Objective:To explore the characteristics of brain functional connectivity changes associated with repetitive transcranial magnetic stimulation (rTMS) in adolescents with anhedonia symptoms, and to develop a predictive model of treatment efficacy based on baseline functional connectivity.Methods:A total of 88 adolescents (aged 13-18 years) with major depressive disorder and comorbid anhedonia, diagnosed according to the Diagnostic And Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), were enrolled in a randomized, double-blind, block-design trial. Participants received either active rTMS ( n=44) or sham stimulation ( n=44) for 15 consecutive days with individualized targeting. Resting-state functional magnetic resonance imaging (fMRI) data and clinical assessments were collected before and after the intervention. Brain regions were parcellated using the Brainnetome Atlas to construct whole-brain functional connectivity matrices. Linear mixed-effects models were used to identify functional connections showing significant group×time interaction effects. The percentage change in Snaith-Hamilton Pleasure Scale (SHAPS) scores (ΔSHAPS) served as the dependent variable in multiple regression analyses to examine the explanatory power of connectivity changes for treatment response. A connectome-based predictive modeling (CPM) approach was employed to predict individual treatment responses based on baseline functional connectivity with permutation testing used to validate model robustness. Results:Thirty-one functional connections showing significant group×time interaction ( F=6.67-15.69, all P<0.01) were identified between the active and sham stimulation groups, primarily involving the subcortical network (SCN), dorsal attention network (DAN), limbic network (LN), and default mode network (DMN). Changes in these connections accounted for 53% of the variance in ΔSHAPS (adjusted R2=0.53, F=4.574, P=0.001). The CPM model based on baseline connectivity showed strong predictive performance (10-fold cross-validation: r=0.65, R2=0.40, MAE=0.095, permutation P<0.001; leave-one-out cross-validation: r=0.74, R2=0.52, MAE=0.013, permutation P<0.001). Among the 59 predictive features, those originating from the LN contributed most substantially, particularly cross-network connections with the DMN and SCN. Correlation analyses revealed widespread associations between baseline predictive features and rTMS-induced connectivity changes, including significant negative correlations between baseline LN-DMN connectivity and post-treatment changes in DAN and subcortical connectivity. Conclusion:rTMS significantly alleviates anhedonia symptoms in adolescents with depression and induces widespread reconfiguration of functional connectivity across multiple brain networks. The CPM model based on baseline connectivity features effectively predicts rTMS treatment efficacy for anhedonia, providing new insights for individualized treatment strategies in adolescent depression.

Objective:To explore the characteristics of brain functional connectivity changes associated with repetitive transcranial magnetic stimulation (rTMS) in adolescents with anhedonia symptoms, and to develop a predictive model of treatment efficacy based on baseline functional connectivity.Methods:A total of 88 adolescents (aged 13-18 years) with major depressive disorder and comorbid anhedonia, diagnosed according to the Diagnostic And Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), were enrolled in a randomized, double-blind, block-design trial. Participants received either active rTMS ( n=44) or sham stimulation ( n=44) for 15 consecutive days with individualized targeting. Resting-state functional magnetic resonance imaging (fMRI) data and clinical assessments were collected before and after the intervention. Brain regions were parcellated using the Brainnetome Atlas to construct whole-brain functional connectivity matrices. Linear mixed-effects models were used to identify functional connections showing significant group×time interaction effects. The percentage change in Snaith-Hamilton Pleasure Scale (SHAPS) scores (ΔSHAPS) served as the dependent variable in multiple regression analyses to examine the explanatory power of connectivity changes for treatment response. A connectome-based predictive modeling (CPM) approach was employed to predict individual treatment responses based on baseline functional connectivity with permutation testing used to validate model robustness. Results:Thirty-one functional connections showing significant group×time interaction ( F=6.67-15.69, all P<0.01) were identified between the active and sham stimulation groups, primarily involving the subcortical network (SCN), dorsal attention network (DAN), limbic network (LN), and default mode network (DMN). Changes in these connections accounted for 53% of the variance in ΔSHAPS (adjusted R2=0.53, F=4.574, P=0.001). The CPM model based on baseline connectivity showed strong predictive performance (10-fold cross-validation: r=0.65, R2=0.40, MAE=0.095, permutation P<0.001; leave-one-out cross-validation: r=0.74, R2=0.52, MAE=0.013, permutation P<0.001). Among the 59 predictive features, those originating from the LN contributed most substantially, particularly cross-network connections with the DMN and SCN. Correlation analyses revealed widespread associations between baseline predictive features and rTMS-induced connectivity changes, including significant negative correlations between baseline LN-DMN connectivity and post-treatment changes in DAN and subcortical connectivity. Conclusion:rTMS significantly alleviates anhedonia symptoms in adolescents with depression and induces widespread reconfiguration of functional connectivity across multiple brain networks. The CPM model based on baseline connectivity features effectively predicts rTMS treatment efficacy for anhedonia, providing new insights for individualized treatment strategies in adolescent depression.

Objective To explore the effects of screw configurations on bone healing, so as to provide the basis for related fracture treatment. Methods The process of bone healing under different screw configurations was studied by finite element method, and the change in the process of callus growth during healing periods was simulated by interfragmentary strain theory (IFS). The iterative process for renewing callus modulus in every finite element was conducted by the second-developed ABAQUS based on Python scripting language, thus the process of fracture healing was simulated. Results The effect from different numbers of screws on bone healing was smaller than that from different working length of bone plates. On the premise of stable fixation, given the certain working length of bone plates, the effect from different screw numbers on stress distributions in plates or screws was relatively small, while the effect from different working length on stress distributions in plates or screws was relatively large, and the stress distribution in plates was larger than that in screws at different working length. Conclusions It is necessary to take more consideration on working length of bone plates than the number of screws when they are under a stable fixed situation, and it is a wise choice to reduce the screw numbers and choose a suitable working length for bone healing process.

Objective To evaluate the effects of locked plates with different screw layouts on stability of the internal fixation system for femoral shaft fracture, so as to provide guidance for clinical study on screw layout of the internal fixation system for femoral shaft fracture. Methods Orthogonal test design method was used with 4 groups of screws at 3 parametric levels. Biomechanical properties of the internal fixation system for femoral shaft fracture under 9 screw layouts were analyzed respectively by the finite element method, and 4 inspection indices, namely, the maximum equivalent stress of the plate, the screw and the femur, and the maximum axial displacement of the internal fixation system were obtained from each group of the experiment, and the orthogonal test weight matrix analysis method was conducted for data analysis. Results The influences of different screw parameters on stability of the internal fixation system varied, and the maximum weights of the 4 sets of screw parameters in stability of the internal fixation system were 13.86%, 7.57%, 7.53% and 5.91%, respectively. Conclusions The best scheme for screw layout in orthogonal test is the screw with none-fixed threaded holes in the first group, the single cortical screws with fixed threaded holes in the second group and the double cortical screws with fixed threaded holes in the third and fourth group.

Objective:To establish an HPLC-MS/MS method for the determination of lamotrigine ( LTG) in human plasma to be applied in the clinical therapeutic drug monitoring. Methods:LTG was analyzed on a Kromasil C8 (50 mm × 2. 1 mm,5μm) column. Methanol and water (both containing 0. 1% formic acid) was used as the mobile phase with gradient elution. The flow rate was 0. 6 ml ·min-1 at the column temperature of 40℃. The ion transitions under an ESI positive model were performed at m/z 256. 0>211. 0 and m/z 264. 1>154. 0 for LTG and ticlopidine (internal standard, IS), respectively. Results: The calibration curve of LTG was linear within the range of 0. 02-2 μg · ml-1 . The recoveries of LTG at three quality control levels were within the range of 91. 94%-100. 28%. LTG was stable under all tested conditions and the dilution (the dilution factor was 10) had no influence on the accuracy and precision of LTG determination. Conclusion:The HPLC-MS/MS method for the determination of LTG developed in the study is accuracy, stable and convenient, and is applicable in the clinical therapeutic drug monitoring of LTG.