Objective To observe the influence of functional ankle instability(FAI)on balance and lower limb explosive power. Methods A total of 26 male FAI participants,13 bilateral(bilateral group)and 13 left(left group),who regularly en-gaged in high-intensity exercise,were recruited at Harbin Sport University in May,2024.Meanwhile,13 unin-jured male participants who engaged in high-intensity exercise were recruited as control group.They were mea-sured the moving area of the left foot,right foot and body center of gravity standing on feet with the eyes opened and closed;as well as the sway angle,confidence ellipse diameter(maximum and minimum)to circle area ratio,sway ratio and confidence ellipse standing on single foot,with Gaitview plantar pressure analysis system.They were also tested with Y-balance test(YBT),and were measured flight time and center of gravity height during jumps single leg left/right drift,stiffness and counter movement jump using Opto-jump Optical Measurement of Motor Quality. Results There were significant differences among the groups in swing angle,confidence ellipse diameter(maximum and minimum)to circle area ratio,swing ratio and confidence ellipse as left-leg stance with eyes closed(F>3.300,P<0.05),which was the least in the control group(P<0.05).Swing angle,swing ratio and confidence ellipse were also different among the groups as right-leg stance with eyes closed(F>4.404,P<0.05),and they were less in the control group than in the bilateral group(P<0.05),and less in the left group than in the bilateral group(P<0.05),except swing angle.There was a significant difference in YBT results(F>3.649,P<0.05),which was the least in the bilateral group(P<0.05).There were significant differences in the flight time and center of gravity height during counter movement jump(F>7.458,P<0.01),which was the least in the bilateral group(P<0.05). Conclusion FAI may impair the static balance as single-leg stance with eyes closed,dynamic balance and lower limb ex-plosive power.

Objective:To analysis the molecular characteristics of chronic rhinosinusitis with nasal polyps (CRSwNP), to unravel its pathophysiological mechanisms, and to develop a prognostic model capable of effectively predicting postoperative recurrence.Methods:The data from three datasets (GSE198950, GSE179265, and GSE136825) were integrated, comprising 39 control cases, 16 cases of chronic rhinosinusitis without nasal polyps, and 89 cases of CRSwNP. Differential expression genes (DEGs) were identified based on adjusted P<0.05 and Log2FC>1. KEGG and GO enrichment analyses, as well as STRING node scoring, were conducted. Variable selection was performed using random forest and least absolute shrinkage and selection operator regression (LASSO), with key nodes identified through intersection analysis. Mann-Whitney U test was applied, and variables with P<0.05 were included in the model. A prognostic model for CRSwNP was constructed using logistic regression, externally validated using RNA-seq data, and evaluated with receiver operating characteristic (ROC) curve analysis to calculate the area under the curve (AUC). Results:This research illustrated both upregulated and downregulated DEGs in CRSwNP, activating pathways like neuroactive ligand-receptor interaction and IL-17 signaling, while inhibiting calcium signaling and gap junctions. Key nodes identified through random forest and LASSO, including G protein subunit γ4 ( U=3.00 P=0.028), Cholecystokinin ( U=0.50, P=0.006), Epidermal growth factor ( U=1.00 P=0.008), and Neurexin-1 ( U=0.00, P=0.004), showing statistical significance in external validation. The prognostic model, visualized in a line graph, exhibited high reliability (C-index=0.875,AUC=0.866). The ROC curve in external validation indicated its effectiveness in predicting postoperative recurrence (AUC=0.859). Conclusions:This study integrates multiple datasets on CRSwNP to provide a comprehensive description of its molecular features. The prognostic model, built upon key nodes identified through random forest and LASSO analyses, demonstrates high accuracy in both internal and external validations, thus providing robust support for the development of personalized treatment strategies for CRSwNP.

Objective:To explore the application value of histogram and texture feature analysis based on CT images in distinguishing long bone osteosarcoma (OS) from Ewing sarcoma (ES).Methods:A retrospective collection of 25 patients with long bone osteosarcoma and 25 patients with Ewing sarcoma confirmed by surgery and pathology in National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Qilu Hospital of Shandong University and Nanjing Drum Tower Hospital, Nanjing University Medical School, from March 2018 to May 2023 was conducted. All patients were randomly divided into a training set (21 cases of OS and 19 cases of ES) and a validation set (4 cases of OS and 6 cases of ES) in an 8∶2 ratio. The region of interest (ROI) on CT images to extract texture feature parameters was manually sketched. Random forest and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature screening. Logistic regression (LR), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were used to establish models respectively. Receiver operating characteristic (ROC)curve was drawn and area under the curve (AUC) was calculated to evaluate the diagnostic efficiency of the four models.Results:A total of 100 texture parameters were extracted from CT images, and 8 feature parameters (maximum 3D diameter, 10th percentile, kurtosis, maximum pixel intensity value, inverse normalization, grayscale level variance, long range high grayscale emphasis, and low grayscale area emphasis) were obtained through screening. Four classifiers were used to establish models, and the AUC values of the four models (LR, RF, SVM, KNN) in the validation group were 0.92, 0.79, 0.83, and 0.73, respectively. LR and SVM classifier algorithm trains models had high diagnostic efficiency, with an accuracy of 90%, sensitivity of 83%, specificity of 100%, and AUC of 92% for the LR classifier validation set; the accuracy of SVM classifier validation set was 80%, sensitivity was 67%, specificity was 100%, and AUC was 83%.Conclusions:LR and SVM models have high value in distinguishing OS and ES.

Objective:The purpose of this study was to investigate the clinical value of CT-guided localization of pulmonary nodules with soft wire hook-wire by trailing technique.Methods:The clinical data of 211 pulmonary nodules of 185 patients from November 2020 to March 2022 in Beijing Aerospace General Hospital were retrospectively analyzed. The pulmonary nodules were localized with soft wire hook-wire by trailing technique before video-assisted thoracic surgery (VATS). The success rate, complications, pathological results and localization operations related data were statistically analyzed.Results:The success rate of localization was 97.63% (206/211), and the success rate of VATS removal was 99.53% (210/211). The average operation time was (7.19 ± 2.62) min, and the average time required for resection of lesions was 27 min (10 to 126 min). During the surgery, the soft wire hook-wire of two patient was found to be dislocated and retracted into the chest wall. The pulmonary nodules were successfully located and removed according traces left by puncture points on the lung surface. It was found that the hook-wire was located in the interlobar fissure in 3 patients. The pulmonary nodules were successfully removed by the hook-wire position and appropriately expanding the resection range. A minor pneumothorax occurred in 49 patients, but no closed drainage was needed; 12 patients developed intrapulmonary hematoma; 15 patients with chest pain were treated with analgesia.Conclusions:For small pulmonary nodules requiring thoracoscopic surgery, the computed tomography-guided pulmonary nodule localization with soft wire hook-wire by trailing technique is more convenient, safe and effective, and is worthy of promotion to use.

Objective:To evaluate the long-term safety, tolerability and efficacy of Lacosamide add-on therapy in Chinese children with partial-onset seizures.Methods:SP848 was a global multicenter single-arm study involving 60 Chinese children with partial-onset seizures with the age of 4-17 years who were managed by Lacosamide add-on therapy at seven hospitals across China from April 2018 to May 2019.After treatment with at least two kinds of anti-seizure medications simultaneously or sequentially, partial seizures were still poorly controlled and Lacosamide oral solution (syrup) or tablets were added.The minimum initial oral dose was 2 mg/(kg·d), and the maximum allowable dose was 12 mg/(kg·d)or 600 mg/d during the study period.The dose was adjusted according to the tolerance and seizure control level of partial-onset seizures children.Seizure frequency and the median percentage change in partial-onset seizures per 28 days from baseline to the final visit were recorded, including 50% responder rate and 75% responder rate.Results:A total of 60 Chinese children with the mean age of 9.18 (4.00-15.40) years were included in this interim analysis, involving 39 males and 21 females.The mean course of epilepsy was 5.04 (0.50-15.20) years.A total of 43 patients (71.7%) still have been treated.One patient (1.7%) has completed the 6-12 months of follow-up, and 14 patients (23.3%) have completed the follow-up for less than 6 months.The median change in the frequency of partial seizures every 28 days from baseline to the last visit was -2.91, with its median percentage as -25.46%, and the proportions of ≥50%, while ≥75% responder rate were 40.0% and 28.3%, respectively.A total of 52 patients (86.7%) had 265 treatment emergent adverse events (TEAE), 11 patients (18.3%) had 19 serious TEAE, 37 patients (61.7%) had 127 drug-related TEAE, and 11 patients (18.3%) had 16 TEAE leading to the discontinuation of the trial.The most common TEAE were upper respiratory tract infections (20 cases, 33.3%), followed by drowsiness (16 cases, 26.7%), dizziness (15 cases, 25.0%) and vomiting (13 cases, 21.7%). There were no abnormal changes in the electrocardiographic findings during the treatment.Conclusions:For Chinese patients with partial seizures who are older than the age of 4 years and poorly controlled by other drugs, Lacosamide is effective and well tolerated as an add-on therapy drug.The safety characteristics are consistent with those reported in children and adults.No new safety concerns are identified.

Antibodies, Monoclonal, Humanized/therapeutic use* ; Humans ; Hyperlipidemias ; Hypertriglyceridemia/drug therapy* ; Proprotein Convertases ; Subtilisins

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.

Objective:This study aims to compare the applicability value of autoregressive integrated moving average model(ARIMA) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250-400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, using the liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected data for establishing prediction models of liposuction operation. Using Matlab 2017, 30 couples of liposuction data set were extracted by simple random sampling, and the dynamic time warping (DTW) value of each couple of data sets was calculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction results of the two models. Matlab 2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0.05 indicated statistically significant difference. Results:Eighteen patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36.6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0.048±0.028. The mean DTW value between the ARIMA model predicted data and the actual data was 0.660±0.577, which was statistically significant compared with the test standard ( P<0.05). The mean DTW value between the deep learning model predicted data and the actual data was 0.052±0.030, which was not significantly different compared to the test standard ( P>0.05). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.