Computational Characterization of Environmental Enteropathy

环境性肠病的计算表征

• 批准号: 10413870
• 负责人: Sana Syed
• 金额: $ 19.26万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10413870
• 关键词: 5 year old; Address; Age; Antigens; Asia; Bangladeshi; Bile Acids; Biochemical; Biological; Biological Markers; Biopsy; Birth; Caring; Celiac Disease; Cessation of life; Child; Childhood; Chronic; Classification; Clinical; Clinical Markers; Clinical Research; Cognitive deficits; Cohort Studies; Collaborations; Computing Methodologies; Data Science; Detection; Development; Development Plans; Diagnosis; Diagnostic; Duodenum; Environmental Risk Factor; Etiology; Exhibits; Exposure to; Failure; Foundations; Funding; Gastroenterologist; Genetic Risk; Genetic Transcription; Gluten; Goals; Gold; Growth; Health; Histologic; Histology; Histopathology; Human Pathology; Image; Impairment; Inflammation; Injury; Intestinal permeability; Intestines; K-Series Research Career Programs; Knowledge; Lactulose; Lamina Propria; Lead; Length; Link; Lymphocytosis; MS4A1 gene; Malnutrition; Measurement; Measures; Mediating; Mentors; Mentorship; Metabolic; Milieu Therapy; Morphology; Mucositis; Multiomic Data; Neurocognitive; Pakistan; Pathogenesis; Pathologic; Pathway interactions; Pattern; Physicians; Population; Populations at Risk; Prevalence; Publications; Research; Resource-limited setting; Rhamnose; Risk Factors; Role; Scientist; Severities; Small Intestines; Standardization; Structure; System; Time; Tissues; Training; Translational Research; Universities; Validation; Villous; Villous Atrophy; Virginia; Visual; Work; absorption; base; career; career development; circulating biomarkers; clinical phenotype; cohort; deep learning; diagnostic algorithm; dietary; disorder control; effective intervention; effective therapy; enteric pathogen; epithelium regeneration; imaging approach; improved; intraepithelial; low and middle-income countries; microbiome; mortality; multiple omics; novel; novel marker; novel therapeutics; nutrient absorption; nutrition; oral vaccine; patient oriented; socioeconomics; systemic inflammatory response; tissue injury; transcriptome; urinary; vaccine immunogenicity

PROJECT SUMMARY/ABSTRACT Undernutrition afflicts 20% of children < 5 years of age in low- and middle-income countries (LMICs) and is a major risk factor for mortality. Linear growth failure (or stunting) in children is tightly linked to irreversible physical and cognitive deficits, with profound implications for development. A common cause of stunting in LMICs is Environmental Enteropathy (EE) which has also been linked to decreased oral vaccine immunogenicity. To date, there are no universally accepted, clear diagnostic algorithms or non-invasive biomarkers for EE making this a critical priority. In this K23 Mentored Career Development Award application, Dr. Sana Syed, a Pediatric Gastroenterologist with advanced training in Nutrition at the University of Virginia, proposes to 1) Develop and validate a Deep Learning Net to identify morphological features of EE versus celiac and healthy small intestinal tissue, 2) correlate the Deep Learning Net identified distinguishing EE intestinal tissue findings with clinical phenotype, measures of gut barrier and absorption, and bile acid deconjugation, and 3) Use a Deep Learning Net computational approach to identify distinguishing multiomic patterns of EE versus celiac disease. This work will be carried out in the context of an ongoing birth cohort study of environmental enteropathy in Pakistan (SEEM). Dr. Syed proposes a career development plan which includes mentorship, fieldwork, coursework, publications, and clinical time that will situate her as an independent physician-scientist with expertise in translational research employing computational `omics and image approaches to elucidate biologic mechanisms of stunting pathways and in identification of novel and effective therapies for EE.

项目概要/摘要 在低收入和中等收入国家 (LMIC)，20% 的 5 岁以下儿童面临营养不良问题， 死亡的主要危险因素。儿童线性生长障碍（或发育迟缓）与不可逆的生长密切相关 身体和认知缺陷，对发展具有深远影响。发育迟缓的一个常见原因 LMIC 是环境性肠病 (EE)，也与口服疫苗减少有关 免疫原性。迄今为止，还没有普遍接受的、明确的诊断算法或非侵入性的诊断算法。 EE 的生物标志物使其成为重中之重。在这份 K23 指导职业发展奖申请中， Sana Syed 博士是一位儿科胃肠病学家，曾在弗吉尼亚大学接受过营养学高级培训， 建议 1) 开发并验证深度学习网络来识别 EE 与 乳糜泻和健康小肠组织，2) 将深度学习网络识别出的区分 EE 关联起来 肠道组织发现与临床表型、肠道屏障和吸收的测量以及胆汁酸 解共轭，以及 3) 使用深度学习网络计算方法来识别不同的多组学 EE 与乳糜泻的模式。这项工作将在持续出生队列的背景下进行 巴基斯坦环境性肠病研究（SEEM）。 Syed 博士提出了职业发展计划 包括指导、实地考察、课程作业、出版物和临床时间，这将使她成为一名 独立医师科学家，拥有利用计算组学和转化研究的专业知识 图像方法阐明发育迟缓途径的生物学机制并识别新的和 EE 的有效治疗方法。