Purpose: The aim of the present study was to evaluate the electrophysiological, biochemical and ultrastructural changes on the rat sciatic nerve after radiotherapy. Material and Methods: Thirty male Wistar albino rats were divided into three groups as: Control group (n = 10), Group I: 3 months after radiotherapy (n = 10), and Group II: 6 months after radiotherapy (n = 10). Groups I and II were irradiated with a ⁶⁰Co gamma source. A dose of 20 Gy in 10 fractions was applied to Groups I and II. Compound motor action potentials (CMAP) were recorded in all groups. Superoxide dismutase (SOD) and catalase (CAT) activities and malondialdehyde (MDA) levels were measured in the sciatic nerve of rats using the biochemical methods. Ultrastructural changes were determined by electron microscopy. Results: In Groups I and II, the amplitude of CMAP was significantly lower and the latency was significantly higher than that of the control group. There were no significant differences between Groups I and II regarding the CMAP amplitude and latency. The MDA levels were significantly increased, whereas the SOD and CAT activities were significantly decreased in experimental groups when compared with the control group. However, there were no significant changes in these parameters between Groups I and II. Degeneration in myelinated nerve fibers was observed ultrastructurally only in the experimental groups. Significant changes were observed between the control group and experimental groups in terms of ultrastructural myelin grading score and axonal damage score. No significant differences were found between Groups I and II. Conclusions: These findings indicated that the dose of 20 Gy in 10 fractions radiotherapy caused neuropathic damages in normal rat sciatic nerve 3 and 6 months after irradiation.

This study presents a deep learning-based approach for the automated detection of Quiet Eye (QE) durations from electrooculography (EOG) signals in archery. QE—the final fixation or tracking of the gaze before executing a motor action—is a critical factor in precision sports. Traditional detection methods, which rely on expert evaluations, are inherently subjective, time-consuming, and inconsistent. To overcome these limitations, EOG data were collected from 10 licensed archers during controlled shooting sessions and preprocessed using a wavelet transform and a Butterworth bandpass filter for noise reduction. We implemented and compared a traditional model (SVM) and five deep learning models—CNN + LSTM, CNN + GRU, Transformer, UNet, and 1D CNN—for QE detection. The CNN + LSTM model achieved the highest accuracy (95%), followed closely by CNN + GRU (93%), demonstrating superior performance in capturing both spatial and temporal dependencies in the EOG signals. Although Transformer-based and UNet models performed competitively, they exhibited lower precision in distinguishing QE periods. The performance of the traditional model was inferior to deep learning approaches. These results indicate that deep learning provides an effective and scalable solution for objective QE analysis, substantially reducing the dependence on expert annotations. This automated approach can enhance sports training by offering real-time, data-driven feedback to athletes and coaches. Furthermore, the methodology holds promise for broader applications in cognitive and motor skill assessments across various domains. Future work will focus on expanding the dataset, enabling real-time deployment, and evaluating model generalizability across different skill levels and sports disciplines.