Grant List
Represents Grant table in the DB
GET /v1/grants?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=1392&sort=-start_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-start_date", "prev": null }, "data": [ { "type": "Grant", "id": "15643", "attributes": { "award_id": "2442970", "title": "CAREER: Mechanism-Informed AI for Biological Systems-of-Systems to Accelerate Biomanufacturing Systems Integration and Innovations", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "MSI-Manufacturing Systms Integ" ], "program_reference_codes": [], "program_officials": [ { "id": 31283, "first_name": "Janis", "last_name": "Terpenny", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-09-01", "end_date": null, "award_amount": 596920, "principal_investigator": { "id": 32147, "first_name": "Wei", "last_name": "Xie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Unlike traditional pharmaceuticals, biopharmaceuticals use living organisms, e.g., cells, as factories to provide essential life-saving treatments for severe and chronic diseases (including cancers, metabolic diseases, and infectious diseases such as COVID-19) often with advantages such as increased efficacy and reduced side effects. However, current manufacturing systems lack the flexibility to produce existing and new biopharmaceuticals on demand. This is mainly because biomanufacturing processes are highly complex and variable, with hundreds of biological, physical, and chemical factors dynamically interacting at molecular, cellular, and macroscopic scales. Further, bioprocessing mechanisms are not systematically understood, and data are often very limited, sparse, and heterogeneous. To address these challenges, this Faculty Early Career Development (CAREER) project aims to optimize biomanufacturing processes via a bioprocess-specific AI that integrates uncertainty, intelligence, and science (i.e., systems and synthetic biology). Leveraging emerging sensing technologies that can monitor bioprocesses at molecular and cellular scales, this AI can also efficiently decode fundamental mechanisms. Moreover, by transferring this AI to industry practice, it is hoped this research will help make life-saving biopharmaceuticals rapidly available by accelerating biomanufacturing systems integration and automation with dramatically improved capabilities. The project will in parallel create a world-leading workforce pipeline from training the current workforce to educating (under)graduate and K-12 students. This project will create a mechanism-informed AI platform on Biological Systems-of-Systems to enable the quick assembly of flexible and robust biomanufacturing systems. To support biomanufacturing systems integration and accelerate the development of flexible optimal robust manufacturing systems, this research will answer two fundamental questions: (1) how to create a unified knowledge representation that enables integration of heterogeneous data collected at molecular, cellular, and macroscopic scales in different production processes; and (2) how to enable sample-efficient and interpretable learning for fundamental mechanisms and optimal control strategies within and across different scales. These questions will be addressed through three integrated research efforts: (i) creating a multi-scale probabilistic knowledge graph (pKG) hybrid (mechanistic + statistical) model with a modular design capable of representing spatial-temporal causal interdependencies from molecular- to cellular- to macroscopic scales for different biomanufacturing processes; (ii) developing interpretable federated learning to quickly fuse sparse and heterogeneous data collected from different production processes to advance scientific understanding and track critical latent states through sequential Bayesian inference on the pKG; and (iii) constructing new provably efficient model-based reinforcement learning schemes on Bayesian pKG, accounting for model uncertainty, informing design of experiments for digital twin calibration, and streamlining the policy search on optimal robust biomanufacturing systems. 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": "15644", "attributes": { "award_id": "2504217", "title": "EAGER: LGBTQI+ DCL:Exploring the influence of community cultural wealth on nonbinary engineering students professional formation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "EngEd-Engineering Education" ], "program_reference_codes": [], "program_officials": [ { "id": 31129, "first_name": "Matthew A.", "last_name": "Verleger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-06-01", "end_date": null, "award_amount": 292815, "principal_investigator": { "id": 32148, "first_name": "Kerrie", "last_name": "Douglas", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 252, "ror": "", "name": "Purdue University", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "Current and future US engineering workforce demands require research to better understand how to support the professional formation of all engineering students. The number of enrolled engineering students nation-wide had the sharpest decline in a generation. Further complicating the problem is the decreased math and reading scores across the US since the pandemic, adding an additional filter of who can enter into engineering. Projected national shortages of engineers are in the tens and hundreds of thousands of workers in some sectors. Simultaneously, fewer young people are entering into four-year degrees. Once a student has enrolled in an undergraduate engineering program, they become a valuable asset for meeting the workforce demands and need support to continue in their professional formation. However, researchers have found that some subgroups of students are at a particularly high-risk of leaving engineering. Among those subgroups of at-risk learners are nonbinary engineering students. Researchers know very little about factors supporting or hindering nonbinary students engineering professional formation. This project serves to help understand how these students leverage identity-specific strengths from their communities, known as community cultural wealth, to succeed in their academic careers. This novel, transformational EAGER proposal will explore their community cultural wealth—that is, for example, how these students sustain hopes and goals, successfully navigate their majors, receive support from family-style relationships, leverage their social network, transgress expectations and resist negative stereotypes and microaggressions—as a means to thrive in engineering. We will interview twenty nonbinary engineering students at various stages of their academic careers using narrative inquiry. Through this project, we aim to raise awareness of the unique assets of the nonbinary engineering community so that engineering students feel affirmed and heard, and engineering educators may design inclusive education practices and advocate on behalf of the nonbinary community in engineering. This project outcomes will result in the development of resources that can be shared to support the professional formation of nonbinary students, as well as the broader engineering student population. The purpose of this asset-based qualitative study is to investigate how nonbinary engineering students leverage their community cultural wealth to support their wellbeing, belonging, and persistence during their professional formation. We are guided by two research questions: 1) How do nonbinary engineering students access community cultural wealth within engineering and queer communities, and 2) how do nonbinary engineering students mobilize their cultural capital to support their wellbeing, sense of belonging and persistence? We will interview 20 engineering students at various stages of their professional formation using composite narrative inquiry and critical incident technique. By interviewing students at various stages of professional formation, we will explore how capital is accrued and how different forms of capital impact students’ persistence at differing stages of their identity development. Our findings will generate new knowledge about how nonbinary students draw upon their personal assets and those of the LGBTQ+ and ally communities during their professional formation. Nonbinary participants will benefit from being heard, affirmed, and seen throughout the interview process, and from reading the narratives of other nonbinary engineering students leveraging their assets to persist, belong and thrive in engineering. To reach students outside of the study, we will disseminate the composite narratives to LGBTQ+ STEM focused social media and professional organizations. 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": "15645", "attributes": { "award_id": "2449371", "title": "I-Corps: Translation Potential of a Handheld Standoff Photothermal Spectroscopy System for Real-time Indication of Viral Epidemics", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 31316, "first_name": "Jaime A.", "last_name": "Camelio", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-15", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32149, "first_name": "Thomas", "last_name": "Thundat", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 422, "ror": "", "name": "SUNY at Buffalo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "This I-Corps project is focused on the development of an innovative. non-invasive, diagnostic tool for viral infections. The technology is able to provide rapid, accurate, and real-time detection of influenza, respiratory syncytial virus, and COVID-19. The tool's portability ensures that it can be widely distributed, making it accessible in a variety of healthcare settings, including clinics, hospitals, and in remote areas with limited medical infrastructure. By enabling early and precise diagnosis, this tool can improve patient outcomes, reduce the spread of infectious diseases, and alleviate the burden on healthcare systems. Furthermore, its scalability and low manufacturing costs position it as a viable option for mass production and global distribution, addressing urgent public health needs. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a method to determine multiple pathological conditions simultaneously. The solution detects the infrared signatures produced by the resonant excitation of certain molecules using a tunable source. This approach achieves a limit of detection that is orders of magnitude higher than available nanosensors. By applying machine learning techniques to analyze the nanomechanical infrared response profile, multiple pathological conditions can be identified simultaneously. This device is capable of continuous miniaturization, making it portable and affordable for widespread deployment. 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": "15675", "attributes": { "award_id": "1R01EB037031-01", "title": "Point-of-care DNA diagnostics from raw samples", "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": 32518, "first_name": "KRISTIN HEDGEPATH", "last_name": "GILCHRIST", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-05", "end_date": "2029-03-31", "award_amount": 257807, "principal_investigator": { "id": 32519, "first_name": "Robert M", "last_name": "Cooper", "orcid": "", "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": "The proposed project will develop living biosensors for detecting and analyzing DNA at the single- base level, without requiring sample purification or any equipment. DNA is the prime information carrier for life, and DNA analysis provides valuable information for, e.g., diagnosing microbial infections or tracking disease outbreaks. Many techniques exist for detecting and analyzing DNA, but these generally require processing steps to extract and purify samples, and most require expensive equipment and significant training and expertise. This proposal will transfer that complexity into the biosensor itself, harnessing functions that evolved into living bacteria over billions of years to pull DNA out of raw samples, analyze it, and produce easily read output. The biosensors will pull in DNA using natural competence, and analyze it with single-base precision using their endogenous CRISPR-Cas system. Upon detecting a target sequence, the living biosensors will release thousands of signal molecules that can be detected using a lateral flow assay, similar to a consumer pregnancy or Covid-19 test. Several target DNA sequences will be used for demonstrations: urinary tract pathogens, E. coli, and Salmonella. The target uropathogens are difficult to diagnose with standard culture tests. Using single-base sequence analysis, the biosensors will subtype E. coli as likely pathogenic or likely commensal. A similar strategy will be employed to detect single-base mutations responsible for the majority of fluoroquinolone-resistant Salmonella isolates. DNA biosensing will be demonstrated in clinically relevant human samples, without the extensive purification required by other methods. The result will be a hybrid living biosensor / lateral flow assay that requires minimal sample preparation, produces rapid results, and can achieve single-base resolution. The biosensors developed in this project could find applications any time DNA monitoring is needed that is inexpensive, requires minimal sample preparation, equipment, and expertise, or takes place at the point of care. Examples include clinical diagnostics, monitoring disease outbreaks for public health, or environmental monitoring, with particular benefits where resources are limited.", "keywords": [ "Antibiotic Resistance", "Architecture", "Bacteria", "Base Sequence", "Binding", "Biological", "Biological Assay", "Biosensing Techniques", "Biosensor", "Blood", "Buffers", "COVID-19 test", "Clinic", "Clinical", "Clustered Regularly Interspaced Short Palindromic Repeats", "Colon", "Colorectal Neoplasms", "Competence", "Complex", "Coupled", "Custom", "DNA", "DNA Sequence", "DNA analysis", "Detection", "Diagnosis", "Diagnostic", "Disease Outbreaks", "Engineering", "Ensure", "Environment", "Environmental Monitoring", "Epitopes", "Equipment", "Escherichia coli", "Genetic", "Genomic DNA", "Goals", "Human", "Hybrids", "In Situ", "In Vitro", "Infection", "Lateral", "Life", "Medical", "Methods", "Monitor", "Mus", "Mutation", "Mutation Detection", "Oncogenic", "Output", "Pathogenicity", "Performance", "Pregnancy Tests", "Preparation", "Proteins", "Public Health", "Publishing", "RNA", "Rapid diagnostics", "Readability", "Reporter", "Resolution", "Resource-limited setting", "Resources", "Salmonella", "Salmonella enterica", "Sampling", "Scheme", "Science", "Sensitivity and Specificity", "Sequence Analysis", "Signal Transduction", "Signaling Molecule", "Source", "Specificity", "Sputum", "Synthetic Genes", "System", "Techniques", "Testing", "Time", "Training", "Urinary tract", "Urinary tract infection", "Urine", "Uropathogen", "Work", "base", "cancer cell", "clinical diagnostics", "clinically relevant", "cost", "diagnostic platform", "fluoroquinolone resistance", "improved", "in vivo", "interest", "lateral flow assay", "microbial", "nanoshell", "pathogen", "point of care", "point-of-care diagnostics", "screening", "tumor" ], "approved": true } }, { "type": "Grant", "id": "15673", "attributes": { "award_id": "1R01HL172872-01A1", "title": "Targeting Angiopoietin-like 4 (ANGPTL4) in Severe Community Acquired Pneumonia", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 32514, "first_name": "EMMANUEL FRANCK", "last_name": "MONGODIN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-04", "end_date": "2029-01-31", "award_amount": 827909, "principal_investigator": { "id": 32515, "first_name": "William A", "last_name": "Altemeier", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32516, "first_name": "Pavan Kumar", "last_name": "Bhatraju", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Community-acquired pneumonia (CAP) is a common cause of morbidity and mortality in hospitalized patients but therapeutics are limited. In response, identification of modifiable pathways to alter host response and improve outcomes in patients with severe CAP has been highlighted as a NHLBI research priority. Our research group has identified angiopoietin-like 4 (ANGPTL4) as a potential mediator in adverse outcomes in CAP from viral and bacterial pathogens. We have generated preliminary data in a discovery proteomic analysis of 5000 different plasma proteins. We found that ANGPTL4 was one of the top proteins associated with fewer ventilator free days and worse hospital mortality in severe CAP due to COVID-19. Next, in a multi-center cohort, we replicated these findings in COVID-19 that higher ANGPTL4 concentrations were associated with worse clinical outcomes, and obtained preliminary evidence that ANGPTL4 is also associated with outcomes in severe CAP due to bacteria3. We also have generated data that genetically targeting Angptl4 is protective in mice with severe influenza, a finding that is supported by pre-clinical data that inhibition of ANGPTL4 signaling through a monoclonal antibody is protective in viral pneumonia. In addition, independent research groups have also found that ANGPTL4 is associated with clinical outcomes in severe CAP. Together, these findings support our hypothesis that ANGPTL4 expression is a significant determinant of outcomes from CAP, independent of pathogen type, and that modulation can lead to improved clinical outcomes. To further examine this hypothesis, we will use complementary clinical and pre-clinical studies in the following aims. In Aim 1, we will determine the relationship between plasma ANGPTL4 levels and outcomes in a hospitalized population with varying severity at enrollment (acute care and ICU) and pathogen type (viral and bacterial). In Aim 2, we will infer causal relationships between ANGPTL4 concentrations and risk for pulmonary and extra- pulmonary organ dysfunction using a non-overlapping 2-sample Mendelian randomization genetic approach. In Aim 3, we will evaluate the role of ANGPTL4 in pre-clinical models of viral and bacterial pneumonia and determine the relative contributions of the proteolytically processed cANGPTL4 and nANGPTL4 peptides. The outstanding qualifications of our team in the fields of sepsis, community acquired pneumonia, molecular epidemiology, and pre-clinical models uniquely position us to deliver an integrated molecular view of host response in CAP that is not only responsive to the challenges in severe CAP care identified by global leaders, but could fundamentally alter paradigms of patient care in severe CAP. The long-term goals are to delineate the role of ANGPTL4 in severe CAP through understanding which clinical outcomes are most closely linked with ANGPTL4 levels through epidemiological and genetic causal inference analyses and to understand the cell of origin and relative contributions of different cleavage products of ANGPTL4 through pre-clinical studies.", "keywords": [ "ANGPTL4 gene", "Acute Renal Failure with Renal Papillary Necrosis", "American", "Bacteria", "Bacterial Pneumonia", "Biometry", "C-terminal", "COVID-19", "COVID-19 pandemic", "COVID-19 pneumonia", "Caring", "Cells", "Cessation of life", "Chest", "Clinical", "Clinical Research", "Clinical Trials", "Data", "Development", "Enrollment", "Epidemiology", "Follow-Up Studies", "Functional disorder", "Genetic", "Genetic Models", "Genotype", "Goals", "Health Care Costs", "Hospital Mortality", "Hospitalization", "Hour", "Human Genetics", "Immune response", "Infection", "Influenza", "Link", "Lung", "Measures", "Mechanical ventilation", "Mediator", "Mendelian randomization", "Metabolic Diseases", "Molecular", "Monoclonal Antibodies", "Morbidity - disease rate", "Mus", "N-terminal", "National Heart Lung and Blood Institute", "Organ", "Outcome", "Pathway interactions", "Patient Care", "Patients", "Peptides", "Permeability", "Plasma", "Plasma Proteins", "Pneumonia", "Population", "Positioning Attribute", "Pre-Clinical Model", "Preclinical data", "Process", "Protein Secretion", "Proteins", "Proteomics", "Pulmonary Inflammation", "Pulmonology", "Qualifying", "Research", "Research Priority", "Resolution", "Risk", "Role", "SARS-CoV-2 infection", "Sample Size", "Sampling", "Sepsis", "Severities", "Severity of illness", "Shock", "Signal Transduction", "Societies", "Source", "Streptococcus pneumoniae", "Testing", "Therapeutic", "Translational Research", "Variant", "Vascular Permeabilities", "Ventilator", "Viral", "Viral Pneumonia", "Virus", "acute care", "adverse outcome", "antimicrobial", "bacterial community", "biobank", "biological heterogeneity", "clinical heterogeneity", "clinical phenotype", "clinical translation", "cohort", "community acquired pneumonia", "epidemiological model", "experimental study", "genetic approach", "genetic epidemiology", "improved", "improved outcome", "influenza infection", "insight", "lipoprotein lipase", "molecular modeling", "mortality", "mouse model", "multidisciplinary", "pathogen", "pathogenic bacteria", "pathogenic virus", "patient population", "patient response", "pneumonia model", "pre-clinical", "preclinical study", "protein expression", "response" ], "approved": true } }, { "type": "Grant", "id": "15679", "attributes": { "award_id": "1R01HL176493-01", "title": "Pathogenic Mechanism and Therapeutic Approaches for Exercise Intolerance in Post-Acute Sequelae of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 32514, "first_name": "EMMANUEL FRANCK", "last_name": "MONGODIN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-01", "end_date": "2029-01-31", "award_amount": 633045, "principal_investigator": { "id": 32524, "first_name": "Michael G", "last_name": "Risbano", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32525, "first_name": "Lianghui", "last_name": "Zhang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 848, "ror": "", "name": "UNIVERSITY OF PITTSBURGH AT PITTSBURGH", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Post-acute sequelae of COVID-19 (PASC) is an emerging public health priority with up to 18% prevalence. Noteably, almost 30% patients diagnosed with PASC experence exercise intolerance. This activity limitation continues to negatively impact our workforce, and poses a persistent socialeconimic burden on our society. Our Post-Covid Recovery Clinic, a RECOVERY Vital site, has evaluated exercise intolerant PASC for nearly 4 years. We recently discovered pathophysiologic endotypes that contribute to exercise intolerance in PASC via invasive cardiopulmonary exercise testing (iCPET). Yet, the molecular drivers for this population remain elusive. Four- years after the onset of the pandemic we are left without PASC-defining biomarkers, or targeted therapeutics. Thus, it is crucial to investigate the interconnected molecular and pathophysiologic links in exercise intolerant PASC, a task uniquely within our team’s expertise. Angiotensin-converting enzyme 2 (ACE2) is not just an entry receptor for SARS-CoV-2 but also an enzyme with a protective function through regulation of the renin- angiotensin system. Studies have shown that a high level of plasma ACE2 is associated with an increased risk of SARS-CoV-2-related mortality. Our preliminary data showed that the catalytic activity of increased plasma ACE2 was significantly impaired in the exercise intolerant PASC patients, and closely correlated with reduced exercise capacity as measured by peak oxygen consumption evaluated during iCPET. Furthermore, to study the pathogenic mechanism of exercise intolerance in PASC, we established a novel PASC mouse model. In this model, we observed the persistence of the SARS-CoV-2 RNAs in lung microvascular ECs, impaired ACE2 activity, chronic pulmonary inflammation, along with a significant reduction in exercise capacity. Thus, we hypothesize that dysfunctional ACE2 shed from pulmonary ECs is a major driver for exercise intolerance in PASC and an engineered solube ACE2 with enhanced ACE2 activity will improve exercise capacity of PASC. To test our hypotheses, we will investigate the predictive value of ACE2 activity as a clinical biomarker and assess its association with exercise capacity over 12 months in PASC patients in Aim 1. We will define an engineered soluble ACE2 with enhanced ACE2 activity as an innovative therapeutic intervention to improve exercise capacity and vascular function in the PASC mouse model in Aim 2. Furthermore, we will explore the mechanism of ACE2 dysfunction shed from the pulmonary vasculature in Aim 3. If successful, we will identify a diagnostic and therapeutic paradigm urgently needed for PASC patients experiencing exercise intolerance, and remediate the deficient response to this global public health threat.", "keywords": [ "2019-nCoV", "ACE2", "Acute Lung Injury", "Adult", "Affect", "Binding", "Biological Markers", "Blood Vessels", "COVID-19", "COVID-19 mortality", "COVID-19 patient", "Cardiopulmonary", "Cell surface", "Characteristics", "Chronic", "Circulation", "Clinic", "Clinical assessments", "Data", "Diagnosis", "Diagnostic", "Disease Progression", "Disintegrins", "Endothelial Cells", "Endothelium", "Engineering", "Enzymes", "Exercise", "Exercise Test", "Fatigue", "Functional disorder", "Health", "Impairment", "Inflammation", "Knock-in", "Knockout Mice", "Left", "Link", "Long COVID", "Lung", "Measures", "Medicine", "Metalloproteases", "Modeling", "Molecular", "Outpatients", "Oxygen Consumption", "Pathogenicity", "Pathology", "Patients", "Peptides", "Plasma", "Population", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Predictive Value", "Prevalence", "Proteins", "Public Health", "Pulmonary Inflammation", "Questionnaires", "RNA", "Recovery", "Regulation", "Renin-Angiotensin System", "Risk", "SARS-CoV-2 infection", "Site", "Societies", "Symptoms", "Testing", "Therapeutic", "Therapeutic Intervention", "clinical biomarkers", "clinical infrastructure", "design", "dosage", "endothelial dysfunction", "exercise capacity", "exercise intolerance", "experience", "improved", "innovation", "knock-down", "lung microvascular endothelial cells", "mortality", "mouse model", "novel", "pandemic disease", "post SARS-CoV-2 infection", "post-COVID-19", "public health priorities", "receptor", "remediation", "research clinical testing", "response", "symptom cluster", "targeted treatment", "treatment optimization" ], "approved": true } }, { "type": "Grant", "id": "15648", "attributes": { "award_id": "2439345", "title": "CAREER: Intelligent Biomarker Analysis based on Wearable Distributed Computing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "CAREER: FACULTY EARLY CAR DEV" ], "program_reference_codes": [], "program_officials": [ { "id": 12587, "first_name": "Juan", "last_name": "Li", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 340, "ror": "", "name": "North Dakota State University Fargo", "address": "", "city": "", "state": "ND", "zip": "", "country": "United States", "approved": true } ] } ], "start_date": "2025-04-01", "end_date": null, "award_amount": 503930, "principal_investigator": { "id": 32151, "first_name": "Juan", "last_name": "Patarroyo", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1024, "ror": "", "name": "University of Puerto Rico Mayaguez", "address": "", "city": "", "state": "PR", "zip": "", "country": "United States", "approved": true }, "abstract": "Some of the challenges associated with wearable technologies are the limitation on computational power, battery capacity, data privacy, user interface design, and the need for seamless integration into user lifestyles without causing discomfort. These challenges limit the on-device implementation of machine learning methods, which are suitable for classifying and estimating medical conditions based on the biomarkers sensed by the wearable devices. This research addresses these problems by developing a scheme that distributes the computational load of machine-learning models across wearable devices. Results from this research contribute to deploying advanced health monitoring tools for in-home care of frail populations, such as post-COVID patients. This is aligned with the NSF mission to promote the progress of science and advance national health. The development of this project involves multidisciplinary efforts from computer science, bioengineering, and electrical engineering, as well as educational activities with the participation of students from underrepresented groups. This project focuses on developing a wearable sensor network scheme with distributed and interconnected computing capabilities. As an application case, the wearable computing sensor network is aimed at biomechanics analysis for frail populations. The research plan is geared toward creating an advanced scheme of wearable devices to improve power consumption, data privacy, and computational performance for advanced health monitoring and analysis. To fulfill the strict requirements of size, computational load, and energy consumption, a novel distributed machine learning architecture is designed and deployed on each wearable sensor using field programmable gate arrays. The deployed architecture is a simplified version of the parallel-computing architecture found in commercial graphics processing units, which have been demonstrated to be suitable for machine-learning applications. In addition, this architecture contains additional hardware components for estimating missing data, synchronization, and addressing communication errors between the devices. This project addresses realistic challenges in biomedical and wearable technologies research, including (i) segmenting and training machine learning models considering the nature of biomechanical data and wearable inertial sensors without affecting accuracy, (ii) modeling a lightweight computer architecture for performing distributed machine learning inference in real time, (iii) estimating detailed body motion dynamics using a reduced amount of inertial sensors, and (iv) integrating reliable and state-of-the-art data analytics environments for efficient real-time analysis and visualization. The education plan tackles three major areas: (i) research training and competitive experiences for graduate and undergraduate students in the areas of computer science, computer architecture, and health-related areas, (ii) course development in topics related to edge computing, real-time systems, and machine learning applications to healthcare, and (iii) outreach to K-12 students and professionals by the introduction of competitive activities. Most of the students and contributors for this project are Hispanic, and this project supports broader access to and training in cutting-edge research in computational applications. 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": "15646", "attributes": { "award_id": "2422986", "title": "PFI (MCA): Integration of Protein Engineering and Electrochemical Biosensors for Virus Detection", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Special Projects" ], "program_reference_codes": [], "program_officials": [ { "id": 2267, "first_name": "Samir M.", "last_name": "Iqbal", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-01", "end_date": null, "award_amount": 331189, "principal_investigator": { "id": 32150, "first_name": "Karin", "last_name": "Chumbimuni-Torres", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 173, "ror": "", "name": "The University of Central Florida Board of Trustees", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "This Partnerships for Innovation – Mid Career Advancement (PFI-MCA) project is focused on the development of a new, affordable technology to test for viruses with high accuracy. The project innovation is in the use of stable enzymes that can be stored at room temperature without requiring cold environments. The enzymes, combined with an electrochemical biosensor and microfluidics technology, will create a portable, affordable platform for virus detection in resource-limited environments. This technology can impact areas like health diagnostics, national security, and food safety. The research is multidisciplinary as it integrates chemistry, biology, and engineering. The project will give students hands-on experience in research scientific fields. The commercial impact of this technology will be important since it has potential to develop technology for virus detection that will be low cost and portable so it can be used anywhere and can supplement virus outbreak surveillance. This project will also translate the technology to manufacturing and commercialization. This project employs protein engineering to make stable enzymes that can be stored at room temperature without requiring cold environments. These enzymes are used to perform isothermal amplification of a virus fragment for posterior detection with electrochemical biosensors. By combining these two technologies, the project will develop a virus detection platform that works even in areas with limited resources, making it more accessible and cost-effective. The recent pandemic has shown the urgent need for affordable and quick virus detection methods. Currently, the most common virus detection method, Reverse Transcriptase Polymerase Chain Reaction, is expensive, requires special equipment and trained staff, and is mostly available in large labs, which makes it hard to use in areas with limit resources. This project aims to develop a more affordable and practical solution by using protein engineering to create stable, cost-effective enzymes that can be used with Nucleic Acid Sequence-Based Amplification technique at a single temperature. The enzymes, combined with an electrochemical biosensor and microfluidics technology, will create a portable, affordable platform for virus detection in resource-limited environments. 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": "15676", "attributes": { "award_id": "1F32HD116425-01A1", "title": "Developing a Biomimetic Lactating Mammary Lobe for Therapeutic Safety", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 32520, "first_name": "KATIE MARIE", "last_name": "VANCE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-01", "end_date": "2027-07-31", "award_amount": 76756, "principal_investigator": { "id": 32521, "first_name": "Amy H", "last_name": "Lee", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 210, "ror": "https://ror.org/042nb2s44", "name": "Massachusetts Institute of Technology", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "PROJECT SUMMARY. Breast milk is rich with bioactive components that are critical to an infant’s development. It is highly recommended that infants ingest breast milk; but, fluctuating maternal hormones and substandard post-parturition health directly mediate breast milk production. Maternal ingestion of small molecule drugs further compounds decreased breast milk synthesis and secretion, and adversely compromises breast milk quality. Although the majority of actively breastfeeding women consume medication or receive therapeutics, small drug molecule transport from maternal plasma to synthesized breast milk remains largely unknown. Important strides in understanding pharmacokinetics in milk-producing mammary glands have yet to occur because of the lack of engineered bioinspired mammary lobe systems that mimic complex in vivo signatures— topographical lobule microcurves, spiked levels of lactogenic hormones, cellular landscapes, and mechanically-driven lobe expansion and contraction. The objective of this proposal is to determine if our established microengineered mammary lobe system, which integrates key physiological characteristics, i.) faithfully mirrors multifactorial breast milk synthesis processes and ii.) could be employed as a versatile screening testbed for evaluating drug and therapeutic safety during lactation. The project is based on the central hypothesis that exogenous stimuli that reflect in vivo mechanisms, such as hormone levels, dynamic mechanical lobe stimulation, and passive transport of small drug molecules, will potentiate differential cellular landscape phenotypes and lead to unique content differences in engineered breast milk. This could develop a new in vitro preclinical model that promotes the cognizance of drugs or therapeutics that are safe to ingest or receive during lactation. We believe this contributes to improving important women’s health issues. Our hypothesis will be tested through the following two aims. Aim 1 will develop a 3D mammary lobe model and determine how in vivo relevant parameters alter physical and molecular mammary cell phenotypes, and regulate the secretion of important breast milk components. Aim 2 will investigate the pharmacokinetics of small molecule drugs or therapeutics that passively diffuse into the engineered breast milk. Nicotine or mRNA encoding for SARS-CoV-2 will serve as a model drug or therapeutic, respectively. We will pursue these aims using an innovative combination of analytical and adaptable techniques from engineering and biological sciences. These include the development of a scalable lobe model, by which the application of physiologically relevant stimuli and compartments can mimic breast milk synthesis and drug distribution. The engineering approaches that we leverage will develop foundational resources for the ongoing efforts and research revolving lactation and post-parturition health equity. The expected outcome of this work will highlight the importance of engineering new microsystems for in vivo mimicry. These platforms can facilitate clinical translation of rapid drug and therapeutic safety screening. The results will have a significant positive impact to women and will encourage the ongoing efforts to support women during their breastfeeding journey.", "keywords": [ "2019-nCoV", "3-Dimensional", "Affect", "Air", "Apical", "Biological Sciences", "Biomimetics", "Birth", "Breast Feeding", "COVID-19", "COVID-19 vaccine", "Carrier Proteins", "Caseins", "Cell Polarity", "Cell Proliferation", "Cell-Cell Adhesion", "Cells", "Characteristics", "Chemicals", "Circulation", "Complex", "Consumption", "Cultured Cells", "Cytoskeletal Proteins", "Development", "Diffuse", "Diffusion", "Drug Kinetics", "Drug Modelings", "Engineering", "Enzyme-Linked Immunosorbent Assay", "Excretory function", "Exhibits", "Exposure to", "Gland", "Goals", "Harvest", "Health", "High Pressure Liquid Chromatography", "Hormones", "Human Milk", "In Vitro", "Individual", "Infant", "Infant Development", "Ingestion", "Lactation", "Lipids", "Lobe", "Lobule", "Mammary gland", "Maternal health equity", "Mechanical Stimulation", "Mechanical Stress", "Mechanics", "Mediating", "Membrane", "Messenger RNA", "Milk Proteins", "Mission", "Modeling", "Molds", "Molecular", "Mothers", "Motion", "Nicotine", "Nutrient", "Outcome", "Oxytocin", "Perfusion", "Periodicity", "Pharmaceutical Preparations", "Phenotype", "Physiological", "Plasma", "Pre-Clinical Model", "Pregnancy", "Process", "Prolactin", "Protein Biosynthesis", "Protein Secretion", "Proteins", "Proteomics", "Public Health", "Pump", "RNA vaccine", "Recommendation", "Regulation", "Reporting", "Research", "Resources", "Safety", "Side", "Stains", "Stimulus", "Structure", "Surface", "System", "Techniques", "Testing", "Therapeutic", "Tight Junctions", "United States National Institutes of Health", "Variant", "Ventilator", "Woman", "Women&apos", "s Health", "Work", "clinical translation", "drug distribution", "drug testing", "extracellular vesicles", "health equity", "high throughput screening", "improved", "in vivo", "innovation", "interstitial", "lactogenesis", "lipidomics", "mammary", "maternal vaccination", "mechanical drive", "mechanical properties", "mechanical stimulus", "microsystems", "milk expression", "milk production", "milk secretion", "mimicry", "molecular phenotype", "nicotine use", "passive transport", "pre-clinical", "protein expression", "reconstitution", "screening", "secretory protein", "small molecule", "therapeutic evaluation" ], "approved": true } }, { "type": "Grant", "id": "15647", "attributes": { "award_id": "2525962", "title": "Conference: International Symposium on the Infectious Diseases of Bats:Fourth International Symposium on the Infectious Diseases of Bats (BatID 2025), Chicago, IL", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "Symbiosis Infection & Immunity" ], "program_reference_codes": [], "program_officials": [ { "id": 2558, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-04-01", "end_date": null, "award_amount": 14868, "principal_investigator": { "id": 26531, "first_name": "Cara", "last_name": "Brook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 289, "ror": "https://ror.org/024mw5h28", "name": "University of Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "This award supports the Fourth International Symposium on the Infectious Diseases of Bats (‘BatID 2025’), an international conference which brings together researchers from the disparate fields of virology, immunology, biochemistry, ecology, physiology, and genetics to investigate the role of bats as unique pathogen hosts. Bats are natural reservoir hosts for several high profile emerging human pathogens—including SARS-related coronaviruses, the likely precursors to the COVID-19 pandemic—yet they demonstrate limited pathology upon infection with viruses that cause extreme disease in non-bat (including human) hosts. Studying the mechanisms by which bats avoid disease from infection offers opportunities to translate bat-inspired immunological approaches into human disease therapeutics. This meeting offers opportunities for early career trainees, particularly graduate students and postdoctoral scholars, to share ideas and research findings with experts across this wide-ranging and interdisciplinary field. The meeting also aims to facilitate conversation between bat ecologists and conservationists with those engaged in more molecular approaches to understanding bat infectious disease to reconcile bats’ roles as major pathogen reservoirs. BatID 2025 is organized around three major conference objectives: (1) to disseminate research on the unique role of bats as pathogen hosts, (2) to foster collaborations and expand the field of bat infectious disease research, and (3) to identify a priority future research agenda in the study of bat infectious diseases. The first objective highlights this meeting’s utility as a research-sharing forum that welcomes representatives from disparate disciplines, who may not closely follow research outputs from other fields. The second objective seeks to turn this idea exchange into action by fostering collaborations among attendees, this year with a particular emphasis on recruiting participants and speakers who have not previously attended this meeting. Finally, the third objective seeks to organize the community around future research goals through an open discussion at the end of the two-day program. As an output, the Conference Organizing Committee will produce a peer-reviewed ‘Conference Proceedings’ article (to be led by junior researcher attendees) that shares these goals with the broader scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1392, "count": 13920 } } }