Machine learning based frailty index for the genetically diverse mice
基于机器学习的遗传多样性小鼠的衰弱指数
基本信息
- 批准号:10513177
- 负责人:
- 金额:$ 33.6万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AdoptedAffectAgeAgingAnimal ModelAnimalsApplied ResearchAutomationBasic ScienceBehaviorBehavioralBehavioral ModelBiologicalBiological AgingBiological AssayCaloric RestrictionChronicChronologyClinicalCollaborationsComputer Vision SystemsDataData SetDietDietary InterventionDiseaseEnvironmental Risk FactorEtiologyFatty acid glycerol estersFutureGeneticGerontologyHealthHealth StatusHeterogeneityHigh Fat DietHourHumanIndividualInterventionIntervention StudiesJudgmentLettersLongevityMachine LearningManualsMeasuresMethodsMonitorMouse StrainsMusNesting BehaviorOrganismOutcomePhasePhenotypePhysiologicalPhysiologyPopulationPopulation HeterogeneityProcessPublic HealthReproducibilityResearchShockSleepSocial BehaviorSystemTestingThe Jackson LaboratoryTherapeuticTimeTrainingTranslational ResearchVisualage relatedagedbasebiological systemsclinical heterogeneitydietary restrictiondiverse datadrinking behaviorfeedingfrailtyhands-on learninghealthy aginghigh rewardhigh riskhuman modelimprovedindexingmachine learning modelmortalitymortality risknovelopen field behaviorpre-clinicalpreclinical studysocialsugartooltranslational study
项目摘要
PROJECT SUMMARY
Aging is a terminal process that affects all biological systems. Biological aging—in contrast to chronological
aging—occurs at different rates for different individuals. In humans, growing old comes with increased health
issues and mortality rates, yet some individuals live long and healthy lives, and others succumb earlier to
diseases and disorders. The concept of frailty is used to quantify this heterogeneity and is defined as the state
of increased vulnerability to adverse health outcomes. The frailty index (FI) is an invaluable and widely used tool
which outperforms other methods to quantify frailty. FIs have been adapted for use in mice using a variety of
both behavioral and physiological measures as index items. However, because conducting mouse FI requires
trained individuals for manual scoring, it often limits the scalability of the tool. Thus, although the FI is an
extremely useful tool for aging research, an increase in its scalability, reliability, and reproducibility through
automation would enhance its utility. We used machine learning applied to video data to create an automated
visual FI (vFI). The is easy to implement, unbiased, and scalable. Here we propose to improve our tool and carry
out an interventional study. We will adopt the vFI to function with genetically diverse mice (R61: Aim 1). We will
also create features from long-term monitoring to increase accuracy and breadth of systems measured in the
vFI (R61: Aim 2). Finally, we will apply the vFI to a diet intervention study to show its utility for large scale studies
(R33: Aim 3). We will test a high fat high sugar diet (increased frailty) and caloric restriction group (decreased
frailty) with normal chow (control) in a Diversity Outbred population of mice. The result of this project will be a
fully validated and automated vFI that can be used for high-throughput interventional studies, enabling
therapeutics for healthy aging.
项目摘要
衰老是影响所有生物系统的终末过程。生物衰老 - 与年代相反
衰老 - 不同个体以不同的速度涉及。在人类中,变老会随着健康增长而增加
问题和死亡率率,但有些人过着长寿,健康的生活,而另一些人则早些时候屈服于
疾病和疾病。脆弱的概念用于量化这种异质性,并将其定义为状态
增加对不利健康结果的脆弱性。脆弱指数(FI)是一种有趣且广泛使用的工具
这超出了量化脆弱的其他方法。 FIS已适用于使用多种
行为和物理措施作为索引项目。但是,因为进行鼠标FI需要
受过训练的个人进行了手动评分,它通常会限制工具的可扩展性。那,尽管FI是
非常有用的工具,用于老化研究,通过
自动化将增强其效用。我们使用了应用于视频数据的机器学习来创建自动化
视觉FI(VFI)。易于实现,公正和可扩展。在这里,我们建议改善我们的工具并携带
介入研究。我们将采用VFI与一般的大型小鼠一起起作用(R61:AIM 1)。我们将
还可以创建长期监控的功能,以提高在
VFI(R61:AIM 2)。最后,我们将将VFI应用于饮食干预研究,以显示其用于大规模研究的效用
(R33:AIM 3)。我们将测试高脂肪高糖饮食(增加脆弱)和热量限制组(减少
脆弱)在多样性的小鼠种群中具有正常的食物(对照)。这个项目的结果将是
完全验证和自动化的VFI,可用于高通量介入研究,使得
健康衰老的治疗剂。
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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VIVEK KUMAR其他文献
VIVEK KUMAR的其他文献
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{{ truncateString('VIVEK KUMAR', 18)}}的其他基金
Machine learning based frailty index for the genetically diverse mice
基于机器学习的遗传多样性小鼠的衰弱指数
- 批准号:
10688138 - 财政年份:2022
- 资助金额:
$ 33.6万 - 项目类别:
The Short Course on the Application of Machine Learning for Automated Quantification of Behavior
机器学习在行为自动量化中的应用短期课程
- 批准号:
10600079 - 财政年份:2022
- 资助金额:
$ 33.6万 - 项目类别:
Google Cloud Pipeline for mouse behavior and frailty assessment for the aging research community
Google Cloud Pipeline 用于衰老研究社区的小鼠行为和虚弱评估
- 批准号:
10827671 - 财政年份:2022
- 资助金额:
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Establishment and Characterization of Novel Mutant Mouse Models for the Addiction Research Community
成瘾研究界新型突变小鼠模型的建立和表征
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10647879 - 财政年份:2021
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Impacts of Sleep and Circadian Biology on Alzheimer's Disease and Aging: A Focus on Genetics and Genomics
睡眠和昼夜节律生物学对阿尔茨海默病和衰老的影响:关注遗传学和基因组学
- 批准号:
10606644 - 财政年份:2021
- 资助金额:
$ 33.6万 - 项目类别:
Impacts of Sleep and Circadian Biology on Alzheimer's Disease and Aging: A Focus on Genetics and Genomics
睡眠和昼夜节律生物学对阿尔茨海默病和衰老的影响:关注遗传学和基因组学
- 批准号:
10378650 - 财政年份:2021
- 资助金额:
$ 33.6万 - 项目类别:
Impacts of Sleep and Circadian Biology on Alzheimer's Disease and Aging: A Focus on Genetics and Genomics
睡眠和昼夜节律生物学对阿尔茨海默病和衰老的影响:关注遗传学和基因组学
- 批准号:
10237478 - 财政年份:2021
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Dissection of Addiction Relevant Signal Integration by Cyfip2 through Precise Genome Engineering
Cyfip2 通过精确基因组工程解析成瘾相关信号整合
- 批准号:
10450066 - 财政年份:2020
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Dissection of Addiction Relevant Signal Integration by Cyfip2 through Precise Genome Engineering
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10074946 - 财政年份:2020
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$ 33.6万 - 项目类别:
Dissection of Addiction Relevant Signal Integration by Cyfip2 through Precise Genome Engineering
Cyfip2 通过精确基因组工程解析成瘾相关信号整合
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