Objective The differences in biomechanical characteristics of the lower limbs between individuals with chronic ankle instability(CAI)and healthy individuals during unanticipated and anticipated jumping were compared,in order to provide practical references and ideas for the prevention and treatment of recurrent ankle sprains.Methods Thirty subjects were recruited,including 15 patients with CAI and 15 healthy volunteers.All subjects completed unanticipated and anticipated jumping tests in a random order,with a 1-week interval between the two tests.Kinematic and kinetic data of lower limbs were collected synchronously using Vicon infrared high-speed motion capture system and Kistler three-dimensional force platform.Results At the moment of touchdown,knee flexion angle was significantly greater during unanticipated jumping than that during anticipated jumping(P=0.009),while ankle eversion angle was notably lower(P=0.043).During the early landing phase,unanticipated jumping showed significantly greater peak hip flexion and abduction angles,as well as knee flexion(P=0.038,P=0.036,P=0.04),while peak ankle dorsiflexion and eversion angles were significantly lower(P=0.001,P=0.01)compared to anticipated jumping.Additionally,peak hip abduction moment during unanticipated jumping was significantly higher in patients with CAI than that during anticipated jumping(P=0.028).Conclusions Unanticipated jumping reduced ankle dorsiflexion and eversion angles in individuals with CAI,putting the ankle in an open,sprain-prone position.Individuals with CAI compensated proximally by increasing hip flexion,abduction,knee flexion angles,and hip extension moment to stabilize the ankle.

Objective To evaluate the safety and therapeutic efficacy of three-dimensional(3D)-printed personalized cervical correction pillows for treating cervical spondylotic radiculopathy.Methods A finite element model was established to simulate and analyze the biomechanical changes in cervical spine before and after using the pillow.Additionally,20 patients with chronic neck pain were included to analyze changes in visual analogue scale(VAS)scores,neck disability index(NDI),pressure pain threshold(PPT),Borden value,cervical lordosis,T1 slope,cervical slope,and thoracic inlet angle before and after using the pillow.Results Finite element analysis indicated that the maximum stress on vertebral bodies increased by 64.35％and the maximum stress on cartilage tissues by 5.09％after using the pillow.The Borden value improved by 45.75％.Clinical studies showed a significant reduction in VAS scores,NDI,and PPT after treatment(P＜0.05),while PPT,Borden value,cervical lordosis,T1 slope,and thoracic inlet angle significantly increased(P＜0.05).Conclusions The 3D-printed personalized cervical correction pillow is safe and effective in alleviating neck pain and improving cervical curvature,and it provides a new and effective non-surgical treatment option for cervical spondylotic radiculopathy,with significant clinical implications.

Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.

Objective A Cobb angle prediction model for adolescent idiopathic scoliosis(AIS)based on three-point mechanical data from three-dimensional(3D)-printed orthotics and various machine learning algorithms was developed,so as to provide an innovative,radiation-free method for early clinical screening and monitoring of AIS.Methods Clinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender,age,disease type,weight,and Risser score.Six algorithms,namely,random forest,support vector regression,gradient boosting regressor,extreme gradient boosting,lightgbm,and catboost,were used to construct and evaluate the performance of Cobb angle prediction models.Results The gradient boosting regressor model had the best performance on several evaluation metrics,achieving a precision rate of 0.937,recall rate of 0.818,F1-score of 0.949,and an area under curve(AUC)value of 0.843.In the validation set,the model's predictions reached an accuracy rate of 0.942,fitting well with the actual Cobb values.Conclusions The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening.It provides a non-invasive assessment for AIS patients,enhancing the safety and efficiency of screening and monitoring,and offering a powerful decision-making tool for clinicians,with a great clinical significance.

Objective A Cobb angle prediction model for adolescent idiopathic scoliosis(AIS)based on three-point mechanical data from three-dimensional(3D)-printed orthotics and various machine learning algorithms was developed,so as to provide an innovative,radiation-free method for early clinical screening and monitoring of AIS.Methods Clinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender,age,disease type,weight,and Risser score.Six algorithms,namely,random forest,support vector regression,gradient boosting regressor,extreme gradient boosting,lightgbm,and catboost,were used to construct and evaluate the performance of Cobb angle prediction models.Results The gradient boosting regressor model had the best performance on several evaluation metrics,achieving a precision rate of 0.937,recall rate of 0.818,F1-score of 0.949,and an area under curve(AUC)value of 0.843.In the validation set,the model's predictions reached an accuracy rate of 0.942,fitting well with the actual Cobb values.Conclusions The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening.It provides a non-invasive assessment for AIS patients,enhancing the safety and efficiency of screening and monitoring,and offering a powerful decision-making tool for clinicians,with a great clinical significance.

Objective To study the effect of medial collateral ligament(MCL)release on the squatting motion followling total knee arthroplasty(TKA)and provide reference data for ligament release during knee replacement surgery.Methods Based on CT and MRI images of a volunteer,a three-dimensional(3D)geometric anatomical model of the natural knee joint including bone tissues and major soft tissues was established.A finite element model of the artificial knee joint was established by simulating TKA surgery.The squatting motion after 30％release of the upper end,lower end,and both ends of the MCL was simulated,and motion characteristic data of the knee joint at flexion/extension angles from 0° to 135° were obtained.Results The effects of ligament release at different locations on knee squatting motion varied.After releasing the lower end,the medial translation,posterior translation,superior translation,and adduction of the femur relative to the tibia increased by 13.74％,3.83％,9.74％,and 2.37％,respectively,while the external rotation decreased by 36.8％.After releasing the upper end,the medial translation and posterior translation increased by 10.65％and 10％,respectively,while the superior translation,adduction,and external rotation decreased by 4.52％,33.89％,and 67.1％,respectively.After releasing both ends,the medial translation,posterior translation,and superior translation increased by 14.77％,9.39％,and 22.56％,respectively,while the adduction and external rotation decreased by 15.62％and 47.3％,respectively.Conclusions After MCL released,the medial translation,anterior translation,superior translation,and abduction of the femur relative to the tibia increased,while the external rotation decreased.Releasing the lower end had the least effect on these femoral movements,showing an obvious advantage.

Objective To study the effect of medial collateral ligament(MCL)release on the squatting motion followling total knee arthroplasty(TKA)and provide reference data for ligament release during knee replacement surgery.Methods Based on CT and MRI images of a volunteer,a three-dimensional(3D)geometric anatomical model of the natural knee joint including bone tissues and major soft tissues was established.A finite element model of the artificial knee joint was established by simulating TKA surgery.The squatting motion after 30％release of the upper end,lower end,and both ends of the MCL was simulated,and motion characteristic data of the knee joint at flexion/extension angles from 0° to 135° were obtained.Results The effects of ligament release at different locations on knee squatting motion varied.After releasing the lower end,the medial translation,posterior translation,superior translation,and adduction of the femur relative to the tibia increased by 13.74％,3.83％,9.74％,and 2.37％,respectively,while the external rotation decreased by 36.8％.After releasing the upper end,the medial translation and posterior translation increased by 10.65％and 10％,respectively,while the superior translation,adduction,and external rotation decreased by 4.52％,33.89％,and 67.1％,respectively.After releasing both ends,the medial translation,posterior translation,and superior translation increased by 14.77％,9.39％,and 22.56％,respectively,while the adduction and external rotation decreased by 15.62％and 47.3％,respectively.Conclusions After MCL released,the medial translation,anterior translation,superior translation,and abduction of the femur relative to the tibia increased,while the external rotation decreased.Releasing the lower end had the least effect on these femoral movements,showing an obvious advantage.

Objective The differences in biomechanical characteristics of the lower limbs between individuals with chronic ankle instability(CAI)and healthy individuals during unanticipated and anticipated jumping were compared,in order to provide practical references and ideas for the prevention and treatment of recurrent ankle sprains.Methods Thirty subjects were recruited,including 15 patients with CAI and 15 healthy volunteers.All subjects completed unanticipated and anticipated jumping tests in a random order,with a 1-week interval between the two tests.Kinematic and kinetic data of lower limbs were collected synchronously using Vicon infrared high-speed motion capture system and Kistler three-dimensional force platform.Results At the moment of touchdown,knee flexion angle was significantly greater during unanticipated jumping than that during anticipated jumping(P=0.009),while ankle eversion angle was notably lower(P=0.043).During the early landing phase,unanticipated jumping showed significantly greater peak hip flexion and abduction angles,as well as knee flexion(P=0.038,P=0.036,P=0.04),while peak ankle dorsiflexion and eversion angles were significantly lower(P=0.001,P=0.01)compared to anticipated jumping.Additionally,peak hip abduction moment during unanticipated jumping was significantly higher in patients with CAI than that during anticipated jumping(P=0.028).Conclusions Unanticipated jumping reduced ankle dorsiflexion and eversion angles in individuals with CAI,putting the ankle in an open,sprain-prone position.Individuals with CAI compensated proximally by increasing hip flexion,abduction,knee flexion angles,and hip extension moment to stabilize the ankle.

Objective To evaluate the safety and therapeutic efficacy of three-dimensional(3D)-printed personalized cervical correction pillows for treating cervical spondylotic radiculopathy.Methods A finite element model was established to simulate and analyze the biomechanical changes in cervical spine before and after using the pillow.Additionally,20 patients with chronic neck pain were included to analyze changes in visual analogue scale(VAS)scores,neck disability index(NDI),pressure pain threshold(PPT),Borden value,cervical lordosis,T1 slope,cervical slope,and thoracic inlet angle before and after using the pillow.Results Finite element analysis indicated that the maximum stress on vertebral bodies increased by 64.35％and the maximum stress on cartilage tissues by 5.09％after using the pillow.The Borden value improved by 45.75％.Clinical studies showed a significant reduction in VAS scores,NDI,and PPT after treatment(P＜0.05),while PPT,Borden value,cervical lordosis,T1 slope,and thoracic inlet angle significantly increased(P＜0.05).Conclusions The 3D-printed personalized cervical correction pillow is safe and effective in alleviating neck pain and improving cervical curvature,and it provides a new and effective non-surgical treatment option for cervical spondylotic radiculopathy,with significant clinical implications.

Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.