Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "7093", "attributes": { "award_id": "3R01AG066749-01S1", "title": "Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related Dementias", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Aging (NIA)" ], "program_reference_codes": [], "program_officials": [ { "id": 21472, "first_name": "Jean", "last_name": "Yuan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-01", "end_date": "2025-03-31", "award_amount": 389580, "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": 22889, "first_name": "Cui", "last_name": "Tao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 22890, "first_name": "WENJIN Jim", "last_name": "ZHENG", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 788, "ror": "", "name": "UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "AD/ADRD are highly complex diseases characterized by distinct molecular pathways and neuropathological phenotypes. Unfortunately, the treatment remains at best modestly effective and no new drugs have been approved since 2003. Combinatorial drug therapy for AD/ADRD treatment has not been intensively studied but it is highly promising. We hypothesize that finding repositioned drug combinations through innovative exploration of big data may uncover effective AD/ADRD treatments, with implicit advantages in overcoming drug resistance and targeting multiple biomarkers. We will combine big biomedical data from complementary sources, novel and advanced informatics models, clinical domain expertise, as well as biology knowledge and validation into a coherent framework to tackle AD/ADRD with potential combinatorial drug therapies. In an exponentially larger and more challenging space of combinatorial drug therapy, opportunities are also exponentially larger when compared with traditional single-drug models but many computational challenges need to be carefully handled. We will develop multiple computational models under two philosophical umbrellas, with focuses on quantifiable screening and biological understanding. Our findings will be validated with biological experiments from cell to mouse. If successful, we will significantly advance AD/ADRD research and benefit patients with safe and effective treatment.", "keywords": [ "2019-nCoV", "Address", "Administrative Supplement", "Adult", "Alzheimer&apos", "s Disease", "Alzheimer&apos", "s disease related dementia", "Alzheimer&apos", "s disease therapy", "Antiviral Agents", "Automobile Driving", "Big Data", "Biological", "Biological Markers", "Biology", "Blood - brain barrier anatomy", "COVID-19", "Cells", "Classification", "Clinical", "Combination Drug Therapy", "Complex", "Computer Models", "Coronavirus", "Custom", "Data", "Databases", "Development", "Disease", "Drug Combinations", "Drug Modelings", "Drug Targeting", "Drug resistance", "Elderly", "Engineering", "Exposure to", "Functional disorder", "Goals", "Healthcare", "Hour", "Human", "Informatics", "Information Retrieval", "Investigation", "Knowledge", "Literature", "Machine Learning", "Manuals", "Mediating", "Metadata", "Methods", "Middle East Respiratory Syndrome", "Mining", "Modeling", "Molecular", "Mus", "Natural Language Processing", "Nature", "Ontology", "Paper", "Parents", "Pathway interactions", "Patients", "Performance", "Pharmaceutical Preparations", "Pharmacogenomics", "Pharmacotherapy", "Phenotype", "Prevention", "Protective Agents", "Protocols documentation", "PubMed", "Publications", "Publishing", "Research", "Research Personnel", "Rest", "Seeds", "Services", "Severe Acute Respiratory Syndrome", "Source", "Surveys", "System", "Update", "Validation", "Viral", "Virus", "base", "big biomedical data", "clinical practice", "combat", "combinatorial", "data mining", "drug candidate", "effective therapy", "experimental study", "human-in-the-loop", "improved", "information model", "innovation", "insight", "knowledge base", "machine learning algorithm", "novel", "novel drug combination", "novel therapeutics", "prevent", "relating to nervous system", "research study", "screening", "software development", "text searching", "tool", "user-friendly" ], "approved": true } }, { "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": "10843", "attributes": { "award_id": "7R01LM013712-06", "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": 21523, "first_name": "LYNDA R", "last_name": "HARDY", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-01", "end_date": "2025-12-31", "award_amount": 613743, "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": 452, "ror": "https://ror.org/03v76x132", "name": "Yale University", "address": "", "city": "", "state": "CT", "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": [ "Acceleration", "Acute", "Address", "Algorithms", "Artificial Intelligence", "COVID-19", "COVID-19 patient", "COVID-19 risk", "Calibration", "California", "Cessation of life", "Chronic", "Chronic Disease", "Clinical", "Communication", "Consent", "Country", "County", "Data", "Data Analyses", "Data Discovery", "Data Linkages", "Decentralization", "Development", "Disease", "Equilibrium", "Evaluation", "Event", "Genome", "Geography", "Goals", "Health", "Health Insurance Portability and Accountability Act", "Health Sciences", "Health protection", "Healthcare", "Heterogeneity", "Hospitalization", "Image", "Incidence", "Institution", "Intervention", "Knowledge", "Laboratories", "Learning", "Linear Models", "Link", "Literature", "Marketing", "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", "Running", "Sample Size", "Security", "Site", "Source", "Structure", "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", "federated learning", "future outbreak", "hospital readmission", "individual patient", "large datasets", "model building", "mortality", "multimodality", "new pandemic", "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", "transmission process", "unsupervised learning", "virtual" ], "approved": true } }, { "type": "Grant", "id": "673", "attributes": { "award_id": "2049300", "title": "Collaborative research: The Intergenerational Effects of the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1536, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-12-31", "award_amount": 174032, "principal_investigator": { "id": 1538, "first_name": "Jenna", "last_name": "Nobles", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 1537, "first_name": "Felix", "last_name": "Elwert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 263, "ror": "", "name": "University of Wisconsin-Madison", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the effects of COVID-19 exposure during pregnancy on birth outcomes, over time, and across different groups defined by different sources of disadvantage. The COVID-19 pandemic is a large shock likely to affect infant health through multiple pathways including maternal infection, stress and anxiety, economic hardship, and access to prenatal care. Because these factors differ across groups in the United States, the impact of COVID-19 on birth outcomes will likely be stronger among groups with fewer advantages and certain demographic groups, exacerbating differences in the United States. These effects are critical to understand because birth outcomes predict health and socioeconomic attainment throughout the life course. This study relies on causal inference techniques exploiting variation in infection rates across time and place to capture the consequences of the pandemic on differences in birth outcomes, in particular intrauterine growth restriction, a key predictor of early-life cognition, education, and ultimately earnings. Birth records obtained at the state level with early release are used to provide the earliest possible evidence. Research focuses on six states that provide large and diverse samples across areas where the pandemic has unfolded with significant variation. Time-varying data on COVID incidence and mortality, and local official responses are linked to these data.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 } }, { "type": "Grant", "id": "672", "attributes": { "award_id": "2049529", "title": "Collaborative research: The Intergenerational Effects of the COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1534, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-12-31", "award_amount": 155773, "principal_investigator": { "id": 1535, "first_name": "Florencia", "last_name": "Torche", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 266, "ror": "https://ror.org/00f54p054", "name": "Stanford University", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project examines the effects of COVID-19 exposure during pregnancy on birth outcomes, over time, and across different groups defined by different sources of disadvantage. The COVID-19 pandemic is a large shock likely to affect infant health through multiple pathways including maternal infection, stress and anxiety, economic hardship, and access to prenatal care. Because these factors differ across groups in the United States, the impact of COVID-19 on birth outcomes will likely be stronger among groups with fewer advantages and certain demographic groups, exacerbating differences in the United States. These effects are critical to understand because birth outcomes predict health and socioeconomic attainment throughout the life course. This study relies on causal inference techniques exploiting variation in infection rates across time and place to capture the consequences of the pandemic on differences in birth outcomes, in particular intrauterine growth restriction, a key predictor of early-life cognition, education, and ultimately earnings. Birth records obtained at the state level with early release are used to provide the earliest possible evidence. Research focuses on six states that provide large and diverse samples across areas where the pandemic has unfolded with significant variation. Time-varying data on COVID incidence and mortality, and local official responses are linked to these data.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 } }, { "type": "Grant", "id": "671", "attributes": { "award_id": "2105671", "title": "RAPID: Collaborative Research: The Integrity of Mail Voting", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [], "program_officials": [ { "id": 1532, "first_name": "Lee", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-01", "end_date": "2021-11-30", "award_amount": 120283, "principal_investigator": { "id": 1533, "first_name": "Robert M", "last_name": "Stein", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 357, "ror": "", "name": "William Marsh Rice University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 357, "ror": "", "name": "William Marsh Rice University", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Given the increase use of vote by mail (VBM) due to the COVID-19 pandemic, the investigators focus on mail-in voters' experience with how they seek, obtain, complete, return and have their mail ballots counted. They examine citizens' perceptions about the integrity of elections using VBM by assessing concerns about variation in the institutional structures of VBM, the secrecy of VBM the process, and the coercion that VBM voters may face from their peer group. The investigators use a state-centric survey design that includes an oversample in 10 states that have a mixture of VBM systems. The oversample is important and gives additional statistical power and leverage about state election administration and laws. The sample consist of only registered voters who report voting by mail in the 2020 election. Surveys are solicited online using Qualtrics software. E-mail addresses for registered voters by state will be purchased from L2. The investigators completed 200 online surveys with mail voters in 40 states, and 1,000 with mail voters in 10 states, along with 100 in-person voters in each these states, for a total sample of 23,000. This research provides a baseline for concerns about voter integrity related to mail balloting. Furthermore, it establishes whether alternative mail voting systems differ in terms of when, where, and with whom mail ballots are completed and returned. This project should inform scholars and policy makers in several areas of VBM policy administration. Findings from the study are also valuable in increasing public confidence in VBM election procedures. The research involves undergraduate researchers at all three campuses of the collaborative study.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 } }, { "type": "Grant", "id": "670", "attributes": { "award_id": "2032097", "title": "SBIR Phase I: AK-423: A broad-spectrum antiviral and immunomodulatory agent (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1530, "first_name": "Kaitlin", "last_name": "Bratlie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2022-06-30", "award_amount": 256000, "principal_investigator": { "id": 1531, "first_name": "Sherine", "last_name": "Abdelmawla", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 356, "ror": "", "name": "Akanocure Pharmaceuticals, Inc.", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 356, "ror": "", "name": "Akanocure Pharmaceuticals, Inc.", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project stems from the development of an efficient broad-spectrum antiviral agent addressing the current COVID-19 pandemic. The approach can also address future outbreaks of other viruses. The project is targeting the group of COVID-19 patients who will develop severe illness featuring multiple organ dysfunction. Those patients need ICU units and ventilators in amounts that can overwhelm the health care system. This project will develop an antiviral agent to mitigate the social distancing measures and improve quality of life. This Small Business Innovation Research (SBIR) Phase I project focuses on the development AK-423, a potential efficient antiviral and immunomodulatory agent against COVID-19. The technical objectives focus on testing AK-423 (in-vitro and in-vivo) against COVID-19. Recent reports suggest that the multi-organ damage that occurs during COVID-19 infection is characterized by an exaggerated inflammatory response indicative of cytokine storm, auto-immunity, and a sepsis syndrome caused by complex abnormal immune reactions. An ideal treatment would not only suppress viral replication but would also regulate the abnormal immune response. AK-423 targets a host metabolic process that the virus hijacks. It is also a key process in the differentiation of lymphocytes. Inhibition of such process is proven to dampen the immune response, minimize immune response-induced tissue damage, inhibit production of pro-inflammatory cytokines, and efficiently shut down viral replication. This strategy will deliver an efficient broad-spectrum antiviral agent.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 } }, { "type": "Grant", "id": "669", "attributes": { "award_id": "2036690", "title": "SBIR Phase I: Artificial Intelligence (AI) Enabled 3-Dimensional (3D) Imaging for COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1528, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2021-09-30", "award_amount": 256000, "principal_investigator": { "id": 1529, "first_name": "Cristian", "last_name": "Atria", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 355, "ror": "", "name": "nView medical Inc.", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 355, "ror": "", "name": "nView medical Inc.", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve COVID-19 treatments by providing imaging information to assess the severity and progression of the disease. Chest computed tomography (CT) has been shown to be sensitive to COVID-19 via the observation of ground-glass opacities and is being used on patients with acute symptoms. Practical considerations such as high radiation to the patient and cross contamination risk from moving the patient for imaging must be taken into account in the decision to image with CT. A 3D imaging solution that is low cost, low radiation, and mobile could provide advantages and bring quality of care while integrating efficiently in the hospital workflow. Along with addressing the current crisis, the usage of this solution to address other respiratory diseases would secure strong commercial potential for this research.This Small Business Innovation Research (SBIR) Phase I project seeks to develop and validate an artificial intelligence (AI)-enabled 3D imaging reconstruction algorithm that can be used to assess the severity and progression of respiratory diseases such as COVID-19. Current chest imaging technologies can either provide adequate image quality or efficient imaging of the lungs, but not both. Two major advances could make the imaging more efficient while also providing the required image quality. Scatter modeling has been shown to be successful in improving image quality when reconstructing from few radiographs; Preliminary data shows how Machine Learning (ML) can be integrated to enhance efficient imaging to provide higher quality images. A 3D image creation algorithm that models X-ray scatter and uses ML to reconstruct 3D images from rapid radiographs will enable using 3D imaging for respiratory diseases including COVID-19. This algorithm will be validated on cadaveric models to assess if an AI-enabled imaging system that is mobile, that can be used bedside, and that is easily draped for sterile utilization is feasible.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 } }, { "type": "Grant", "id": "668", "attributes": { "award_id": "2034020", "title": "SBIR Phase I: Auto Pairing Direct to Cellular Telehealth Gateway for Improved COVID-19 Home Health Monitoring Adherence", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)" ], "program_reference_codes": [], "program_officials": [ { "id": 1526, "first_name": "Muralidharan", "last_name": "Nair", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-12-15", "end_date": "2021-09-30", "award_amount": 255430, "principal_investigator": { "id": 1527, "first_name": "John E", "last_name": "Fitch", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 354, "ror": "", "name": "Birkeland Current LLC", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 354, "ror": "", "name": "Birkeland Current LLC", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovative Research (SBIR) Phase I project includes the ability to eliminate two of the primary barriers in dealing with seniors and technology: user interfaces and internet availability. The approach makes use of recent (2019) national network coverage for Narrow-Band Internet of Things (NB-IoT) enabling low power, low cost access for direct to cellular low bandwidth applications. The auto pairing direct to cellular gateway provides COVID-19 diagnosed patients with the capability to effectively monitor symptoms from home resulting in improved disease impact tracking and monitoring adherence while reducing hospital demand, disease spread, and system costs over current smart phone-based system. Current systems require lengthy user training and account setup as well as cumbersome measurement data transfers using an app during each reading. This currently challenging interface would be replaced with a single device requiring no set up or direct interaction with the user. The approach greatly simplifies and streamlines the disease related measurements while reducing the time and cost of getting devices provisioned and into the end users’ hands. Although focused on COVID-19 monitoring, the technology also provides broad application for effective telemedicine adoption by seniors for chronic care monitoring. This Small Business Innovative Research (SBIR) Phase I project seeks to provide a solution which is more cost effective and enables greater adoption and compliance for long-term, in-home, self-monitoring of seniors and at-risk populations diagnosed with COVID-19. The research goals of this project include demonstrating an auto pairing direct to cellular device that meets the requirements for COVID-19 monitoring under the global COVID-19 Emergency Response Solution. The research approach includes: demonstrating a design with optimized narrow band performance; demonstrating FCC/IC end unit certification; and demonstrating testing required for network certifications. The anticipated technical results of this Phase I project enable smartphone-based monitoring systems and a gateway into senior populations for chronic care monitoring.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 } }, { "type": "Grant", "id": "667", "attributes": { "award_id": "2108526", "title": "RAPID: Real-time Forecasting of COVID-19 risk in the USA", "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": 1524, "first_name": "Katharina", "last_name": "Dittmar", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-15", "end_date": "2022-12-31", "award_amount": 200000, "principal_investigator": { "id": 1525, "first_name": "Lauren M", "last_name": "Gardner", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 344, "ror": "https://ror.org/00za53h95", "name": "Johns Hopkins University", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "In an effort to support decision making by governments and individuals related to the COVID-19 pandemic, the researchers will develop a set of epidemic forecast models to accurately assess the risk presented by COVID-19 in the United States at county, state, and national levels. The models will build on epidemiological data from the CSSE team’s publicly available COVID-19 tracking map, along with anonymized mobile phone data, demographic and socioeconomic information, climate and seasonality factors, and various health and behavioral metrics. The modeling framework will be flexible, and thus able to provide decision support for various policy needs and mitigation strategies. The team will make concerted efforts to maximize the model’s usefulness to decision-makers and ensure the successful translation of modeling outcomes into useful actions. In the short term, the model outputs generated by the team will contribute to the CDC’s COVID-19 national forecasting efforts through the COVID-19 Forecast Hub. In the long term, the systems engineering approach to this research effort will contribute to the establishment of a robust, vetted set of tools that can be used for epidemic forecasting, prior to and during the next pandemic. This project will also support the training of graduate students. The forecasting model will utilize an empirical machine learning approach that combines disparate data inputs into a meaningful predictive model using a combination of raw data and novel metrics generated in-house as inputs. The research team will explore, evaluate and compare the performance of different statistical methodologies for answering different proposed modeling objectives, in addition to developing new techniques to further improve predictive capabilities such as ensemble approaches and input clustering. Various combinations of methodologies and research objectives will be considered and optimized to find the best pairing. The team will make a concerted effort to continually validate the model based on observed data, and in response, continue to refine the model to both increase the accuracy of the predictions and infer the most important factors driving the outbreak, thus improving our general understanding of COVID-19 transmission risk. The proposed modeling effort will simultaneously build on the research team’s ongoing data collection effort that supports the JHU CSSE COVID-19 Dashboard and data set, and thus enable the team to further improve the quality of the data, as well as improve the communication, documentation, and management of the dataset, which has become the authoritative source of COVID-19 case and death data globally serves as the foundation for national and local level COVID-19 modeling conducted by dozens of research teams, governmental organizations and public health agencies around the world.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": 1424, "count": 14236 } } }