Computational Medicine in the Heart, Integrated Training Program

心脏计算医学综合培训计划

• 批准号: 10556918
• 负责人: Alison L Marsden
• 金额: $ 20.1万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10556918
• 关键词: Heart; Medicine; Training Programs

Project Summary Cardiovascular (CV) diseases are rising causes of morbidity and mortality worldwide. There is excitement that computational medicine, an emerging field combining engineering disciplines with the life sciences, will enable scientific and clinical breakthroughs and accelerate bench-to-bedside translation. However, few training programs exist in this field, and so trainees often learn ad hoc. We seek funding for a new multidisciplinary T32 program in Computational medicine in the Heart: Integrated training Program (CHIP) at Stanford. CHIP will provide cutting-edge training for 3 post-PhD, -MD or -MD/PhD fellows annually, each undergoing 2 years of training at the intersection of engineering, CV physiology and medicine. Trainees will pursue a cutting-edge research project mentored by faculty with complementary expertise in engineering and the life sciences, and select didactic courses to build expertise, grow professionally, and develop community. The forward-looking vision of CHIP addresses key priorities of several National Agencies and fills current gaps in interdisciplinary training. Stanford CHIP leverages faculty and resources at top-ranked Schools of Engineering, Medicine and Humanities and Sciences. The T32 is co-directed by a physician-engineer and an engineer-physiologist, bringing 38 faculty from 13 Departments and Divisions with strong emphasis on women and under - represented minorities. Key support is provided by the inter-disciplinary Cardiovascular Institute (CVI) and the Institute for Computational and Mathematical Engineering (ICME) at Stanford. Faculty will provide trainees with research opportunities in CV science spanning cell-to-organ and bench-to-bedside, as well as computational science, clinical care, and therapeutic innovation. The faculty are highly collaborative and have exceptional track records of launching trainees into independent scientific careers. Applicant selection and all aspects of the program stress inclusion of trainees from under-represented groups. Trainees will not be required to have backgrounds in both engineering and life sciences. The T32 will provide tailored teaching of CV science to engineers, engineering to life-science fellows, and advanced cross- disciplinary topics to each. Core didactics also include ethics, the responsible conduct of science, methods to ensure reproducibility. Diversity, equity and inclusion are central for trainees and faculty. Evaluation will be both constructive and bidirectional between trainees and faculty. In summary, the CHIP T32 at Stanford provides post-MD, post-PhD and post-MD/PhD graduates with world-class training at the intersection of bioengineering, CV science and medicine. The T32 is well positioned for success due to the co-location of these top-tier resources on a single campus in Silicon Valley, a hub of innovation in data science, artificial intelligence and therapy. Our aspirational goal is that CHIP graduates will become global scientific leaders at the cusp of engineering, physiology and medicine.

项目概要 心血管（CV）疾病是全球发病率和死亡率上升的原因。有 令人兴奋的是计算医学，一个将工程学科与生活相结合的新兴领域 科学，将实现科学和临床突破并加速实验室到临床的转化。 然而，该领域的培训项目很少，因此受训者通常是临时学习。 我们为心脏计算医学的新多学科 T32 项目寻求资金： 斯坦福大学综合培训计划（CHIP）。 CHIP 将为 3 名博士后、医学博士或 - 每年都有医学博士/博士研究员，每人接受为期 2 年的工程、简历交叉培训 生理学和医学。学员将在教师的指导下进行尖端研究项目 工程和生命科学方面的互补专业知识，并选择教学课程来建立专业知识， 专业成长，发展社区。 CHIP 的前瞻性愿景解决了以下关键优先事项 多个国家机构并填补了当前跨学科培训的空白。 斯坦福大学 CHIP 利用顶尖工程学院、医学院和医学院的师资和资源 人文与科学。 T32 由一名医师工程师和一名工程师生理学家共同指导， 汇集了来自 13 个系和部门的 38 名教职人员，重点关注女性和以下人员： 代表少数群体。跨学科心血管研究所 (CVI) 和 斯坦福大学计算与数学工程研究所 (ICME)。学院将为学员提供 CV科学领域的研究机会，涵盖细胞到器官、实验室到临床以及计算 科学、临床护理和治疗创新。教师们高度协作并具有卓越的 将受训者带入独立科学职业的记录。 申请人的选择和该计划的各个方面都强调纳入来自代表性不足的学员 组。学员不需要同时具备工程和生命科学背景。 T32将 为工程师提供量身定制的简历科学教学，为生命科学研究人员提供工程设计以及高级跨学科教学 每个人的纪律主题。核心教学法还包括伦理学、负责任的科学行为、科学方法 确保再现性。多样性、公平性和包容性是学员和教师的核心。评价将是 学员和教师之间都是建设性的、双向的。 总之，斯坦福大学的 CHIP T32 为医学博士后、博士后和医学博士/博士后毕业生提供了 生物工程、CV 科学和医学交叉领域的世界一流培训。 T32定位良好 之所以能取得成功，是因为这些顶级资源都集中在硅谷的一个园区内，而硅谷是一个 数据科学、人工智能和治疗方面的创新。我们的理想目标是 CHIP 毕业生 将成为工程、生理学和医学尖端的全球科学领导者。