A New Perspective on Mental Health Assessment in Laboratory Animals: Stress Response Monitoring Based on Hair Characteristics
10.12300/j.issn.1674-5817.2025.166
- VernacularTitle:实验动物精神健康评估新视角：基于毛发特征的应激反应监测
- Author:
Hongman LI
1
;
Yutong CHEN
1
;
Yingpei SHI
1
;
Yijing WANG
1
;
Yan PAN
1
;
Tong XU
1
;
Yi ZHOU
1
;
Qiyue DENG
1
;
Xue LIU
2
Author Information
1. School of Basic Medicine, Army Medical University, Chongqing 400038, China
2. Department of Biomedical Engineering and Medical Imaging, Army Medical University, Chongqing 400038, China
- Publication Type:Journal Article
- Keywords:
Laboratory animals;
Mental health management;
Stress;
Hair;
Artificial intelligence
- From:
Laboratory Animal and Comparative Medicine
2026;46(4):553-563
- CountryChina
- Language:Chinese
-
Abstract:
The mental health management of laboratory animals is a critical factor in ensuring the reliability of scientific research data. However, due to the subtle nature of mental state alterations and the limitations of current assessment methods, the mental health of laboratory animals is often overlooked by researchers. Therefore, there is an urgent need to explore a new, objective, simple, and practical method for evaluating mental health. Stress, as a primary factor inducing alterations in the mental state of animals, can influence experimental results across multiple research fields through the neuroendocrine-immune network. This paper first elucidates the necessity of mental health management in laboratory animals from three perspectives: factors contributing to stress, neural mechanisms of stress, and research areas affected by stress. It highlights that excluding animals with abnormal mental states before experiments can enhance the efficiency and reproducibility of biomedical studies. Second, this paper briefly summarizes existing methods for assessing the health of laboratory animals, pointing out that approaches such as behavioral tests and metabolomics have many limitations in evaluating stress responses. Existing methods struggle to meet the demand for simple, objective, and non-invasive assessment methods in animal management. Therefore, this paper focuses on hair, a biological sample with the advantages of cumulative information and non-invasive collection, and systematically describes the theoretical basis and technological advances in using hair characteristics as indicators of stress responses from three perspectives: hair traits, the levels of substances in hair, and artificial intelligence (AI). It proposes and supports an innovative technical pathway that combines hair traits with AI-based image analysis, with the aim of offering an improved solution for non-invasive and objective stress assessment, while providing theoretical support for improving the health management system of laboratory animals.
