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:
- 金额:$ 19.26万
- 依托单位:
- 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:
- 关键词:5 year oldAddressAgeAlgorithmsAntigensAsiaBangladeshiBile AcidsBiochemicalBiologicalBiological MarkersBiopsyBirthCaringCeliac DiseaseCessation of lifeChildChildhoodChronicClassificationClinicalClinical MarkersClinical ResearchCognitive deficitsCohort StudiesCollaborationsComputing MethodologiesData ScienceDetectionDevelopmentDevelopment PlansDiagnosisDiagnosticDuodenumEnvironmental Risk FactorEtiologyExhibitsExposure toFailureFoundationsFundingGastroenterologistGenetic RiskGenetic TranscriptionGlutenGoalsGoldGrowthHealthHistologicHistologyHistopathologyHuman PathologyImageImmune responseImpairmentInflammationInjuryIntestinal permeabilityIntestinesK-Series Research Career ProgramsKnowledgeLactuloseLamina PropriaLeadLengthLinkLymphocytosisMS4A1 geneMalnutritionMeasurementMeasuresMediatingMentorsMentorshipMetabolicMilieu TherapyMorphologyMucositisMultiomic DataNeurocognitivePakistanPathogenesisPathologicPathway interactionsPatternPhysiciansPopulationPopulations at RiskPrevalencePublicationsResearchResourcesRhamnoseRisk FactorsRoleScientistSeveritiesSmall IntestinesStandardizationStructureSystemTimeTissuesTrainingTranslational ResearchUniversitiesVaccinesValidationVillousVillous AtrophyVirginiaVisualWorkabsorptionbasecareercareer developmentcirculating biomarkersclinical phenotypecohortdeep learningdietarydisorder controleffective interventioneffective therapyenteric pathogenepithelium regenerationimaging approachimmunogenicityimprovedintraepitheliallow and middle-income countriesmicrobiomemortalitymultiple omicsnovelnovel markernovel therapeuticsnutrient absorptionnutritionoral vaccinepatient orientedsocioeconomicssystemic inflammatory responsetissue injurytranscriptomeurinary
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Sana Syed其他文献
Sana Syed的其他文献
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{{ truncateString('Sana Syed', 18)}}的其他基金
Predicting Clinical Phenotypes in Crohn's Disease Using Machine Learning and Single-Cell 'omics
使用机器学习和单细胞组学预测克罗恩病的临床表型
- 批准号:
10586795 - 财政年份:2023
- 资助金额:
$ 19.26万 - 项目类别:
Population-Based Characterization of Metabolic Pathways to Predict Pediatric Crohn's Disease Outcomes
基于人群的代谢途径特征预测儿童克罗恩病结果
- 批准号:
10418965 - 财政年份:2022
- 资助金额:
$ 19.26万 - 项目类别:
Population-Based Characterization of Metabolic Pathways to Predict Pediatric Crohn's Disease Outcomes
基于人群的代谢途径特征预测儿童克罗恩病结果
- 批准号:
10660989 - 财政年份:2022
- 资助金额:
$ 19.26万 - 项目类别:
Computational Characterization of Environmental Enteropathy
环境性肠病的计算表征
- 批准号:
10627838 - 财政年份:2019
- 资助金额:
$ 19.26万 - 项目类别:
Computational Characterization of Environmental Enteropathy
环境性肠病的计算表征
- 批准号:
10413870 - 财政年份:2019
- 资助金额:
$ 19.26万 - 项目类别:
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