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中文摘要
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摘要-项目4:虚拟公共卫生精准营养实验室 当谈到更好地理解和解决精确营养问题时,重要的是要考虑 无论是在皮肤上还是皮肤下都会发生什么。作用于皮肤之上的因素代表着来自 在个人的身体之外,会影响他们的营养和健康。这包括社交、构建、 以及个人接触到的更广泛的宏观环境,例如来自朋友/家人社交的影响 网络、食物获取和当地政策。有证据表明,人外的因素会影响他们的饮食 以及他们改变饮食的能力。研究还表明,一个人的外部因素是如何“潜伏”的。 影响对营养加工和长期慢性健康状况至关重要的生物过程。在 同时,生物过程和健康状况也会影响一个人以外的因素。忽略因素 由于对营养机制的不完全理解,外界可能会限制精确营养的影响。 以及不准确的干预方法,引入了偏见,加剧了差距。因此,精准营养 应该考虑饮食如何与基于个体水平因素定义的表型相适应,以及 为了缩小干预范围,人们暴露在社会、建筑和宏观环境中--实施 差距。营养建议可能需要根据人们生活和饮食的背景而量身定做,而 人们生活和饮食的环境可能需要干预,以支持对饮食的坚持 建议。因此,这个拟议项目的目标是开发和利用项目3: 精准营养的虚拟人(项目4),与精准的虚拟人一样 营养(项目3)可以作为一个“虚拟实验室”来测试不同饮食对不同食物的影响 并更好地理解和预测结果。区别在于, 虽然基于虚拟人代理的模型(ABM)将关注个体及其皮肤下的一切, 虚拟公共卫生精准营养实验室将纳入外部的关键因素和流程 个人。这将包括个人在社会、经济和建筑环境中的暴露情况。这些 对饮食行为的不同影响形成反馈循环和相互作用,这需要模型 代表自主决策和复杂的适应性行为。目标1将开发样品的ABM 纽约州(NYS)和洛杉矶(LA)地区模拟物理环境如何影响不同 随着时间的推移,人们的营养摄入量、饮食行为、遵循特定饮食的能力以及由此产生的健康状况。目标 2将在ABMS中结合不同代理的社会环境的计算表示,以 模拟这些因素如何影响他们的饮食、营养摄入量、对饮食建议的遵守情况,以及结果 随着时间的推移保持健康。目标3将演示如何使用ABM来评估不同人群的 饮食可能需要根据他们的社会、经济和建筑环境以及其他哪些政策和 可能需要干预措施来促进对特定饮食的坚持。
英文摘要
Abstract-Project 4: Virtual Public Health Precision Nutrition Laboratory When it comes to better understanding and addressing precision nutrition, it is important to consider what happens "above the skin" as well as under. Factors operating “above the skin” represent impacts from outside an individual’s body that can influence their nutrition and health. This includes facets of the social, built, and broader macro-environments that an individual is exposed to, such as influences from friend/family social networks, food access, and local policies. Evidence shows that factors outside a person can affect their diet and their capacity for dietary change. Studies also show how factors outside a person get “under the skin,” affecting biological processes that matter to nutrient processing and long-term chronic health conditions. At the same time, biological processes and health conditions can affect factors outside a person. Neglecting factors outside a person may limit the impact of precision nutrition due to an incomplete understanding of mechanisms and inaccurate intervention approaches that introduce bias and worsen disparities. Thus, precision nutrition should consider how diets fit with phenotypes that are defined based on individual-level factors as well as social, built, and macro-environments people are exposed to in order to narrow the intervention-implementation gap. Nutrition recommendations may need to be tailored to the contexts in which people live and eat, and the contexts in which people live and eat may need to be intervened upon to support adherence to dietary recommendations. Therefore, the goal of this proposed project is to develop and utilize the Project 3: The Virtual Human for Precision Nutrition (Project 4), which like the Virtual Human for Precision Nutrition (Project 3) could serve as a "virtual laboratory" to test different diets on different types/groups of people and better understand and predict the resulting responses. The difference is that while the Virtual Human agent-based model (ABM) will focus on the individual and everything under their skin, the Virtual Public Health Precision Nutrition Laboratory will incorporate the key factors and processes outside the individual. This will include the person's exposure to social, economic, and built environments. These different influences on dietary behaviors form feedback loops and interactions that require the model to represent autonomous decision making and complex, adaptive behaviors. Aim 1 will develop ABMs of sample New York State (NYS) and Los Angeles (LA) areas to simulate how physical environments may affect different people's nutrient intake, dietary behaviors, ability to follow particular diets, and resulting health over time. Aim 2 will incorporate into the ABMs computational representations of different agents' social environments, to simulate how these affect their diet, nutrient intake, adherence to dietary recommendations, and resulting health over time. Aim 3 will demonstrate how the ABM can be used to evaluate how much different peoples’ diets may need to be tailored to their social, economic, and built environment and what other policies and interventions may be needed to facilitate adherence to a given diet.
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Simulating the Spread and Control of Multiple MDROs Across a Network of Different Nursing Homes
  • 批准号:
    10549492
  • 项目类别:
  • 资助金额:
    $52.63万
  • 财政年份:
    2023
  • 负责人:
    Bruce Y Lee
  • 依托单位:
Artificial Intelligence, Modeling, and Informatics for Nutrition Guidance and Systems (AIMINGS) Center
Administration and Coordination Core (ACC)
Project 3: The Virtual Human for Precision Nutrition
海外基金