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人工智能和精准营养方法在改善资源匮乏地区母婴健康方面的进展。

Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings.

作者信息

Mehta Saurabh, Huey Samantha L, Fahim Shah Mohammad, Sinha Srishti, Rajagopalan Kripa, Ahmed Tahmeed, Knight Rob, Finkelstein Julia L

机构信息

Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA.

Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA.

出版信息

Nat Commun. 2025 Aug 18;16(1):7673. doi: 10.1038/s41467-025-62985-3.

Abstract

Malnutrition continues to be a major threat to health, particularly maternal and child health in low resource settings, resulting in impairments in cognitive function, growth, and development, and metabolic diseases later in life. Nutritional assessment is a cornerstone of any successful nutrition intervention or program whether in the community or at the clinic. Improved computational power and advances in technology may enable precision nutrition-based approaches for maternal and child health, which can complement current methods for nutritional assessment to identify clinical, biochemical, microbiome-related, social, and environmental characteristics to predict responses to nutritional interventions or programs. Precision nutrition has the potential to complement program monitoring, efficacy evaluation, and ultimately to inform design of interventions to improve maternal and child health.

摘要

营养不良仍然是对健康的重大威胁,尤其是在资源匮乏地区对母婴健康构成威胁,会导致认知功能、生长发育受损,以及日后出现代谢性疾病。营养评估是任何成功的营养干预或项目的基石,无论是在社区还是在诊所。计算能力的提高和技术进步可能使基于精准营养的母婴健康方法成为可能,这可以补充当前的营养评估方法,以识别临床、生化、微生物组相关、社会和环境特征,从而预测对营养干预或项目的反应。精准营养有潜力补充项目监测、疗效评估,并最终为改善母婴健康的干预措施设计提供依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a74d/12361493/760084ddb65d/41467_2025_62985_Fig1_HTML.jpg

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