Quantitative relationship between hippocampal injury and mechanical load and body weight in closed head injury of male rats using quadratic orthogonal regression - Peng Wang; Xuewei Song; Qiushi Wang; Jinlong Qiu; Xiyan Zhu; Tianming Yu; Hongyi Xiang; Hui Zhao (Scientific Reports)
Abstract Memory dysfunction, a common symptom following closed head impact injuries, suggests deficits in hippocampal function. This defect is thought to be related to impact strength and individual animal weight. In this study, head impact experiments were performed on male Sprague Dawley rats to establish a quantitative relationship between impact strength, body weight and hippocampal injury. A two-factor, three-level experimental design was established using the L9(34) orthogonal table, with impact strength and body weight as the variables. According to the protocol, rats were injured using a BIM-IV animal impact machine, and the effects of impact strength and body weight factors on hippocampal injury were evaluated by the results of Morris water maze and hematoxylin–eosin staining of the hippocampus at 24 h post-injury. Using the percentage of cell loss in the hippocampus as an indicator, quadratic regression models were established to describe the effects of impact strength and body weight on cell loss in the CA1, CA3, and DG regions of the hippocampus. The results revealed a quadratic, non-linear relationship between impact strength, body weight and the percentage of cell loss in these regions. Specifically, increasing impact strength resulted in a higher proportion of cell loss, whereas increasing body weight was associated with a reduction in cell loss. Consistent with these pathological findings, higher impact strength prolonged the time required for rats to locate the hidden platform in the Morris water maze, while higher body weight shortened the platform-finding time under the same impact strength. The quadratic orthogonal regression equations for hippocampal injury as a function of mechanical load and body weight provide valuable insights for predicting hippocampal damage and understanding its underlying mechanisms.