteristics, facilitate trial conduct, and assist physicians in selecting optimal treatment for their patients. The overarching goal is to develop Bayesian adaptive methods to identify superior treatments or doses across various diseases and clinical settings, ultimately aiming to achieve greater anti-disease e ects, improved safety, and enhanced survival outcomes.", "keywords": [ "Acute", "Address", "Alzheimer&apos", "s Disease", "Azacitidine", "Bayesian Method", "Bayesian Modeling", "Biological", "Biological Markers", "COVID-19", "COVID-19 pandemic", "Calibration", "Cations", "Characteristics", "Clinic", "Clinical", "Clinical Trials", "Combined Modality Therapy", "Complex", "Computer software", "Consumption", "Decision Making", "Development", "Disease", "Disease Management", "Dose", "Drug Evaluation", "Dysmyelopoietic Syndromes", "Ensure", "Evaluation", "Excision", "Family", "Futility", "Future", "Goals", "Hybrids", "Individual", "Interferons", "Ischemic Stroke", "JAK1 gene", "Knowledge", "Malignant Breast Neoplasm", "Malignant Neoplasms", "Maps", "Measurement", "Medical", "Medicine", "Methods", "Modeling", "Modernization", "Monitor", "Nature", "Outcome", "Patient-Focused Outcomes", "Patients", "Pharmaceutical Preparations", "Phase", "Physicians", "Placebo Control", "Pneumonia", "Population", "Predictive Analytics", "Prevalence", "Probability", "Procedures", "Process", "Property", "Randomized", "Research", "Resources", "Rheumatism", "Safety", "Savings", "Structure", "System", "Systemic Lupus Erythematosus", "Testing", "Therapeutic Agents", "Time", "Toxic effect", "Variant", "active control", "arm", "control trial", "cost", "design", "dosage", "drug development", "graphical user interface", "high risk", "implementation design", "improved", "individual patient", "inhibitor", "innovation", "novel", "novel therapeutics", "optimal treatments", "patient safety", "personalized medicine", "phase 2 designs", "pragmatic implementation", "programs", "receptor", "response", "simulation", "social", "software development", "standard of care", "success", "survival outcome", "targeted agent", "timeline", "tool", "treatment arm", "treatment strategy", "treatment trial", "trial comparing", "trial design", "user friendly software", "user-friendly" ], "approved": true } }, { "type": "Grant", "id": "15063", "attributes": { "award_id": "5R01DA058352-02", "title": "In pursuit of a one-stop shop: a hybrid type 1 effectiveness-implementation trial of comprehensive tele-harm reduction for people who inject drugs", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 31614, "first_name": "ANGELA EUNJI", "last_name": "Lee-Winn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-06-01", "end_date": "2028-03-31", "award_amount": 619921, "principal_investigator": { "id": 31615, "first_name": "Joshua Adam", "last_name": "Barocas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 24139, "first_name": "Hansel Emory", "last_name": "Tookes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 872, "ror": "", "name": "UNIVERSITY OF MIAMI SCHOOL OF MEDICINE", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true } ] }, { "id": 27403, "first_name": "Tyler Scott", "last_name": "Bartholomew", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 872, "ror": "", "name": "UNIVERSITY OF MIAMI SCHOOL OF MEDICINE", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "People who inject drugs (PWID) remain a high priority population under the Ending the HIV Epidemic: Plan for America (EHE) with 11% of new HIV infections attributable to injection drug use (IDU). IDU has led to multiple recent outbreaks of HIV in the US, driven primarily by the ongoing opioid and stimulant crises, creating an obstacle in meeting EHE goals of a 90% reduction in incident HIV infections by 2030 through the 4 pillars – diagnose, treat, prevent, and respond. EHE has identified evidence-based interventions within these pillars, including rapid HIV testing, antiretrovirals, comprehensive syringe services programs (SSPs), and PrEP that need to be implemented, scaled, and sustained within communities most affected by HIV. To maximize and extend the effectiveness of these interventions among PWID, differentiated, simplified, integrated, and comprehensive healthcare models need to be developed, tested, and deployed where they are in comfortable, destigmatizing environments that simultaneously address a key driver of HIV—substance use disorder (SUD). In addition to HIV, PWID continue to be impacted by a myriad of harmful health conditions such as hepatitis C virus (HCV), overdose, bacterial infections and sexually transmitted infections (STIs) due to structural, economic, social, and policy constraints. PWID often experience discrimination, stigma, and considerable social disadvantage, leading to almost universal poorer health outcomes than comparable populations who do not inject drugs. The need for innovative, efficacious, scalable, and community-driven models of healthcare in destigmatizing settings for PWID is crucial. Our team has led the development and testing of Tele-Harm Reduction (THR): a telehealth-based, multicomponent, adaptive care model for PWID living with HIV. Building on this work, we now seek to rapidly adapt and test Comprehensive-THR (C-THR) for comprehensive HIV prevention services delivered via an SSP. We propose a hybrid type I effectiveness-implementation randomized controlled trial (n=350) to evaluate the efficacy of the C-THR model vs. offsite referral and peer navigation for engagement in HIV prevention (i.e., PrEP or medications for OUD). PWID will be recruited from an academic medical center-based syringe services program (SSP) in Miami, FL (IDEA Miami) from both fixed and mobile SSP modalities. There are three overall aims of the proposed study: (1) to determine if the C-THR model increases engagement in HIV prevention compared to offsite referral and peer navigation, (2) to examine the long-term clinical and cost-effectiveness of the C-THR model, and (3) to assess the implementation and scalability of the C-THR model in diverse SSP settings. The co-primary outcome is tenofovir on dried blood spot or buprenorphine on urine drug screen across follow-up at 3,6,9 and 12 months. Secondary outcomes will include engagement in HIV/HCV/STI testing and sustained virologic response (SVR, cure) for HCV. The cost- effectiveness analysis, long-term modeling, and mixed-methods implementation and scalability evaluation will provide compelling data on the sustainability and possible impact of C-THR on comprehensive HIV prevention delivered via SSPs in the COVID era and beyond.", "keywords": [ "AIDS prevention", "Academic Medical Centers", "Address", "Adherence", "Adoption", "Affect", "Anti-Retroviral Agents", "Award", "Bacterial Infections", "Blood", "Buprenorphine", "COVID-19 pandemic", "Caring", "Clinic", "Clinical effectiveness", "Communities", "Comprehensive Health Care", "Cost Effectiveness Analysis", "Data", "Development", "Diagnosis", "Discrimination", "Disease Outbreaks", "Drug Screening", "Drug usage", "Drug user", "Dryness", "Economics", "Effectiveness", "Effectiveness of Interventions", "Enrollment", "Environment", "Epidemic", "Evaluation", "Evidence based intervention", "Foundations", "Future", "Goals", "HIV", "HIV Infections", "HIV risk", "HIV/HCV", "Harm Reduction", "Health", "Healthcare Systems", "Hepatitis C", "Hepatitis C virus", "Human immunodeficiency virus test", "Hybrids", "Infection", "Injecting drug user", "Institution", "Interdisciplinary Study", "Intervention", "Interview", "Methods", "Modality", "Modeling", "Naloxone", "National Institute of Drug Abuse", "Needle-Exchange Programs", "Opioid", "Overdose", "Participant", "Persons", "Pharmaceutical Preparations", "Policies", "Population", "Positioning Attribute", "Practical Robust Implementation and Sustainability Model", "Randomized", "Randomized Controlled Trials", "Research", "Services", "Sexually Transmitted Diseases", "Site", "Spottings", "Stimulant", "Substance Use Disorder", "Surveys", "Syringes", "Tenofovir", "Testing", "Trust", "Urine", "Viral", "Work", "acceptability and feasibility", "antiretroviral therapy", "care outcomes", "care systems", "cost effective", "cost effectiveness", "economic evaluation", "effectiveness-implementation RCT", "effectiveness/implementation trial", "efficacy evaluation", "efficacy testing", "evidence base", "experience", "follow-up", "health care model", "implementation barriers", "implementation evaluation", "implementation process", "implementation/effectiveness", "injection drug use", "innovation", "medication for opioid use disorder", "meetings", "models and simulation", "novel", "opioid epidemic", "overdose education", "patient navigation", "peer", "perceived discrimination", "pilot test", "poor health outcome", "pre-exposure prophylaxis", "prevent", "prevention service", "primary outcome", "process evaluation", "recruit", "response", "scale up", "screening", "secondary outcome", "social", "social disparities", "social stig" ], "approved": true } }, { "type": "Grant", "id": "15087", "attributes": { "award_id": "2346334", "title": "Collaborative Research: CIRC: New: Facilitating Language Technologies for Crisis Preparedness and Response", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "CCRI-CISE Cmnty Rsrch Infrstrc" ], "program_reference_codes": [], "program_officials": [ { "id": 31622, "first_name": "Cindy", "last_name": "Bethel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 1383654, "principal_investigator": { "id": 27143, "first_name": "Antonios", "last_name": "Anastasopoulos", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Language technologies are promising and could have strong impact during disaster responses. they can help to triage text messages in a disaster to determine what aid to provide. Language technologies can translate vast amounts of data related to an ongoing pandemic. Responders can use these technologies to converse with victims during disaster responses. However, advances in language technologies to date are limited. They focus on a few dozen of the more than 6500 languages spoken or signed in the world today. Current language technologies neglect millions of people. This especially impacts those who are most at risk for experiencing disasters. This project provides an infrastructure for language technology advancements for crisis response. The results will be useful for everyone, no matter the language they speak.<br/><br/>This project builds datasets of crisis communications using dedicated data collections and social media harvesting. These datasets will be applicable to curated crisis scenarios. They will use common language scenarios necessary to communicate with vulnerable populations. This approach helps people for whom language technologies are not typically developed. The project will bring together researchers from different disciplines. These include language technology researchers, experts in disaster relief, linguistics, and human-computer interaction. The project will target representatives from the local speech communities to take part. To coordinate this effort, the project will organize yearly workshops and shared tasks with the communities.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1397, "count": 13961 } } }