Objective To report the first case of renal calculi in a patient with horseshoe kidney (HSK) treated with simultaneous bilateral minimally invasive percutaneous nephrolithotomy (SB-mPCNL) combined with antegrade flexible ureteroscopy lithotripsy (A-FURS), and to analyze the feasibility of the surgical approach. Case Report A 57-year-old male patient was admitted due to “right lumbago and abdominal pain for 10 days”.Computed tomography urography (CTU) revealed bilateral nephrolithiasis with HSK.The patient underwent SB-mPCNL combined with A-FURS.The operation time was 185 min, with the lithotripsy and stone clearance time accounting for 80 min, and the estimated blood loss being approximately 30 mL.A plain abdominal radiograph conducted on the 4th postoperative day showed complete clearance of stones.Bilateral nephrostomy tubes were removed on the 6th postoperative day.The patient recovered uneventfully and was discharged on the 7th postoperative day.The safety and feasibility of this combined surgical modality were analyzed based on relevant literature. Conclusion SB-mPCNL combined with A-FURS is a safe, effective and feasible therapeutic option for bilateral nephrolithiasis in patients with HSK, which is worthy of further clinical verification.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

BACKGROUND:Percutaneous kyphoplasty was a common surgical procedure for the treatment of osteoporotic vertebral compression fracture.However,there was no research to confirm whether percutaneous kyphoplasty could effectively correct the local kyphoplasty of the spine in patients over 80 years old with osteoporotic vertebral compression fracture.OBJECTIVE:To investigate the effect of percutaneous kyphoplasty on local kyphosis in super-aging patients with osteoporotic vertebral compression fracture.METHODS:Single-segment osteoporotic vertebral compression fracture patients treated with percutaneous kyphoplasty at the Department of Spinal Surgery,Peking University People's Hospital,from March 2016 to August 2022,were selected as the research cohort,and the follow-up data of patients in hospital and out-patient were collected.According to patients'age,patients were divided into the advanced age group(60-79 years old,n=126)and the super-aged group(＞80 years old,n=52).According to gender,body mass index,basic diseases(hypertension,diabetes,and cardiovascular diseases),fracture segments and the presence or absence of preoperative intravertebral cleft,the two groups of patients were matched 1:2 by propensity score matching.The lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative and postoperative Cobb angle,recovery rate of Cobb angle,distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture were compared between the two groups.RESULTS AND CONCLUSION:(1)After matching the propensity score,115 patients were included,with 71 patients in the advanced age group and 44 patients in the super-aged group.There was no statistically significant difference in baseline data,including gender,body mass index,hypertension ratio,diabetes ratio,cardiovascular disease ratio,fracture section,and preoperative intravertebral cleft,between the two groups(P＞0.05).The postoperative Cobb angle of the super-aged patients was significantly smaller than that of the elderly patients(P＜0.05).There was no significant difference in lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative Cobb angle,recovery rate of Cobb angle,postoperative distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture ratio between the two groups(P＞0.05).(2)These findings indicate that percutaneous kyphoplasty can effectively correct local kyphosis of the spine in super-aging patients with osteoporotic vertebral compression fractures.

BACKGROUND:Percutaneous kyphoplasty was a common surgical procedure for the treatment of osteoporotic vertebral compression fracture.However,there was no research to confirm whether percutaneous kyphoplasty could effectively correct the local kyphoplasty of the spine in patients over 80 years old with osteoporotic vertebral compression fracture.OBJECTIVE:To investigate the effect of percutaneous kyphoplasty on local kyphosis in super-aging patients with osteoporotic vertebral compression fracture.METHODS:Single-segment osteoporotic vertebral compression fracture patients treated with percutaneous kyphoplasty at the Department of Spinal Surgery,Peking University People's Hospital,from March 2016 to August 2022,were selected as the research cohort,and the follow-up data of patients in hospital and out-patient were collected.According to patients'age,patients were divided into the advanced age group(60-79 years old,n=126)and the super-aged group(＞80 years old,n=52).According to gender,body mass index,basic diseases(hypertension,diabetes,and cardiovascular diseases),fracture segments and the presence or absence of preoperative intravertebral cleft,the two groups of patients were matched 1:2 by propensity score matching.The lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative and postoperative Cobb angle,recovery rate of Cobb angle,distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture were compared between the two groups.RESULTS AND CONCLUSION:(1)After matching the propensity score,115 patients were included,with 71 patients in the advanced age group and 44 patients in the super-aged group.There was no statistically significant difference in baseline data,including gender,body mass index,hypertension ratio,diabetes ratio,cardiovascular disease ratio,fracture section,and preoperative intravertebral cleft,between the two groups(P＞0.05).The postoperative Cobb angle of the super-aged patients was significantly smaller than that of the elderly patients(P＜0.05).There was no significant difference in lumbar CT values,injection amount of bone cement,preoperative and postoperative vertebral height,preoperative collapse rate of the vertebral body,preoperative Cobb angle,recovery rate of Cobb angle,postoperative distance between the bone cement and anterior edge of the vertebral body,sagittal position of cement filling,contact between the bone cement and endplate,distance between the bone cement and vertebral endplates,bone cement distribution score,bone cement leakage,and vertebral refracture ratio between the two groups(P＞0.05).(2)These findings indicate that percutaneous kyphoplasty can effectively correct local kyphosis of the spine in super-aging patients with osteoporotic vertebral compression fractures.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective To conduct a comparative analysis of the biomechanical characteristics of the lower limbs during stair descent in patients with hemiplegia using different method to provide theoretical references for reducing fall risk during stair descent.Methods Ten healthy subjects and 20 patients with hemiplegia were selected,and their kinematic and dynamic data during stair descent were collected using the Qualisys Motion capture system and the Kistler three-dimensional dynamometer.Their biomechanical characteristics and fall risks were also analyzed.Results Compared with that of healthy subjects and patients that step on the healthy side(SHS),the range of motion(ROM)of the affected side in the lower-limb joints of patients that step on the affected side(SAS)was smaller.SHS reduced the flexion and extension ranges of the healthy side of the knee joint,and the ROM of the affected side in the lower-limb joints of SHS patients was greater than that of SAS patients.The ground reaction force(GRF)curve changes of SAS patients in left and right directions during stair descent were relatively consistent with those of normal subjects.The maximum vertical GRF of the affected side in SAS patients at the moment of landing was 1.05 times the body weight,whereas that of the healthy side was 1.25 times the body weight,which was lower than that of normal subjects(1.5 times the body weight).The maximum vertical GRF of the healthy side in SHS patients at the moment of landing was 1.85 times the body weight,which was higher than that of SAS patients and normal subjects.Conclusions Compared with that of SAS patients,the affected limb side of SHS patients has a greater ROM and vertical GRF at the moment of landing during stair descent,making SHS difficult to master.SAS is most consistent with the biomechanical characteristics during stair descent of patients with hemiplegia.

Objective:To explore the value of nodal integrated topological attributes (NITA) based on machine learning model in identifying schizophrenia.Methods:A total of 56 patients with first-onset schizophrenia admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical University from January 2022 to August 2023 and 56 healthy volunteers recruited from community were selected. Functional MRI data were collected, and brain functional networks were constructed after preprocessing. Global and nodal topological attributes were extracted using graph theory as training features. Participants were divided into training set (46 schizophrenia patients and 46 heathy volunteers) and testing set (10 schizophrenia patients and 10 heathy volunteers). Random Forest Classifier (RFC), Support Vector Machine (SVM), and Gradient Boosting Tree (XGBoost) models were fitted to global and nodal topological attributes in the training set to calculate the accuracy, recall rate, F1 value, and area under receiver operating characteristic curve (AUC) of each model. Generalization ability was analyzed based on the performance of testing set, and excellent topological attributes were screened out. Selected topological attributes were reduced to one-dimensional features through principal component analysis,and then fitted to the above models, and feature-adapted model was selected based on the performances of training and testing sets. Statistical analysis of the new dimensional features of each brain region of schizophrenia patients and heathy volunteers was performed. Combined with false discovery rate (FDR), new dimension features with significant differences were selected and fitted with the adapted model.Results:In the training set, machine learning models using node topological attributes achieved higher accuracy, recall rate, F1 scores, and AUC compared with those using global topological attributes. In the test set, the SVM model using node topological attributes showed stable generalizability (accuracy=75.00%, recall rate=100.00%, F1 score=0.80, AUC=0.92). The node topological attribute metrics were down-dimensionally named NITA. Based on validation results of SVM model using NITA in the training set (accuracy of 77.00%, recall of 72.00%, F1 value of 0.76, AUC of 0.86) and performance in the testing set (accuracy of 66.67%, recall of 83.33%, F1 value of 0.71, AUC of 0.61), SVM was selected as the adapted model. NITA in the right middle frontal gyrus ventrolateral area, left inferior frontal gyrus dorsal area, right precentral gyrus caudal ventrolateral area, left superior temporal gyrus rostral area, right fusiform gyrus lateroventral area, right inferior parietal lobule rostrodorsal area, left occipital polar cortex showed significant difference between patients and volunteers ( P<0.05, FDR-corrected). The optimal model (FDR-PCAN-SVM) obtained via NITA being trained on corresponding brain area reached an accuracy of 93.74%, recall rate of 98.00%, F1 value of 0.94, and AUC of 0.96 in the training set and accuracy of 83.33%, recall rate of 66.67%, F1 value of 0.80, and AUC of 0.92 in the testing set. Conclusion:NITA may serve as a potential image biomarker for schizophrenia identification; brain regions with abnormal NITA is key nodes in information exchange and integration within the brain networks in schizophrenia patients.

Objective To predict the torque on the affected side of the hip,knee,and ankle joints in stroke patients during walking using principal component analysis(PCA)and backpropagation(BP)neural networks.Methods Kinematic and dynamic data from 30 stroke patients were synchronously collected using an 8-lens Qualisys infrared point high-speed motion capture system and Kistler three-dimensional(3D)force measurement platform.The torques of the hip,knee,and ankle joints in the stroke patients were calculated using OpenSim,and the initial variables with a cumulative contribution rate of 99％were screened using PCA.The normalized root mean square error(NRMSE),root mean square error(RMSE),mean absolute percentage error(MAPE),mean absolute error(MAE),and R2 were used as evaluation indicators for the PCA-BP model.The consistency between the calculated joint torque and predicted torque was evaluated using Kendall's W coefficient.Results PCA data showed that the trunk,pelvis,and affected sides of the hip,knee,and ankle joints had a significant impact on the torque of the affected sides of the hip,knee,and ankle joints on the x,y,and z axes(sagittal,coronal,and vertical axes,respectively).The NRMSE between predicted and measured values was 5.14％-8.86％,RMSE was 0.184-0.371,MAPE was 3.5％-4.0％,MAE was 0.143-0.248,and R was 0.998-0.999.Conclusions The established PCA-BP model can accurately predict the torque of the hip,knee,and ankle joints in stroke patients during walking,with a significantly shortened measurement time.This model can replace traditional joint torque calculation in the gait analysis of stroke patients,provides a new approach to obtaining biomechanical data of stroke patients,and is an effective method for the clinical treatment of stroke patients.