Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&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=1385&sort=award_id", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1383&sort=award_id" }, "data": [ { "type": "Grant", "id": "15506", "attributes": { "award_id": "75N95024D00006-0-759502400007-1", "title": "TASK ORDER 7 - ODP", "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": "2024-05-31", "end_date": "2024-09-30", "award_amount": 1825925, "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 and the NIGMS-supported Institutional Development Award Networks for Clinical and Translational Research (IDeA-CTR), 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": "15551", "attributes": { "award_id": "75N95024D00006-P00001-759502400001-1", "title": "TO1 - STSS PROGRAM SUPPORT 1.0", "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": "2024-02-08", "end_date": "2024-05-31", "award_amount": 10367151, "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": 2550, "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": "15544", "attributes": { "award_id": "75N95024F00064-0-0-1", "title": "LINKAGE HONEST BROKER SERVICES FOR THE N3C DATA ENCLAVE", "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": "2024-03-01", "end_date": "2024-10-31", "award_amount": 3237500, "principal_investigator": { "id": 26571, "first_name": "SHAUN", "last_name": "GRANNIS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1823, "ror": "", "name": "REGENSTRIEF INSTITUTE, INC.", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "National COVID-19 Cohort Collaborative (N3C): The N3C Data Enclave is a secure platform storing harmonized clinical data provided by more than 50 contributing members. The Enclave hosts over 651 million clinical observations on over 6.5 million persons, including over 2.1 million COVID cases, amounting to more than 7.3 billion rows of data. Harmonization, anonymity, and security is accomplished through the N3C privacy-preserving record linkage (PPRL). The PPRL uses a de-identified, software-generated token applied by a data contributor to each patient record. A linkage honest broker holds the de-identified tokens and provides a service matching token generated across disparate data sets without knowledge of the identity of the patients. The de-identified tokens are held separately from data residing within the data enclave. As an illustration of the PPRL process, Regenstrief generates a series of de-identified tokens using software provided by Datavant, Inc. The tokens are provided to a data provider, such as Hospital Network Alpha (HNA). HNA strips identifying PII from its clinical records and replaces the PII with the tokens. HNA then uploads the de-identified clinical records to N3C. If researchers require additional information about those records, they provide the token to Regenstrief, who passes it on to HNA without any knowledge of the underlying clinical data. HNA then provides the appropriate response. The linkage honest broker function is critical to the PPRL, which in turn is critical to continued use of the N3C Data Enclave by researchers. The salient characteristic of this requirement is the vendor?s ability to provide uninterrupted linkage honest broker support (harmonization, anonymity, and security) for the N3C Data Enclave.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12422", "attributes": { "award_id": "75N97023F00096-0-0-2", "title": "ACTIV TRACE support at NCBI for SARS-CoV-2", "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": [], "start_date": "2023-09-28", "end_date": "2028-09-27", "award_amount": 2354179, "principal_investigator": { "id": 28370, "first_name": "", "last_name": "", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2103, "ror": "", "name": "A-TEK", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "To monitor and evaluate SARS-CoV-2 genome evolution and characterize representative variant viruses for their potential impact on medical countermeasures.", "keywords": [ "2019-nCoV", "Basic Science", "Evolution", "Monitor", "SARS-CoV-2 genome", "Variant", "Virus", "medical countermeasure" ], "approved": true } }, { "type": "Grant", "id": "12249", "attributes": { "award_id": "7DP2AI170485-02", "title": "Real-time predictive modeling for public health departments to control infectious diseases", "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": 12912, "first_name": "Misrak", "last_name": "Gezmu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-07-01", "end_date": "2027-07-31", "award_amount": 463425, "principal_investigator": { "id": 25652, "first_name": "Nathan", "last_name": "Lo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "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": "Public health departments increasingly use predictive modeling to guide decisions and resource allocation for the control of infectious diseases in the United States, especially during the COVID-19 pandemic. These novel predictive models offer promise to better identify high-risk populations to precisely deploy interventions such as vaccination, yet there is limited evidence on how these models are used by public health departments and whether they translate into policy that reduces infectious diseases. The major scientific problem I seek to address is to identify whether, and to what degree, predictive models can be incorporated into public health practice and translated into policy by public health departments to improve the control of infectious diseases. By leveraging a key collaboration with the California Department of Public Health (CDPH) and rich epidemiologic data sources, I will address a key public health challenge of how to optimally allocate limited resources for targeted vaccination against pertussis, seasonal influenza, and hepatitis A. The goal is to target vaccines to the highest-risk locations and populations to reduce the number of outbreaks and infections. My hypothesis is that public health departments can effectively incorporate predictive mathematical models on optimal targeting of vaccination into their policy decisions. I will apply my expertise in predictive modeling and infectious diseases to develop open-source, predictive modeling tools for county public health departments to allocate targeted vaccination to the highest-risk populations, and study the step-by-step implementation of these models in public health use. My broad, long-range goal is to evaluate the causal public health impact of using predictive models to guide decisions on vaccination in public health departments. In Aim 1, I will develop and validate predictive models to optimally target vaccines to high-risk locations and populations (age, demographic and risk factor) for pertussis, seasonal influenza, and hepatitis A. The model will provide comparative effectiveness and costs of various targeted vaccination strategies, and an overall vaccine recommendation specific to the county and infectious disease. In Aim 2, I will apply methods from implementation science to optimize the user experience for public health officials to maximize usability, communication, and uptake of model-based vaccine recommendations. In Aim 3, I will implement the predictive models of targeted vaccination in California public health departments and measure implementation outcomes in a pilot study. This work will provide the foundation for a future innovative trial with CDPH that randomizes county public health departments and evaluates whether using model-based predictions on optimal vaccine allocation can causally reduce cases and outbreaks. This proposed work has the potential to unlock new scientific directions of translating predictive models into common practice in public health, which can then be applied across many infectious diseases.", "keywords": [ "Address", "Age", "Age Factors", "COVID-19 pandemic", "California", "Collaborations", "Communicable Diseases", "Communication", "County", "Data", "Data Sources", "Decision Making", "Demographic Factors", "Disease", "Disease Outbreaks", "Epidemiology", "Equilibrium", "Evaluation", "Evolution", "Feasibility Studies", "Foundations", "Future", "Goals", "Hepatitis A", "Heterogeneity", "Individual", "Infection", "Influenza A virus", "Intervention", "Interview", "Investigation", "Location", "Measures", "Methods", "Modeling", "Online Systems", "Outcome", "Pertussis", "Pilot Projects", "Policies", "Population", "Public Health", "Public Health Practice", "Public Policy", "Randomized", "Recommendation", "Research", "Resource Allocation", "Resources", "Risk Factors", "Seasons", "Standardization", "Surveys", "Testing", "Time", "Translating", "United States", "Vaccination", "Vaccines", "Validation", "Work", "comparative effectiveness", "cost", "design", "epidemiologic data", "experience", "experimental study", "high risk", "high risk population", "implementation measures", "implementation outcomes", "implementation science", "improved", "infection risk", "innovation", "insight", "mathematical model", "model development", "novel", "open source", "post-doctoral training", "predictive modeling", "predictive tools", "randomized trial", "routine practice", "seasonal influenza", "surveillance data", "tool", "uptake", "usability", "vaccination strategy", "web-based tool" ], "approved": true } }, { "type": "Grant", "id": "15419", "attributes": { "award_id": "7DP5OD033362-03", "title": "Self-Assembling Spike-EBR Nanoparticles as a Vaccine Platform Technology Against SARS-CoV-2 and Future Pandemic Coronaviruses", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Dental and Craniofacial Research (NIDCR)", "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 23244, "first_name": "Becky", "last_name": "Miller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-22", "end_date": "2027-08-31", "award_amount": 472500, "principal_investigator": { "id": 26589, "first_name": "Magnus Adrian Gero", "last_name": "Hoffmann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 917, "ror": "", "name": "J. DAVID GLADSTONE INSTITUTES", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic represents the 3rd outbreak caused by zoonotic transmission of a beta-coronavirus (beta-CoV) in the last 20 years. Hence there is an urgent need for new vaccine strategies to control the ongoing pandemic and prevent future CoV outbreaks. mRNA vaccines have emerged as an ideal platform for the development of rapid-response vaccines, but clinical studies have shown that neutralizing antibody titers elicited by mRNA vaccines are ~10-fold lower than titers elicited by protein nanoparticle (NP) vaccines. This is a concern with regards to the emergence of SARS-CoV-2 variants of concern (VOCs) that are less sensitive to vaccine- induced antibodies. In addition, less than 25% of the world population is fully vaccinated. Thus, rapid-response vaccine technologies are needed that elicit potent antibody responses with a single injection and/or lower doses, to ensure lasting protection against VOCs, reduce costs, and accelerate global distribution. Moreover, prevention of future CoV pandemics requires the development of a universal CoV vaccine that elicits cross-reactive immune responses against a broad spectrum of CoV strains by focusing responses to conserved epitopes. The scope of the proposed research is to design and evaluate new vaccine strategies to enhance the potency of mRNA-based rapid-response vaccines and facilitate universal CoV vaccine development. The proposal is based on the EBR NP technology, which modifies membrane proteins such as CoV spike (S) proteins to self-assemble into virus- resembling NPs that bud from the cell surface. NP assembly is induced by inserting a short amino acid sequence into the cytoplasmic tail designed to recruit proteins from the endosomal sorting complex required for transport (ESCRT) pathway. Initial studies in mice showed that low-dose injections of EBR NPs presenting the SARS- CoV-2 S protein elicited 10-fold higher neutralizing antibody titers than soluble S protein and protein-based NPs that displayed the receptor-binding domain (RBD) of the S protein. The EBR NP technology will be applied to accomplish three goals: i) Design a hybrid mRNA vaccine encoding the modified SARS-CoV-2 S-EBR construct that would be expressed at the cell surface and self-assemble into virus-resembling NPs to elicit more potent antibody responses than the approved Pfizer/Moderna vaccines, while retaining the manufacturing properties and T-cell activation of mRNA vaccines. ii) Engineer S-EBR NPs to package and deliver S or S-EBR mRNA vaccines as an alternative to lipid NPs. This delivery approach would enhance mRNA vaccine potency as S proteins presented on S-EBR NPs induce potent antibody responses, facilitate efficient intracellular delivery, and target mRNA vaccines to tissues that are naturally infected by SARS-CoV-2 to induce local immune responses. iii) Design and evaluate mosaic S-EBR NP-based universal CoV vaccine candidates that present full-length membrane-associated S proteins from multiple CoV strains to elicit cross-reactive immune responses against a broad spectrum of CoVs and protect against future outbreaks. The proposed vaccine strategies could have direct impact on the COVID-19 global health crisis and advance our emergency preparedness for the next pandemic.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15426", "attributes": { "award_id": "7G08LM014107-02", "title": "Reading Bees: Adapting and Testing a Mobile App Designed to Empower Families to Read more Interactively with Children in Distinct Geographical and Cultural Contexts", "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": 26724, "first_name": "Meryl", "last_name": "Sufian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-01-23", "end_date": "2026-07-31", "award_amount": 120271, "principal_investigator": { "id": 32033, "first_name": "John S.", "last_name": "Hutton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1215, "ror": "", "name": "UT SOUTHWESTERN MEDICAL CENTER", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Many children arrive at kindergarten unprepared to learn to read, at-risk of falling more behind, with major inequities linked to race, geography and poverty (rates >50%). These are amplified during disruptions such as COVID, when access to information and resources is perturbed. Low proficiency is strongly linked to adverse school, vocational and health outcomes, with estimated costs >$350 billion/year. As parents are a child’s “first and most important teachers,” home reading routines have a large impact on these outcomes. However, there are wide disparities in these between high- and low-resource families, fueled by household stressors, cultural differences, literacy challenges and other factors. Marginalized families also often face barriers to access of reliable literacy-promoting information, programs and resources, worsening disparities. Given trusted access to families when parenting routines are shaped, health providers are poised to help mitigate these barriers, yet guidance tends to be general, inconsistent and can fade-out at home. The objective of the proposed project is to enhance, “localize” and test a new, free mobile app designed to provide reliable shared reading guidance and resources for parents (Reading Bees; RB) in an efficient, engaging way. The rationale is that no similar approach exists, RB is free and designed to enhance existing programs, and there is evidence that its features will be useful and effective. Content is evidence-based and has been co-developed with input from community stakeholders and families from disadvantaged backgrounds. Core principles are clarity, credibility, flexibility (e.g., parents set their own goals), responsiveness (child age, family concerns, ZIP), engaging content (tips, videos, resources) and positive reinforcement (“LitCoin” awards). The long-term goal of this project is to use RB to help improve reading and literacy outcomes. To achieve this, teams in 3 culturally distinct areas (OH, WV, FL) will collaborate in a 3-year project. Content will first be added to address needs in each community: lists of local reading-related resources curated by area stakeholders and a Spanish language version of RB. Enhanced, “localized” RB will then be tested with parents in each area, first through focus groups to gauge usefulness and guide refinement, and then by providing RB to parents (ages 0-6) during clinic visits and measuring use over the next 2 months. Outcome measures involve feasibility, acceptance and useflness. The central hypothesis is that local stakeholders will be engaged by the opportunity to highlight resources in their area; families will rate RB content as useful and use RB often, especially to earn LitCoin awards; and improved access to information and resources will fuel better reading and literacy outcomes. This work is significant and innovative as it involves a tech-enabled, user-centered approach that is scalable within existing pediatric, library and program infrastructure and empowers parents to read more interactively and access reliable information. The expected outcome is that this work will provide vital enhancements to RB, show feasibility and usefulness and provide a flexible, collaborative model to “localize” and scale use of RB into other areas.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15430", "attributes": { "award_id": "7I01RX004572-02", "title": "Modifying Adiposity Through Behavioral Strategies to Improve COVID-19 Rehabilitation", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2023-11-01", "end_date": "2029-10-31", "award_amount": null, "principal_investigator": { "id": 32034, "first_name": "KATHLEEN A", "last_name": "GRIFFITH", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26754, "first_name": "ALICE S.", "last_name": "RYAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1532, "ror": "https://ror.org/036a0e562", "name": "Baltimore VA Medical Center", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Findings of post-acute sequelae of Post-COVID Conditions (PCC) manifestations of fatigue, pain, dyspnea, and muscle weakness, provide a strong rationale for rehabilitation; yet few formal studies exist and the effects of severe acute respiratory syndrome coronavirus-2 infection on function are not well described. Notably, two- thirds of Veterans are overweight and obese, rendering excess adiposity a significant risk factor and a high- priority area related to PCC prevention and care. Obesity increases the risk of severe illness in Veterans recovering from PCC, but how it does so is not fully understood. Recent research suggests that excess adipose tissue is associated with adverse changes in adipose cellular function, and that these variations may be involved in the biology of aging and the etiology of aging- related diseases. Adipose tissue contains cells that have undergone cellular senescence, which induces inflammation, cytotoxicity, and metabolic dysfunction in other cells and tissues. However, the precise role of adipose tissue cellular composition on PCC recovery is limited. Thus, we propose to evaluate the role of obesity and PCC on physical functioning, health-related quality of life (HRQOL), and systemic and adipose tissue inflammatory and cellular senescence profiles in ethnically diverse older Veterans from the Audie Murphy (San Antonio) and Baltimore VA Medical Centers. Further, we propose a randomized controlled trial to determine whether a reduction in body weight and increased physical function by a weight loss intervention (WL), including dietary modification and exercise, in obese Veterans with PCC will reduce systemic and adipose tissue inflammation and senescence, which will have important implications for PCC recovery. We will pursue the following aims: Aim 1: To compare physical function, body composition, HRQOL, PCC symptoms, and adipose tissue molecular profiling in four cohorts of Veterans at baseline: lean PCC naïve, lean with PCC, obese PCC naïve, and obese with PCC (N=150). Aim 2: To compare in Veterans with obesity: a) a 12-week randomized WL vs. weight stability (WS) intervention (30/group) on physical function, body composition, HRQOL, and PCC symptoms together with changes in the global molecular profile in adipose tissue in Veterans with PCC and b) the WL intervention in PCC naïve vs. with PCC (N=30/group) on these outcomes. Older (55-80 years) men and women Veterans will be recruited. We will perform a standard functional battery (maximal aerobic capacity [VO2max; primary outcome], usual gait speed, six min walk distance, timed up and go, and handgrip strength), body composition (dual energy x-ray absorptiometry and computed tomography scans), HRQOL (NIH PROMIS-57), and PCC symptoms (COVID-19 Yorkshire Rehabilitation Scale [C19-YRS]) and adipose tissue will be collected. Further, we will test, in a randomized controlled trial, the hypothesis that a WL intervention, compared to weight stability (WS), improves physical function, body composition, and HRQOL and reduces inflammation and senescent cell burden and to a similar extent as the PCC naïve group with obesity. A deeper understanding of the relationship between adipose tissue and PCC will likely reveal factors that predispose to or protect against aging-related functional declines. Moreover, a better understanding of the effects of a lifestyle intervention on the molecular profile of adipose tissue will help to determine how changes in adipose tissue contribute to PCC and PCC recovery. Lastly, this research will provide important mechanistic insights into how cellular senescence influences the pathophysiology of physical, mental, and social dysfunction in older Veterans. Our findings could provide evidence-based recommendations to promote this type of intervention in Veterans recovering from PCC.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14833", "attributes": { "award_id": "7K01HD110683-02", "title": "State economic support policies on the prevention of child abuse and neglect during and post the COVID-19 pandemic: Bridging evidence with policy implementation", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 27515, "first_name": "LEAH KAYE", "last_name": "Gilbert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-12-01", "end_date": "2028-08-31", "award_amount": 132381, "principal_investigator": { "id": 28356, "first_name": "Liwei", "last_name": "Zhang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 888, "ror": "https://ror.org/00te3t702", "name": "University of Georgia", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Child abuse and neglect (CAN) is a pressing and preventable public health issue with negative lifelong consequences, including early deaths. Children living in economically disadvantaged families and communities are at high risk for CAN. Economic support policies may be effective strategies towards reducing CAN through strengthening families' financial well-being and reducing related stress. Because states vary in policy selection and implementation (e.g., eligibility and spending), understanding the effects of varying state-level economic support policies can inform the larger-scale implementation of economic policies to prevent CAN. Yet, there is a lack of research examining the causal effects of state economic support policies on CAN prevention. Also, little effort has been made to bridge macro-level policy evaluation with community-based CAN prevention strategies. Since the Covid-19 pandemic, many states have adjusted pre-existing programs and enacted Covid-related support policies (e.g., eviction moratoria and extended unemployment benefits). Evaluating the impact of changing policies on CAN during pre- Covid, Covid, and Covid-recovery eras can help determine how to direct economic support resources to families at risk of CAN during recovery and plan for future disasters. Leveraging a natural experimental design with nationwide data, along with a community-based participatory design, this study will 1) identify and synthesize state-level economic support policies during pre-Covid, Covid, and Covid-recovery eras, to examine how these policies, individually and in synergy with each other, impact county-level CAN report rates; 2) investigate how states' policy effects on CAN are mediated by county-level poverty and unemployment rates, and whether the effects vary by age, gender, race/ethnicity, rural/urban status, and CAN subtype; 3) develop and implement advocacy strategies with local communities to increase access to empirically informed economic support services that prevent CAN. To accomplish the proposed project goals, the PI will receive mentorship from a group of interdisciplinary experts, including Drs. Melissa Jonson-Reid, Derek Brown, and Patricia Kohl, take full advantage of the extensive resources at the NIH-funded Center for Innovation in Child Maltreatment Policy, Research and Training (P50HD096719), and extend existing partnerships with community stakeholders in St. Louis, Missouri. The PI will receive training in 1) performing policy evaluations with rigorous causal inference methodologies; 2) managing and analyzing large-scale ecological data; 3) conducting community-based participatory research; 4) developing NIH grants and disseminating research evidence for CAN prevention. This K01 award will enable the PI to conduct independent, community-engaged, and policy-relevant research informing states' selection and implementation of policies to prevent CAN.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11453", "attributes": { "award_id": "7K01MH127306-03", "title": "Effects of adolescent social isolation on adult decision making and corticostriatal circuitry", "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": 24237, "first_name": "Ashlee V", "last_name": "Van't Veer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-03-15", "end_date": "2025-06-30", "award_amount": 113971, "principal_investigator": { "id": 24238, "first_name": "Elizabeth N.", "last_name": "Holly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1061, "ror": "", "name": "RUTGERS THE STATE UNIV OF NJ NEWARK", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Adolescence is a particularly important period in social and cognitive development, characterized in part by rapid increases in exploration, social interaction, and neural connectivity. Social isolation in adolescence has a clear, profound impact on a wide range of behavioral and physiological endpoints extending into adulthood. The overarching research goal of this proposal is to elucidate how adolescent social isolation in male and female mice alters value-based decision making in adulthood, as well as the underlying corticostriatal circuitry driving these complex goal-directed behaviors. This work is timely and vitally important as COVID-19 has subjected an increasing number of adolescents to social isolation through school closures and stay-at-home orders. A first aim of this proposal is to use operant tasks to systematically investigate how adolescent social isolation impacts how mice later value reward benefits and integrate expected costs during decision making. Preliminary data suggests that adolescent social isolation amplifies reward value, but specific aspects of decision-making behavior will be disentangled with computational modeling of value-based choice. The second aim builds on this behavioral work to test the hypothesis that adolescent social isolation disrupts corticostriatal circuitry and striatal output during adult value-based decision-making. A distributed neural network is engaged during decision- making, and the dorsomedial striatum (DMS) is a key node in this network. Prefrontal inputs to the DMS from the medial prefrontal cortex (mPFC) and orbitofrontal cortex (OFC) are critically involved in action selection and outcome valuation, respectively. All three of these nodes undergo maturation and refinement during adolescence, and adolescent social isolation disrupts this development. However, how this impacts adult corticostriatal function remains unknown. Using in vivo electrophysiology, local field potential (LFP) and single- unit recordings will be used to test how adolescent social isolation affects synaptic strength and connectivity from these cortical regions to the DMS during value-based decision-making behavior. This work proposed in the Mentored Research Scientist Development Award will provide Dr. Elizabeth Holly with training in computational modeling of decision-making behavior and in vivo electrophysiology, which will be an important part of the foundation of her independent research career. By completion of this Award, the goal is for Dr. Holly to transition to a tenure-track faculty position and apply for an R01. The mentorship team Dr. Holly has assembled will ensure her successful training in these techniques, and prepare her to transition to her own independent research laboratory.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1384, "pages": 1392, "count": 13920 } } }