ObjectiveThis study aims to explore the mechanism by which Yishen Huoxue Tongqiao Formula improves metabolism-neuroplasticity and treats unilateral vestibular labyrinth destruction by regulating the metabolic balance of glutamate （Glu）/γ-aminobutyric acid （GABA）. Methods48 Sprague-Dawley （SD） adult rats were randomly divided into the sham operation group， model group， Yishen Huoxue Tongqiao Formula groups with low， medium， and high doses （9.20， 18.39， 36.78 g·kg^-1）， and betahistine group （1.62 mg·kg^-1）. A unilateral vestibular labyrinth destruction （vestibular dysfunction） model was established by intratympanic injection of chloroform into the right ear， while the control group received intratympanic injection of normal saline. Drugs were administered once daily for seven consecutive days. During the period， behavioral tests were performed to evaluate the behaviors of rats after unilateral vestibular labyrinth destruction. Hematoxylin-eosin （HE） staining and Nissl staining were used to observe the neuronal morphology in the medial vestibular nucleus. Golgi staining was employed to assess the number of dendritic spines of neurons in the medial vestibular nucleus. Ultra-performance liquid chromatography-tandem mass spectrometry （LC-ESI-MS/MS） was utilized to detect Glu/GABA. Immunofluorescence and immunohistochemistry were used to detect the expressions of neuronal nuclei （NeuN）， growth-associated protein 43 （GAP-43）， and glial fibrillary acidic protein （GFAP）. Western blot and real-time fluorescent quantitative polymerase chain reaction （Real-time PCR） were applied to determine the expressions of glutamate-immunoreactive （Glu-IR）， GABA， GFAP， postsynaptic density protein 95 （PSD-95）， and GAP-43. ResultsCompared with the sham operation group， the model group presented with head deviation， balance disorder， increased tail suspension score， nuclear consolidation of medial vestibular nerve neurons， and decreased Nissl bodies （P<0.01）. The number of dendritic spines in neurons and NeuN-positive cells decreased. The content of Glu decreased. The content of GABA increased （Glu/GABA decreased）. The expression of GAP-43 was down-regulated， and GFAP was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PSD-95， and GAP-43 proteins， as well as Glu-IR mRNA decreased， while the expressions of GABA and GFAP proteins and mRNA increased （P<0.05， P<0.01）. Compared with those in the model group， the head deviation， imbalanced behavior， and tail suspension scores in each treatment group decreased， with alleviated neuronal injury and recovered Nissl bodies （P<0.01）. The number of dendritic spines of neurons increased， and the number of NeuN-positive cells rebounded. The content of Glu increased， and the content of GABA decreased （Glu/GABA increased）. GFAP was down-regulated， and GAP-43 was up-regulated （P<0.05， P<0.01）. The expressions of Glu-IR， PMD-95， and GAP-43 proteins， as well as Glu-IR mRNA increased， while the expressions of GABA and GFAP proteins and mRNA decreased. The effect was more significant in the high-dose group （P<0.01）. ConclusionThe Yishen Huoxue Tongqiao Formula can alleviate vestibular dysfunction， and its mechanism may be associated with regulating the metabolic balance of Glu/GABA， mitigating neural damage， improving synaptic plasticity （promoting GAP-43 expression and inhibiting GFAP expression）， and facilitating vestibular compensation.

Objective Based on data from the Global Burden of Disease Study 2023 (GBD 2023), this study analyzes the trends in disease burden and years lived with disability (YLD) of bladder cancer in China from 1990 to 2023. Methods Epidemiological data on bladder cancer in China were obtained from the GBD 2023 database.The age-standardized prevalence rate (ASPR), YLD and population attributable fraction (PAF) of risk factors were calculated.Temporal trends were evaluated using the estimated annual percentage change (EAPC), and Joinpoint regression was applied to compute the annual percent change (APC) and average annual percent change (AAPC) for trend analysis. Results Between 1990 and 2023, the number of bladder cancer patients requiring rehabilitation services increased from 213 568 cases to 587 191 cases, representing a 175.0% increase.The YLD rose from 22 158 to 56 620, marking a 155.6% growth.The ASPR increased from 22.6/100 000 to 25.7/100 000 (EAPC=0.06), indicating a stable trend.The age-standardized YLD rate (ASYR) remained at 2.5/100 000 (EAPC=-0.32), indicating a downward trend.Notably, gender disparities in disease burden were evident, with male patients and YLD being 4.63 times and 4.25 times higher than their female counterparts, respectively.Risk factor analysis showed that smoking and high fasting plasma glucose were the main risk factors for bladder cancer.The ASYR attributable to high fasting plasma glucose showed an increasing trend in males (EAPC=0.13) but a decreasing trend in females (EAPC=-1.41). Conclusion The demand for rehabilitation and disease burden of bladder cancer in China have significantly increased, with particularly heavy burdens among males and the elderly population.It is urgent to formulate targeted interventions, strengthen tobacco control, occupational protection, and weight management, and establish an integrated rehabilitation service system to address the increasingly severe disease burden caused by bladder cancer.

Objective:To compare the clinical outcomes of immediate breast reconstruction using latissimus dorsi flap with implant versus mesh with implant based on BREAST-Q evaluation.Methods:From the clinical database of the First Affiliated Hospital of Nanjing Medical University, the patients who underwent immediate breast reconstruction after total mastectomy from January 2020 to December 2023 were selected as the research subjects. All breast reconstruction surgeries were performed by the same surgeon. Patients were divided into two groups according to surgical methods: the latissimus dorsi muscle flap combined with implant immediate breast reconstruction group (LD group) and the mesh combined with implant immediate breast reconstruction group (mesh group). Patients were followed up in outpatient clinics or by telephone one year after surgery. The BREAST-Q was used to evaluate the surgical outcomes of both groups from four dimensions: psychosocial well-being, sexual well-being, chest-physical well-being, and breast satisfaction. The score range for each dimension was 0-100, with higher scores indicating greater patient satisfaction with quality of life and surgical outcomes. Statistical analysis was performed using SPSS 22.0 software. Normally distributed measurement data were expressed as Mean ± SD, and comparisons between the two groups were performed using independent sample t-test. Count data were expressed as number of cases and percentages, and comparisons between groups were performed using chi-square test or Fisher’s exact test. P<0.05 was considered statistically significant. Results:A total of 123 patients were included, with 59 patients in the LD group and 64 patients in the mesh group. In the LD group, the mean age was (37.7±7.0) years, body mass index (BMI) was (22.6±2.6) kg/m 2, and clinical tumor staging showed 2, 22, 30, and 5 cases for stages 0, Ⅰ, Ⅱ, and Ⅲ, respectively. In the mesh group, the mean age was (39.1±7.0) years, BMI was (22.6±2.8) kg/m 2, and clinical tumor staging showed 1, 25, 38, and 0 cases for stages 0, Ⅰ, Ⅱ, and Ⅲ, respectively. There were no statistically significant differences between the two groups in baseline characteristics including age, BMI, and clinical tumor staging (all P>0.05). One year after surgery, the BREAST-Q result showed no statistically significant differences between the LD group and mesh group in psychosocial well-being [(83.0±19.8) points vs. (80.8±19.3) points] and sexual well-being [(62.1±30.4) points vs. (65.8±25.6) points] (all P>0.05). However, the LD group had lower chest-physical well-being scores than the mesh group [(40.6±9.7) points vs. (45.1±9.6) points, P<0.05], while breast satisfaction scores were higher in the LD group than in the mesh group [(68.0±17.8) points vs. (59.8±12.6) points, P<0.01]. Conclusion:Immediate breast reconstruction by both latissimus dorsi flap with implant and mesh with implant can improve patients’ psychosocial and sexual well-being by enhancing breast appearance. However, LD technique provides better breast satisfaction, while the mesh technique offers advantages in physical well-being of the chest wall and upper body. Surgeons should select the most appropriate breast reconstruction technique based on patients’ anatomical conditions, treatment history, and individual needs to optimize postoperative quality of life and satisfaction.

With the advancement of economic development and clear aligner technology,invisible orthodontics has become an important treatment approach for malocclusion.However,in extraction cases involving anterior tooth retraction,the limited three-dimensional force control of aligners often leads to lingual tipping of incisors,deepened overbite,and anchorage loss of posterior teeth,thereby increasing the complexity and uncertainty of orthodontic treatment.As an effective tool for simulating complex biomechanical behaviors,finite element analysis(FEA)enables quantitative assessment of tooth movement and stress distribution in the periodontium,providing theoretical support for optimizing aligner design.This review focuses on FEA studies of anterior tooth retraction using clear aligners in extraction cases,summarizes tooth movement characteristics,and discusses key influencing factors including aligner parameters,movement patterns,attachment design,and auxiliary appliances.Finally,the future directions of FEA in dynamic simulation and personalized modeling are explored to support more predictable and effective clinical outcomes.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.

With the advancement of economic development and clear aligner technology,invisible orthodontics has become an important treatment approach for malocclusion.However,in extraction cases involving anterior tooth retraction,the limited three-dimensional force control of aligners often leads to lingual tipping of incisors,deepened overbite,and anchorage loss of posterior teeth,thereby increasing the complexity and uncertainty of orthodontic treatment.As an effective tool for simulating complex biomechanical behaviors,finite element analysis(FEA)enables quantitative assessment of tooth movement and stress distribution in the periodontium,providing theoretical support for optimizing aligner design.This review focuses on FEA studies of anterior tooth retraction using clear aligners in extraction cases,summarizes tooth movement characteristics,and discusses key influencing factors including aligner parameters,movement patterns,attachment design,and auxiliary appliances.Finally,the future directions of FEA in dynamic simulation and personalized modeling are explored to support more predictable and effective clinical outcomes.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.

With the rapid development of information technology,WeChat official accounts have emerged as an essential tool in the education sector,standing out among numerous platforms due to their simplicity and versatile functions.This paper examines the chal-lenges in traditional orthodontic practice teaching and explores innovative approaches and practical outcomes of empowering undergradu-ate orthodontic practice teaching through WeChat official accounts.Educators utilize the platform to upload instructional videos on arch-wire bending,creating a"mobile and shared"teaching platform that provides students with additional support for self-learning and clin-ical practice.Through personalized instruction,students develop a"mind-eye-hand"three-dimensional operational skillset.The study reveals that empowering orthodontic education with WeChat official accounts effectively enhances students' learning interest,optimizes classroom time,and facilitates a mutual workload reduction for both teachers and students during teaching reforms.Furthermore,it pro-motes the homogenization,flexibility,and ecological development of teaching content.This model demonstrates promising application prospects and offers new ideas and practical pathways for digital innovation and transformation in dental education.