Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=2&sort=-funder
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=-funder", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=-funder", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-funder" }, "data": [ { "type": "Grant", "id": "10819", "attributes": { "award_id": "1R01AG080583-01", "title": "The Effects of the COVID-19 Pandemic on Long-Term Care for High-Need Older Adults with and without 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": 7229, "first_name": "Elena", "last_name": "Fazio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-02-01", "end_date": "2028-01-31", "award_amount": 604912, "principal_investigator": { "id": 26903, "first_name": "Yulya", "last_name": "Truskinovsky", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 179, "ror": "https://ror.org/01070mq45", "name": "Wayne State University", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "Nearly 20 million adults (38%) aged 65 and older have limitations with one or more self-care activities (e.g., dressing, getting out of bed) and one in ten older adults are living with Alzheimer’s Disease and Related Dementias (ADRD). Together these two overlapping groups of “high-need” older adults typically rely on a variety of long-term care (LTC) sources to assist with daily activities, including family and unpaid care, paid care in the home, residential care such as assisted living and nursing home care. Inadequate care may lead to adverse consequences in daily self-care and avoidable health care utilization. The sudden onset of the COVID- 19 pandemic may have profoundly affected access to and use of LTC and contributed to further adverse consequences for high-need older adults, particularly for those living with ADRD. This project will draw upon two complementary longitudinal, nationally representative surveys of older adults–the Health and Retirement Study (HRS) and the National Health and Aging Trends Study (NHATS)–linked to geographic data and Medicare claims. Using statistical approaches that strengthen our ability to draw causal inferences, we will: 1) Evaluate the short-term impact (2018-2020) of the COVID-19 pandemic on the type and amount of LTC use, comparing high-need older adults with and without ADRD and identify arrangements more likely to be “stable” with lower risks of change. 2) Determine whether care trajectories were disrupted after the start of the pandemic, comparing high-need older adults with and without ADRD from 2016 through 2024/2025. 3) Assess the impact of COVID-19 on adverse consequences related to care gaps among high-need older adults with and without ADRD. We will estimate the effect of the COVID-19 pandemic on self-reports of unmet need (using NHATS) and claims-based measures of avoidable hospitalizations and emergency department visits (using HRS) for those with and without ADRD. Detailed geographic data will allow us to take into account local conditions while identifying more “vulnerable” care arrangements with higher risks of adverse consequences. The results of this project will provide a comprehensive understanding of the COVID-19 pandemic’s impact on LTC outcomes in the short and longer term. This study aligns with NIA’s priority to understand community support for dementia care, in particular the determinants of availability LTC, LTC utilization and how the effects of community level factors including infrastructure and risk environment.", "keywords": [ "Address", "Adult", "Affect", "Aging", "Alzheimer&apos", "s disease related dementia", "Area", "Assisted Living Facilities", "Beds", "COVID-19", "COVID-19 impact", "COVID-19 pandemic", "COVID-19 pandemic effects", "Caregivers", "Caring", "Cessation of life", "Communities", "Data", "Dementia", "Elderly", "Emergency department visit", "Environment", "Family", "Family Caregiver", "Generations", "Geography", "Health", "Health and Retirement Study", "Healthcare", "Home", "Home Nursing Care", "Hospitalization", "Impaired cognition", "Infrastructure", "Link", "Long-Term Care", "Marketing", "Measures", "Medicare claim", "Nursing Homes", "Patient Self-Report", "Persons", "Public Health", "Reporting", "Research", "Residential Treatment", "Risk", "Schools", "Self Care", "Services", "Signal Transduction", "Soil", "Source", "Sterile coverings", "Surveys", "Techniques", "Time", "Training", "Variant", "Virus", "Work", "adverse outcome", "aging population", "base", "care outcomes", "community-level factor", "dementia care", "geographic difference", "health care availability", "health care service utilization", "health data", "high risk", "human old age (65+)", "improved", "mortality", "mortality risk", "neglect", "pandemic disease", "pandemic potential", "patient home care", "programs", "trend" ], "approved": true } }, { "type": "Grant", "id": "10779", "attributes": { "award_id": "1I21RX004376-01", "title": "Impact of Veteran Voices & Visions Peer Support Groups on Social Integration for Veterans with SMI/Psychosis", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2022-12-01", "end_date": "2024-11-30", "award_amount": null, "principal_investigator": { "id": 26853, "first_name": "Ippolytos Andreas", "last_name": "Kalofonos", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1708, "ror": "https://ror.org/05xcarb80", "name": "VA Greater Los Angeles Healthcare System", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Veterans with Serious Mental Illness (SMI) struggle with social integration - participation in work, housing, and citizenship - due to symptoms, stigma, and psychosocial functioning deficits. This has a tremendous impact on mortality, comparable to that of smoking and greater than obesity and alcohol abuse. Despite considerable VA efforts to provide mental health care to Veterans with SMI, programs that promote social integration are lacking. Veterans with SMI are at especially high risk for poor social integration and suicidal ideation during the COVID-19 pandemic. There is an urgency to advance treatments targeting Veterans' social integration. This project addresses this need with a group-based, peer specialist (PS) co-facilitated psychosocial intervention for Veterans with SMI, called “Veteran Voices and Visions” (VVV). VVV targets Veterans with SMI who experience psychosis, a group particularly in need of support with social integration. Virtual VVV groups are co-led by VA mental health clinicians (MHCs) and PSs via online video conference. VVV is an adaptation of a community-based support group model called the Hearing Voices (HV) approach that was developed over 30 years ago in the Netherlands. It has since spread to over twenty-five countries, representing hundreds of support groups worldwide. The approach facilitates group cohesion around and normalization of the common psychotic symptoms of SMI: hallucinations, delusions, and social isolation. Despite its global scope, this approach has neither been formally adapted nor rigorously studied in public health systems, including the VA. This intervention has the potential to create and foster a supportive community that improves the social integration of participants by reducing their distress and self-stigma, and increasing self-efficacy. These three process outcomes are strongly associated with social integration. This proposal is directly aligned with VA priorities to advance the breadth of existing psychosocial interventions for Veterans with SMI, improve access via telehealth services, and support Veterans' independence, wellbeing, empowerment, and whole health. The goal of this proposal is (1) to develop a manual, training guidelines, and a fidelity scale for VVV, (2) to assess the feasibility and acceptability of VVV, and (3) collect pilot outcome data. The manual, training guidelines, and fidelity scale will be developed by the research team in collaboration with 4 advisory panels: Veterans with SMI, MHCs, PSs, and non-VA experts in HV. Then, 5 MHCs and 5 PSs will be trained to use the new VVV manual, and each MHC-PS pair will conduct a group with the new protocol. Thirty Veterans will be recruited to participate in these 5 groups and assessments will be conducted at baseline, midpoint, and post- intervention. Baseline assessments include measurement of psychiatric symptoms, level of distress from psychosis, internalized self-stigma, self-efficacy, sense of belonging, recovery, and social integration. Follow- up assessments conducted at 10 and at 20 weeks will include these same measurements as baseline, as well as a survey and qualitative interview on intervention acceptability. Feasibility data on numbers approached, enrolled, and retained, and treatment fidelity will be collected. Fidelity will be assessed in two ways: (1) facilitators will complete self-administered reflection worksheets after each session and (2) research team members will rate 5 randomly selected audio-recorded sessions from each group for formal fidelity ratings. The goal of the within-subjects trial evaluating feasibility and acceptability of the manualized VVV protocol for improving self-efficacy, self-stigma and social functioning in Veterans with psychosis. To that end, the study will be run using the full collection of measures that would be included in a subsequent RCT. However, since the study is designed to assess whether the proposed intervention can be successfully implemented in the target population and to evaluate the properties of a candidate set of outcome measures, rather than demonstrate efficacy, most of the analyses will be descriptive. This will lay the groundwork for a future Merit that will support a controlled efficacy study.", "keywords": [ "Address", "Alcohol abuse", "Belief", "COVID-19 pandemic", "Caring", "Collaborations", "Collection", "Communities", "Country", "Curiosities", "Data", "Delusions", "Development", "Distress", "Education", "Employment", "Enrollment", "Feeling suicidal", "Fostering", "Frequencies", "Friendships", "Future", "Geography", "Goals", "Grant", "Guidelines", "Hallucinations", "Health", "Health system", "Hearing", "Housing", "Improve Access", "Intervention", "Interview", "Life", "Link", "Loneliness", "Manuals", "Marriage", "Measurement", "Measures", "Mental Health", "Mental Health Services", "Meta-Analysis", "Modeling", "Netherlands", "Obesity", "Occupations", "Outcome", "Outcome Measure", "Participant", "Personal Satisfaction", "Phase", "Population", "Process", "Property", "Protocols documentation", "Psychoses", "Psychotic Disorders", "Public Health", "Quality of life", "Recovery", "Research", "Running", "Safety", "Self Administration", "Self Efficacy", "Services", "Smoking", "Social Functioning", "Social isolation", "Specialist", "Standardization", "Support Groups", "Surveys", "Symptoms", "Target Populations", "Training", "Training Programs", "Transportation", "Trust", "Veterans", "Videoconferencing", "Vision", "Voice", "Vulnerable Populations", "Work", "acceptability and feasibility", "adverse outcome", "base", "cohesion", "design", "efficacy study", "empowerment", "experience", "follow up assessment", "health related quality of life", "high risk", "improved", "innovation", "insight", "internalized stigma", "member", "mortality", "peer", "peer support", "pilot trial", "post intervention", "programs", "psychiatric symptom", "psychosocial", "psychotic symptoms", "recruit", "remediation", "service engagement", "severe mental illness", "social integration", "social movement", "social stigma", "suicidal risk", "telehealth", "virtual", "virtual platform" ], "approved": true } }, { "type": "Grant", "id": "10867", "attributes": { "award_id": "1I01HX003574-01", "title": "Impact of COVID-era Disrupted Care on Disparities in Outcomes among Veterans with Kidney Failure", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2022-08-01", "end_date": "2024-07-31", "award_amount": null, "principal_investigator": { "id": 12730, "first_name": "AMAL N.", "last_name": "TRIVEDI", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 222, "ror": "https://ror.org/05gq02987", "name": "Brown University", "address": "", "city": "", "state": "RI", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 26960, "first_name": "Virginia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1827, "ror": "https://ror.org/034adnw64", "name": "Durham VA Medical Center", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Background: The COVID-19 pandemic significantly disrupted care delivery that limited access to providers, acute and timely non-acute evaluation, and clinical intervention. Dialysis patients with kidney failure are particularly vulnerable to COVID-19 infection, COVID-19 related morbidity and mortality because they commonly have multiple chronic conditions, typically require thrice weekly life-sustaining dialysis treatment in close- quartered clinic settings, are already prone to fragmented care, and especially susceptible to disrupted care. The COVID-19 pandemic presents fundamental threats to dialysis patients, and there is an urgent need to examine the impact of disrupted care for this vulnerable and uniquely healthcare reliant population of patients. Over 16,000 of enrolled Veterans receive chronic dialysis through Veterans Health Administration (VA) Kidney Program’s VA and VA Community Care providers. VA Community Care improves access to life- sustaining dialysis services for 80% of Veterans with kidney failure with limited access to the VHA’s 70 dialysis units. In prior work, we found lower 2-year mortality among Veterans receiving dialysis exclusively in VA compared to those receiving dialysis in non-VA settings, consistent with VA and non-VA comparative studies in other clinical contexts. These differences may be due to more comprehensive integrated services (e.g., co- located primary and tertiary care services, care management, social work, national electronic medical record) in VA, compared with the more fragmented and siloed dialysis care in private sector community settings. Significance: Pandemic-related disruptions may disproportionately affect Veterans with serious conditions and social risk factors like those with kidney failure and the nature and impact of pandemic-related disruptions may differ in VA and non-VA systems. However, rigorous comparisons of COVID-related care disruptions and outcomes between Veterans receiving healthcare in VA and non-VA settings are lacking. Specific Aims: Building on our team’s research expertise, this study will: 1) Quantify the impact of COVID-19 on 1a) disrupted care for prevalent patients and 1b) deferred care for incident patients in a national cohort of Veterans with ESKD and compare the extent of these impacts between VA- financed dialysis care in VA and VA Community Care settings from 2018-2022. 2) Compare patient-level outcomes and racial and socioeconomic disparities in outcomes in VA and non-VA dialysis settings before and during the COVID pandemic time periods. Methodology: We will conduct a longitudinal cohort study of all VA-enrolled patients with end-stage kidney disease receiving VA-financed dialysis care between 2018 and 2022, to observe care patterns before the pre- COVID phase (Jan 2018-Feb 2020), the acute COVID phase defined as the first case of COVID until authorization of the vaccine (Mar 2020-Dec 2020) and the recovery phase when COVID-19 vaccination was available (Jan 2021-Dec 2022). Study data will be derived from linkages of VA and Medicare administrative data with community-level national COVID-19 tracking and neighborhood socioeconomic status data to assess the impacts of pandemic-related disruptions in care on disparities in outcomes among Veterans receiving dialysis in VA and non-VA settings. Next Steps/Implementation: This research is directly responsive to the COVID RFA HX-21-025: Pandemic related disrupted and deferred care and three VA HSR&D priority areas (Access to Care, Complex Disease Management, Social Determinants of Health). The study team is conducting this work partnership with the VHA National Program for Kidney Disease to ensure that our work is poised to shape evolving VA policy around provision of community care and to improve care for Veterans during the COVID-19 pandemic, recovery, and future public health crises. Results will inform how VA manages its Veterans in disaster scenarios, particularly with provider partners in VA community care, for whom the VA relies on for reliable and accessible dialysis care.", "keywords": [ "Acute", "Address", "Affect", "Ambulatory Care", "Area", "Authorization documentation", "Blood Vessels", "COVID-19", "COVID-19 impact", "COVID-19 morbidity", "COVID-19 pandemic", "COVID-19 vaccination", "Caring", "Chronic", "Clinic", "Clinical", "Collaborations", "Communities", "Community Financing", "Comparative Study", "Complex", "Computerized Medical Record", "Critical Care", "Data", "Dedications", "Diagnostic", "Dialysis patients", "Dialysis procedure", "Disasters", "Disease Management", "Emergency Situation", "End stage renal failure", "Enrollment", "Ensure", "Evaluation", "Frequencies", "Future", "Health", "Health Personnel", "Health Services Accessibility", "Health Services Administration", "Healthcare", "Immunocompromised Host", "Improve Access", "Integrated Health Care Systems", "Intervention", "Kidney", "Kidney Diseases", "Kidney Failure", "Life", "Longitudinal cohort study", "Maintenance", "Managed Care", "Medicare", "Methodology", "Morbidity - disease rate", "Nature", "Neighborhoods", "Outcome", "Patient Transfer", "Patients", "Patterns of Care", "Phase", "Policies", "Predisposition", "Primary Care", "Private Sector", "Procedures", "Process", "Provider", "Public Health", "Recovery", "Renal function", "Research", "Resources", "Risk Factors", "SARS-CoV-2 exposure", "SARS-CoV-2 infection", "Services", "Shapes", "Social Work", "Socioeconomic Status", "Structure", "Survival Rate", "System", "Time", "Vaccines", "Veterans", "Veterans Health Administration", "Work", "authority", "base", "care delivery", "care fragmentation", "care providers", "care systems", "cohort", "community setting", "coronavirus disease", "disadvantaged population", "ethnic minority", "experience", "health equity", "improved", "medical specialties", "military veteran", "mortality", "multiple chronic conditions", "pandemic disease", "participant enrollment", "patient population", "programs", "racial and ethnic", "social", "social health determinants", "socioeconomic disadvantage", "socioeconomic disparity", "tertiary care", "treatment adherence" ], "approved": true } }, { "type": "Grant", "id": "10875", "attributes": { "award_id": "5R01DK132350-02", "title": "Health Impacts of City-Wide Zero-Fare Bus Transit: A Natural Experiment", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)" ], "program_reference_codes": [], "program_officials": [ { "id": 22391, "first_name": "Mary", "last_name": "Evans", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-15", "end_date": "2026-01-31", "award_amount": 667617, "principal_investigator": { "id": 6100, "first_name": "Jannette Yvonne", "last_name": "Berkley-Patton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 753, "ror": "", "name": "UNIVERSITY OF MISSOURI KANSAS CITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22393, "first_name": "Jordan A.", "last_name": "Carlson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 753, "ror": "", "name": "UNIVERSITY OF MISSOURI KANSAS CITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Wide-reaching efforts are needed to increase population levels of physical activity and healthy eating in low- income groups for obesity- and type 2 diabetes prevention/control. Low-income groups experience higher rates of obesity and diabetes than the general population and the COVID-19 pandemic has made these groups even more vulnerable to developing these preventable chronic diseases. Active transportation is an underused source of physical activity but is particularly relevant to low-income groups. A major and consistent correlate of active transportation is use of public transit, and transit users engage in 5-15 more minutes/day of overall PA that non-users. Public transit may also support access to healthy eating and health services. Citywide policies to increase use of public transit have promise for improving health markers but have been substantially underexplored. As an effort to improve economic conditions among low-income groups, Kansas City, MO (KCMO; 500K residents; 43,000 daily bus trips) has become the only major city in the U.S. to permanently adopt an ongoing zero-fare bus transit (ZBT) policy. The policy has eliminated all bus fares across the city. This provides an extraordinary opportunity to examine impacts of such policies on bus ridership and subsequently on bus users' physical activity, healthy eating, and weight status. In this proposed study, we will collect bus ridership data before and up to 3 years after ZBT in KCMO and multiple comparison cities. To investigate health information, study participants will be recruited from a large primary care health system serving low- income communities. Participants will complete measures of bus use, and height and weight information will be obtained from the health system's electronic health record before and up to 3 years after ZBT. A subsample of participants will complete a 7-day objective physical activity assessment and questionnaires on their healthy eating, perceptions of the ZBT policy, and barriers/facilitators to riding the bus. Community residents will collect neighborhood environment information around bus stops to test as barriers to bus ridership and support advocacy efforts. A state-of-the-art synthetic control approach will be used to compare ridership trends across cities and weight status trajectories between post-ZBT bus users and non-bus users. The synthetic controls will be a weighted combination of multiple control participants to provide a better comparison than any single control alone. This study has significant implications for advancing knowledge and evidence on the potential health impacts of ZBT policies. Findings will produce information that will be usable by other natural experiment researchers and healthcare entities, as well as by local, state, and federal governments in making determinations on use of public policy approaches such as ZBT as a lever for obesity- and diabetes-related risk reduction in low-income communities.", "keywords": [ "Accelerometer", "Address", "Adopted", "Advocacy", "Authorization documentation", "Body mass index", "COVID-19 pandemic", "Characteristics", "Chronic Disease", "Cities", "Communities", "Crime", "Data", "Diabetes Mellitus", "Diabetes prevention", "Economic Conditions", "Economics", "Electronic Health Record", "Environment", "Evaluation", "Federal Government", "Focus Groups", "General Population", "Health", "Health Food", "Health Services", "Health Services Accessibility", "Health system", "Healthcare", "Healthcare Systems", "Healthy Eating", "Height", "Income", "Infrastructure", "Kansas", "Knowledge", "Lead", "Local Government", "Low income", "Measures", "Methods", "Natural experiment", "Neighborhoods", "Non-Insulin-Dependent Diabetes Mellitus", "Obesity", "Outcome", "Participant", "Patients", "Perception", "Physical activity", "Policies", "Policy Maker", "Population", "Primary Health Care", "Public Policy", "Quasi-experiment", "Questionnaires", "Research Personnel", "Risk Reduction", "Sampling", "Selection Bias", "Source", "State Government", "Surveys", "Testing", "Transportation", "Weight", "authority", "behavioral economics", "built environment", "density", "design", "experience", "health economics", "improved", "pandemic disease", "recruit", "sociodemographics", "socioeconomics", "tool", "trend", "usability", "walkability" ], "approved": true } }, { "type": "Grant", "id": "10787", "attributes": { "award_id": "1I01HX003576-01A1", "title": "Impact of COVID-19 on Continuity of Care for Veterans on Antipsychotic Medications", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2022-11-01", "end_date": "2024-10-31", "award_amount": null, "principal_investigator": { "id": 26863, "first_name": "ANOUK L", "last_name": "GRUBAUGH", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1782, "ror": "", "name": "RALPH H JOHNSON VA MEDICAL CENTER", "address": "", "city": "", "state": "SC", "zip": "", "country": "United States", "approved": true }, "abstract": "Background: Severe mental illnesses are consistently ranked as some of the most debilitating health conditions worldwide due to their early age of onset, chronicity, and impact on functioning. Fortunately, a number of antipsychotic medications have been found to be effective for managing the symptoms of severe mental illness (SMI) and for preventing relapse and rehospitalization. Despite their efficacy, treatment non-compliance for individuals on these medications is high due to a number of factors including poor insight into illness, negative attitudes about medication, and medication related side effects. Further complicating care and outcomes for this clinical population, providers must maintain close oversight of patients on antipsychotics due to the impact of these medications on metabolic and cardiac functioning which confer an increased risk of obesity, diabetes, heart problems, and other chronic illnesses. This oversight includes regular monitoring of weight, blood pressure, fasting blood glucose, and lipid levels. Additionally, clozapine, indicated for treatment-resistant schizophrenia, requires weekly-to-monthly monitoring of absolute neutrophil counts to prevent potentially fatal clozapine- induced agranulocytosis. Significance: The proposed project has significant and immediate relevance to Veterans and the VHA in that it seeks to better understand if and to what extent COVID-19 related care disruptions impacted care and outcomes for Veterans with SMI prescribed antipsychotic medications. Given pre- existing challenges in the treatment of this Veteran population, this is an important area of inquiry as well as one for which little is known. Added strengths of the proposed study include the use of a mixed methods approach that includes national level data from multiple sources. Aside from addressing a critical knowledge gap, the proposed study targets what is unarguably one of the most vulnerable patient populations within the VA and other healthcare systems—patients with SMI prescribed antipsychotic medications. Specific Aims: Aim 1: To assess the impact of COVID-19 related care disruptions on healthcare use and outcomes for Veterans on antipsychotic medications using robust statistical methods and national level data; Aim 2: To assess whether the impact of COVID-19 related care disruptions differ by race/ethnicity, gender, age, and rural/urban status using national level data; Aim 3: To conduct thematic interviews with provider and patient stakeholder groups at the national level to better understand COVID-19 related care disruptions. Provider stakeholders (e.g., psychiatrists, advanced nurse practitioners) will be interviewed to better understand COVID-19 related changes in practice behaviors, the perceived impact of these changes on care continuity and outcomes, and to solicit suggestions to mitigate the impact of interrupted care in the future; Veterans prescribed antipsychotic medication prescriptions in the pre COVID-19 window will be interviewed to better understand the impact of COVID-19 related care disruptions on treatment seeking behaviors, obstacles encountered with regard to access, and to explore other factors potentially impacting outcomes in this patient group. Methodology: The proposed study will employ a mixed-methods (quantitative/qualitative) approach. For Aims 1 & 2 we will employ retrospective, observational analyses using a national cohort of Veterans (N>250,000) with an ICD-CM-10 diagnostic code for schizophrenia or bipolar disorder prescribed a first-generation or second-generation antipsychotic [1/19-12/21]. Veterans of all ages, genders, racial groups, military eras will be included in the cohort. Aim 3 will involve individual thematic interviews with provider (n=35-45) and patient stakeholders (n=50-60). Next Steps/Implementation: Findings from this 2-year project will be of immediate relevance and impact for local, regional, and national level administrators and mental health providers as well as the VA Office of Mental Health and Suicide Prevention and VA Pharmacy Benefits Management Services. Collectively, data from this project will serve to identify potential strategies to further mitigate the impact of COVID-19 on the care and outcomes of Veterans with SMI as well as prepare for future public health and/or other national emergencies.", "keywords": [ "Address", "Adherence", "Administrator", "Admission activity", "Age", "Age of Onset", "Agranulocytosis", "Antipsychotic Agents", "Area", "Attitude", "Behavior", "Bipolar Disorder", "Blood Glucose", "Blood Pressure", "COVID-19", "COVID-19 impact", "Caring", "Chronic", "Chronic Disease", "Clinical", "Clozapine", "Code", "Continuity of Patient Care", "Data", "Data Sources", "Diabetes Mellitus", "Diagnostic", "Disease", "Drug Prescriptions", "Emergency Care", "Emergency Situation", "Ethnic Origin", "Event", "Fasting", "Future", "Gender", "Generations", "Guidelines", "Health", "Health Personnel", "Healthcare", "Healthcare Systems", "Heart", "Heart Diseases", "Individual", "Inpatients", "Interruption", "Interview", "Knowledge", "Lipids", "Malignant Neoplasms", "Mental Health", "Mental disorders", "Metabolic", "Methodology", "Methods", "Military Personnel", "Minority Women", "Monitor", "Nurse Practitioners", "Outcome", "Outpatients", "Patients", "Pharmaceutical Preparations", "Pharmacists", "Population", "Provider", "Psychiatrist", "Public Health", "Race", "Relapse", "Research Design", "Resistance", "Rural", "Safety", "Schizophrenia", "Services", "Statistical Methods", "Suggestion", "Suicide", "Suicide prevention", "System", "Testing", "Time", "Veterans", "Visit", "Weight", "behavioral outcome", "care outcomes", "cohort", "cost", "economic impact", "experience", "health care availability", "health care delivery", "health care service utilization", "heart function", "hospital readmission", "insight", "military veteran", "mortality", "multiple data sources", "neutrophil", "non-compliance", "obesity risk", "patient population", "pharmacy benefit", "post-COVID-19", "prevent", "racial and ethnic", "rural area", "severe mental illness", "side effect", "symptom management", "urban dwelling" ], "approved": true } }, { "type": "Grant", "id": "10883", "attributes": { "award_id": "5R03AI163978-02", "title": "The use of online obituaries as a tool for public health surveillance", "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": 26420, "first_name": "MARY KATHERINE", "last_name": "Bradford", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2024-01-31", "award_amount": 78000, "principal_investigator": { "id": 22259, "first_name": "Maria Liliana", "last_name": "Alva", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 181, "ror": "https://ror.org/05vzafd60", "name": "Georgetown University", "address": "", "city": "", "state": "DC", "zip": "", "country": "United States", "approved": true }, "abstract": "This study aims to (1) establish the degree of representativeness across age, sex, and race of obituary data by comparing that information with death certificate records to understand open-source data's reliability and measurement properties. (2) Build a model that uses online obituary data to predict administrative records. During health care emergencies, it is essential to monitor all-cause mortality and not just cause-specific deaths and to calculate the number of excess deaths for several reasons: (1) official statistics on cause-specific deaths might undercount people who did not test positive before dying; (2) hospitals and civil registries may not process death certificates for several days, or even weeks, which creates lags in the data; (3) the person completing the death certificate does not have access to the complete medical record or otherwise know about a positive test or symptoms; (4) pandemic and health emergencies divert attention and resources away from other conditions (e.g., cancer patients have seen delays and postponing treatment) and discouraged people from going to the hospital when needed (e.g., strokes), which may have indirectly caused an increase in fatalities from diseases other than COVID-19. Automated data collection from text mining of openly available online obituaries could allow us to derive quick predictions of age and sex distribution of death by location in a cost-effective way. Currently, publicly available datasets have a two-year lag. From the moment death records are captured to the time these are released, this delay hampers monitoring efforts. Providing information on sex, age, and race is critical because health emergencies might directly or indirectly cause a disproportionate increase in fatalities among certain groups. In places where mortality is exceptionally high (or low) based on obituary data, this form of monitoring can inform the policy response's effectiveness. This work can also be foundational for disease monitoring should future pandemics arise because online death records are easier and cheaper to access than administrative data.", "keywords": [ "Acquired Immunodeficiency Syndrome", "African American", "African American population", "Age", "Age Distribution", "Attention", "Awareness", "COVID-19", "COVID-19 monitoring", "COVID-19 mortality", "Cancer Patient", "Cause of Death", "Centers for Disease Control and Prevention (U.S.)", "Cessation of life", "Communicable Diseases", "Communication", "Containment", "Data", "Data Analyses", "Data Collection", "Data Linkages", "Data Science", "Data Set", "Death Certificates", "Death Records", "Disease", "Disease Marker", "Effectiveness", "Elderly", "Emergency Care", "Emergency Situation", "Epidemiology", "Ethnic Origin", "Event", "Excess Mortality", "Exercise", "Exhibits", "Fatality rate", "Federal Government", "Future", "Geography", "Health", "Health Policy", "Home", "Hospitalization", "Hospitals", "Latinx", "Life Expectancy", "Link", "Location", "Measurement", "Measures", "Medical Records", "Modeling", "Monitor", "Newspapers", "Not Hispanic or Latino", "Online Systems", "Persons", "Policies", "Policy Maker", "Population Surveillance", "Property", "Public Health", "Race", "Records", "Registries", "Research", "Resources", "Risk Factors", "Severity of illness", "Sex Distribution", "Stroke", "Structure", "Symptoms", "Testing", "Time", "Training", "Variant", "Virus", "Work", "age group", "application programming interface", "caucasian American", "coronavirus disease", "cost", "cost effective", "data access", "disadvantaged population", "future outbreak", "health disparity", "hispanic community", "innovation", "male", "mortality", "open data", "open source", "opioid overdose", "pandemic disease", "premature", "pressure", "prospective", "racial minority", "response", "sex", "statistics", "supervised learning", "text searching", "tool", "web page", "web site" ], "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": "10827", "attributes": { "award_id": "1R21AI171944-01A1", "title": "Extracellular vesicles encapsulating CRISPR machinery for treatment of SARS-CoV-2 infection", "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": 6115, "first_name": "DIPANWITA", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-19", "end_date": "2024-12-31", "award_amount": 221972, "principal_investigator": { "id": 8711, "first_name": "Houjian", "last_name": "Cai", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 888, "ror": "https://ror.org/00te3t702", "name": "University of Georgia", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 888, "ror": "https://ror.org/00te3t702", "name": "University of Georgia", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Summary/Abstract SARS-CoV-2 has caused the deaths of millions of people globally. Effective antiviral therapeutic treatment options are urgently needed. CRISPR-mediated genome editing has provided a very promising avenue for treatment of a variety of genetic diseases. Particularly, the CRISPR-Cas13 system has been demonstrated to possess the potential of inhibiting SARS-CoV-2 and influenza infections by degradation of viral genomic RNA and viral mRNA. However, it is still very challenging to deliver the CRISPR machinery to initiate genome editing efficiently in vivo. Extracellular vesicles (EVs) contain a variety of molecular components including lipids, mRNA, microRNAs, and proteins. A large body of studies has shown that EVs mediate cell-to-cell communication by transmitting their encapsulated contents. This proposal intends to construct EVs encapsulating the CRISPR machinery and deliver the EVs to respiratory epithelial cells to inhibit SARS-CoV-2 proliferation in vivo. We will therefore genetically engineer the Cas13d protein so that Cas13d/CRISPR-RNA (crRNA) ribonucleoprotein complex can be encapsulated into EVs. We will also engineer the membrane of EVs, such that EVs target respiratory epithelial cells and deliver Cas13d/crRNA for inhibiting SARS-CoV-2 viral assembly and proliferation, thereby inhibiting COVID-19. This study will allow us to understand the feasibility of an EVs-based vehicle to deliver genome editing machinery to inhibit SARS-CoV-2 proliferation in lung epithelial cells. This study will provide a treatment option for COVID-19 patients to reduce disease severity and mortality.", "keywords": [ "2019-nCoV", "ACE2", "Antiviral Agents", "BCAR1 gene", "Binding", "Biology", "C14 isotope", "COVID-19", "COVID-19 patient", "COVID-19 treatment", "CRISPR/Cas technology", "Cause of Death", "Cell Communication", "Cell membrane", "Cells", "Clustered Regularly Interspaced Short Palindromic Repeats", "Coated vesicle", "Complex", "Conditioned Culture Media", "Data", "Encapsulated", "Engineering", "Epithelial Cells", "Event", "Future", "Gene Expression", "Generations", "Genes", "Genetic", "Genetic Diseases", "Genetic Engineering", "Glycine", "In Vitro", "Knock-out", "Knowledge", "Lipids", "Lung", "Mediating", "Membrane", "Mesocricetus auratus", "Messenger RNA", "Methods", "MicroRNAs", "Modeling", "Modification", "Molecular", "N-terminal", "Nucleocapsid", "Persons", "Play", "Production", "Proliferating", "Proteins", "RNA", "RNA Sequences", "Ribonucleoproteins", "Role", "SARS-CoV-2 infection", "SARS-CoV-2 inhibitor", "SARS-CoV-2 spike protein", "Saturated Fatty Acids", "Severity of illness", "Specificity", "Structure of parenchyma of lung", "System", "Technology", "Testing", "Tissues", "Transfection", "Vaccines", "Variant", "Viral", "Viral Load result", "Viral Proteins", "Virus Assembly", "airway epithelium", "base", "extracellular vesicles", "genome editing", "genomic RNA", "improved", "in vivo", "influenza infection", "mortality", "myristoylation", "nanobiologic", "nanoparticle", "pandemic disease", "posttranscriptional", "receptor", "src-Family Kinases", "therapeutic genome editing", "transmission process", "treatment strategy", "viral genomics" ], "approved": true } }, { "type": "Grant", "id": "10851", "attributes": { "award_id": "1R01AI176639-01", "title": "Innate immunity against viral infection in intestinal epithelial cells of C. elegans", "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": 8224, "first_name": "Kentner L.", "last_name": "Singleton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-01-20", "end_date": "2027-12-31", "award_amount": 387195, "principal_investigator": { "id": 26936, "first_name": "Emily R", "last_name": "Troemel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "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": "RNA viruses have had an immense impact on human health. SARS-CoV-2 is only the most recent of many RNA viral zoonoses, and, even disregarding pandemics, the health burden of endemic RNA viruses, particularly in vulnerable populations, is substantial. Epithelial cells, abundant and exposed at mucosal surfaces, are often the first to be infected by RNA viruses, and are therefore often the first cell type to detect and respond to viral infection. However, unlike circulating immune cells, their in vivo behaviors cannot be measured from blood draws, and their behavior ex vivo may poorly correlate with in vivo dynamics. Our long-term goal is to understand how epithelial cells coordinate anti-viral responses in a whole-animal setting. Our previous work demonstrated that the RIG-I-like receptor (RLR) DRH-1 in the nematode C. elegans activates an anti-viral transcriptional response in intestinal epithelial cells that we named the Intracellular Pathogen Response (IPR), which protects against infections by viruses and other intracellular pathogens. We found that DRH-1 responds to infection with Orsay virus–a single-stranded, positive-sense RNA virus that naturally infects C. elegans intestinal epithelial cells. The objective of this proposal is to determine where and how DRH-1 triggers resistance to Orsay virus infection, and investigate whether in C. elegans, which lacks identified homologs of interferons, there is a role for bystander cells in mounting an immune response. The central hypothesis is that upon Orsay virus infection, DRH-1 in intestinal epithelial cells detects viral replication and induces the IPR, signaling to neighboring cells through an as-yet undescribed pathway. The rationale is based on our genetic analysis of DRH-1 and its role in anti-viral responses, and our visualization of IPR gene expression and DRH-1 localization dynamics in the context of infection. Our work is innovative because we are pursuing the IPR, which shares similarity with the type-I interferon (IFN-I) response in humans, but excitingly, appears to signal through novel factors, as homologs of MAVS, IRF3, NFkB, TNF-alpha and IFN-I itself are absent from the C. elegans genome. We will test our hypothesis with three specific aims: Aim 1) Where and how does DRH-1/RLR promote anti-viral defense in C. elegans? Aim 2) What signaling pathway is activated downstream of DRH-1/RLR in C. elegans? Aim 3) Which host cells mount an anti-viral immune response in C. elegans? The expected outcomes are to establish the signaling cascade used by DRH-1/RLR to trigger the protective IPR immune response in intestinal epithelial cells of C. elegans, and to identify the components of a systemic defense system. The proposed research is significant, because it could lead to new treatments for infections by RNA viruses, as well as a better understanding of epithelial immune defense and inflammatory diseases.", "keywords": [ "2019-nCoV", "Animals", "Antiviral Response", "Behavior", "Binding", "Blood", "C. elegans genome", "Caenorhabditis elegans", "Cells", "Data", "Disease", "Double-Stranded RNA", "Epithelial", "Epithelial Cells", "Fluorescent in Situ Hybridization", "France", "Gene Expression", "Genes", "Genetic", "Genetic Transcription", "Genomics", "Goals", "Health", "Homologous Gene", "Human", "IRF3 gene", "Immune", "Immune response", "Infection", "Inflammatory", "Innate Immune Response", "Interferon Type I", "Interferons", "Intestines", "Ions", "Knowledge", "Mammals", "Measures", "Methods", "Microscopy", "Microsporidia", "Mission", "Modeling", "Molecular", "Mucous Membrane", "Muscle", "NF-kappa B", "Names", "Natural Immunity", "Nematoda", "Neurons", "Organism", "Outcome", "Pathway interactions", "Pattern", "Planets", "Proteins", "Proteomics", "Public Health", "RNA", "RNA Interference", "RNA Virus Infections", "RNA Viruses", "Regulation", "Reporter", "Research", "Resistance", "Role", "Signal Pathway", "Signal Transduction", "Surface", "System", "TNF gene", "Testing", "Tissues", "United States National Institutes of Health", "Up-Regulation", "Viral", "Virus", "Virus Activation", "Virus Diseases", "Virus Replication", "Visualization", "Vulnerable Populations", "Work", "Zoonoses", "antiviral immunity", "burden of illness", "candidate identification", "cell type", "cytosolic receptor", "fighting", "genetic analysis", "helicase", "in vivo", "innovation", "insight", "intestinal epithelium", "mutant", "novel", "overexpression", "pandemic disease", "pathogen", "pathogenic fungus", "receptor", "response", "tool", "transcriptomics", "tripolyphosphate", "viral detection" ], "approved": true } }, { "type": "Grant", "id": "10891", "attributes": { "award_id": "5R01EB032708-02", "title": "Fetal MRI: robust self-driving brain acquisition and body movement quantification", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Biomedical Imaging and Bioengineering (NIBIB)" ], "program_reference_codes": [], "program_officials": [ { "id": 22716, "first_name": "GUOYING", "last_name": "LIU", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2025-11-30", "award_amount": 690807, "principal_investigator": { "id": 22717, "first_name": "ELFAR", "last_name": "ADALSTEINSSON", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 798, "ror": "https://ror.org/00dvg7y05", "name": "Boston Children's Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 10236, "first_name": "Patricia Ellen", "last_name": "Grant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 798, "ror": "https://ror.org/00dvg7y05", "name": "Boston Children's Hospital", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "PROJECT SUMMARY/ ABSTRACT Our premise is that the fetal stage of human brain development is the most dynamic, the most vulnerable and the most important for lifelong behavioral and cognitive function. As many neurological disorders have their genesis in fetal life, there is a need to accurately quantify normal and abnormal fetal brain development from both the perspective of fetal brain structure and body motion. Better imaging tools would enable us to explore how fetal neurological disorders as well as environmental exposures, such as opioids, maternal obesity, or COVID-19, impact early brain structure and body movements. Magnetic resonance imaging (MRI) T2-weighted, single-shot fast-spin-echo (e.g. HASTE) images provide a unique window into this critical phase of structural brain development, with the potential to detect subtle abnormalities. However, fetal brain MRI is challenging due to fetal motion, which leads to image artifacts, double oblique acquisitions and incomplete brain coverage. As a result, trained MR technologists must “chase the fetus” to amass the necessary images to diagnose the presence or absence of lesions, resulting in long scan times and higher RF energy deposition. Thus, fetal brain MRI is inefficient, limited to specialized centers, and diagnosis is still difficult because fetal motion results in each image being an independent slice that cannot be referenced to another slice, making confirmation of suspicious findings difficult. At the same time, fetal motion is an important measure of functional neurological integrity, informing postnatal outcomes. However, current clinical MR and ultrasound assessments of fetal motion do not fully capture the complex 3D motions of all body parts simultaneously. Better assessment of fetal neurological health requires novel tools to automatically and efficiently obtain coherent, high quality HASTE fetal brain volumes and to characterize 3D fetal whole-body motion. To address these unmet needs, we will leverage convolutional neural network (CNN) models and propose the following aims: (1) Develop a self-driving engine for efficient acquisition of high-quality HASTE fetal brain volumes and (2) Enable automated fetal whole-body motion tracking and characterization. We will deploy the proposed tools in a prospective study that compares fetuses with Chiari II malformation (spina bifida), a disorder known to have brain abnormalities and often associated with decreased leg movement, to typical fetuses with the following aim: (3) Assess performance of the self-driving HASTE engine and whole-body motion characterization in Chiari II vs typical fetuses. For Aims 1 and 2, we will include data from collaborating sites and strategies for CNN generalization to increase robustness and potential to deploy our tools to other scanners. The ability to automatically obtain high-quality coherent fetal brain volumes and characterize fetal motion will improve stratification for fetal treatments and characterization of response to fetal interventions. 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