Advancing Research on Empirically-Supported Interventions for Older Adults Living with Serious Mental Illness (SMI) (R01 Clinical Trial Optional)

The overall purpose of the Older Adult Home Modification Program (OAHMP) is to assist experienced nonprofit organizations, state and local governments, and public housing authorities in undertaking comprehensive programs that make safety and functional home modification repairs and renovations to meet the needs of low-income seniors. The goal of the home modification program is to enable low-income elderly persons to remain in their homes through low-cost, low barrier, high impact home modifications to reduce older adults" risk of falling, improve general safety, increase accessibility, and to improve their functional abilities in their home. This will enable older adults to remain in their homes, that is, to "age in place," rather than move to nursing homes or other assisted care facilities.

The NPRC will serve as the nation"s go‐to source for trusted information, real‐time help, community capacity‐building, and peer support and community-based resources that promote independence and participation. The NPRC will advance independence, informed choice, and full participation in community life for individuals living with paralysis. The NPRC will improve the health and quality of life of people living with paralysis by reducing environmental, social, and systemic barriers to health and community living. The program will also strengthen community capacity through competitive subawards and tracks progress through performance measures and a public dashboard.

The Medical Student Education (MSE) Program provides support to public medical schools in the top quartile of states with a projected primary care provider shortage to expand or support education for medical students preparing to become physicians.

The overarching goal of this notice of funding opportunity (NOFO) and its companion opportunities is to establish the Precision Medicine with AI: Integrating Imaging with Multimodal Data (PRIMED-AI) Program to support development of innovative, reliable, cost-effective, and sustainable multimodal AI-based clinical decision support (CDS) tools. PRIMED-AI CDS tools are based on the integration of clinical imaging with other types of multimodal health data to enhance care for patients with a wide range of health conditions. The PRIMED-AI Program seeks to catalyze the adoption of AI-based CDS tools into clinical workflows to enable novel personalized medicine strategies that address significant health challenges. The purpose of this NOFO is to solicit applications for the creation of a Validation Center to serve as a dedicated hub for the comprehensive evaluation and characterization of AI-enabled, image-based, multimodal CDS tools developed by the PRIMED-AI Consortium, to ensure these tools are reliable, reproducible, and generalizable. The Validation Center will focus on verification, validation, interoperability, and uncertainty quantification to comprehensively characterize performance of the CDS tools developed by the program. The core functions of the Validation Center will involve the systematic validation of PRIMED-AI Consortium deliverables, including those emerging from award recipients under the Playbook, Data-to-Model Academic-Industrial Partnerships, and Model-to-Clinic NOFOs.

In response to a longstanding congressional mandate (P.L. 98-619, Departments of Labor, Health and Human Services, and Education and Related Agencies Appropriation Act, 1985), NIMHD invites applications from eligible institutions to establish a national coordinating center (CC) for the Research Centers in Minority Institutions (RCMIs) initiative. The RCMI CC will coordinate activities across funded RCMI U54 Centers, prospectively evaluate the overall RCMI program, and serve as a national resource to help these centers achieve their primary goals. The RCMI CC will be responsive to requests generated by RCMI site key personnel, NIMHD, NIH, the scientific community, and the general public.

The purpose of this Notice of Funding Opportunity (NOFO) is to support the next 5-year award period of the Clinician-Scientist Transdisciplinary Aging Research Coordinating Center (Clin-STAR CC), whose mission is to serve as a national resource for clinician-investigators across specialties and disciplines who are focusing their careers on aging research. This NOFO intends to build upon NIA's substantial investments to foster the development of clinician-scientists across specialties and disciplines. The specific goals of this initiative are to convey scientific and research knowledge on aging research; foster networking and collaboration between clinician-scientist leaders in aging research and clinician-investigators across specialties and disciplines who wish to focus on aging research; provide mentoring and career development support for emerging clinician-scientists committed to pursuing aging research in their clinical specialty or discipline; and advance interdisciplinary research projects in aging. Ultimately, the Clin-STAR CC is intended to provide a multi-faceted national research platform leading to improved patient-centered care for older adults across specialties and disciplines.

The NIH Director"s Pioneer Award supports individual scientists of exceptional creativity who propose bold and highly innovative research projects with the potential to produce a major impact on broad, important areas relevant to the mission of NIH. To be considered pioneering, the proposed research must reflect substantially different scientific directions from those already being pursued in the investigator"s research program or elsewhere. Applications in all topics relevant to the broad mission of NIH are welcome. Research may involve basic, translational, or clinical research. The NIH Director"s Pioneer Award is a component of the High-Risk, High-Reward Research (HRHR) program of the NIH Common Fund.

Through this Notice of Funding Opportunity (NOFO), the National Cancer Institute (NCI) solicits applications for research on developing a comprehensive and cohesive understanding of cancer metastasis. Towards this goal, the NCI will support U54 Specialized Centers that together will continue to serve as the hub of the Metastasis Research Network (MetNet). Each MetNet Center should propose an overarching scientific theme that will be pursued through two or three scientific inter- and multidisciplinary basic research projects. The projects should use integrative system level approaches to elucidate and integrate a mechanistic understanding of the non-linear, dynamic, and emergent processes in metastasis. Overarching themes should encompass the interplay between at least two central elements in the metastatic process such as early dissemination, cellular, soluble and/or physical microenvironment crosstalk, dormancy, or mechanisms of responses by metastatic cells to therapies. Studies on all types of cancer are welcome, including rare and pediatric metastatic cancers.Research proposed by MetNet Centers is expected to advance our understanding towards a more comprehensive appreciation of metastasis as a whole body, systems-level problem.

This Notice of Funding Opportunity (NOFO) invites a single cooperative agreement application for the Resource Center for the Cardiovascular Repository for Type 1 Diabetes (CaRe-T1D). The purpose of CaRe-T1D is to support innovative, discovery and mechanistic research through the study of human cardiovascular (CV) tissue. This research seeks to better understand cardiovascular disease (CVD) in type 1 diabetes, highlight differences compared to type 2 diabetes, and ultimately guide the development of therapies to treat this major cause of morbidity and mortality for individuals with type 1 diabetes. To achieve these goals, the Resource Center will expand the CaRe-T1D biorepository of human CV tissue, further develop the data commons for data sharing and for artificial intelligence (AI) tools, coordinate the research activities of the investigative teams in the CaRe-T1D consortium, and make CaRe-T1D resources accessible to the broader research community. This NOFO is associated with the Special Diabetes Program (https://www.niddk.nih.gov/about-niddk/research-areas/diabetes/type-1-diabetes-special-statutory-funding-program/about-special-diabetes-program) which funds research on the prevention, treatment, and cure of type 1 diabetes and its complications, including unique, innovative, and collaborative research consortia and clinical trials networks.This is a Notice of Funding Opportunity (NOFO) for a Single Source that will invite application(s) from eligible organization(s) to apply. Please see Section III. Eligibility for additional information. In accordance with NIH standard peer-review processes, the application(s) will be peer-reviewed, and only meritorious application(s) will be considered for funding.

Through this Notice of Funding Opportunity (NOFO), the National Cancer Institute (NCI) invites applications for projects to expand, improve, or transform the utility of mammalian cancer and tumor models for translational research. With this NOFO, the NCI intends to encourage submission of projects devoted to demonstrating that mammalian models or their derivatives used for translational research are robust representations of human biology, are appropriate to test questions of clinical importance, and provide reliable information for patients' benefit. These practical goals contrast with the goals of many mechanistic, NCI-supported R01 projects that employ mammals, or develop and use mammalian cancer models, transplantation tumor models, or models derived from mammalian or human tissues or cells for hypothesis-testing, non-clinical research. Among many other possible endeavors, applicants in response to this FOA could propose demonstrations of how to overcome translational deficiencies of mammalian oncology models, define new uses of mammalian models or their genetics for unexplored translational challenges, advance standard practices for use of translational models, test approaches to validate and credential models, or challenge current practices for how models are used translationally