Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=-award_id
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-award_id", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-award_id", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-award_id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-award_id" }, "data": [ { "type": "Grant", "id": "7884", "attributes": { "award_id": "9R01LM013712-05A1", "title": "Decentralized differentially-private methods for dynamic data release and analysis", "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": 23263, "first_name": "JANE", "last_name": "YE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-01", "end_date": "2025-12-31", "award_amount": 647096, "principal_investigator": { "id": 1540, "first_name": "Xiaoqian", "last_name": "Jiang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 480, "ror": "https://ror.org/03gds6c39", "name": "The University of Texas Health Science Center at Houston", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 23731, "first_name": "LUCILA", "last_name": "OHNO-MACHADO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 760, "ror": "https://ror.org/0168r3w48", "name": "University of California, San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Large data sets are important in the development and evaluation of artificial intelligence (AI) and statistical learning models to predict morbidity, mortality, and other important health outcomes. Healthcare institutions are stewards of their patients’ data, and want to contribute to the development, evaluation, and utilization of predictive analytics tools. However, they also know that simple “de-identification” per HIPAA rules is not sufficient to protect patient privacy. Additionally, other factors such as protection of market share, lack of control about who uses shared data for what purposes, and concerns about patients’ reactions to having their data shared without explicit consent make initiatives such as certain registries and centralized repositories difficult to implement. We have shown that it is possible to decompose algorithms so that they can run on data that stays at each healthcare center, thus mitigating the concerns about control and potential misuse. In the first phase of this project, we concentrated on demonstrating the accuracy and performance of these algorithms for the study of chronic diseases in which (1) acquisition of new knowledge about the condition is slow (i.e., the disease is well understood, so scientific discoveries are not being published at a rapid pace); and (2) the incidence and presentation of the disease do not vary dramatically from place to place, and from person to person. In this competitive renewal, we propose to develop decentralized predictive models that meet all requirements for chronic diseases, but the methods are also applicable to rapidly evolving acute conditions such as COVID-19. We propose new approaches to deal with sites that may be missing certain patient profiles or certain variables but can still participate in model learning, evaluation and implementation. These new AI algorithms will permit supervised and unsupervised learning across institutions, using data from multiple modalities (e.g., imaging, genomes, laboratory tests), and will allow privacy-protecting record linkage. We will test these algorithms and approaches in data from three highly diverse medical centers across the US: Emory University in Atlanta, University of Texas Health Science Center at Houston, and University of California, San Diego.", "keywords": [ "Acute", "Address", "Algorithms", "Artificial Intelligence", "COVID-19", "COVID-19 patient", "California", "Cessation of life", "Chronic", "Chronic Disease", "Clinical", "Communication", "Consent", "Country", "County", "Data", "Data Analyses", "Data Discovery", "Data Linkages", "Decentralization", "Development", "Disease", "Disease Outbreaks", "Equilibrium", "Evaluation", "Event", "Future", "Genome", "Geography", "Goals", "Health", "Health Insurance Portability and Accountability Act", "Health Sciences", "Healthcare", "Heterogeneity", "Hospitalization", "Image", "Incidence", "Institution", "Intervention", "Knowledge", "Laboratories", "Learning", "Linear Models", "Link", "Literature", "Medical center", "Methodology", "Methods", "Modality", "Modeling", "Morbidity - disease rate", "Outcome", "Parents", "Patient-Focused Outcomes", "Patients", "Pattern", "Performance", "Persons", "Phase", "Phenotype", "Population", "Predictive Analytics", "Privacy", "Privatization", "Publishing", "Reaction", "Records", "Recovery", "Registries", "Research", "Research Personnel", "Resolution", "Resource Allocation", "Resources", "Risk", "Running", "Sample Size", "Security", "Site", "Source", "Structure", "Supervision", "Techniques", "Testing", "Texas", "Time", "Training", "Underrepresented Minority", "Universities", "Visit", "analytical tool", "artificial intelligence algorithm", "base", "clinical decision support", "clinical decision-making", "combat", "combinatorial", "cost", "data dissemination", "data integration", "data privacy", "data sharing", "design", "distributed data", "federated computing", "hospital readmission", "individual patient", "large datasets", "mortality", "multimodality", "novel", "novel strategies", "outcome prediction", "pandemic disease", "patient privacy", "predictive modeling", "privacy preservation", "privacy protection", "profiles in patients", "repository", "software development", "statistical learning", "supervised learning", "unsupervised learning", "virtual" ], "approved": true } }, { "type": "Grant", "id": "9782", "attributes": { "award_id": "9R01HL159711-07A1", "title": "hHv1 channels in neutrophils and the innate immune inflammatory response", "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": 22454, "first_name": "GUOFEI", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-05", "end_date": "2026-06-30", "award_amount": 642261, "principal_investigator": { "id": 25632, "first_name": "Steve A N", "last_name": "Goldstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 25633, "first_name": "Ruiming", "last_name": "Zhao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 971, "ror": "", "name": "UNIVERSITY OF CALIFORNIA-IRVINE", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "SUMMARY/ABSTRACT (30 lines) Relevance to public health. Polymorphonuclear leukocytes (PMN, neutrophils) release reactive oxygen species (ROS) to combat infection, but this inflammatory response can also initiate and propa- gate lung damage. Acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS) that is fatal in 40% of patients, are characterized by accumulation of albumin-rich fluid in the pulmonary air spaces. Drug therapies focused on downstream cytokine actions have failed to improve morbidity or mortality; we hypothesize, and offer evidence, that targeting the human voltage-gated pro- ton channel (hHv1) at early steps can be more effective. We propose to target hHv1 because (i) the chan- nel in PMN initiates and sustains the inflammatory response, (ii) C6, a unique blocker of hHv1 sup- presses human PMN ROS production, and (iii) C6 suppresses lung compromise in an ALI mouse model. Brief background. This application builds on advances in the last period when we created the first high-affinity and specific direct blocker of hHv1 (C6 peptide) and used it to show, first, that human sperm require hHv1-mediated H+ efflux to initiate capacitation, allowing the acrosomal reaction, and oocyte fertilization and, second, that hHv1 in human PMN is required to produce and sustain release of inflammatory agents, including ROS and proteases, during the innate immune inflammatory response. Unique features and innovation. Our pilot data reveal a second target in the pathway: albumin (Alb) is required to activate hHv1 in human PMN and we describe a peptide (L*) that blocks Alb-activa- tion and ROS production. Supporting our driving hypothesis, we show here that both C6 and L* inhibit hHv1 in human PMN, decreasing ROS production, and that C6 protects in an ALI mouse model, restor- ing lung compliance, and decreasing ROS, proinflammatory cytokines, protein, and PMN in bron- choalveolar lavage fluid. We employ our novel membrane tethered (T-peptide) method to speed struc- ture-function studies and peptide design, show a bivalent C6 (C62) that fully inhibits open hHv1 chan- nels, benefit from advanced biophysical and in vivo methods, and two expert collaborators. Three specific aims. (1) Alb activation of hHv1 seeks the structural and mechanistic basis for the action of Alb and a more potent natural metabolite. (2) Alb regulation of the PMN inflammatory re- sponse seeks to delineate the role of hHv1 in PMN using C6, C62 and L* and the basis for peptide action. (3) Inhibiting acute lung injury with Hv1 inhibitors studies an ALI model in WT and Hv1 KO mice. Significance. This work addresses an unmet medical need, recently made more apparent by the ad- vent of COVID-19-related ALI/ARDS and has broader influence because Hv1 in PMN and other phago- cytes is complicit in additional acute and chronic inflammatory disorders. We propose to apply unique hHv1 inhibitors and innovative methods to understand and suppress this pathophysiology.", "keywords": [ "2019-nCoV", "Acrosome Reaction", "Acute", "Acute Lung Injury", "Acute Respiratory Distress Syndrome", "Address", "Affinity", "Air", "Albumins", "Anti-Inflammatory Agents", "Automobile Driving", "Bacteria", "Bacterial Pneumonia", "Binding", "Biophysics", "Bronchoalveolar Lavage Fluid", "COVID-19", "COVID-19 pandemic", "Cells", "Cessation of life", "Chronic", "Complex", "Data", "Development", "Disease", "Effectiveness", "Elastases", "Fertilization", "Fluorescence Resonance Energy Transfer", "Functional disorder", "Generations", "Goals", "Grant", "Health", "Hospitalization", "Host Defense", "Human", "Hypoxemia", "Immune", "Immunization", "In Vitro", "Infection", "Inflammation", "Inflammation Mediators", "Inflammatory", "Inflammatory Response", "Innate Immune System", "Ischemic Stroke", "Knock-out", "Knockout Mice", "Leukocytes", "Life", "Ligands", "Liquid substance", "Lung", "Lung Compliance", "Mediating", "Medical", "Membrane", "Methods", "Microscopy", "Modeling", "Morbidity - disease rate", "Mus", "Oocytes", "Oxidants", "Oxidases", "Oxides", "Pathology", "Pathway interactions", "Patients", "Peptide Hydrolases", "Peptides", "Phagocytes", "Pharmaceutical Preparations", "Pharmacology", "Pharmacology Study", "Pharmacotherapy", "Physiology", "Pneumonia", "Production", "Proteins", "Protons", "Public Health", "Publishing", "Reactive Oxygen Species", "Regulation", "Reporting", "Research", "Respiratory Burst", "Role", "Sepsis", "Site", "Speed", "Sperm Capacitation", "Structure", "Variant", "Vent", "Virus", "Wild Type Mouse", "Work", "base", "chronic inflammatory disease", "combat", "cytokine", "design", "experience", "extracellular", "fungus", "improved", "in vivo", "inhibitor", "innovation", "lung injury", "mortality", "mouse model", "neutrophil", "novel", "novel strategies", "operation", "peptide L", "public health relevance", "response", "single molecule", "sperm cell", "structural biology", "success", "tool", "voltage" ], "approved": true } }, { "type": "Grant", "id": "440", "attributes": { "award_id": "955897", "title": "EcoHealthNet: Ecology, Environmental Science and Health Research Network", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [], "program_officials": [ { "id": 855, "first_name": "Samuel", "last_name": "Scheiner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2010-09-01", "end_date": "2015-08-31", "award_amount": 497121, "principal_investigator": { "id": 860, "first_name": "Peter", "last_name": "Daszak", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 234, "ror": "", "name": "Ecohealth Alliance inc.", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 856, "first_name": "Gregory E", "last_name": "Glass", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 857, "first_name": "Jonathan A", "last_name": "Patz", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 858, "first_name": "Alonso", "last_name": "Aguirre", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 859, "first_name": "Jonathan H", "last_name": "Epstein", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 234, "ror": "", "name": "Ecohealth Alliance inc.", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 234, "ror": "", "name": "Ecohealth Alliance inc.", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Infectious diseases play a key role in ecosystems: regulating wildlife populations, mediating inter-specific competition, causing dramatic population declines, even driving local extinctions of wildlife. During the last two decades, ecologists have led the growth of a new field of disease ecology, developing theoretical and practical research that understands these interactions. However, integration of disease ecology into veterinary and human health sciences has been slow, partly due to the slow response of health science curricula to incorporate these advances. This lack of integration, and the significance of pathogens shared among animals and humans (zoonoes) has led to repeated calls for increased collaboration among ecologists, veterinarians and public health researchers. The objective of this award is to develop \"EcoHealthNet,\" a new \"Ecohealth Alliance\" linking Centers of Excellence in NGOs, Universities, and Research Societies and fusing the fields of Conservation Medicine, Medical Geography, and the \"One Medicine\" or \"One Health\" concept. EcoHealthNet will fill a critical role in bringing together ecologists, environmental biologists and the disciplines more traditionally involved in infectious diseases - veterinary medical, human medical and public health researchers. It will provide mentored training opportunities for more than 100 graduate students, openly recruited from the medical (human and veterinary), ecological, epidemiological, microbiological, economic, and environmental science fields. The training will include workshops in epidemiology, mathematical modeling of infectious disease, and field epidemiology; and international applied field research in ongoing, well-supported programs such as the ecology of Nipah virus, Avian Influenza, rodent pathogen diversity in urban America; West Nile Virus and SARS ecology. Over the five years of this project, over 100 students from diverse backgrounds will be trained in tackling the global problem of emerging diseases which threaten wildlife conservation, public health and development. Research findings will be disseminated through peer-reviewed publications, media interviews, conference presentations, and congressional briefings, in close collaboration with national and intergovernmental agencies that cover conservation, development, trade issues, and public health. Network members will help make data publicly available via online databases, via the student section of the International EcoHealth Association, the Wildlife Trust Alliance and the EcoHealth Alliance.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15494", "attributes": { "award_id": "95023A00195024F00015-0-0-1", "title": "NCATS SCIENTIFIC AND TECHNOLOGY SUPPORT SERVICES (STSS)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2024-09-30", "end_date": "2024-11-29", "award_amount": 3152339, "principal_investigator": { "id": 26488, "first_name": "GARY", "last_name": "MAYS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "National COVID-19 Cohort Collaborative (N3C): The National COVID-19 Cohort Collaborative (N3C) sponsors the NIH COVID-19 Data Enclave, one of the largest data enclaves in the world supporting COVID-19 research. N3C is a partnership among the NCATS-supported Clinical and Translational Science Awards (CTSA) Program hubs, with overall stewardship by NCATS. The N3C program consists of thousands of researchers, requiring enterprise level information technology (IT) support as part of a virtual research organization (VRO). This contract is necessary to ensure that NCATS and N3C can continue to provide adequate support for a secure, collaborative, VRO. This contract allows for continued support of the VRO which supports all of the required information technology functions to support an environment of over 4,000 users, including cloud-based productivity tools, a service desk, commercial and open-source deployments of analytical tools for the community to use, and expansion of the data types available for analysis, such as imaging, viral variant genomic sequences, etc. The common need is to share a collaborative cloud environment capable of ingesting billions of data points and performing a variety of complex analyses against multimodal data types, ranging from pathology and radiology data, synthetic data, genomic information, electronic health records (EHRs) and a wide variety of others. All of this must be done while meeting the highest levels of security and privacy, given the sensitivity of some of the data types being collected and the importance of the work being done in the environment. This contract provides support for all of these enterprise IT efforts.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15495", "attributes": { "award_id": "95023A00195024F00013-0-0-1", "title": "NATIONAL CLINICAL DATA COLLABORATIVE COUNTS (NACDACC)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2024-09-30", "end_date": "2025-09-29", "award_amount": 12038222, "principal_investigator": { "id": 26488, "first_name": "GARY", "last_name": "MAYS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "National COVID-19 Cohort Collaborative (N3C): The National COVID-19 Cohort Collaborative (N3C) sponsors the NIH COVID-19 Data Enclave, one of the largest data enclaves in the world supporting COVID-19 research. N3C is a partnership among the NCATS-supported Clinical and Translational Science Awards (CTSA) Program hubs with overall stewardship by NCATS. The N3C program consists of thousands of researchers, requiring enterprise level information technology (IT) support as part of a virtual research organization (VRO). This contract is necessary to ensure that NCATS and N3C can continue to provide adequate support for a secure, collaborative, VRO. This contract allows for continued support of the VRO which supports all of the required information technology functions to support an environment of over 4,000 users, including cloud-based productivity tools, a service desk, commercial and open-source deployments of analytical tools for the community to use, and expansion of the data types available for analysis, such as imaging, viral variant genomic sequences, etc. The common need is to share a collaborative cloud environment capable of ingesting billions of data points and performing a variety of complex analyses against multimodal data types, ranging from pathology and radiology data, synthetic data, genomic information, electronic health records (EHRs) and a wide variety of others. All of this must be done while meeting the highest levels of security and privacy, given the sensitivity of some of the data types being collected and the importance of the work being done in the environment. This contract provides support for all of these enterprise IT efforts.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15500", "attributes": { "award_id": "95023A00195024F00010-0-0-1", "title": "BO 011 - UNA OPERATIONS AND TECHNICAL SUPPORT SERVICES (OTSS)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2024-07-18", "end_date": "2024-10-17", "award_amount": 1603808, "principal_investigator": { "id": 32047, "first_name": "JACK", "last_name": "COLLINS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1701, "ror": "", "name": "AXLE INFORMATICS, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "National COVID-19 Cohort Collaborative (N3C): The National COVID-19 Cohort Collaborative (N3C) sponsors the NIH COVID-19 Data Enclave, one of the largest data enclaves in the world supporting COVID-19 research. N3C is a partnership among the NCATS-supported Clinical and Translational Science Awards (CTSA) Program hubs, with overall stewardship by NCATS. The N3C program consists of thousands of researchers, requiring enterprise level information technology (IT) support as part of a virtual research organization (VRO). This contract is necessary to ensure that NCATS and N3C can continue to provide adequate support for a secure, collaborative, VRO. This contract allows for continued support of the VRO which supports all of the required information technology functions to support an environment of over 4,000 users, including cloud-based productivity tools, a service desk, commercial and open-source deployments of analytical tools for the community to use, and expansion of the data types available for analysis, such as imaging, viral variant genomic sequences, etc. The common need is to share a collaborative cloud environment capable of ingesting billions of data points and performing a variety of complex analyses against multimodal data types, ranging from pathology and radiology data, synthetic data, genomic information, electronic health records (EHRs) and a wide variety of others. All of this must be done while meeting the highest levels of security and privacy, given the sensitivity of some of the data types being collected and the importance of the work being done in the environment. This contract provides support for all of these enterprise IT efforts.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "490", "attributes": { "award_id": "913691", "title": "EAPSI: Elucidation of Determinants of Coronavirus Cross-Species Transmission", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown" ], "program_reference_codes": [], "program_officials": [], "start_date": "2009-06-01", "end_date": "2010-05-31", "award_amount": 5678, "principal_investigator": { "id": 991, "first_name": "Jeffrey E", "last_name": "Teigler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 268, "ror": "", "name": "Teigler Jeffrey E", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 268, "ror": "", "name": "Teigler Jeffrey E", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "2009 EAPSI Fellowship - CHINAThis award supports a U.S. graduate student to conduct an individual research project at one of seven locations in East Asia and the Pacific region. The research project will provide the student with a first-hand mentored research experience, an introduction to science and science policy infrastructure, and an orientation to the culture and language of the location. The primary goals of the East Asia Summer Institute program are to expose students to science and engineering in the context of a research laboratory, and to initiate early-career professional relationships that will foster research collaborations with foreign counterparts in the future.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10844", "attributes": { "award_id": "7U24LM013755-03", "title": "RADx-Rad Discoveries & Data: Consortium Coordination Center Program Organization", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 10715, "first_name": "YANLI", "last_name": "WANG", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-01", "end_date": "2024-11-30", "award_amount": 5853027, "principal_investigator": { "id": 25146, "first_name": "Eliah S", "last_name": "Aronoff-Spencer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 760, "ror": "https://ror.org/0168r3w48", "name": "University of California, San Diego", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 23731, "first_name": "LUCILA", "last_name": "OHNO-MACHADO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 25147, "first_name": "HUA", "last_name": "XU", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "ABSTRACT Preparing SARS-CoV-2 testing data for reuse requires making the data syntactically and semantically equivalent. Standardization of terminologies and a common data model accomplish the former, while the latter is accomplished through understanding the data and making it comparable across RADx-rad awardees by benchmarking against known gold standards. The standardization of samples is as important as standardizing the data, particularly in the highly innovative RADx-rad program, where new technologies will be developed or optimized for deployment in various settings. Highly motivated RADx-rad awardees will receive advice on how their diagnostics compare to FDA-approved ones, with each other, how their diagnostic performs in independent testing, as well as how to ensure the tests are usable in real world settings. In collaboration with University of Texas Health Science Center at Houston, University of California San Diego researchers in informatics/data science and infectious diseases with ample experience in leading large consortia have designed a unique RADx- rad Consortium Data and Coordination Center (radCDCC). This center is based on three pillars: (1) effective administration and coordination among awardees, NIH, and other programs;; (2) innovative approaches and tools to collect and standardize data and metadata to promote findability, accessibility, interoperability and reuse (FAIR) for data sharing;; and (3) principled preparation of standardized samples with known quantities of viral loads, and standardized procedures for testing new diagnostics to allow comparison across tests and calibration of new technologies. Backed by sophisticated HIPAA-compliant cloud services, user friendly web-tools, and extensive support from UCSD’s facilities for computation and for clinical research, the radCDCC will interface with other RADx programs and other COVID-19 focused programs at NIH to ensure alignment of awardees, NIH and the public in the pursuit of effective, affordable, and deployable new technologies for testing.", "keywords": [ "Agreement", "Antibodies", "Area", "Award", "Back", "Benchmarking", "Biological Assay", "COVID-19 diagnostic", "COVID-19 testing", "Calibration", "California", "Clinical Pathology", "Clinical Research", "Cloud Computing", "Cloud Service", "Collaborations", "Communicable Diseases", "Communication", "Communities", "Computer software", "Consultations", "Creativeness", "Data", "Data Collection", "Data Discovery", "Data Science", "Data Scientist", "Data Sources", "Development", "Diagnostic", "Diagnostic tests", "Ensure", "Evaluation", "FAIR principles", "FDA approved", "Funding", "Goals", "Gold", "Health Insurance Portability and Accountability Act", "Health Sciences", "Infection", "Informatics", "Infusion procedures", "Internet", "Knowledge", "Laboratories", "Logistics", "Metadata", "Methods", "Online Systems", "Persons", "Positioning Attribute", "Preparation", "Privacy", "Procedures", "Process", "Protocols documentation", "Quality Control", "RADx", "RADx Radical", "Reporting", "Research Personnel", "Resources", "Risk", "Role", "Sampling", "Science", "Secure", "Security", "Semantics", "Signal Transduction", "Specialist", "Specimen", "Standardization", "Structure", "Technology", "Terminology", "Testing", "Texas", "United States National Institutes of Health", "Universities", "Update", "Validation", "Viral", "Viral Load result", "Virus", "Visualization", "Work", "coronavirus disease", "dashboard", "data access", "data centers", "data exchange", "data management", "data modeling", "data reuse", "data sharing", "data standards", "design", "detection method", "diagnostic technologies", "distributed data", "expectation", "experience", "help-seeking behavior", "indexing", "innovation", "interoperability", "meetings", "member", "new technology", "news", "novel diagnostics", "pandemic disease", "programs", "quality assurance", "repository", "search engine", "syntax", "testing services", "tool", "transmission process", "usability", "user centered design", "user-friendly", "virology" ], "approved": true } }, { "type": "Grant", "id": "12165", "attributes": { "award_id": "7U01MH129968-03", "title": "The Impacts of County-Level COVID- 19 -Related Public Health and Social Policies on Racial/Ethnic and Socioeconomic Disparities in Mental Health and Healthcare Utilization", "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": 6350, "first_name": "Jennifer", "last_name": "Humensky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-05", "end_date": "2026-07-31", "award_amount": 509205, "principal_investigator": { "id": 10475, "first_name": "Rita", "last_name": "Hamad", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 768, "ror": "https://ror.org/043mz5j54", "name": "University of California, San Francisco", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 961, "ror": "", "name": "HARVARD SCHOOL OF PUBLIC HEALTH", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic and related mitigating measures have brought on tremendous financial hardship for vulnerable Americans, with nearly 15% unemployment at its peak and millions falling into poverty. There have been striking racial and socioeconomic disparities in these hardships, particularly for Black and Latinx families, which build on longstanding inequities in income, housing, and other social determinants of health. Recent research has demonstrated the impact of the pandemic and its resulting socioeconomic inequities on disparities in mental health and healthcare utilization. It is increasingly recognized that policies play a role in mitigating or exacerbating these disparities. For example, mandated closures lead to job and income loss, which disproportionately affect low-income and minority women. Conversely, economic support policies ensure that vulnerable families have the resources to stay healthy at home. While some studies have examined the effects of state-level COVID-19-related policies, there has been little systematic documentation of county-level policies and their effects on disadvantaged groups. The goal of this study is to leverage, expand upon, and link existing national data sets to test the hypothesis that county-level public health and social policies have affected disparities in mental health and healthcare utilization. In Aim 1, we will gather county-level COVID-19- related policy data for 2020-2021 for a nationwide sample of 250 counties, selected to ensure coverage of over half of the U.S. population as well as diversity in racial/ethnic, socioeconomic, and urban/rural composition. Policies will be drawn from online sources and grouped into 3 domains chosen due to likely impacts on mental health and utilization: (1) containment and closure, (2) economic support, and (3) public health. We will characterize geographic and temporal variation in county-level policies and make this database freely available. In Aim 2, we will examine which county-level COVID-19-related policies contributed to or ameliorated pandemic-related disparities in mental health and health behaviors, linking the policy database from Aim 1 with national health surveys that provide individual-level data on self-reported psychological distress, smoking, alcohol use, and illicit drug use. In Aim 3, we will estimate the association of county-level COVID-19-related policies with disparities in healthcare utilization for mental health problems, linking the policy database from Aim 1 with national data sets that provide individual-level data on healthcare utilization. Aims 2 and 3 exploit temporal and geographic variation in county-level policies and employ quasi-experimental methods to estimate policy effects. Conducted in close collaboration with other SBECCC investigators, the expected outcome of this research is the creation of a county-level policy database that will serve as a valuable resource for researchers and stakeholders working to understand how local policies contributed to and continue to influence pandemic- related health disparities, as well as specific evidence on policy effects on mental health outcomes. This will guide policies and interventions to reduce mental health burden especially for vulnerable communities.", "keywords": [ "Acute", "Address", "Adult", "Adverse effects", "Affect", "Alcohol consumption", "American", "Anxiety", "Behavioral Risk Factor Surveillance System", "Black race", "Businesses", "COVID-19", "COVID-19 pandemic", "Catalogs", "Collaborations", "Contact Tracing", "Containment", "County", "County Government", "Data", "Data Set", "Databases", "Disparity", "Disparity population", "Documentation", "Drug usage", "Economic Policy", "Economics", "Ensure", "Ethnic Origin", "Expenditure", "Experimental Models", "Family", "Financial Hardship", "Geography", "Goals", "Health", "Health Policy", "Health Services Accessibility", "Health Surveys", "Health behavior", "Home", "Household", "Housing", "Income", "Individual", "Inequity", "Intervention", "Job loss", "Latinx", "Lead", "Leadership", "Link", "Low Income Population", "Low income", "Measures", "Medicaid", "Medical", "Mental Depression", "Mental Health", "Mental Health Services", "Minority Women", "National Health Interview Survey", "Occupations", "Outcome", "Outcomes Research", "Patient Self-Report", "Persons", "Policies", "Policy Making", "Population", "Poverty", "Primary Care", "Public Health", "Quasi-experiment", "Race", "Reporting", "Research", "Research Personnel", "Resources", "Rural", "Sampling", "Services", "Smoking", "Social Policies", "Socioeconomic Status", "Source", "Stress", "Subgroup", "Surveys", "Testing", "Unemployment", "Vaccination", "Variant", "Vulnerable Populations", "Work", "behavioral health", "coronavirus disease", "ethnic disparity", "ethnic minority", "experience", "falls", "geographic difference", "health care disparity", "health care service utilization", "health disparity", "high risk population", "illicit drug use", "long-standing disparities", "medical specialties", "pandemic disease", "pandemic impact", "people of color", "psychological distress", "public health intervention", "racial disparity", "racial minority", "social determinants", "social health determinants", "socioeconomic disparity", "socioeconomics", "trend", "urban residence", "vulnerable community" ], "approved": true } }, { "type": "Grant", "id": "9375", "attributes": { "award_id": "7U01DC019579-02", "title": "Longitudinal At Home Smell Testing to Detect Infection by SARS-CoV-2", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 25126, "first_name": "Janet", "last_name": "Cyr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-21", "end_date": "2023-05-31", "award_amount": 877287, "principal_investigator": { "id": 25127, "first_name": "MARK W", "last_name": "ALBERS", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 736, "ror": "https://ror.org/002pd6e78", "name": "Massachusetts General Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 736, "ror": "https://ror.org/002pd6e78", "name": "Massachusetts General Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Abstract: Self-report of sudden loss of smell or taste substantially increases the odds of being infected with SARS-CoV-2 (10 – 37-fold). However, self-report of smell function is an unreliable predictor of smell loss. Based on our experience developing smell tests with personalized algorithms for asymptomatic detection of Alzheimer's disease, we created a self-administered easy to use “at home” 5-minute objective smell test to uncover alterations in smell function unbeknownst to many individuals, and confer increased risk of infection by SARS- CoV-2. Our new smell test consists of a physical smell card containing peel and sniff odor labels and a web- based application. The disposable smell card reduces the risk that the smell test serves as a vector of transmission to other patients, research staff, and to health care workers. Each participant accesses the web- based app on their own smartphone, tablet, or computer. In our pilot studies, we validated each participant's COVID status by extracting results of clinical SARS-CoV-2 RT PCR assays from electronic health records. The smell test provides better area under the curve for SARS-CoV-2 infection (0.83 – 0.85) in both US and Argentinian symptomatic patients (ages 19 - 87) than symptom tracking alone (0.66). We are expanding the smell test from one smell card with three odors to 6 smell cards, each with 3 different odors (18 odors total). Having six different versions of the smell card will afford longitudinal screening several times per week and provide data to construct personalized thresholds for changes in olfactory function - each person serving as their own control and monitoring for diminishment of their expected performance based on their personal trajectory rather than being based on population norms. Here we propose to develop a native app to conduct longitudinal COVID Smell Test (Aim 1), collect data on asymptomatic health care workers, symptomatic patients, and undergraduates (Aim 2) and develop algorithms for the longitudinal smell test for personalized thresholds, differentiate smell loss from COVID relative to influenza, and assess risk of developing pulmonary disease in COVID infected patients (Aim 3). The Longitudinal COVID Smell Test is accessible, affordable, and readily scalable. Effective screening with the COVID smell test will better inform students and employees if they should not report to school or work, and seek an evaluation by a healthcare professional and molecular testing at an early, often asymptomatic, stage of the disease.", "keywords": [ "2019-nCoV", "Age", "Algorithms", "Alzheimer disease detection", "Anosmia", "Area Under Curve", "Argentine", "Biological Assay", "COVID diagnosis", "COVID-19 pandemic", "COVID-19 patient", "COVID-19 test", "Cellular Phone", "Clinic", "Clinical", "Computers", "Data", "Development", "Disease", "Electronic Health Record", "Elements", "Employee", "Evaluation", "General Hospitals", "Health Personnel", "Health Professional", "Home", "Hospitals", "Individual", "Infection", "Influenza", "International", "Label", "Lung diseases", "Maryland", "Massachusetts", "Molecular", "Molecular Diagnosis", "Monitor", "Odors", "Olfactory dysfunction", "Online Systems", "Participant", "Patient Self-Report", "Patients", "Performance", "Persons", "PhenX Toolkit", "Phenotype", "Pilot Projects", "Population", "Population Heterogeneity", "Predictive Value", "Probability", "Prognosis", "Quarantine", "Rehabilitation therapy", "Research", "Risk", "Risk Estimate", "SARS-CoV-2 infection", "Safety", "Schools", "Self Administration", "Sensitivity and Specificity", "Signal Transduction", "Site", "Smell Perception", "Students", "Symptoms", "Syndrome", "Tablets", "Taste Perception", "Testing", "Time", "Universities", "Update", "Virus", "Woman", "Work", "algorithm development", "base", "coronavirus disease", "experience", "infection risk", "influenza infection", "innovation", "new technology", "respiratory", "screening", "smell test", "social health determinants", "software development", "undergraduate student", "university student", "vector transmission" ], "approved": true } } ], "meta": { "pagination": { "page": 2, "pages": 1392, "count": 13920 } } }