Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1391&sort=funder_divisions
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=funder_divisions", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=funder_divisions", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=funder_divisions", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1390&sort=funder_divisions" }, "data": [ { "type": "Grant", "id": "11311", "attributes": { "award_id": "2111696", "title": "SBIR Phase I: Efficient Arithmetic on Quasi-Compressed Data for Performance Improvement", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1211, "first_name": "Peter", "last_name": "Atherton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-10-31", "award_amount": 256000, "principal_investigator": { "id": 27350, "first_name": "David", "last_name": "Chen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2012, "ror": "", "name": "ARITH INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve the performance of semiconductor chips. Specifically, it develops a novel method to operate directly on compressed data, saving time, energy, and latency. This improved performance will affect computationally intensive applications such as medical imaging (e.g., CT/MRI), climate simulation, hurricane warnings, and earthquake alerts.\n\nThis Small Business Innovation Research (SBIR) Phase I project develops a method to operate on compressed data. Today, computers apply data compression to identify and remove redundancy in the data in order to save storage space. Computers apply arithmetic to compute in integers or real numbers (usually represented internally as floating-point data). However, today's computers first decompress the data, compute, and then compress the computed result, consuming additional time and energy. This project develops an arithmetic and math hardware accelerator capable of processing compressed data directly to dramatically improve computation performance, storage effectiveness, and energy efficiency. This project combines data compression and floating-point engineering to deliver the first-ever Compressed Floating-Point Unit (CFPU) that minimizes semiconductor and energy usage and reduces computation latency.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11312", "attributes": { "award_id": "2111696", "title": "SBIR Phase I: Efficient Arithmetic on Quasi-Compressed Data for Performance Improvement", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1211, "first_name": "Peter", "last_name": "Atherton", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-01", "end_date": "2023-10-31", "award_amount": 256000, "principal_investigator": { "id": 27350, "first_name": "David", "last_name": "Chen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2012, "ror": "", "name": "ARITH INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve the performance of semiconductor chips. Specifically, it develops a novel method to operate directly on compressed data, saving time, energy, and latency. This improved performance will affect computationally intensive applications such as medical imaging (e.g., CT/MRI), climate simulation, hurricane warnings, and earthquake alerts.\n\nThis Small Business Innovation Research (SBIR) Phase I project develops a method to operate on compressed data. Today, computers apply data compression to identify and remove redundancy in the data in order to save storage space. Computers apply arithmetic to compute in integers or real numbers (usually represented internally as floating-point data). However, today's computers first decompress the data, compute, and then compress the computed result, consuming additional time and energy. This project develops an arithmetic and math hardware accelerator capable of processing compressed data directly to dramatically improve computation performance, storage effectiveness, and energy efficiency. This project combines data compression and floating-point engineering to deliver the first-ever Compressed Floating-Point Unit (CFPU) that minimizes semiconductor and energy usage and reduces computation latency.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11845", "attributes": { "award_id": "2208120", "title": "SBIR Phase I: Combining Machine Learning with Clinical Expertise to Assess and Mitigate Risk in Healthcare", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2023-05-31", "award_amount": 256000, "principal_investigator": { "id": 27736, "first_name": "James", "last_name": "Levett", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2051, "ror": "", "name": "PRESAJ, INC.", "address": "", "city": "", "state": "IA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will improve health care outcomes associated with complex procedures. Approximately 1 in 7 major surgical procedures in the US is associated with a medical complication, totaling more than 4 million complications and $80 billion in costs per year. Many more complications occur in non-surgical settings. This project will use machine learning to combine proven engineering principles with clinical expertise to identify and address specific risks for each procedure, care facility, and patient (accounting for high-impact risk factors ranging from diabetes to social determinants of health). This technology will augment existing standardized, outcome-oriented quality-improvement tools with cost-effective customized, process-oriented tools in a novel way, with an envisioned initial application for the ~5,100 community hospitals in the US. A modest improvement of 1% of complications would annually reduce costs by nearly $1 billion and will save 4,500+ lives.\n\nThis Small Business Innovation Research (SBIR) Phase I project will use a systems-based approach to examine process-level risk in healthcare. Big data in healthcare is inconsistently structured and not optimized to directly improve patient outcomes. The large datasets for most procedures provide only high-level conclusions regarding risk; they do not pinpoint the specific steps in provider workflow with high risk or the role of external factors, such as comorbidities or facility age. This project will determine the feasibility of using machine learning supervised by experienced clinicians to assess risk using principles from Failure Modes and Effects Analysis. The project will develop a proof-of-concept machine-learning system that uses a proprietary risk taxonomy and modifiers to combine national, state, facility, and actuarial datasets to generate risk priority numbers for each step for a service line. This system will then be applied to coronary artery bypass graft surgery to assess its validity and clinical value. Monte Carlo simulations and clinician focus groups using a Likert scale will determine the significance of the results.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11850", "attributes": { "award_id": "2136307", "title": "SBIR Phase I: AI-assisted identification of small molecules for targeted repair of vascular barrier dysfunctions", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-12-01", "end_date": "2023-05-31", "award_amount": 255487, "principal_investigator": { "id": 27742, "first_name": "Mario", "last_name": "Dipaola", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2053, "ror": "", "name": "AKTTYVA THERAPEUTICS, INC.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this small Business Innovation Research (SBIR) Phase I project is the identification of treatments for vascular leak disorders. Uncontrolled vascular leak disorders are common pathological processes that lead to tissue damage across multiple organs and over 60 clinical conditions. Currently, there are no drug-based therapies that address vascular leaks. Available solutions are focused on providing supportive care or decreasing inflammation, without addressing the underlying mechanism. This project is proposing a new approach to repair vascular leaks as a therapeutic intervention. By establishing the first drug discovery workflow for the identification of small molecules that repair vascular leakage, this project will enable the development of a pipeline of drugs for multiple conditions. The first condition targeted will be acute respiratory distress syndrome (ARDS), which accounts for 10% of intensive care unit (ICU) admissions and is the leading cause of mortality in ICU. Globally, it affects more than 3 million patients yearly. The proposed solution will decrease the number of deaths and the costs for ICU.\n\nThis Small Business Innovation Research (SBIR) Phase I project seeks to validate a new structure-based drug screening platform designed to identify small molecules that activate the molecular pathways responsible for repairing vascular leakage. The proposed platform consists of a unique combination of novel machine learning methods for ligand-binding site prediction, fast docking algorithm capable of screening ultra-large (over a billion molecule) compound libraries with targeted absorption, distribution, metabolism, and excretion-toxicity (ADME-Tox) profile within minutes (5,000 compounds/second). The AI-guided docking approach is combined with in vitro high-throughput assays measuring the mechanisms of vascular leak in a physiologically relevant microenvironment of human tissues to select candidates targeting vascular leak disorders. In this project, the steps of this tiered workflow will be validated and applied to the first target, leading to the identification of a set of new drug candidates.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11972", "attributes": { "award_id": "2304483", "title": "SBIR Phase I: Combating Pathogens, Helios-1 Onsite Universal Detection", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-01", "end_date": "2024-07-31", "award_amount": 275000, "principal_investigator": { "id": 27855, "first_name": "Darrell", "last_name": "Marshall", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2057, "ror": "", "name": "TOTAL ANALYSIS L.L.C.", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is protection against pathogen-related infections. Currently, detection tests for pathological agents are laborious, time-consuming, expensive, and require advanced technical expertise to conduct. The proposed portable, onsite pathogen detector will allow for fast, specific, sensitive, and cost-effective pathogen tests that can be conducted with minimal personnel training and equipment. The solution is intended to be used at healthcare centers, transport nodes, defense facilities, and any other site where the spread of infectious diseases is a possibility. This technology will benefit the population’s health and welfare, by facilitating the implementation of pathogen detection routines that reduce the risk of large-scale infections. Such infections disproportionally affect under-represented groups. The solution will also improve the national defense against bioterrorism, since the proposed technology could be as standard as a typical metal detector used in large, populated venues, on the battlefield protecting troops, or at airports to keep the traveling public safe. The nation’s economic competitiveness may also improve, since the proposed solution could mitigate and even avoid the economic consequences of a health crisis.\n\nThe proposed project seeks to prove that Matrix Assisted Ionization can be coupled with Ion-Mobility Spectrometry (MAI-IMS) for pathogen detection and identification. The recent pandemic outbreak has demonstrated the necessity of rapid, on-site, and accurate pathogen detection devices. The proposed method is to use the existing IMS technology and modify it to detect pathogens by fabricating a Matrix assisted ionization vault (Helios-1) that overcomes the biomolecule volatility restriction of all current ion mobility spectrometers. A crucial technical hurdle is finding the device's optimal ionization and operational environment. To overcome this challenge, the most similar conditions to mass spectrometry must be found, which will involve experimental tests to determine the adequate environmental conditions and the engineering modifications of the MAI extension chamber to adapt IMS for non-volatile biomolecule detection. Standardize organism sample conditions and protocols are also needed. This challenge represents a critical step to prevent variation caused by the extraction of the sampling procedure. This challenge will be tackled by testing different extraction procedures until they meet the criteria for satisfactory performance. Additionally, machine learning algorithms will be employed for pathogen recognition. All of the above will help prove the feasibility of the proposed MAI-IMS-based pathogen detection and identification platform.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10736", "attributes": { "award_id": "2213051", "title": "SBIR Phase II: Microbial Discovery and Biosynthesis of Targeted Protease Inhibitors (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-12-01", "end_date": "2024-11-30", "award_amount": 1000000, "principal_investigator": { "id": 26782, "first_name": "Matthew", "last_name": "Traylor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 349, "ror": "", "name": "THINK BIOSCIENCE, INC.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research Phase II project is to develop a mature, market-ready approach for building targeted, readily synthesizable inhibitors of viral proteases. The technology will extend the discovery platform to new targets and disease indications and build a biochemical foundation for progressing preclinical programs to promising leads, starting with a potent lead candidate for treating COVID-19. The project seeks to generate new intellectual property that covers the discovery platform and promising small molecules, and it will support new opportunities to partner with pharmaceutical companies on antiviral therapeutics, which continue to be an important unmet medical need. \n\nThis Small Business Innovation Research Phase II project seeks to expand and industrialize the company’s recently demonstrated approach for using microbial systems to guide the discovery and assembly of protease inhibitors. The project focuses on COVID-19 and other viral diseases that lack effective treatments, exhibit significant epidemic potential, and/or remain relevant to U.S. biodefense. The research program may uncover inhibitors of a broad set of viral proteases and as it screens large libraries of biosynthetic pathways for targeted inhibitors. This solution complements the multi-part effort by developing a potent lead candidate for treating COVID-19 and a general workflow for the (bio)synthetic optimization of hits identified. Success in these tasks may stretch contemporary approaches to synthetic biology by applying them to the discovery and assembly of new biologically active compounds and may develop a supporting (bio)synthetic workflow—one that that combines applied enzymology and synthetic chemistry.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10737", "attributes": { "award_id": "2212920", "title": "SBIR Phase II: A Rapid, Sensitive Pathogen Typing and Antibiotic Sensitivity Test for Bloodstream Infections (COVID-19)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-12-01", "end_date": "2024-11-30", "award_amount": 999999, "principal_investigator": { "id": 2057, "first_name": "Rachel", "last_name": "Tinker-Kulberg", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 445, "ror": "", "name": "Kepley Biosystems Incorporated", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 445, "ror": "", "name": "Kepley Biosystems Incorporated", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase II project seeks to develop an improved era of infectious disease management, allowing rapid intervention for antibiotic therapy to stem the 30% mortality rate and associated cost impact of sepsis. With some 49 million cases worldwide and a 25-30% mortality rate, sepsis claims 11 million lives annually. Sepsis cases have been increasing 8.7% per year. To address the full spectrum of infectious diseases, innovations must deliver simple and affordable testing capabilities similar to routine hospital admission blood analyses. The proposed antifungal and antibacterial susceptibility test for the detection and treatment of bloodstream infections could benefit patients by improving patient management and hospital logistics. Sepsis is the most expensive healthcare challenge, with an estimated financial impact of more than $62 billion per year. This bloodstream infection screening assay could impact the entire continuum of care – from initial hospital interactions through patient care and discharge – by identifying infections early, optimizing treatment, and increasing survival. Direct customer survey-based estimates and independent information sources project a U.S. commercial opportunity of 226 million annual assays (36 million hospital admissions, 40 million intensive care patients, 130 million emergency walk-ins, and 20 million presurgical evaluations).\n \nThe proposed project could result in the development of a user-friendly and affordable analytical tool for early detection of bloodstream infections that differentiates bacterial and fungal pathogens associated with sepsis and determine their antibiotic sensitivity in hours. Sepsis is a major public health and economic concern that results in one human death every 2.8 seconds. If bloodstream infections go undetected or untreated, patients can quickly escalate into sepsis or septic shock with mortality chances increasing by 8% per hour without appropriate antibiotic administration. Rapid and accurate detection of a bloodstream infections prior to the onset of sepsis is critical to limit the extent of tissue and organ damage, mortality, and associated hospital costs. The proposed innovation includes the use of an FDA-approved reagent called Limulus Amebocyte Lysate for clinical bloodstream infections and antifungal antibacterial susceptibility testing to guide therapeutic interventions, and routine surveillance of high-risk patient populations. The technical approach for this Phase II encompasses proficiency studies that would validate high-throughput detection of pathogens, as well as their antimicrobial sensitivity and resistance profiles in clinical blood specimens. Additionally, assay miniaturization and automation would be performed and are considered critical for future in vitro diagnostic partnership adoption.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "5210", "attributes": { "award_id": "0923706", "title": "SBIR Phase II: Relief-Free Infrared Diffractive Optics Based on Semiconductor Materials", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [], "start_date": "2009-08-15", "end_date": "2011-07-31", "award_amount": 500000, "principal_investigator": { "id": 18439, "first_name": "Sergei", "last_name": "Krivoshlykov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1381, "ror": "", "name": "ANTEOS, Inc.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 1381, "ror": "", "name": "ANTEOS, Inc.", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "\"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).\"\n\nThis Small Business Innovation Research (SBIR) Phase II project will develop a new generation of relief-free thin-plate components of diffractive optics operating in the infrared region of spectrum. The diffractive optics employs volume phase holographic structures, which are optically recorded in semiconductor materials transparent at the infrared wavelengths using proprietary process of photo-modification for producing dramatic change of the material refractive index under illumination with low intensity light. Phase I of this project proved feasibility of the proposed concept by demonstrating photo modification of ZnSe infrared material and fabricating the first model components. The developed technology can be immediately applied to fabrication of diffractive optics, volume phase holographic gratings, and phase retardation plates for wavelengths up to 1.9 ìm, as well as antireflection layers for wavelengths up to 8 ìm. In Phase II project the technology will be optimized and applied to fabrication of the prototype components of infrared diffractive optics operating at longer wavelengths, including the important wavelength of CO2 laser 10.6 ìm and windows of atmospheric transparency 3-5 and 8-12 ìm. \n\n\nThe developed photo-modification process is highly adaptable and creates a rich technology platform for fabrication of a broad range of products for a large variety of markets. Successful implementation of this technology will result in a new generation of high efficiency relief-free infrared diffractive optics and sub-wavelength components, including diffraction gratings, beam splitters, beam shapers, semiconductor materials with artificial birefringence, phase retardation plates and wave plates. The relief-free components of infrared diffractive optics based on semiconductor materials are capable to withstand high light intensities and perform complicated light management functions. Another important application is the fabrication of highly stable anti-reflection (AR) layers on infrared semiconductor optics. The market for infrared diffractive optics includes defense and airspace industry, laser industry, spectral devices, sensors and detectors, night vision optics, industrial process control, material processing, cutting and welding, environmental monitoring, medical diagnostics and surgery.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10212", "attributes": { "award_id": "2136470", "title": "SBIR Phase II: Single wearable patch for cost-effective, reliable, and accurate home sleep apnea testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 936, "first_name": "Henry", "last_name": "Ahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2023-12-31", "award_amount": 995933, "principal_investigator": { "id": 26167, "first_name": "Brennan", "last_name": "Torstrick", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1895, "ror": "", "name": "HUXLEY MEDICAL, INC.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to help patients with cardiorespiratory conditions, including sleep apnea and cardiac arrhythmia. The project will develop a wearable sensor platform to simultaneously diagnose and monitor conditions. Multiple physiological measurements are collected by a comfortable, wireless sensor patch, resulting in a convenient remote monitoring clinical framework. Interfacing sensor data with an efficient cloud-based provider portal and automated algorithms will enable rapid screening of the 24 million undiagnosed sleep apnea patients in the United States. The proposed innovation will also provide insight into the practical clinical benefits and efficiencies to be gained by bundling multiple comorbid or otherwise related diagnostic pathways into a single workflow, such as reducing time to treatment for comorbid atrial fibrillation. This remote monitoring bundle concept represents the only all-in-one device capable of servicing multiple highly pervasive health challenges in a method unobtrusive and user-friendly for both the patient and the provider - particularly for telemedicine applications made more urgent by the global pandemic.\n\nThis Small Business Innovation Research (SBIR) Phase II project aims to develop a simple, accurate, cloud-connected wearable patch and collect clinical comparison data to develop automated, low-power algorithms to simultaneously detect sleep-disordered breathing, sleep stages, and cardiac arrhythmias. The project integrates materials science, mechanical engineering, and signal processing approaches to detect critical physiological signals from the torso, including oxygen saturation and several hemodynamic metrics. The project will also conduct studies that offer early insights into the clinical benefits of bundled workflows across cardiac and sleep medicine specialties.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "9558", "attributes": { "award_id": "2127436", "title": "SBIR Phase II: COVID-19 Rapid Sensing Using Structural DNA Biosensors", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase II" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-08-01", "end_date": "2024-07-31", "award_amount": 998507, "principal_investigator": { "id": 25169, "first_name": "Xiaohu", "last_name": "Yao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 557, "ror": "", "name": "ATOM BIOWORKS INC", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 557, "ror": "", "name": "ATOM BIOWORKS INC", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be the development of a platform technology for creating rapid virus diagnostics that directly recognizes the virus surface protein pattern from a patient sample and generates accurate results within minutes. Current standards for high-fidelity viral pathogen diagnostics require complex instruments, technical expertise to run the instruments, and hours to produce and interpret results. The proposed platform creates a virus-specific biosensor that selectively binds to the target virus and produces visible results without time-consuming pre-processing or expensive instruments. Lower cost tests and faster sample-to-result turnovers could result in more effective control of disease spread.\n\nThis project seeks to develop a highly functional, sensitive, and specific diagnostic for the detection of coronavirus. This technology is based on the company’s Pattern-Recognition Enhanced Sensing and Therapeutics (PEST) concept. The solution is a first-in-class diagnostics that uses algorithmically-designed structural DNA to form a trap that may detect and selectively bind a signature pattern of the pathogen. This recognition and binding may generate visual signals without the need of DNA/RNA preprocessing or amplification associated with the current generation of molecular tests (polymerase chain reactions, PCRs). This project will involve building a preclinical prototype of PEST-enabled lateral flow based COVID-19 rapid diagnostics with a goal of providing results for each sample within 5 minutes. This fast test result will be followed by preclinical validation to determine the test’s specificity, limits of detection, and implement mechanism to improve the assay specificity and to avoid cross-reaction to other virus types.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1391, "pages": 1419, "count": 14184 } } }