Search

The physiological similarities between mice and humans make them vital animal models in biological and medical research. This paper explores the application of artificial intelligence (AI) in analyzing mice behavior, emphasizing AI's potential to identify and classify these behaviors. Traditional methods struggle to capture subtle behavioral features, whereas AI can automatically extract quantitative features from large datasets. Consequently, this study aims to leverage AI to enhance the efficiency and accuracy of mice behavior analysis. The paper reviews various applications of mice behavior analysis, categorizes deep learning tasks based on an AI pyramid, and summarizes AI methods for addressing these tasks. The findings indicate that AI technologies are increasingly applied in mice behavior analysis, including disease detection, assessment of external stimuli effects, social behavior analysis, and neurobehavioral assessment. The selection of AI methods is crucial and must align with specific applications. Despite AI's promising potential in mice behavior analysis, challenges such as insufficient datasets and benchmarks remain. Furthermore, there is a need for a more integrated AI platform, along with standardized datasets and benchmarks, to support these analyses and further advance AI-driven mice behavior analysis. [ABSTRACT FROM AUTHOR]

Objective: To identify the disease-causing gene mutations in three Chinese pedigrees affected with congenital inherited cataract, in ordre to provide genetic counseling and prenatal diagnosis., Methods: Using exons combined target region capture sequencing chip to screen the candidate disease-causing mutations, Sanger sequencing was used to confirm the disease-causing mutations., Results: Family 1 was polymorphic cataract, family 2 was cerulean cataract, family 3 was coralliform cataract. The inheritance mode of the three pedigrees consisted with autosomal dominant inheritance. In family 1, a nonsense mutation of CRYβB2 gene c.463C>T in exon 6 result in a p.Q155X amino acid change. In family 2, a missense mutation of of CRYGD gene c.43C>T in exon 2 result in a p.R14C amino acid change. In family 3, a missense mutation of CRYGD gene c.70C>A in exon 2 result in a p.P23T amino aid change. No above-mentioned mutations were found in normal individuals., Conclusion: The nonsense mutation c.463C>T (p.Q155X) of CRYβB2 gene, the heterozygous mutations c.43C>T(p.R14C) of CRYGD gene and c.70C>A( p.P23T) of CRYGD gene was the disease-causing gene mutation in family 1, 2 and 3 respectively, our results provid genetic counseling and prenatal diagnosis for these three families.