Ke Si, PhD | Zhejiang University; Xiaogang Xu, PhD; Junnan Xu, MS; Hui Yu, PhD; Kaihua Liu, BS
Competition Sponsor: Chinese Academy of Medical Sciences
Awardee Year: 2024
At present, population aging has become a global phenomenon. Particularly in our country, the situation is increasingly severe. Aging and its related diseases have imposed a heavy burden on social development and people’s lives. Among them, behavioral abnormalities triggered by the decline of brain function are an important manifestation of aging. Therefore, establishing effective aging models and intervention methods is a crucial issue in scientific research. However, the existing molecular and cellular models, as well as single-behavior outcome and subjective aging index assessments, still cannot meet practical needs. For these reasons, this project proposes an innovative computerized visual aging evaluation system. We will study the multi-dimensional behavioral changes in aging animal models of different ages, establish a comprehensive behavioral dataset, and combine it with multi-omics sequencing technology to further optimize system performance. By developing multimodal feature extraction and behavioral analysis techniques, we aim to construct an efficient aging behavioral analysis scheme and apply it to human behavioral aging evaluation and screening of anti-aging drugs.
The core innovation of this project lies in establishing a multi-dimensional computer vision behavioral aging evaluation model based on the joint optimization of genomics, proteomics, transcriptomics, and metabolomics across movement, memory, and emotion. This breaks away from the traditional single-behavior results and provides a more accurate and objective evaluation mechanism. We will establish an intelligent system integrating aging evaluation and anti-aging treatment screening, providing high-throughput and precise research tools for research on the mechanisms of aging-related diseases and the development of anti-aging drugs.