Objective To investigate the effects on one hand of training the other with isometric wrist extension and flexion training and its underlying mechanism.Methods Twenty healthy young girls were recruited and randomized into a training group and a control group with 10 subjects in each,using a random number table.The subjects in the training group were accepted isometric training of the wrist extensors and flexors on the right side once every other day,4 days a week for 6 weeks according to a pre-programmed protocol,while those in the control group had no intervention.Peak torque and surface electromyography (sEMG) were recorded and assessed before and after 6 weeks of training.Results In the training group,the average peak torque of right wrist flexion and extension were both significantly higher than before training (within-group comparison) and higher than in the control group (between-group comparison) after 6 weeks of training.The average peak torques of the left wrist in the training group in flexion and extension were (12.9±2.0) Nm and (6.4 ± 1.3) Nm after training,both significantly higher than before the training and stronger than the control group.In the sEMG traces during wrist extension,after training the ascend velocity of the right extensor carpi ulnaris and the integrated area of the left extensor carpi ulnaris in the training group were significantly smaller than before training and in the control group.Conclusion Unilateral isometric resistance training of the wrist muscles can transfer to the contralateral side,probably by altering muscle recruitment.

Objective To investigate the extent of cross-education between the pronation and supination muscles of the right and left forearms after unilateral isometric training.Methods Twenty healthy young girls were randomized into a training group and a control group using a random number table.The training group underwent isometric training of their right forearms for six weeks,while the control group continued ordinary life without exercises.Pronation peak torque (PPT) and supination peak torque (SPT) were assessed before and after the training for both groups.Results The differences in PPT or SPT between the training group and the control group were not significant before the training program.When the training had been completed,however,the average PPT and SPT on right side of those trained of course had increased significantly compared to before training or to the control group.More significantly,the average PPT and SPT on the left side in the training group were also significantly better than before training or in the control group after training.Conclusion Unilateral isometric training of the forearm pronation and supination muscles for six weeks can significantly increase muscle strength bilaterally,indicating good cross-education.

Objective:To explore the feasibility and validity of constructing an intensity-modulated radiotherapy gamma pass rate prediction model after combining the SHAP values with the extreme gradient boosting tree (XGBoost) algorithm feature selection technique, and to deliver corresponding model interpretation.Methods:The dose validation results of 196 patients with pelvic tumors receiving fixed-field intensity-modulated radiotherapy using modality-based measurements with a gamma pass rate criterion of 3%/2 mm and 10% dose threshold in Hunan Provincial Tumor Hospital from November 2020 to November 2021 were retrospectively analyzed. Prediction models were constructed by extracting radiomic features based on dose files and using SHAP values combined with the XGBoost algorithm for feature filtering. Four machine learning classification models were constructed when the number of features was 50, 80, 110 and 140, respectively. The area under the receiver operating characteristic curve (AUC), recall rate and F1 score were calculated to assess the classification performance of the prediction models.Results:The AUC of prediction model constructed with 110 features selected based on the SHAP-valued features was 0.81, the recall rate was 0.93 and the F1 score was 0.82, which were all better than the other 3 models.Conclusion:For intensity-modulated radiotherapy of pelvic tumor, SHAP values can be used in combination with the XGBoost algorithm to select the optimal subset of radiomic features to construct predictive models of gamma pass rates, and deliver an interpretation of the model output by SHAP values, which may provide value in understanding the prediction by machine learning-dependent models.