Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1405&sort=title
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=title", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=title", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1406&sort=title", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1404&sort=title" }, "data": [ { "type": "Grant", "id": "8008", "attributes": { "award_id": "1U01DA053903-01", "title": "Wastewater Assessment for Coronavirus in Kentucky: Implementing Enhanced Surveillance Technology", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 1834258, "principal_investigator": { "id": 22395, "first_name": "Scott M", "last_name": "Berry", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1162, "ror": "https://ror.org/02k3smh20", "name": "University of Kentucky", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1162, "ror": "https://ror.org/02k3smh20", "name": "University of Kentucky", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "Wastewater Assessment for Coronavirus in Kentucky – Implementing Enhanced Surveillance Technology Surveillance for SARS-CoV-2 is hindered by the availability of testing, particularly in remote and rural areas. Screening of wastewater for SARS-CoV-2 viral biomarkers offers a viable alternative to individual testing and it can identify communities and facilities that are at risk of becoming hotspots.Wastewater surveillance overcomes several limitations of clinical surveillance, such as the need for robust healthcare and laboratory infrastructure and the lack of representative and comprehensive testing within communities. Conventional wastewater surveillance takes samples from sewer systems or wastewater treatment facilities and uses a series of extraction steps prior to advanced PCR technology to quantitate the viral biomarker (RNA). This approach is time and resource-intensive, which limits its wide-scale application. Developing next generation technology to simplify wastewater RNA extraction and quantitation will make it feasible to use more broadly at facilities and in rural communities. The limited clinical testing for COVID-19 in rural Southeastern Kentucky hampers disease surveillance and prevents informed public action to mitigate and contain the spread of disease. Wastewater testing for SARS-CoV- 2 in these communities using field-friendly technology will provide important information to local authorities and citizens about the spread and trend of SARS-CoV-2 infection in their communities. Our project will accomplish two aims: 1) Develop next generation wastewater assessment technology and 2) Implement and evaluate the next generation wastewater assay. For Aim 1 we adapt technology invented by our team termed exclusion-based sample preparation (ESP) to simplify and improve RNA extraction from wastewater. We will pair ESP with loop-mediated isothermal amplification (LAMP) technology for RNA detection to create a sensitive, robust, and field-friendly platform for testing wastewater for SARS- CoV-2 RNA. We will compare the next generation assay with established techniques on metrics of sensitivity, specificity, and usability (e.g., assay time, number of assay steps). For Aim 2 we will first validate the next generation assay in the field at congregate living facilities in a side-by-side comparison with conventional wastewater surveillance. Next, building on existing relationships in Appalachian Kentucky, we will recruit and train a purposive group of wastewater treatment plant operators, watershed watch citizen scientists, and school science teachers to test wastewater in their communities and schools using the field-friendly next generation wastewater assay. Field results will be validated in the lab. A robust mixed methods evaluation using the RE-AIM framework will assess community perceptions of feasibility, acceptability, and utility of wastewater surveillance for SARS-CoV-2 and identify community measures taken in response to test results.", "keywords": [ "2019-nCoV", "Appalachian Region", "Biological Assay", "Biological Markers", "COVID-19 assay", "COVID-19 detection", "COVID-19 outbreak", "COVID-19 surveillance", "COVID-19 testing", "Characteristics", "Cities", "Clinical", "Cold Chains", "Communities", "Coronavirus", "Detection", "Disease", "Disease Surveillance", "Evaluation", "Exclusion", "Feces", "Goals", "Healthcare", "Heating", "Hour", "Individual", "Infection", "Infection prevention", "Infrastructure", "Kentucky", "Laboratories", "Measures", "Mediating", "Methods", "Molecular Biology Techniques", "Monitor", "Nursing Homes", "Perception", "Performance", "Persons", "Plants", "Population", "Preparation", "Prevention Measures", "Prisons", "RADx Radical", "RNA", "RNA Degradation", "Reach Effectiveness Adoption Implementation and Maintenance", "Resources", "Reverse Transcriptase Polymerase Chain Reaction", "Ribonucleases", "Risk", "Rural", "Rural Appalachia", "Rural Community", "Rural Population", "SARS-CoV-2 infection", "Sampling", "Schools", "Sensitivity and Specificity", "Series", "Side", "Signal Transduction", "Surveillance Methods", "Syncope", "System", "Techniques", "Technology", "Technology Assessment", "Test Result", "Testing", "Time", "Training", "United States", "United States National Institutes of Health", "Universities", "Viral", "Vulnerable Populations", "authority", "base", "citizen science", "community based participatory research", "experience", "field study", "high risk", "improved", "innovation", "isothermal amplification", "new technology", "next generation", "point of care", "prevent", "recruit", "research clinical testing", "response", "rural area", "rural setting", "sample collection", "science teacher", "trend", "usability", "wastewater samples", "wastewater sampling", "wastewater screening", "wastewater surveillance", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "6687", "attributes": { "award_id": "4U01DA053903-02", "title": "Wastewater Assessment for Coronavirus in Kentucky: Implementing Enhanced Surveillance Technology", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 1546299, "principal_investigator": { "id": 22395, "first_name": "Scott M", "last_name": "Berry", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1162, "ror": "https://ror.org/02k3smh20", "name": "University of Kentucky", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1162, "ror": "https://ror.org/02k3smh20", "name": "University of Kentucky", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "Wastewater Assessment for Coronavirus in Kentucky – Implementing Enhanced Surveillance Technology Surveillance for SARS-CoV-2 is hindered by the availability of testing, particularly in remote and rural areas. Screening of wastewater for SARS-CoV-2 viral biomarkers offers a viable alternative to individual testing and it can identify communities and facilities that are at risk of becoming hotspots.Wastewater surveillance overcomes several limitations of clinical surveillance, such as the need for robust healthcare and laboratory infrastructure and the lack of representative and comprehensive testing within communities. Conventional wastewater surveillance takes samples from sewer systems or wastewater treatment facilities and uses a series of extraction steps prior to advanced PCR technology to quantitate the viral biomarker (RNA). This approach is time and resource-intensive, which limits its wide-scale application. Developing next generation technology to simplify wastewater RNA extraction and quantitation will make it feasible to use more broadly at facilities and in rural communities. The limited clinical testing for COVID-19 in rural Southeastern Kentucky hampers disease surveillance and prevents informed public action to mitigate and contain the spread of disease. Wastewater testing for SARS-CoV- 2 in these communities using field-friendly technology will provide important information to local authorities and citizens about the spread and trend of SARS-CoV-2 infection in their communities. Our project will accomplish two aims: 1) Develop next generation wastewater assessment technology and 2) Implement and evaluate the next generation wastewater assay. For Aim 1 we adapt technology invented by our team termed exclusion-based sample preparation (ESP) to simplify and improve RNA extraction from wastewater. We will pair ESP with loop-mediated isothermal amplification (LAMP) technology for RNA detection to create a sensitive, robust, and field-friendly platform for testing wastewater for SARS- CoV-2 RNA. We will compare the next generation assay with established techniques on metrics of sensitivity, specificity, and usability (e.g., assay time, number of assay steps). For Aim 2 we will first validate the next generation assay in the field at congregate living facilities in a side-by-side comparison with conventional wastewater surveillance. Next, building on existing relationships in Appalachian Kentucky, we will recruit and train a purposive group of wastewater treatment plant operators, watershed watch citizen scientists, and school science teachers to test wastewater in their communities and schools using the field-friendly next generation wastewater assay. Field results will be validated in the lab. A robust mixed methods evaluation using the RE-AIM framework will assess community perceptions of feasibility, acceptability, and utility of wastewater surveillance for SARS-CoV-2 and identify community measures taken in response to test results.", "keywords": [ "2019-nCoV", "Appalachian Region", "Biological Assay", "Biological Markers", "COVID-19 assay", "COVID-19 detection", "COVID-19 outbreak", "COVID-19 surveillance", "COVID-19 testing", "Characteristics", "Cities", "Clinical", "Cold Chains", "Communities", "Coronavirus", "Detection", "Disease", "Disease Surveillance", "Evaluation", "Exclusion", "Feces", "Goals", "Healthcare", "Heating", "Hour", "Individual", "Infection", "Infection prevention", "Infrastructure", "Kentucky", "Laboratories", "Measures", "Mediating", "Methods", "Molecular Biology Techniques", "Monitor", "Nursing Homes", "Perception", "Performance", "Persons", "Plants", "Population", "Preparation", "Prevention Measures", "Prisons", "RADx Radical", "RNA", "RNA Degradation", "Reach Effectiveness Adoption Implementation and Maintenance", "Resources", "Reverse Transcriptase Polymerase Chain Reaction", "Ribonucleases", "Risk", "Rural", "Rural Appalachia", "Rural Community", "Rural Population", "SARS-CoV-2 infection", "Sampling", "Schools", "Sensitivity and Specificity", "Series", "Side", "Signal Transduction", "Surveillance Methods", "Syncope", "System", "Techniques", "Technology", "Technology Assessment", "Test Result", "Testing", "Time", "Training", "United States", "United States National Institutes of Health", "Universities", "Viral", "Vulnerable Populations", "authority", "base", "citizen science", "community based participatory research", "experience", "field study", "high risk", "improved", "innovation", "isothermal amplification", "new technology", "next generation", "point of care", "prevent", "recruit", "research clinical testing", "response", "rural area", "rural setting", "sample collection", "science teacher", "trend", "usability", "wastewater samples", "wastewater sampling", "wastewater screening", "wastewater surveillance", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "5342", "attributes": { "award_id": "1R43AI170537-01", "title": "Wastewater data integration and modelling to accurately predict community and organizational outbreaks due to viral pathogens", "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": 18735, "first_name": "MICHAEL JOHN", "last_name": "Cooper", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-11", "end_date": "2023-04-30", "award_amount": 259613, "principal_investigator": { "id": 18736, "first_name": "Nathan L", "last_name": "Tintle", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 904, "ror": "", "name": "SUPERIOR STATISTICAL RESEARCH, LLC", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "Project Summary. The COVID-19 pandemic has magnified the need for enhanced ability to accurately anticipate future outbreaks due to novel and endemic viral pathogens. Without systematic surveillance, the ability to head off outbreaks before they occur is challenging: the data from positive human test results is often too late to prevent a major outbreak from occurring, despite substantial lockdown efforts. The key reason for this delay is that people are infectious for days before (and if) they are diagnosed positive. We can no longer rely on population-based testing, which (a) is delayed; (b) is non-random and expensive, exacerbating well- known and understood health disparities; and (c) relies on highly accurate, widely distributed test availability and use. Over the last fourteen months, our team of affiliated scientists has developed and implemented a wastewater-sampling approach to monitor for COVID-19 and other viral pathogens. Our approach utilizes unique genomic signatures of SARS-CoV-2 (the virus that causes COVID-19) to detect this pathogen in wastewater, providing inexpensive and unbiased real-time data on COVID-19 infections in communities and organizations. Our group has begun to contract with municipalities, academic entities and large manufacturing companies to provide real-time, unbiased data on the presence of COVID-19. Currently, however, wastewater COVID-19 data has primarily been used solely to determine the presence/absence of SARS-CoV-2 in samples. We see a highly innovative and impactful opportunity to leverage these data further to anticipate the timing, location, and severity of future outbreaks from SARS-CoV-2 and other novel and endemic viral pathogens. The Superior Statistical Research (SSR) R&D team is an internationally recognized group of wastewater and public health experts with cross-cutting expertise in statistics, data analysis, modelling, computing, wastewater monitoring, and the ability to translate wastewater and health information into actionable steps for organizations and communities. To address this opportunity, we propose a Phase I proof- of-concept SBIR project with two Aims. First, we will demonstrate that it is possible to anticipate locations and organizations with future outbreaks of COVID-19 with significant lead time. Second, we will demonstrate how model predictions can be optimized to be useful for municipalities and organizations. Feasibility will be determined by having models with excellent predictive ability (R2>0.90) (Aim 1) and by demonstrating the profitability of the commercialization pathway (Aim 2). Phase I feasibility will allow us to extend modelling capabilities beyond SARS-CoV-2 to other viral pathogens (e.g., influenza, norovirus, HIV): expanding wastewater testing capabilities for these additional pathogens, and further roll-out and improvement of the machine-learning/modelling effort in Phase II. Ultimately, we will have a full-service commercial set of predictive models (Phase III) that can be combined with wastewater-monitoring programs at the community and organizational level, leading to dramatic reductions in viral disease outbreaks.", "keywords": [ "2019-nCoV", "Address", "Biological", "COVID-19", "COVID-19 diagnosis", "COVID-19 monitoring", "COVID-19 outbreak", "COVID-19 pandemic", "Cessation of life", "Communities", "Complication", "Consult", "Contracts", "Cost Savings", "Data", "Data Analyses", "Data Science", "Diagnosis", "Disease", "Disease Outbreaks", "Economics", "Effectiveness", "Epidemic", "Future", "Growth", "HIV", "Head", "Health", "Hospitalization", "Human", "Influenza", "International", "Knowledge", "Lead", "Life", "Location", "Michigan", "Modeling", "Municipalities", "Norovirus", "Pathway interactions", "Persons", "Phase", "Population", "Prevalence", "Public Health", "Recording of previous events", "Reporting", "Research", "SARS-CoV-2 infection", "SARS-CoV-2 variant", "Sampling", "Scientist", "Services", "Severities", "Small Business Innovation Research Grant", "Statistical Models", "Techniques", "Test Result", "Testing", "Time", "Translating", "Trust", "Variant", "Viral", "Virus", "Virus Diseases", "Work", "base", "commercialization", "community organizations", "cost", "data integration", "data modeling", "genomic signature", "health care availability", "health disparity", "high reward", "high risk", "improved", "innovation", "interest", "machine learning method", "machine learning model", "next generation", "novel", "pathogen", "pathogenic virus", "policy recommendation", "poor communities", "population based", "predictive modeling", "prevent", "programs", "research and development", "statistics", "uptake", "wastewater monitoring", "wastewater sampling", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "8063", "attributes": { "award_id": "4U01DA053893-02", "title": "Wastewater Detection of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 1962927, "principal_investigator": { "id": 23959, "first_name": "Jeff", "last_name": "Wenzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "When faced with a pandemic such as SARS-Coronavirus-2 (SAR-CoV-2), the virus responsible for COVID-19, timely risk assessment and action are required to prevent public health impacts to entire communities. Because infected individuals may not have access to testing or may be asymptomatic and contraction can mean death, a proactive approach to detect the virus is needed to develop public health strategy to mitigate virus spread. Recent studies have detected SAR-CoV-2 genetic material in sewage and demonstrate a positive correlation between the concentration of viral markers and reported cases1-5. The Coronavirus Sewershed Surveillance Project (CSSP) is a collaborative effort to monitor sewersheds for genetic indicators of COVID-19 in wastewater to provide additional, population-level information about virus circulation that is not captured by clinical testing. Untreated wastewater (influent) samples are screened weekly from select sewersheds and targeted micro-sewersheds for detection and “true” prevalence. Congregate facilities provide unique opportunities for study because they are controlled populations where the precise number and timing of infections can be defined. Our team will utilize detailed monitoring of congregate facilities to define the precise per patient contribution and longevity of SARS-COV-2 RNA to wastewater by 1) increasing the number of facilities tested, 2) altering the frequency at which samples are collected, and 3) comparing sewershed data collected to clinical patient case data. Although SARS-COV-2 contribution/patient varies among communities, there have been clear outlier communities that produce little or no genetic material in the wastewater despite the presence of known outbreaks. The reason for this lost signal is not known, so our team will define factors that contribute to SARS- COV-2 signal suppression in wastewater by 1) defining the physical nature of the genetic material in the sewershed to better understand the types of factors that could suppress signal, 2) expanding testing within sewersheds with suppressed signal as well as from additional facilities with similar population and industry demographics as those with suppressed signal to narrow the sources of signal suppression, 3) performing exhaustive chemical characterization comparing wastewater from locations that are suppressed to those that are not to identify candidate compounds that could be causing suppression, and 4) obtaining or generating candidate inhibitors and test their ability to suppress signal from viral genetic material in a controlled experimental setting.", "keywords": [ "2019-nCoV", "Affect", "Biochemical", "Blood Circulation", "COVID-19", "COVID-19 detection", "COVID-19 patient", "COVID-19 testing", "Cessation of life", "Characteristics", "Chemicals", "Clinical", "Collection", "Communities", "Coronavirus", "Data", "Data Set", "Deposition", "Detection", "Disease Outbreaks", "Failure", "Frequencies", "Genetic", "Genetic Materials", "Health", "Immunology", "Individual", "Industry", "Infection", "Infrastructure", "Knowledge", "Laboratories", "Lead", "Location", "Longevity", "Longitudinal Studies", "Measurement", "Methods", "Microbiology", "Missouri", "Molecular", "Monitor", "Municipalities", "Natural Resources", "Nature", "Patients", "Population", "Prevalence", "Property", "Public Health", "RNA", "Recovery", "Reporting", "Research Personnel", "Risk Assessment", "SARS-CoV-2 infection", "Sampling", "Services", "Severities", "Sewage", "Signal Transduction", "Source", "Techniques", "Testing", "Time", "Universities", "Viral", "Viral Markers", "Virus", "demographics", "environmental transport", "exhaustion", "field study", "health record", "individual patient", "inhibitor/antagonist", "novel coronavirus", "pandemic disease", "prevent", "research clinical testing", "trend", "virus genetics", "wastewater samples", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "8064", "attributes": { "award_id": "1U01DA053893-01", "title": "Wastewater Detection of COVID-19", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "NIH Office of the Director" ], "program_reference_codes": [], "program_officials": [ { "id": 22394, "first_name": "Tamara", "last_name": "Slipchenko", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-01-01", "end_date": "2023-05-31", "award_amount": 2000000, "principal_investigator": { "id": 23959, "first_name": "Jeff", "last_name": "Wenzel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1668, "ror": "", "name": "MISSOURI STATE DEPT/ HEALTH & SENIOR SRV", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "When faced with a pandemic such as SARS-Coronavirus-2 (SAR-CoV-2), the virus responsible for COVID-19, timely risk assessment and action are required to prevent public health impacts to entire communities. Because infected individuals may not have access to testing or may be asymptomatic and contraction can mean death, a proactive approach to detect the virus is needed to develop public health strategy to mitigate virus spread. Recent studies have detected SAR-CoV-2 genetic material in sewage and demonstrate a positive correlation between the concentration of viral markers and reported cases1-5. The Coronavirus Sewershed Surveillance Project (CSSP) is a collaborative effort to monitor sewersheds for genetic indicators of COVID-19 in wastewater to provide additional, population-level information about virus circulation that is not captured by clinical testing. Untreated wastewater (influent) samples are screened weekly from select sewersheds and targeted micro-sewersheds for detection and “true” prevalence. Congregate facilities provide unique opportunities for study because they are controlled populations where the precise number and timing of infections can be defined. Our team will utilize detailed monitoring of congregate facilities to define the precise per patient contribution and longevity of SARS-COV-2 RNA to wastewater by 1) increasing the number of facilities tested, 2) altering the frequency at which samples are collected, and 3) comparing sewershed data collected to clinical patient case data. Although SARS-COV-2 contribution/patient varies among communities, there have been clear outlier communities that produce little or no genetic material in the wastewater despite the presence of known outbreaks. The reason for this lost signal is not known, so our team will define factors that contribute to SARS- COV-2 signal suppression in wastewater by 1) defining the physical nature of the genetic material in the sewershed to better understand the types of factors that could suppress signal, 2) expanding testing within sewersheds with suppressed signal as well as from additional facilities with similar population and industry demographics as those with suppressed signal to narrow the sources of signal suppression, 3) performing exhaustive chemical characterization comparing wastewater from locations that are suppressed to those that are not to identify candidate compounds that could be causing suppression, and 4) obtaining or generating candidate inhibitors and test their ability to suppress signal from viral genetic material in a controlled experimental setting.", "keywords": [ "2019-nCoV", "Affect", "Biochemical", "Blood Circulation", "COVID-19", "COVID-19 detection", "COVID-19 patient", "COVID-19 testing", "Cessation of life", "Characteristics", "Chemicals", "Clinical", "Collection", "Communities", "Coronavirus", "Data", "Data Set", "Deposition", "Detection", "Disease Outbreaks", "Failure", "Frequencies", "Genetic", "Genetic Materials", "Health", "Immunology", "Individual", "Industry", "Infection", "Infrastructure", "Knowledge", "Laboratories", "Lead", "Location", "Longevity", "Longitudinal Studies", "Measurement", "Methods", "Microbiology", "Missouri", "Molecular", "Monitor", "Municipalities", "Natural Resources", "Nature", "Patients", "Population", "Prevalence", "Property", "Public Health", "RNA", "Recovery", "Reporting", "Research Personnel", "Risk Assessment", "SARS-CoV-2 infection", "Sampling", "Services", "Severities", "Sewage", "Signal Transduction", "Source", "Techniques", "Testing", "Time", "Universities", "Viral", "Viral Markers", "Virus", "demographics", "environmental transport", "exhaustion", "field study", "health record", "individual patient", "inhibitor/antagonist", "novel coronavirus", "pandemic disease", "prevent", "research clinical testing", "trend", "virus genetics", "wastewater samples", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "15462", "attributes": { "award_id": "5R01DA059394-02", "title": "Wastewater Sampling: A New Tool to Accelerate Ending the HIV Epidemic", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 21884, "first_name": "PETER", "last_name": "HARTSOCK", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-15", "end_date": "2028-07-31", "award_amount": 761163, "principal_investigator": { "id": 28207, "first_name": "Thomas P", "last_name": "Giordano", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 566, "ror": "https://ror.org/02pttbw34", "name": "Baylor College of Medicine", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Current estimates are that 57% of persons in the U.S. with HIV are suppressed, leaving 43% unsuppressed, including about 13% who are undiagnosed and 30% who have been diagnosed but are not currently suppressed. Identifying persons with unsuppressed HIV to link them to care and antiretroviral treatment (ART) is critical for improving health and reducing new infections. New epidemiologic tools that identify in real time communities with high amounts of circulating HIV may enhance efforts to reduce HIV transmission and substantially contribute to ending the HIV epidemic. During the SARS-CoV-2 (SCV2) pandemic, we and others used “wastewater environmental virology” to monitor and respond to COVID. It uses viral capture and PCR detection of viral nucleic acid from wastewater collection sites to detect, quantify, and predict total SCV2 activity in time. We built a robust and mature wastewater sampling program for the Houston region that includes weekly assessment of 39 wastewater collection sites covering about 4 million residents. Our recent preliminary data demonstrate that HIV is detected in wastewater. Wastewater testing is unbiased, comprehensive, real-time, quantitative, and not influenced by access to health care, stigma or denial. We hypothesize that our pioneering wastewater sampling program can be applied as a powerful new tool to identify geographic areas with a high active burden of HIV, reflecting substantial numbers of people with undiagnosed or untreated HIV infection. Resources can then be mobilized to these communities to enhance HIV outreach, testing, prevention and linkage to care. The specifics aims are: 1) To develop a sensitive, reproducible, and streamlined wastewater HIV detection pipeline; 2) To develop epidemiologic models incorporating data from wastewater sampling as a novel and informative parameter along with routine surveillance data on HIV diagnoses and population data; 3) To characterize and incorporate stakeholder preferences, priorities, and recommendations for engaging key community stakeholders in the HIV wastewater sampling program with consideration to the ethical, legal, social, and cultural contexts of individuals living in target neighborhoods; 4) To determine if delivering proven public health interventions to neighborhoods as directed by wastewater data can reduce the wastewater viral load and increase HIV diagnoses in those neighborhoods. This research will enhance the Respond pillar of the End the HIV Epidemic (EHE) strategy. It will leverage and strengthen partnerships between the researchers and the Houston Health Department, the regional public health authority overseeing many EHE programs in Houston. The work in Houston, a high priority EHE region, will result in: (i) the US’s only HIV wastewater sampling program, (ii) epidemiologic models enhanced with wastewater data that identify unmet testing and treatment needs, (iii) community-informed and ethically appropriate, real-time public health monitoring that reduces the number of people with unsuppressed HIV, and (iv) tools and expertise that can be disseminated to other areas.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "12304", "attributes": { "award_id": "1R01DA059394-01A1", "title": "Wastewater Sampling: A New Tool to Accelerate Ending the HIV Epidemic", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 21884, "first_name": "PETER", "last_name": "HARTSOCK", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-09-15", "end_date": "2028-07-31", "award_amount": 776846, "principal_investigator": { "id": 28207, "first_name": "Thomas P", "last_name": "Giordano", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 566, "ror": "https://ror.org/02pttbw34", "name": "Baylor College of Medicine", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Current estimates are that 57% of persons in the U.S. with HIV are suppressed, leaving 43% unsuppressed, including about 13% who are undiagnosed and 30% who have been diagnosed but are not currently suppressed. Identifying persons with unsuppressed HIV to link them to care and antiretroviral treatment (ART) is critical for improving health and reducing new infections. New epidemiologic tools that identify in real time communities with high amounts of circulating HIV may enhance efforts to reduce HIV transmission and substantially contribute to ending the HIV epidemic. During the SARS-CoV-2 (SCV2) pandemic, we and others used “wastewater environmental virology” to monitor and respond to COVID. It uses viral capture and PCR detection of viral nucleic acid from wastewater collection sites to detect, quantify, and predict total SCV2 activity in time. We built a robust and mature wastewater sampling program for the Houston region that includes weekly assessment of 39 wastewater collection sites covering about 4 million residents. Our recent preliminary data demonstrate that HIV is detected in wastewater. Wastewater testing is unbiased, comprehensive, real-time, quantitative, and not influenced by access to health care, stigma or denial. We hypothesize that our pioneering wastewater sampling program can be applied as a powerful new tool to identify geographic areas with a high active burden of HIV, reflecting substantial numbers of people with undiagnosed or untreated HIV infection. Resources can then be mobilized to these communities to enhance HIV outreach, testing, prevention and linkage to care. The specifics aims are: 1) To develop a sensitive, reproducible, and streamlined wastewater HIV detection pipeline; 2) To develop epidemiologic models incorporating data from wastewater sampling as a novel and informative parameter along with routine surveillance data on HIV diagnoses and population data; 3) To characterize and incorporate stakeholder preferences, priorities, and recommendations for engaging key community stakeholders in the HIV wastewater sampling program with consideration to the ethical, legal, social, and cultural contexts of individuals living in target neighborhoods; 4) To determine if delivering proven public health interventions to neighborhoods as directed by wastewater data can reduce the wastewater viral load and increase HIV diagnoses in those neighborhoods. This research will enhance the Respond pillar of the End the HIV Epidemic (EHE) strategy. It will leverage and strengthen partnerships between the researchers and the Houston Health Department, the regional public health authority overseeing many EHE programs in Houston. The work in Houston, a high priority EHE region, will result in: (i) the US’s only HIV wastewater sampling program, (ii) epidemiologic models enhanced with wastewater data that identify unmet testing and treatment needs, (iii) community-informed and ethically appropriate, real-time public health monitoring that reduces the number of people with unsuppressed HIV, and (iv) tools and expertise that can be disseminated to other areas.", "keywords": [ "2019-nCoV", "Acceleration", "Area", "Authorization documentation", "Bioethics Consultants", "COVID-19", "COVID-19 monitoring", "COVID-19 outbreak", "COVID-19 pandemic", "Caring", "Clinical Data", "Collection", "Communities", "Complex", "Data", "Data Reporting", "Detection", "Diagnosis", "Diagnostic", "Diagnostic tests", "Disease Outbreaks", "Epidemic", "Epidemiologist", "Epidemiology", "Ethics", "Geographic Locations", "Geography", "HIV", "HIV Infections", "HIV diagnosis", "Health", "Human", "Human immunodeficiency virus test", "Individual", "Infection", "Laboratories", "Link", "Methods", "Monitor", "Neighborhoods", "Nose", "Nucleic Acids", "Periodicals", "Persons", "Physicians", "Plants", "Poliomyelitis", "Population", "Prevention", "Public Health", "Recommendation", "Reporting", "Reproducibility", "Research", "Research Personnel", "Resolution", "Resources", "Sewage", "Sexual Partners", "Signal Transduction", "Site", "Testing", "Time", "Viral", "Viral Load result", "Work", "antiretroviral therapy", "authority", "coronavirus disease", "epidemiological model", "ethical legal and social implication", "experience", "health care availability", "improved", "novel", "novel strategies", "outreach", "predictive modeling", "preference", "programs", "public health intervention", "routine care", "sewage treatment", "social stigma", "success", "surveillance data", "tool", "transmission process", "viral detection", "virology", "wastewater sampling", "wastewater testing" ], "approved": true } }, { "type": "Grant", "id": "14745", "attributes": { "award_id": "1I21RX004647-01A1", "title": "Water-based Activity to Enhance Recovery in Long COVID", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [], "program_reference_codes": [], "program_officials": [], "start_date": "2024-01-01", "end_date": "2026-09-30", "award_amount": null, "principal_investigator": { "id": 31435, "first_name": "Jennifer Kaci", "last_name": "Fairchild", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1497, "ror": "", "name": "VETERANS ADMIN PALO ALTO HEALTH CARE SYS", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The primary research question of the proposed study is this: Is a water-based exercise + cognitive training (WATER+CT) intervention for Veterans with neurological manifestations of long-COVID feasible? We address the need to enhance recovery in this vulnerable population through an innovative multi-component training program that has successfully been used in other cognitively impaired populations. The aims of the proposed research include: 1) demonstrate feasibility as shown by good recruitment and retention rates and stakeholder ratings; 2) evaluate appropriateness of suggested inclusion and exclusion criteria; 3) evaluate acceptability of water-based physical exercise + cognitive training interventions; 4) assess ability of selected outcome measurement techniques to determine the efficacy of water-based physical exercise + cognitive training; and 5) examine outcome “moderator” and “mediator” measurement techniques. These aims will be tested in a single-blind randomized controlled pilot trial that will establish the feasibility of WATER+CT. This trial will include 50 Veterans, age 18 – 89, experiencing neurological manifestations of long-COVID, with half randomized to WATER+CT and half to usual care. WATER+CT consists of two- phases: 1) an exercise training phase and 2) a cognitive training phase. The exercise training (i.e., WATER) consists of a six-month long individualized exercise program of water-based exercises. During this phase, Veterans will come to thrice-weekly group sessions at Aquatic Therapy Center at VA Palo Alto Health Care System (VAPAHCS). After completion of the exercise program, Veterans will begin classroom-based cognitive training at VAPAHCS for up to two months. The CT is based on an efficacious training program that is structured around two components, pre-training, and mnemonic training, both of which have been used successfully in persons with cognitive impairment. Veterans randomized to the UC control condition will receive educational materials about brain health in addition to their usual care, which is the care they would typically receive in the VA. Assessments of adherence will be administered throughout treatment and measures of feasibility will be completed post-treatment. Participants will complete a variety of neuropsychological measures taping into areas of cognition such as attention, executive functioning, and memory. Participants will also undergo physical fitness assessments including a 6-minute walk test and an exercise treadmill test. To study possible predictors of response to treatment, we will also collect physiological (VO2 max), biological (inflammatory markers and BDNF plasma levels), and genetic data (APOE and BDNF genotypes) from these participants. We hope to provide initial evidence of the feasibility of a water-based exercise training + cognitive training program and provide foundational support for a future VA Merit application targeting enhanced recovery in Veterans with neurological manifestations of long-COVID.", "keywords": [ "2019-nCoV", "Address", "Adherence", "Adult", "Adverse event", "Aftercare", "Age", "Area", "Attention", "Biological", "Brain-Derived Neurotrophic Factor", "COVID-19", "COVID-19 pandemic", "COVID-19 survivors", "Caring", "Clinical Trials", "Cognition", "Cognitive", "Combined Modality Therapy", "Consensus", "Data", "Development", "Educational Intervention", "Educational Materials", "Effectiveness", "Elderly", "Event", "Exclusion Criteria", "Exercise", "Face", "Fatigue", "Future", "Genetic", "Genotype", "Guidelines", "Health", "Healthcare Systems", "Impaired cognition", "Impairment", "Infection", "Intervention", "Long COVID", "Measurement", "Measures", "Mediator", "Memory", "Memory impairment", "Mental Health", "Minority", "Modeling", "Neurologic", "Neurologic Symptoms", "Neuropsychology", "Occupational", "Outcome", "Outcome Measure", "Participant", "Patients", "Persons", "Phase", "Physical Exercise", "Physical Fitness", "Physical Medicine", "Physical Rehabilitation", "Physiological", "Pilot Projects", "Plasma", "Population", "Provider", "Quality of life", "Randomized", "Randomized Controlled Trials", "Recovery", "Reporting", "Research", "Sampling", "Short-Term Memory", "Signal Transduction", "Single-Blind Study", "Structure", "Supervision", "Symptoms", "Techniques", "Testing", "Time", "Training", "Training Programs", "Treadmill Tests", "VO2max", "Veterans", "Virus", "Vulnerable Populations", "Walking", "Water", "Waxes", "Work", "brain fog", "brain health", "clinically relevant", "cognitive function", "cognitive training", "cohort", "debilitating symptom", "effective intervention", "effective therapy", "effectiveness evaluation", "efficacy evaluation", "executive function", "exercise program", "exercise training", "experience", "improved", "inclusion criteria", "inflammatory marker", "innovation", "long term consequences of COVID-19", "mild cognitive impairment", "multi-component intervention", "multimodality", "pilot trial", "predicting response", "processing speed", "programs", "recruit", "retention rate", "success", "treatment as usual", "treatment response" ], "approved": true } }, { "type": "Grant", "id": "13852", "attributes": { "award_id": "2115637", "title": "WaterMarks: An art/science framework for community-engaged learning around water and water management in an urban area", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "AISL" ], "program_reference_codes": [], "program_officials": [ { "id": 4115, "first_name": "Ellen", "last_name": "McCallie", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 2818705, "principal_investigator": { "id": 30216, "first_name": "Mary", "last_name": "Miss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30216, "first_name": "Mary", "last_name": "Miss", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 30217, "first_name": "Donnelley", "last_name": "Hayde", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 30218, "first_name": "Woonsup", "last_name": "Choi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 587, "ror": "", "name": "University of Wisconsin-Milwaukee", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Milwaukee has established itself as a leader in water management and technology, hosting a widely recognized cluster of industrial, governmental, nonprofit, and academic activity focused on freshwater. At the same time, Milwaukee faces a wide range of challenges with freshwater, some unique to the region and others common to cities throughout the country. These challenges include vulnerability to flooding and combined sewer overflows after heavy rainfall, biological and pharmaceutical contamination in surface water, lead in drinking water infrastructure, and inequity in access to beaches and other recreational water amenities. As do other cities, Milwaukee grapples with the challenges for urban water imposed by global climate change, including changing patterns of precipitation and drought. These problems are further complicated by Milwaukee’s acute racial and economic residential segregation. With a population of approximately 595,000, embedded within a metropolitan area of over 1.5 million, Milwaukee remains one of the country’s most segregated cities. There is increasing urgency to engage the public—and especially those who are most vulnerable to environmental impacts—more deeply in the stewardship of urban water and in the task of creating sustainable urban futures. The primary goal of this four-year project is to foster community-engaged learning and environmental stewardship by developing a framework that integrates art with Science, Technology, Engineering, and Mathematics (STEM) experiences along with geography, water management, and social science. Synergies between STEM learning and the arts suggest that collaborations among artists, scientists, and communities can open up ways to bring informal learning about the science of sustainability to communities. Project activities include artist/scientist/community member-led Walks, which are designed to engage multi-generational participants both from the neighborhoods and from across the city, in considering the conditions, characteristics, histories, and ecosystems of neighborhoods. Walks are expanded upon in Workshops with local residents, scientists/experts, and other stakeholders, and include exploring current water-related environmental challenges and proposing solutions. The Workshops draw on diverse perspectives, including lived experience, scientific knowledge, and policy expertise. Art Projects created by local artists amplify community engagement with the topics, including programming for teens and young adults. A website, and free Wi-Fi integrated into various Marker sites around the city, encourage users to pursue self-guided learning to explore the water systems and issues facing surrounding neighborhoods. Programming focuses primarily in Milwaukee’s predominantly African American near North Side and the predominantly Latinx/Hispanic near South Side. Many neighborhoods in these sections are vulnerable to such problems as frequent flooding, lead contamination in drinking water, inequities in safety and maintenance of green space, and less access to Lake Michigan, the city’s primary natural resource and recreational amenity.<br/><br/>The WaterMarks project advances informal STEM learning in at least two ways. First, while the WaterMarks project is designed to fit Milwaukee, the project includes development of an adaptable implementation guide. The guide is designed so that other cities can modify and employ its inclusive structure, programming, and process of collaboration among artists, scientists, partner organizations, and residents to promote citywide civic engagement in urban sustainability through the combination of informal STEM learning and public art. Second, through evaluation and research, the project will build a theoretical model for the relationships among science learning, engagement with the arts, and the distinctive contexts of different neighborhoods within an urban social-ecological system. Evaluation foci include: How does the implementation of WaterMarks support positive outcomes for the project’s communities and the development of an adaptable model for city-scale informal science learning about urban environments? 2. To what extent do the type and degree of outcome-related change experienced by participating community residents vary across and/or between project sites? What factors, if any, appear to be linked to these changes? 3. To what extent and in what ways do the activities of the WaterMarks projects appear to have in situ effects related to the experience of place at project sites? The project’s research questions include: 1. How does participation in Walks focused on visual artistic activities affect outcomes and experiences of informal STEM learning about urban water systems? 2. How do outcomes and experiences of informal STEM learning vary across different urban water topics, participants from different demographic groups, and contrasting sociocultural and biophysical contexts?<br/><br/>This Innovations in Development project is led by the University of Wisconsin-Milwaukee (UWM), in collaboration with City as Living Laboratory (CALL) and the COSI Center for Research and Evaluation. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to (a) advance new approaches to and evidence-based understanding of the design and development of STEM learning in informal environments; (b) provide multiple pathways for broadening access to and engagement in STEM learning experiences; (c) advance innovative research on and assessment of STEM learning in informal environments; and (d) engage the public of all ages in learning STEM in informal environments.<br/><br/>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": "9069", "attributes": { "award_id": "3UM1AI069419-14S1", "title": "WCM-Rutgers NJMS CTU Supplement for COVID Testing", "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": 24868, "first_name": "Teri L.", "last_name": "Greenfield", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-16", "end_date": "2021-11-30", "award_amount": 300000, "principal_investigator": { "id": 24869, "first_name": "ROY M.", "last_name": "GULICK", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 825, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 825, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The Rutgers New Jersey Medical School (NJMS) Clinical Research Site (CRS) 31786 is requesting $300,000 in funding to help support Severe Acute Respiratory Distress Syndrome Coronavirus-2 (SARS-CoV-2) testing. The CRS is part of the Weill Cornell-Rutgers NJMS Clinical Trials Unit and has been a site for two Division of AIDS (DAIDS) funded clinical trials networks: AIDS Clinical Trials Group (ACTG) and HIV Prevention Trials Network (HPTN) for fifteen years. The program plans to provide SARS-CoV-2 testing by leveraging its existing relationships with the local community and partnering with the clinical laboratory located at Public Health Research Institute (PHRI), also a Rutgers facility to perform SARS-CoV-2 testing using the Cepheid testing platform. The Cepheid COVID-19 testing platform has been developed at PHRI with Cepheid and is one of the most sensitive tests currently available to detect SARS-Co-V 2 infection. We will provide SARS-CoV-2 testing at the CRS, in an adjacent building and on a mobile van. These testing locations are well-known and easily accessible to the community, healthcare workers and other high-risk groups. We will also attempt to ensure that special populations such as minorities and the LGBTQ community have access to the test. Finally, this will help the CRS further strengthen its relationship with the community and allow the unit to provide an essential service to this hard-hit community. If funded, this project will allow us to rapidly increase the availability of SARS CoV-2 testing and help contribute to the urgent need for additional COVID testing in an underserved community located near the epicenter of the COVID-19 pandemic in the United States.", "keywords": [ "AIDS clinical trial group", "Acquired Immunodeficiency Syndrome", "Address", "Administrative Supplement", "Adult Respiratory Distress Syndrome", "Affect", "Area", "COVID-19", "COVID-19 pandemic", "Clinical", "Clinical Trials", "Clinical Trials Network", "Clinical Trials Unit", "Communities", "Community Healthcare", "Coronavirus", "Enrollment", "Ensure", "Funding", "Future", "HIV prevention trials network", "Health Personnel", "Individual", "Infection", "Laboratories", "Lesbian Gay Bisexual Transgender Queer", "Location", "Minority", "New Jersey", "Research Institute", "Services", "Severe Acute Respiratory Syndrome", "Site", "Testing", "United States", "Work", "clinical research site", "cohort", "coronavirus disease", "high risk population", "infection rate", "interest", "medical schools", "named group", "pandemic disease", "programs", "public health research" ], "approved": true } } ], "meta": { "pagination": { "page": 1405, "pages": 1419, "count": 14184 } } }