Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1419&sort=start_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=start_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=start_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1420&sort=start_date", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1418&sort=start_date" }, "data": [ { "type": "Grant", "id": "15724", "attributes": { "award_id": "2519776", "title": "The geography of H5N1 avian influenza in the United States: Human-environment ecosystem drivers of transmission and viral evolution", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Human-Envi & Geographical Scis" ], "program_reference_codes": [], "program_officials": [ { "id": 1931, "first_name": "Jeremy", "last_name": "Koster", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-10-01", "end_date": null, "award_amount": 496076, "principal_investigator": { "id": 32774, "first_name": "Michael", "last_name": "Emch", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32773, "first_name": "Xiu-Feng H", "last_name": "Wan", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 166, "ror": "https://ror.org/0130frc33", "name": "University of North Carolina at Chapel Hill", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "This project investigates how avian influenza (bird flu) spreads and undergoes genetic changes and identifies the key factors driving these genetic changes and spread. It elicits the spatial and genetic patterns of avian influenza in birds, mammals, and humans, aiming to assess the pandemic potential of this virus, which has had a 50% mortality rate in people infected during the past 30 years. Understanding the risk of spillover to humans requires a comprehensive understanding of the influenza ecosystem, an interconnected network of factors involving humans, animals, and the environment. The findings are being organized into a database for public access to support translation of what is learned from the project to practice by informing and optimizing measures to mitigate both the economic impacts on the agricultural sector, a core sector of the bioeconomy, and the public health risks posed by emerging influenza variants. This study aims to understand the genetic evolution of avian influenza, particularly a highly pathogenic H5N1 virus lineage over time and identify the ecological factors that drive human infections and viral change. Central to the study is a systematic analysis and characterization of the spatiotemporal distributions of viral genotypes and their genetic divergence from precursor avian influenza viruses. It leverages advanced geospatial modeling, machine learning, and geospatial artificial intelligence (GeoAI) techniques to identify key viral traits, such as transmission potential and virulence, and to elucidate geographic ecosystem factors that influence the spread and evolution of the virus. The study generates a publicly available database that integrates information on more than 20,000 avian influenza viruses with associated human-animal-environment ecosystem variables. This database subserves translational support for research and private-sector preparedness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15810", "attributes": { "award_id": "1I01CX002988-01", "title": "Long-term Cardiometabolic Disease in COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2025-10-01", "end_date": "2029-09-30", "award_amount": null, "principal_investigator": { "id": 44213, "first_name": "Ziyad", "last_name": "Al-Aly", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3364, "ror": "", "name": "ST. LOUIS VA MEDICAL CENTER", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Background: People with COVID-19 have increased risk of death and cardiometabolic disease including cardiovascular disease, diabetes mellitus, dyslipidemia and kidney disease. However, the evidence base is limited in 3 key aspects: (1) Existing studies have characterized the risks of adverse outcomes associated with earlier variants of SARS-CoV-2 and have limited follow-up. (2) It is not clear whether an annual vaccination for COVID-19 beyond the third dose reduces risk of adverse events. (3) Comparative analyses of COVID-19 vs. influenza are helpful to benchmarking risks but are only available for earlier variants and for limited follow up. Significance/Impact: Our proposal aims to answer key questions that are critical to guiding public health policy – including 1) characterizing short- and long-term risks of old, new and yet to emerge variants and subvariants of SARS-CoV-2 (proxied by the era in which they predominate); 2) on an ongoing annual basis, evaluate the effectiveness of COVID-19 vaccines in reducing risks of adverse health outcomes; and 3) on an ongoing basis, provide a comparative assessment of COVID-19 vs seasonal influenza. The proposal is specifically designed to address several key research questions outlined in the US Government National Research Action Plan on Long Covid. The results will have direct and substantial real-world impact in informing policy and clinical care. Innovation: The proposal leverages the unique power of the VA’s large-scale electronic health records and recent methodologic innovations in causal inference and clinical epidemiology to expand the evidence base about the health effects of COVID-19 and the role of vaccines. Specific Aims: To use healthcare data from the VA to: (1) characterize the acute and long-term risks of death and cardiometabolic disease in people with COVID-19 from 2020-2029, cohorted into variant-predominant eras, versus a matched historical control; (2) evaluate the effectiveness of receipt of the COVID-19 vaccine in each year (from 2022-2029) in reducing risk adverse health outcomes in the 12 months after receipt of the vaccine; and (3) comparatively evaluate the acute and long-term risks of death and cardiometabolic disease in people hospitalized for COVID-19, cohorted into variant-predominant eras, versus those hospitalized for seasonal influenza in each influenza season (from 2020 to 2029). Methodologies: VA electronic health record data will be used to construct independent cohorts for each aim and outcome being examined. COVID-19 test results and vaccination data will be collected form the COVID-19 Shared Data Resource, VA laboratory data and Medicare data. Incident outcome definitions validated for use with EHR data will be used. Inverse probability weighing will be used to balance for individual-level patient characteristics (predefined and algorithmically selected covariates), contextual characteristics, and characteristics related to the pandemic. Censoring weights will additionally be used to address situations that may result in informative loss to follow- up. Survival and mixed effect regression models will then be used to estimate differences in risk of outcomes between the COVID-19 exposure group of interest and reference groups, and estimates will be reported as hazard ratios and adjusted incidence rates, or differences in slopes. Implementation/Next Steps: Results from this proposal will inform public health policies (e.g. vaccine policies) and clinical care. Future studies will investigate optimizing care of people with cardiometabolic disease.", "keywords": [ "2019-nCoV", "Acute", "Address", "Algorithms", "Benchmarking", "COVID-19", "COVID-19 impact", "COVID-19 risk", "COVID-19 test", "COVID-19 vaccination", "COVID-19 vaccine", "Cardiometabolic Disease", "Cardiovascular Diseases", "Caring", "Cessation of life", "Characteristics", "Data", "Diabetes Mellitus", "Diagnosis", "Dose", "Dyslipidemias", "Effectiveness", "Electronic Health Record", "Enrollment", "Equilibrium", "Event", "Federal Government", "Future", "Health", "Health Care", "Health Policy", "Hospitalization", "Incidence", "Individual", "Infection", "Influenza", "Interest Group", "Kidney Diseases", "Knowledge", "Laboratories", "Long COVID", "Long-term Follow-up", "Medicare", "Methodology", "Modeling", "Outcome", "Patients", "Persons", "Policies", "Probability", "Proxy", "Public Health", "Recording of previous events", "Reporting", "Research", "Risk", "Risk Reduction", "Role", "SARS-CoV-2 exposure", "SARS-CoV-2 variant", "Test Result", "Testing", "Time", "United States Department of Veterans Affairs", "Vaccination", "Vaccines", "Variant", "Weight", "acute COVID-19", "adverse event risk", "adverse outcome", "cardiometabolic risk", "cardiometabolism", "clinical care", "clinical epidemiology", "cohort", "comparative", "data sharing networks", "design", "effectiveness evaluation", "evidence base", "follow-up", "hazard", "innovation", "mortality risk", "pandemic disease", "seasonal influenza" ], "approved": true } }, { "type": "Grant", "id": "15809", "attributes": { "award_id": "1F30AI194770-01", "title": "Impact of Natural Infection on the Baseline Immune States in Humans", "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": 32556, "first_name": "TIMOTHY A", "last_name": "GONDRE-LEWIS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-11-01", "end_date": "2028-10-31", "award_amount": 34558, "principal_investigator": { "id": 44212, "first_name": "Yona", "last_name": "Lei", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3363, "ror": "", "name": "YALE UNIVERSITY", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "Influenza is a year-round public health burden, causing millions of severe illnesses and hundreds of thousands of respiratory deaths globally. A key challenge in developing more effective vaccines lies in the inherent variability of the human immune system, as vaccine responses are highly variable across individuals, with many failing to develop adequate protective immunity. Low vaccine responsiveness has been associated with specific pre- vaccination baseline immune states. The baseline immune state of an individual determines their immune function and response. We and others have linked inter-individual variations in vaccination outcomes to molecular and cellular immune components that encode the baseline state. Our group previously showed that high vaccine responsiveness is associated with a “naturally adjuvanted” baseline state characterized by enhanced innate immune response potential, a finding supported by corresponding differences in stimulation responses of immune cells from high and low vaccine responders in vitro. We also found that clinically healthy males recovered from mild COVID-19 exhibited a more “poised” baseline state and stronger immune responses to subsequent influenza vaccination. These studies suggest that variations in baseline immune states contribute to heterogenous responses to vaccination, and that prior exposures may establish new baseline states that impact future responses in an antigen-agnostic manner. It remains unclear how infection alters an individual's baseline state over time, how these changes vary across individuals, and if they have functional consequences. Using longitudinal samples from a household cohort that allows control for environmental confounders, and using influenza infection as a model, my proposal aims to address these gaps to better understand the functional impact of infection on baseline immune states. Given the antigen-nonspecific nature of innate immune cells, understanding how infection impacts their function is a key to revealing potential underlying mechanisms. I hypothesize that influenza infection induces durable antigen-agnostic transcriptional and epigenetic changes that give rise to enhanced innate immune response potential. Aim 1 will assess the impact of infection on baseline immune states and innate cell response capacity. Through single-cell multimodal immune profiling, I will assess infection-induced transcriptional and epigenetic changes in peripheral immune cells. Using the same samples, I will examine innate response capacity to in vitro stimulation. Aim 2 will elucidate how infection-induced durable changes mechanistically drive innate cell responses to stimulation. I will implement a causal network inference approach to infer immune determinants of response capacity, followed by experimental validation to establish causality. This work will advance our understanding of infection-induced antigen-agnostic immune reprogramming, potentially revealing key drivers of human immune variation and strategies to modulate baseline states for improving vaccination outcome. Rigorous scientific training will be guided by mentors with experimental and computational expertise, complemented by longitudinal clinical and professional skill development.", "keywords": [ "Address", "Adjuvant", "Age", "Antibody titer measurement", "Antigens", "B-cell receptor repertoire sequencing", "Biological Assay", "COVID-19", "Cells", "Cellular Indexing of Transcriptomes and Epitopes by Sequencing", "Cessation of life", "Chromatin", "Clinical", "Computer Models", "Data", "Effectiveness", "Epigenetic Process", "Etiology", "Exhibits", "Future", "Genetic Transcription", "Goals", "Household", "Human", "Immune", "Immune response", "Immune system", "Immunity", "Immunologic Stimulation", "Immunologics", "Immunology", "In Vitro", "Individual", "Infection", "Influenza", "Influenza vaccination", "Innate Immune Response", "Link", "Lipopolysaccharides", "Mentors", "Modeling", "Molecular", "Nature", "Output", "Peripheral", "Peripheral Blood Mononuclear Cell", "Public Health", "Role", "Same-sex", "Sampling", "Shapes", "Signal Pathway", "Signal Transduction", "Study Subject", "System", "T-Lymphocyte", "Time", "Training", "Translating", "Transposase", "Vaccination", "Vaccine Design", "Vaccines", "Validation", "Variant", "Virus Diseases", "Work", "adaptive immunity", "clinical application", "cohort", "design", "immune function", "implementation strategy", "improved", "influenza infection", "influenza virus vaccine", "innate immune function", "insight", "inter-individual variation", "male", "multimodality", "novel vaccines", "pathogen", "respiratory", "response", "seasonal influenza", "sex", "single-cell RNA sequencing", "skill acquisition", "skills", "universal influenza vaccine", "vaccination outcome", "vaccine response" ], "approved": true } }, { "type": "Grant", "id": "15806", "attributes": { "award_id": "2603320", "title": "Rational Design and Fundamental Understanding of Multimodal Amyloid Probes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "BIOSENS-Biosensing" ], "program_reference_codes": [], "program_officials": [ { "id": 961, "first_name": "Aleksandr", "last_name": "Simonian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-12-01", "end_date": null, "award_amount": 361996, "principal_investigator": { "id": 1842, "first_name": "Jie", "last_name": "Zheng", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 405, "ror": "https://ror.org/02kyckx55", "name": "University of Akron", "address": "", "city": "", "state": "OH", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 240, "ror": "", "name": "University of Texas at San Antonio", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The hallmark of many debilitating diseases, such as Alzeimer’s disease (AD) and type II diabetes (T2D), is the presence of abnormal masses/aggregates of proteins termed “amyloids”. These amyloids, in which composition is disease dependent, are generally considered to be ideal markers for disease diagnosis and therapeutic intervention. Unfortunately, existing probes are limited in that they are only able to detect the presence of a single targeted amyloid protein. This project will develop a new class of generic, multiple-mode, multi-target amyloid probes that will detect a wide variety of proteins associated with different amyloid diseases. Design principles for the multimodal probes can be transformed to numerous molecular-recognition applications for targeted drug therapy, biomarker detection, and disease diagnostics (e.g., cancers and COVID-19). The proposed multi-disciplinary research activities will provide diverse training for students at all levels, especially from underrepresented and low-income families. The students will develop knowledge and skills in data mining, molecular simulations, neuroscience, and lab-on-chip techniques in close relation to public health problems. Finally, the integrated educational and research activities will enrich the curriculum of the Corrosion Engineering program at the University of Akron.\r\n\r\nThe overall objectives of this project are to (1) fully explore, identify, and engineer – with both data-driven simulations and experiments – a new family of AIE@βPs (an aggregation-induced emission (AIE) molecule conjugated with small β-sheet-forming peptides (βPs)) probes capable of early and enhanced detection of multiple pathological aggregates and co-aggregates formed by the same and different amyloid proteins, which co-exist in human body fluids across different amyloid diseases and (2) conduct fundamental sequence-structure-recognition studies on these multi-mode, multiple-target AIE@βPs probes. The AIE molecule targets the aggregated amyloids and avoids the aggregation-induced quenching, while βPs target the β-structures of amyloid aggregates via specific β-sheet interactions. The project’s objectives will be achieved via three tasks: (1) develop a machine-learning model, combined with molecular simulations and biophysical experiments, to screen, identify, and validate a library of βPs capable of self-assembling into β-sheet structures and cross-interacting with both Aβ (associated with AD) and hIAPP (associated with T2D); (2) design and synthesize a series of AIE@βPs probes to detect Aβ, hIAPP, and hybrid Aβ-hIAPP species at different aggregation states for demonstrating “conformational-specific, sequence-independent” mechanisms via synergetic AIE- and βPs-induced binding modes; and (3) transform AIE@βPs probes into different amyloid sensors via surface immobilization by controlling their packing structures, densities, and patterns of AIE@βPs. In parallel, multiscale molecular simulations will be conducted to study the structures, dynamics, and interactions of βPs and AIE@βPs with amyloid aggregates in solution and on surfaces, which will be correlated with amyloid recognition mechanisms of AIE@βPs by experiments. If successful, this work will provide new design principles and sensor systems for early amyloid detection beyond few available today.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15995", "attributes": { "award_id": "1IK2HX003695-01A2", "title": "Improving Specialty Care Through Virtual Care Models", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2026-01-01", "end_date": "2030-12-31", "award_amount": null, "principal_investigator": { "id": 44448, "first_name": "Rebecca", "last_name": "Tisdale", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3442, "ror": "", "name": "VETERANS ADMIN PALO ALTO HEALTH CARE SYS", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "1 Background: Specialty care deserts—the absence of specialists in geographic regions—have led to an access 2 crisis for the VA. In addition to increasing wait times and causing delays in care, these access needs drive many 3 Veterans to seek care outside VA, resulting in fragmented care, increased risks for hospitalization and hospital 4 readmission, and higher costs. In response, VA has launched the Clinical Resource Hub (CRH) program, which 5 seeks to deliver virtual care from “hub” to “spoke” sites in VA. VISN 21 has begun implementing this model in 6 cardiology at several spoke sites, but little is known about how care utilization and quality within the program. 7 Significance/Impact: This work seeks to better understand the effects of a virtual model of specialty care, in 8 this case cardiology care, on Veterans’ care access and quality. In addition, it aligns closely with several VA and 9 HSR&D priorities, chiefly access to care, virtual care/telehealth, and advancing the goals of the MISSION Act. 10 Innovation: The CRH program and the virtual care model at its core have yet to be studied in depth, and there 11 is no research in progress regarding specialty CRH despite strong interest at the national VA level in 12 understanding how specialty CRH is used and associated outcomes. Given that virtual cardiology care was very 13 limited prior to the COVID-19 pandemic, cardiology CRH is particularly novel. Hence, this project would add to 14 the limited body of research examining virtual cardiology care in the VA. In addition, the proposed work seeks to 15 evaluate this virtual care model at a time of unprecedented choice for Veterans between in-person and virtual 16 care, and limited data on how best to integrate these modalities. 17 Specific Aims: The proposed CDA will offer mentorship and training for me to pursue the following aims: 18 Aim 1. Evaluate quality of cardiology care associated with CRH implementation with administrative data. 19 I will use adjusted difference-in-difference event studies to compare cardiology quality metric achievement for 20 patients who received cardiology care via CRH versus those who received conventional VA-based cardiology care. 21 Aim 2. Assess Veteran perceptions of quality of cardiology care delivered via CRH. 22 I will interview Veterans participating in the CRH program and their caregivers regarding their experiences and 23 perceptions of quality of CRH cardiology care and elicit suggestions for key metrics to focus on for improvement. 24 Aim 3. Construct intervention to track and improve access to high-quality, equitable care through CRH. 25 Building on finding from Aims 1 and 2, I will interview clinicians and employ a facilitated deliberative process with 26 an expert advisory group to construct and pilot an intervention to improve quality. 27 Methodology: In Aim 1, I will use a difference-in-difference event study design to assess the impact of the program 28 on a battery of validated and/or guideline-based quality of cardiology care metrics. In Aim 2, guided by the Fortney 29 model of care access and quality, I will conduct semi-structured interviews of Veterans and caregivers receiving 30 care through the VISN 21 CRH program to understand their experiences with the CRH program and what outcomes 31 they recommend to include in a quality improvement intervention. In Aim 3, I will interview clinicians (Aim 3.1) and 32 conduct a facilitated deliberation process (Aim 3.2) to inform the construction of an intervention (proactive panel 33 management using a clinical dashboard tool) to track and improve quality of care and pilot the intervention. 34 Next Steps/Implementation: To continue moving this research into practice to improve health outcomes for 35 Veterans, I will extend the analysis of cardiology quality of care to compare cardiology care in the community to 36 CRH care. In addition, I will assess the effect of the intervention constructed in Aim 3 on patient outcomes and 37 clinician satisfaction via a hybrid implementation-effectiveness trial. I will continue to work with operational partners 38 to ensure cardiology CRH is improving access to high-quality cardiology care for Veterans. This project supports 39 my goal of becoming an independent VA health services researcher and leader in optimizing cardiovascular 40 disease care access, value, and equity for Veterans through virtual care innovations and implementation.", "keywords": [ "Achievement", "Address", "Area", "COVID-19 pandemic", "California", "Cardiology", "Cardiovascular Diseases", "Cardiovascular system", "Caregivers", "Caring", "Characteristics", "Cladribine", "Clinical", "Clinical Services", "Communities", "Community Health Care", "Dangerousness", "Data", "Disease", "Ensure", "Equity", "Evaluation", "Event", "Geographic Locations", "Goals", "Guidelines", "Health", "Health Services", "Health Services Accessibility", "Heart failure", "Homogeneously Staining Region", "Hospitalization", "Hospitals", "Improve Access", "Intervention", "Interview", "Medical", "Mentors", "Mentorship", "Methodology", "Methods", "Modality", "Modeling", "Morbidity - disease rate", "Nevada", "Outcome", "Pacific Islands", "Patient-Focused Outcomes", "Patients", "Perception", "Persons", "Physicians", "Policies", "Positioning Attribute", "Process", "Qualitative Methods", "Quality of Care", "Recommendation", "Research", "Research Design", "Research Personnel", "Resources", "Risk", "Rural Health", "Safety", "Site", "Specialist", "Structure", "Suggestion", "Telemedicine", "Telephone", "Testing", "Time", "Training", "Training Activity", "Veterans", "Visit", "Wait Time", "Work", "adverse outcome", "care fragmentation", "care seeking", "care utilization", "clinical implementation", "connected care", "cost", "dashboard", "design", "effectiveness/implementation trial", "experience", "follow-up", "health economics", "hospital readmission", "hospitalization rates", "implementation efforts", "implementation science", "improved", "innovation", "insight", "interest", "intervention effect", "medical specialties", "mortality", "novel", "operation", "patient subsets", "pilot test", "preference", "programs", "rapid growth", "research to practice", "response", "rural counties", "satisfaction", "sociodemographics", "southern nevada", "telehealth", "therapy design", "tool", "virtual", "virtual delivery", "virtual health care", "virtual model" ], "approved": true } }, { "type": "Grant", "id": "15994", "attributes": { "award_id": "1F31AI181508-01A1", "title": "Investigating the Role of Epstein-Barr Virus in Long COVID Pathogenesis", "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": 32795, "first_name": "EUN-CHUNG", "last_name": "PARK", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-01-16", "end_date": "2028-01-15", "award_amount": 33538, "principal_investigator": { "id": 44447, "first_name": "Alexandra", "last_name": "Tabachnikova", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3441, "ror": "", "name": "YALE UNIVERSITY", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "SARS-CoV-2 infection can result in the development of a constellation of persistent sequelae following acute disease, which is known as Long COVID. Individuals diagnosed with Long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions; however, the basic biological mechanisms responsible for these debilitating symptoms are unclear. Previously, this research group profiled 177 individuals in an exploratory, cross-sectional study encompassing multi-dimensional immune phenotyping in conjunction with machine learning. Key immunological features distinguishing Long COVID were identified and described in the Mount Sinai Yale –Long COVID (MY-LC) study. A striking finding was an elevation in antibodies to lytic antigens of Epstein-Barr Virus (EBV) in Long COVID participants, which may be indicative of more recent reactivation of EBV in these patients. In addition, levels of these antibodies correlated with IL-4, IL-6 cytokine double-producing CD4+ T- cells, which suggests that EBV reactivation is not merely incidental but reflects, mediates or aggravates immune perturbations in these patients. The overarching goal of this proposal is to provide a thorough insight into whether EBV reactivation contributes to LC disease pathogenesis and symptomatology, building on current literature. The research plan proposed will utilize the Iwasaki lab’s expertise in in vitro and in vivo modeling to assess whether SARS-CoV-2 infection can reactivate EBV and contribute to lasting sequelae, as described in Aim 1. Aim 2 will leverage large patient cohorts previously recruited through the MY-LC study and robust sample and data availability to test whether patients with Long COVID characterized by recent EBV reactivation experience unique immune alterations. Aim 2 will also test whether these responses correlate to unique symptoms. The findings uncovered by these studies have the potential to deepen understanding of one cause of Long COVID, and to inform future treatment of a growing, currently largely-untreated patient population. Mentorship from an interdisciplinary group of collaborators, who are experts in the proposed techniques, will facilitate this applicant’s training as an independent immunologist.", "keywords": [ "2019-nCoV", "Acute", "Acute Disease", "Antibodies", "Antigens", "Autoimmunity", "Autonomic Dysfunction", "B-Lymphocytes", "Biological", "Biological Assay", "Blood specimen", "CD4 Positive T Lymphocytes", "COVID-19", "COVID-19 impact", "COVID-19 pathogenesis", "COVID-19 patient", "Cell Culture Techniques", "Cells", "Communication", "Computational Technique", "Cross-Sectional Studies", "DNA Viruses", "Data", "Development", "Diagnosis", "Dimensions", "Disease", "Disease Marker", "EBV reactivation from latency", "Elements", "Epstein-Barr Virus latency", "Exertion", "Exhibits", "Fatigue", "Functional disorder", "Future", "Glycoproteins", "Goals", "Herpesviridae", "Hospitalization", "Human", "Human Herpesvirus 4", "Immune", "Immune response", "Immunologics", "Immunologist", "Impaired cognition", "Impairment", "In Vitro", "Individual", "Infection", "Inflammatory", "Influenza", "Influenza A Virus H1N1 Subtype", "Interleukin-4", "Interleukin-6", "Laboratories", "Literature", "Long COVID", "Lymphopenia", "Lytic", "Lytic Virus", "Machine Learning", "Malaise", "Mediating", "Memory", "Mentorship", "Methods", "Multiple Sclerosis", "Muridae", "Mus", "Neurocognitive", "Participant", "Pathogenesis", "Pathology", "Patients", "Phenotype", "Plasma", "Production", "Quality of life", "Recovery", "Reporting", "Research", "Rheumatoid Arthritis", "Risk", "Role", "SARS-CoV-2 infection", "Sampling", "Serology", "Serum", "Severity of illness", "Study Subject", "Symptoms", "Systemic Lupus Erythematosus", "T cell response", "T-Lymphocyte", "Techniques", "Testing", "Training", "Viral Antigens", "Viremia", "Virus Diseases", "Virus Latency", "Writing", "acute COVID-19", "autoimmune pathogenesis", "brain fog", "chronic infection", "cohort", "comparison control", "cytokine", "daily functioning", "debilitating symptom", "experience", "experimental study", "in vivo", "in vivo Model", "insight", "mortality", "mouse model", "patient population", "persistent symptom", "recruit", "response", "skills", "symptomatology", "virus envelope" ], "approved": true } }, { "type": "Grant", "id": "15993", "attributes": { "award_id": "1F30AI194459-01", "title": "Testing the role of microbial infections in the development of auto-antibodies to type I interferons", "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": 32556, "first_name": "TIMOTHY A", "last_name": "GONDRE-LEWIS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-01-27", "end_date": "2029-01-26", "award_amount": 43914, "principal_investigator": { "id": 44446, "first_name": "Adrianna M.", "last_name": "Rivera-León", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3440, "ror": "", "name": "UNIVERSITY OF MINNESOTA", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "Type I interferons (IFN) are crucial to anti-viral immunity. Neutralizing autoantibodies (AAb) to IFN are found in the general population, increase in prevalence with age, and are linked to worse, often fatal, outcomes in some of the most lethal acute respiratory viral diseases known to date, including fulminant influenza and COVID-19 pneumonia. Despite this, the mechanisms behind the formation of IFN AAb remain unknown. Human data suggest that impairments in thymic tolerance—due to dysfunction of autoimmune regulator (AIRE) and medullary thymic epithelial cells (mTEC)—may be required for the development of IFN AAb. AIRE is a transcription factor expressed by mTEC that is essential for establishing T cell tolerance in the thymus. In mTEC, AIRE promotes the expression and presentation of antigens from extrathymic tissues to developing T cells (thymocytes). This allows for the elimination of auto-reactive thymocyte clones, thereby preventing autoimmunity. Interestingly, AIRE+ mTEC have been shown to express IFN at steady-state conditions in the thymus suggesting that, in this context, AIRE+ mTEC act as antigen-presenting cells to thymocytes to mediate T cell tolerance to IFN. Supporting this idea, individuals with Autoimmune Polyglandular Syndrome 1 (APS1), who lack AIRE and experience T cell tolerance loss, consistently develop IFN AAb. These AAb are isotype- switched and somatically hypermutated, supporting the notion that a failure of T cell tolerance, rather than solely B cell tolerance, is necessary for their generation. However, additional findings suggest that loss of thymic T cell tolerance alone is insufficient for IFN AAb to develop. First, APS1 patients do not typically present IFN AAb at birth or infancy; instead, they develop these AAb later in life after exposure to pathogens is likely to have occurred. Second, IFN AAb have not been observed in specific pathogen-free, Aire-deficient mice. Combined, these observations suggest that pathogen exposure, in addition to AIRE and mTEC dysfunction, may be required for IFN AAb to develop. This proposal aims to understand how infections, combined with AIRE deficiency, contribute to the loss of thymic tolerance to IFN. My central hypothesis is that in individuals with predisposing AIRE deficiency, infections that induce IFN expression act as a double hit, promoting the development of neutralizing IFN AAb. Until now, methods to detect neutralizing IFN AAb in mice have been lacking, which has hindered the field's ability to test this hypothesis. I have developed a novel, sensitive, reproducible, and high-throughput assay for detecting murine neutralizing IFN AAb. This new tool will serve as the basis for this proposal and will facilitate exploration of how microbial infections and thymic defects contribute to the development of IFN AAb in an animal model. The findings from this work will deepen our understanding of how tolerance to IFN is mediated and may inform strategies to prevent IFN AAb development in affected individuals.", "keywords": [ "Academia", "Acute", "Affect", "Affinity", "Age", "Animal Model", "Antibody Affinity", "Antigen Presentation", "Antigen-Presenting Cells", "Antigens", "Autoantibodies", "Autoimmune Polyendocrinopathies", "Autoimmune Regulator", "Autoimmunity", "B-Lymphocytes", "Binding", "Biological Assay", "Birth", "CD4 Positive T Lymphocytes", "COVID-19 pandemic", "COVID-19 pneumonia", "Cells", "Cerebrum", "Chronic", "Clinical", "Clonal Deletion", "Clone Cells", "Competence", "Data", "Defect", "Development", "Epitopes", "Event", "Exposure to", "Failure", "Flow Cytometry", "Frequencies", "Functional disorder", "General Population", "Generations", "Human", "Immune system", "Immunoglobulin Class Switching", "Immunologics", "Impairment", "Individual", "Infection", "Interferon Type I", "Interferons", "Knockout Mice", "Life", "Life Experience", "Link", "Luciferases", "Lymphocytic choriomeningitis virus", "Measures", "Mediating", "Methods", "Microbe", "Modeling", "Mus", "Mutation", "Organ", "Outcome", "Patients", "Peptides", "Play", "Prevalence", "Process", "Regulatory T-Lymphocyte", "Reporting", "Reproducibility", "Research", "Risk Factors", "Role", "Severity of illness", "T-Lymphocyte", "Testing", "Thymic Tissue", "Thymic epithelial cell", "Thymus Gland", "Tissues", "Training", "Virus Diseases", "Wild Type Mouse", "Work", "age related", "aged", "aging population", "antiviral immunity", "autoreactive T cell", "autoreactivity", "career", "comparative", "cytokine", "detection assay", "experience", "experimental study", "germ free condition", "high throughput screening", "human data", "infancy", "influenza pneumonia", "interest", "later life", "loss of function mutation", "microbial", "novel", "pathogen", "pathogen exposure", "peptide vaccination", "prevent", "public health relevance", "respiratory", "response", "severe COVID-19", "thymocyte", "tool", "transcription factor" ], "approved": true } }, { "type": "Grant", "id": "15992", "attributes": { "award_id": "1R01NR021707-01A1", "title": "Organizational Changes to Reduce Nurse Burnout", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Nursing Research (NINR)" ], "program_reference_codes": [], "program_officials": [ { "id": 44444, "first_name": "KAREN MARIE", "last_name": "MCNAMARA", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-01-29", "end_date": "2029-12-31", "award_amount": 2009207, "principal_investigator": { "id": 26621, "first_name": "Karen Blanchette", "last_name": "Lasater", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2627, "ror": "", "name": "UNIVERSITY OF PENNSYLVANIA", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This study evaluates multi-level interventions—ranging from state-level policy action to healthcare organizational strategy and frontline care delivery innovations—to effectively prevent nurse burnout and mitigate the severity of burnout among the roughly half of hospital-based nurses already burned-out. Study objectives will be accomplished by leveraging unique data from thousands of nurses in approximately 535 hospitals in multiple states (CA, FL, NJ, PA) across 4 time-points spanning 20 years. We will generate repeated samples of these hospitals at multiple time-points (already collected: 2006, 2016, 2024, to be collected 2026). Using a repeated cross-sectional design with changing organizational and policy influences overtime, we are uniquely positioned to evaluate potentially causal relationships of modifiable organizational factors and state-level policy interventions on nurse burnout. Each time-period of data includes repeated measures of nurse outcomes (e.g., burnout, job dissatisfaction, intent to leave employment), and hospital factors and models of care (e.g., staffing levels, work environment, Magnet). These cross-sections of data will be linked with contemporaneous American Hospital Association data for considering structural features of hospitals (e.g. teaching status). In combination, we will have 4 cross-sections of data from 535 hospitals (with fluctuating nurse populations), with changing organizational, policy, and other intervening influences (e.g. CA staffing policy relative to non-policy states, 2008 Great Recession, 2020 Covid-19 pandemic). Our quantitative analytic approach uses hierarchical models with time-varying covariates to capture the multilevel structure of the data, as well as difference-in-difference models with propensity score weighting for rigorous causal inferences of changes in organizational factors on changes in outcomes. Using data collected in 2026, we will empirically identify typologies of hospitals with respect to their proportions of nurses with high burnout and average tenure and conduct in-depth interviews with key nurse leaders (hospital nurse executives, nurse managers) in hospitals representative of each of the typologies to elucidate the facilitators and barriers to reducing hospital nurse burnout and turnover. This multi-modal study has novel potential for sustained impact since it will (1) evaluate the impact of modifiable organizational and policy changes on hospital nursing and models of care on nurse burnout; (2) leverage 20 years of repeated cross-sections of data to evaluate potentially causal mechanisms between modifiable hospital factors and external policy interventions on nurse burnout; (3) evaluate currently employed nurses and those who recently left employment to understand whether the reasons nurses say they would leave hospital employment are the same as the reasons they actually leave; (4) integrate quantitative findings with qualitative frontline hospital leadership perspectives to move from evidence to action. The cumulative evidence will inform targeted recommendations for policy and hospital interventions for reducing the unprecedented high rates of nurse burnout and low retention.", "keywords": [ "Address", "American Hospital Association", "Back", "Burn injury", "COVID-19 pandemic", "Care given by nurses", "Caring", "Chronic", "Data", "Discipline of Nursing", "Distress", "Educational process of instructing", "Employment", "Evidence based intervention", "Health system", "Hospital Nursing", "Hospitals", "Intervention", "Interview", "Leadership", "Left", "Length", "Link", "Measures", "Modeling", "Morals", "Nurse Administrator", "Nurse Practitioners", "Nurses", "Nursing Models", "Nursing Staff", "Occupations", "Organizational Change", "Outcome", "Patients", "Policies", "Policy Maker", "Population", "Positioning Attribute", "Registered nurse", "Risk", "Sampling", "Severities", "Span 20", "Stress", "Structure", "Syndrome", "Team Nursing", "Time", "Travel", "Typology", "Work", "Workplace", "burnout", "care delivery", "design", "effective intervention", "experience", "health care service organization", "hospital readmission", "improved", "informant", "innovation", "multimodality", "novel", "pandemic disease", "policy recommendation", "prevent", "professional atmosphere", "recruit", "satisfaction", "virtual" ], "approved": true } }, { "type": "Grant", "id": "15991", "attributes": { "award_id": "1R01NR021708-01A1", "title": "What interventions to reduce hospital nurse burnout are most effective?", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Nursing Research (NINR)" ], "program_reference_codes": [], "program_officials": [ { "id": 44444, "first_name": "KAREN MARIE", "last_name": "MCNAMARA", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-01-30", "end_date": "2029-12-31", "award_amount": 1422387, "principal_investigator": { "id": 44445, "first_name": "EILEEN T", "last_name": "LAKE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2627, "ror": "", "name": "UNIVERSITY OF PENNSYLVANIA", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "What Interventions to Reduce Hospital Nurse Burnout Are Most Effective? Nurse burnout is a threat to healthcare safety, and to nurse and patient outcomes. Burnout among nurses has been a long-standing concern only accelerated by the COVID-19 pandemic. Burnout is a syndrome caused by chronic workplace stress and characterized by feelings of emotional exhaustion, cynicism towards one’s work, and decreased professional efficacy. Pre-pandemic, about 30% of nurses were burned out. Today, nearly half of 4.7 million nurses are experiencing burnout. This unsustainable high level of burnout has dire consequences for nurses and patients alike. Nurse burnout is associated with higher odds of patient mortality, failure to rescue, and prolonged length of stay, as well as nurse job dissatisfaction and turnover. We propose to integrate two approaches to addressing burnout: investigation of organizational characteristics as determinants of burnout, notably conducted by the proposed research team in recent decades, and health system administrators’ current implementation of interventions to reduce nurse burnout. Our preliminary studies reveal that organizational and individual interventions are being implemented nationwide and that nurses prefer organizational ones. It is unknown how preferred and implemented interventions relate to hospitals’ performance on nurse burnout, individual nurse burnout, and reducing burnout over time. Crucially, whether these interventions’ effectiveness depends on the work environment is unknown. Integration of these two approaches will yield a representation of reality across a large, geographically diverse hospital sample to inform whether certain intervention combinations are most effective and in what organizational contexts. The proposed aims address the Notice of Special Interest NOT-NR-23-012, “Addressing Organizational Factors to Prevent or Mitigate Nurse Burnout,” which invites “research studies to develop and evaluate novel organizational interventions to prevent and mitigate nurse burnout,” by identifying the currently preferred and implemented interventions, their work environment contexts, and their relation to nurse burnout, dissatisfaction, and intent to leave and hospital performance on nurse burnout. We propose to conduct a cross-sectional and longitudinal observational study utilizing 2024 and 2026 hospital nurse survey data from 31,942 nurses in 1,278 hospitals (in 2024) in 10 U.S. states to determine how preferred and implemented interventions relate to hospitals’ performance on nurse burnout, individual nurse burnout, and reducing burnout over time. The potential impact of the proposed study would be high because it would provide actionable results to optimize burnout intervention choices and contexts to mitigate pervasive nurse burnout.", "keywords": [ "Acceleration", "Address", "Administrator", "Burn injury", "COVID-19 pandemic", "Characteristics", "Chronic", "Data", "Effectiveness of Interventions", "Emotional", "Failure", "Feeling", "Geography", "Health Care", "Health Resources", "Health system", "Hospital Nursing", "Hospitals", "Individual", "Intervention", "Investigation", "Length of Stay", "Longitudinal observational study", "Mental Health", "Nurses", "Occupations", "Patient-Focused Outcomes", "Patients", "Research", "Respondent", "Safety", "Sampling", "Stress", "Surveys", "Syndrome", "Time", "Work", "Workplace", "burnout", "exhaustion", "experience", "hospital performance", "implementation intervention", "improved", "interest", "mortality", "novel", "pre-pandemic", "prevent", "professional atmosphere", "research study", "success" ], "approved": true } }, { "type": "Grant", "id": "15979", "attributes": { "award_id": "1R21AI188074-01A1", "title": "Identification of RNAi-independent antiviral genes through biased genetic screen in 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": 32860, "first_name": "KENTNER L", "last_name": "SINGLETON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-02-02", "end_date": "2028-01-31", "award_amount": 412500, "principal_investigator": { "id": 44434, "first_name": "Rui", "last_name": "Lu", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3430, "ror": "", "name": "LOUISIANA STATE UNIV A&M COL BATON ROUGE", "address": "", "city": "", "state": "LA", "zip": "", "country": "United States", "approved": true }, "abstract": "Viruses, especially RNA viruses, are formidable pathogens to cellular hosts. Owing to the error-prone nature of their replicases, RNA viruses rapidly accumulate large numbers of genetic mutations in their genome, enabling them to evade immune detection. Some RNA viruses, such as influenza viruses and coronaviruses, can also generate genome variants through genome reassortment or genome recombination mechanisms. It is thus important to study antiviral mechanisms intrinsically resistant to genetic mutations in viral genome, which may lead to the development of novel antiviral strategies. In plants, insects and vertebrates, there are antiviral mechanisms triggered by non-dsRNA products of invading viruses. These antiviral mechanisms provide another layer of protection in case viral dsRNA-triggered immunity is compromised by the invading viruses. In supporting this hypothesis, many plant and animal viruses have been found to produce diverse types of dsRNA binding proteins that are able to bind and sequester viral dsRNA to block immune detection. So far, RNAi is known as the most important antiviral defense mechanism in Caenorhabditis elegans. However, some recent studies on worm antiviral defense suggest that viral infection in C. elegans triggers transcriptional programs that in return provide protection against invading viruses in an RNAi-independent manner. Therefore, very much like what has been demonstrated in plants and insects, RNAi-independent antiviral defense (RiAD) may provide worms another layer of protection against virus in case antiviral RNAi is compromised. To fill the knowledge gap in our understanding of worm RiAD and as proof of principle, the PI’s lab has recently carried out a biased genetic screen of limited scale, aiming to identify genes specifically contributing to worm RiAD. This genetic screen identified 8 candidate genes that confer RiAD targeting a flock house virus (FHV) replicon. 5 of these candidate genes also mediate RiAD against Orsay virus, which naturally infects C. elegans (21). In this application, we propose to continue the biased genetic screen and finish it on a much greater scale to ensure double coverage on all genes involved in RiAD. We will then map and identify the candidate genes through whole genome sequencing combined with feeding RNAi and function rescue assay. Since approximately 70% of C. elegans genes have human homologs, function and mechanism study of the identified genes may not only lead to the identification of novel conserved mechanisms of antiviral innate immunity across kingdoms but also inform the development of novel antiviral strategies.", "keywords": [ "Alleles", "Animals", "Anti-viral Agents", "Anti-viral Response", "Binding", "Binding Proteins", "Biological Assay", "Caenorhabditis elegans", "Candidate Disease Gene", "Complement", "Coronavirus", "Defense Mechanisms", "Detection", "Development", "Double-Stranded RNA", "Ensure", "Genes", "Genetic", "Genetic Recombination", "Genetic Screening", "Genetic Transcription", "Genome", "Homologous Gene", "Human", "Immune", "Immune Evasion", "Immunity", "Insecta", "Interferons", "Invaded", "Knowledge", "Lead", "Mammals", "Maps", "Mediating", "Mutation", "Natural Immunity", "Nature", "Nematoda", "Organism", "Pathway interactions", "Plants", "RNA Interference", "RNA Viruses", "Replicon", "Research", "Resistance", "Variant", "Vertebrates", "Viral", "Viral Genes", "Viral Genome", "Virus", "Virus Diseases", "Work", "antiviral immunity", "candidate identification", "design", "feeding", "gene function", "genome sequencing", "genome-wide", "influenzavirus", "novel", "pathogen", "pathogenic virus", "programs", "protective pathway", "replicase", "response", "whole genome" ], "approved": true } } ], "meta": { "pagination": { "page": 1419, "pages": 1424, "count": 14236 } } }