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Computational Characterization of Environmental Enteropathy

环境性肠病的计算表征

基本信息

批准号：
10164762
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
项目类别：
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/10164762
关键词：
5 year old Address Age Algorithms 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 Immune response 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 Resources Rhamnose Risk Factors Role Scientist Severities Small Intestines Standardization Structure System Time Tissues Training Translational Research Universities Vaccines Validation Villous Villous Atrophy Virginia Visual Work absorption base career career development circulating biomarkers clinical phenotype cohort deep learning dietary disorder control effective intervention effective therapy enteric pathogen epithelium regeneration imaging approach immunogenicity 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

项目摘要

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.

项目总结/摘要营养不良影响着低收入和中等收入国家（LMIC）20%的5岁以下儿童，死亡的主要危险因素。儿童的线性生长障碍（或发育迟缓）与不可逆转的身体和认知缺陷，对发展有深远的影响。发育迟缓的常见原因 LMIC是环境性肠病（EE），也与口服疫苗减少有关免疫原性到目前为止，还没有普遍接受的，明确的诊断算法或非侵入性的 EE的生物标志物，使其成为关键优先事项。在这个K23辅导职业发展奖的应用程序，博士Sana Syed是弗吉尼亚大学接受过高级营养培训的儿科胃肠病学家，建议1）开发和验证深度学习网络，以识别EE的形态特征，腹腔和健康的小肠组织，2）将深度学习网络识别的区分EE 具有临床表型的肠组织发现、肠屏障和吸收的测量以及胆汁酸解偶联，以及3）使用深度学习网络计算方法来识别区分性多组 EE与乳糜泻的模式。这项工作将在正在进行的出生队列研究的背景下进行巴基斯坦环境性肠病研究（SEEM）。赛义德博士提出了一个职业发展计划，包括指导，实地考察，课程，出版物和临床时间，这将使她成为一个独立的物理学家和科学家，具有运用计算`组学进行转化研究的专门知识，成像方法，以阐明发育不良途径生物学机制，并确定新的有效的治疗方法。