Precision Medicine Approaches for Eosinophilic Esophagitis Using Raman Spectroscopy and Machine Learning

项目总结 这个K23指导的以患者为导向的研究职业发展奖的总体目标是 提供基本技能和有指导的研究经验，使Girish Hireath博士成为独立的 他领导着一个多学科的研究团队，专注于嗜酸性食管炎(EoE)。EoE是 一种由过敏原引起的慢性食道炎性疾病。作为最常见的原因之一， 上消化道疾病，在美国，每2000人中就有1人受到影响。我们对此的理解 EoE的发病机制尚不完全，这导致了治疗EoE的繁琐方法。 病人。迫切需要了解与EoE发展相关的所有因素，包括 确定导致未受影响的食道转化的食道生化变化 食管炎症性食管炎。作为一名临床和研究的儿科胃肠病专家 专注于EoE十多年，候选人建议通过建立 关于他发表的工作通过：1)用拉曼光谱验证EoE中的食道生化变化 光谱学(一种独特的、高度敏感的、基于激光的技术)和机器学习，2)确定 拉曼峰与疾病活动性的相关性及密钥的工作特性 区分EoE活动状态中的拉曼峰，以及3)确定基线拉曼的组合， 组织学、内窥镜和临床特征可以预测EoE治疗的反应。通过定义密钥 食道生化改变这项研究将为EoE的发病机制提供新的见解，并通过 确定治疗反应的基线预测因素将为个性化护理奠定框架 EoE患者。这项尖端的和临床相关的研究将在智力刺激的情况下进行 范德比尔特大学的支持性环境。在整个颁奖期间，候选人将是 由Anita Mahadean-Jansen博士(主要导师、拉曼光谱专家)Evan Dellon博士指导 (共同导师，EoE专家)，以及一个在病理学、过敏性炎症、生物统计学方面的杰出导师团队 以及生物信息学和数据科学，他们反映了候选人将领导的 未来，他们杰出的职业道路将成为候选人成功的模板。为了实现他的 长期的职业目标，与他的指导团队一起，候选人有条不紊地开拓出了4- 一年的职业发展计划。这个项目将为他提供拉曼光谱学方面的基本技能， 整合多维生物医学数据、临床转化性研究、EoE、拨款撰写和 领导力。卓越的导师，严谨的职业发展计划，以及在 K23奖项的赞助将带来出版物、数据和经验，这些出版物、数据和经验将独特地 应聘为EoE、生物光子学和数据科学交叉领域的独立研究员。

PROJECT SUMMARY The overarching goal of this K23 Mentored Patient-Oriented Research Career Development Award is to provide essential skills and mentored research experience to prepare Dr. Girish Hiremath as an independent investigator leading a multi-disciplinary team of researchers focused on eosinophilic esophagitis (EoE). EoE is a chronic, allergen-mediated, inflammatory condition of the esophagus. As one of the most common cause of upper gastrointestinal morbidity, it affects 1 out of 2000 individuals in the United States. Our understanding of the pathogenesis of EoE is incomplete and this has contributed towards burdensome approaches to treat EoE patients. There is a critical unmet need to understand all factors related with development of EoE, including determining the biochemical changes in the esophagus that leads to transformation of an unaffected esophagus to an inflamed esophagus in EoE. As a Pediatric Gastroenterologist with clinical and research focus on EoE for more than a decade, the candidate proposes to fill this important knowledge gap by building on his published work through: 1) Validating the esophageal biochemical changes in EoE using Raman spectroscopy (a uniquely suited, highly sensitive, laser-based technique) and machine learning, 2) Determining the correlation between the Raman peaks and the disease activity, and the operating characteristics of the key Raman peaks in distinguishing EoE activity states, and 3) Determining if a combination of the baseline Raman, histologic, endoscopic, and clinical features can predict response to EoE therapy. By defining the key esophageal biochemical alterations this study will offer novel insights into pathogenesis of EoE, and by identifying the baseline predictors of the treatment response it will lay a framework for personalized care of EoE patients. This cutting-edge and clinically relevant research will be conducted in an intellectually stimulating and supportive environment at the Vanderbilt University. Throughout the award period, the candidate will be mentored by Dr. Anita Mahadevan-Jansen (primary mentor, expert in Raman Spectroscopy), Dr. Evan Dellon (co-mentor, expert in EoE), and a team of exceptional mentors in pathology, allergic inflammation, biostatistics and bioinformatics, and data science who mirror the blend of researchers that the candidate will lead in the future Their distinguished career paths will serve as a template for the candidate's success. To achieve his long-term career goal, in conjunction with his mentoring team, the candidate has methodically carved out a 4- year career development program. This program will provide him essential skills in Raman spectroscopy, integration of multidimensional biomedical data, clinical translational research, EoE, grant writing, and leadership. The preeminent mentorship, rigorous career development plan, and clinical time under the auspices of this K23 award will lead to publications, data, and experience that will uniquely situate the candidate as an independent investigator at the intersection of EoE, biophotonics, and data science.