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Winter Conference to Advance New Approach Methodologies (NAMs) in Chemosensory Science

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NIDCD - National Institute on Deafness and Other Communication Disorders

Conference Title: WINTER CONFERENCE TO ADVANCE NEW APPROACH METHODOLOGIES (NAMS) IN CHEMOSENSORY SCIENCE Chemosensory and interoception research still relies on animal models, despite the national momentum to reduce, refine, and replace in vivo testing. To accelerate the adoption of non-animal alternatives, we will convene an inaugural four-day conference (Jan 12-17, 2027, Monell Chemical Senses Center, Philadelphia, PA) that integrates lectures, interactive workshops, and networking sessions focused on in vitro, ex vivo, and in silico NAMs relevant to taste, smell, and interoception. This meeting, to be held annually (2027 to 2031), will bring together ~100 academic investigators, industry scientists, regulators, and trainees, including at least 30 early-career scholars, to create a cross-sector forum for exchanging best practices and catalyzing collaborations. Program elements include: 1. Plenary Sessions highlighting breakthroughs in organoid models (e.g., oral, nasal, gut), high- throughput receptor assays, and AI-enabled chemosensory prediction tools. 2. Hands-on NAM Workshops led by Monell and external experts, covering receptor-based assays and organoid model development, transitional models aimed at reducing animals in research (e.g., ex vivo, in ovo), direct human measures (e.g., behavioral, EEG and fMRI) and machine learning pipelines for data integration and modelling. 3. Regulatory & Ethics Panels featuring scientists on validation standards and pathways for scientific and regulatory acceptance. 4. Trainee Lightning Talks & Mentoring to foster presentation skills and career development. 5. Industry Roundtable with chemosensory scientists, food, fragrance, and biotech companies to identify appropriate NAM uses, translational gaps, and commercialization opportunities. By the conference’s end, participants will: (i) understand the state of the art in NAMs and their potential application for chemosensory and interoception research, (ii) acquire practical skills to incorporate NAMs into grant proposals and product development and testing pipelines, and (iii) contribute to a consensus white paper outlining research priorities, prime applications and validation needs. All slide decks and workshop protocols will be posted on an open-access website; abstracts will also be publicly available. Evaluation surveys will track knowledge gains and subsequent adoption of NAM. This information will be leveraged to invite early NAMs adopters to participate in the Winter Conference in years 2-5. R13 support will offset meeting logistics, trainee travel awards, captioning for accessibility, and post-meeting dissemination, ensuring broad reach and sustained impact across academia, industry, and federal agencies.

Up to $60K
2027-05-31
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

Words to Live by: Leveraging Natural Language Processing and Machine Learning to Enhance Prehospital Triage Algorithms

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NHLBI - National Heart Lung and Blood Institute

Project Summary Abstract Over 1 million EMS clinicians from over 23,000 agencies must determine illness severity and care needs for the 25 million US patients transported annually to emergency departments. Treatment decisions based on these assessments—or 'triage’, such as whether to administer a lifesaving intervention (LSI) or transport to a Level 1 trauma center—proves challenging: Under- and over-estimation of illness severity and care needs occur at rates of 11%-72% and 10%-48%, respectively, which annually leads to thousands of excess deaths and costs the health care system hundreds of millions of dollars. Prehospital clinicians struggle to make accurate triage decisions because they have little available diagnostic information or time for analysis. Prehospital clinicians could benefit if diagnostic information were synthesized via an algorithmic tool to support triage decisions. To this end, prehospital clinicians record several lexical observations regarding patient condition and care needs, including clinician impressions and 9-1-1 telecommunicator notes. These observations mix diagnostic cues (e.g., anatomic injury patterns; possible illness etiology) with provider intuition, which is itself predictive of illness severity and care needs. Words within prehospital lexical observations may inform triage decisions if incorporated into an algorithmic decision tool. This project will involve the first comprehensive test of whether and how lexical prehospital information can be leveraged via natural language processing (NLP) and machine learning (ML) to create triage algorithms. NLP and ML prediction models trained on free-text prehospital clinician impressions will be used to predict illness severity (e.g., Injury Severity Score; mortality; hospital length-of-stay) and administration of prehospital LSI (e.g., intubation; defibrillation; tourniquet). To promote generalizability, models will be built in three large data sets totaling over 12 million prehospital cases; these cohorts vary in transport mode (i.e., ground; air), medical condition (i.e., trauma; non-trauma) and free-text format (i.e., 2-3 word clinician impressions; 4-5 sentence anatomic descriptions; 9-1-1 call notes). Multiple state-of-the-art NLP and ML approaches (e.g., ensemble models using bag-of-words frequencies; transformer models with pretrained embeddings) will be used to balance clinical interpretability and predictive sophistication. Highly predictive ML models could shape triage protocols: Notes from 9-1-1 calls could be fed into an ML prediction model to inform delivery of appropriate support and resources to a patient’s side (e.g., advanced clinicians; blood product); voice-to-text recordings of clinician impressions made upon patient encounter could likewise be leveraged to determine the need for lifesaving care. This work will set the stage for prospective collection of prehospital lexical data, as well as videos of patient encounters in the field, leveraging voice-to-text translation and computer vision-generated scene descriptions to translate these data sources into real-time decision support tools for prehospital triage.

Up to $239K
2028-04-30
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

X-RAD 320 with OptiMax

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OD - NIH Office of the Director

PROJECT SUMMARY/ABSTRACT The Division of Translational Radiation Sciences (DTRS) was established to accelerate the discovery and clinical implementation of new therapeutic strategies in clinical radiotherapy at the University of Maryland School of Medicine. DTRS and the Department of Radiation Oncology have been at the forefront of this field for several decades, having been one of the first institutions to secure a Medical Countermeasures Against Radiological Threats (MCART) consortium award from the NIH in 2005. DTRS currently leads two NIH-sponsored consortia: the Intercollaborative Radiation Countermeasures (INTERACT) Consortium (5U19AI150574-05), and the Radiation Oncology-Biology Integration Network on Oligometastasis (ROBIN OligoMET, 5U54CA273956-03). Our experienced physicists perform both in vitro and in vivo irradiations (in small and large animals) for investigators involved in these two consortia, as well as for other researchers requiring precise and accurate delivery of radiation doses in their experiments. DTRS is not a core facility but operates on a fee-for-service basis to perform and support these procedures for investigators across campus. A large number of our users rely on our current XRAD-320 X-ray irradiator, which has become increasingly unreliable, as the manufacturer no longer services the power generator or offers preventive maintenance, making future repairs potentially impossible. Our department also utilizes cesium-137 irradiators that must be phased out to comply with the U.S. Congress–mandated National Defense Authorization Act (NDAA), which calls for the elimination of all cesium- based irradiators in the U.S. by December 31, 2027, to mitigate national security risks associated with high- activity radioactive sources. We are therefore proposing to replace our aging and unsupported irradiator technology with a modern X-RAD 320 equipped with OptiMAX imaging. This state-of-the-art instrument offers advanced imaging capabilities, enabling precise targeting of radiation delivery to biological tissues. It will support current studies with improved accuracy, allow for the development of new experimental designs, and reduce labor requirements for existing protocols. Additionally, this system is considered one of the most suitable replacements for Cs-137 gamma irradiators. We have identified a group of NIH-funded users within the University of Maryland who rely on the current system or who would benefit from expanded capacity beyond what our aging platform can reliably support. Many of the research projects described in this application are translational in nature and aim to accelerate the understanding of disease mechanisms and the development of novel therapeutic strategies. Our institution is committed to providing substantial support for the installation and long-term operation of this system. The management and operational infrastructure are already in place, led by an outstanding technical team dedicated to delivering high-quality service to all users.

Up to $418K
2027-04-30
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

X-ray Macromolecular Crystallography Detector Upgrade for Structural Biology Facility

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NIGMS - National Institute of General Medical Sciences

Project Summary The University of Nebraska Medical Center (UNMC) requests funds to purchase a Rigaku HyPix-Arc 100° Curved Hybrid Photon Counting (HPC) Detector to provide state-of-the-art crystallographic X-ray diffraction data collection for the three-dimensional solution of medically important biological macromolecules and their complexes. This instrument for macromolecular crystallography is not currently available at UNMC, any nearby university or in the region. The HyPix-Arc 100° is a unique, curved detector using HPC technology. To provide the best data quality it is essential to measure accurate data with minimal correction. With a curved detector arrangement, diffracted beams arrive at the detector as close to perpendicular to the surface as possible. This prevents unwanted reflection enlargement and minimizes the associated corrections. Additionally, the curvature allows the detector to see a higher theta range than larger flat detectors. This means more reflections collected at the same time under the same conditions for truer measurement with better scaling and data quality whilst also increasing measurement speed for sensitive or unstable samples. This instrument will dramatically improve the quality of X-ray diffraction data we collect to solve crystal structures for basic science and drug development projects. The proposed project is viable because of strong investigator support illustrated by the 15 NIH- funded researchers and their projects on nucleosome assembly, ribonucleoproteins, enzyme catalysis, redox biology, cancer therapeutics, neurodegenerative and infectious disease. Participating investigators are from several departments at UNMC, as well as nearby institutions, such as the University of Nebraska-Lincoln, and Creighton University. Each project will benefit directly from the use of the HyPix-Arc 100° detector: enabling sophisticated experiments and expediting progress on NIH-funded science. These projects enjoy strong technical support in the Eppley Structural Biology Facility (ESBF) that includes well-experienced staff that have worked together with the director for over 23 years promoting all aspects of structural biology research in Nebraska and surrounding states. The proposal also has strong institutional support as evidenced by salary and service agreement support by the Vice Chancellor for Research (Dr. Bayles) and the Director of the Eppley Institute for Research in Cancer (Dr. Sweasy). The detector will be housed in the ESBF and installed on the right port of our Rigaku FRE+ ultrahigh-intensity rotating anode X-ray generator. The ESBF is the only structural biology facility in the region and gives easy access for data collection to all interested researchers. The combination of strong user interest, significant research projects, technical expertise, administrative experience, and a solid long-term plan will ensure successful implementation and extensive use of the HyPix-Arc 100° detector.

Up to $565K
2027-05-14
health research

Free to search & build · $99 one-time to unlock the application pack · No subscription

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