OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

Depression is a common mental disorder that falls under the category of "stagnation syndrome" in traditional Chinese medicine （TCM）. Its complex pathogenesis poses challenges for the development of novel therapeutic agents. Currently， clinically used antidepressants are often accompanied by significant side effects， and statistics show that about one-third of patients do not respond to these medications. TCM demonstrates advantages in the treatment of depression through multi-target， multi-pathway and multi-mechanistic approaches. Pinelliae Rhizoma， a phlegm-resolving herb， exhibits effects such as drying dampness and resolving phlegm， as well as eliminating stuffiness and reducing masses. The characteristics of harmonizing Yin and Yang and resolving stagnation in the middle energizer align precisely with the pathogenesis of depression syndrome， demonstrating therapeutic efficacy in affected patients. Literature studies have found that the active ingredients of Pinelliae Rhizoma， such as cavidine， baicalein， β-sitosterol， as well as Pinelliae Rhizoma herb pairs， such as Pinelliae Rhizoma-Magnoliae Officinalis Cortex， Pinelliae Rhizoma-husked sorghum， Pinelliae Rhizoma-Prunellae Spica， exhibit significant antidepressant effects. Furthermore， TCM formulas containing Pinelliae Rhizoma as the principal therapeutic agent， such as Banxia Xiexin Tang， Banxia Houpo Tang， and Wendan Tang， as well as formulas incorporating Pinelliae Rhizoma like compound Xiaochaihu Tang， Chaihu Jia Longgu Muli Tang， and Erchen Tang， have also demonstrated favorable antidepressant efficacy. The antidepressant mechanism of these agents may involve modulation of 5-hydroxytryptamine （5-HT） and dopamine （DA） levels， up-regulation of brain-derived neurotrophic factor （BDNF） expression， regulation of the hypothalamus-pituitary-adrenal （HPA） axis， reduction of oxidative stress， modulation of nuclear transcription factor-κB （NF-κB） signaling pathway， and inhibition of microglia-mediated inflammatory responses. This review summarized the antidepressant mechanisms and clinical applications of the active components， herb pairs， and TCM formulas containing Pinelliae Rhizoma， aiming to provide a reference for modern research on the use of Pinelliae Rhizoma in antidepressant therapy.

Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

Neurological disorders such as post-stroke hemiplegia, spinal cord injury, and Parkinson disease represent a major global health burden. Brain-computer interface (BCI), which creates direct communication pathways between the nervous system and external devices, offers a promising strategy for functional restoration. The long-term efficacy of such BCI fundamentally depends on the performance of biomaterials at the neural interface. Ideal materials must concurrently satisfy biocompatibility, electrical conductivity, enduring chemical stability, and mechanical compatibility with brain tissue. This review systematically outlines the application of conductive polymers, inorganic nanomaterials, natural biomaterials, and composites in BCI, with a focus on how advanced designs, such as bionic and encapsulated electrodes, improve signal fidelity and surgical feasibility through structural innovation. It further summarizes key material-modification techniques and analyzes the complex foreign-body response orchestrated by microglia, astrocytes, and peripheral immune cells. Finally, it provides insights into future research directions and clinical translation of BCI-based neurorehabilitation, while highlighting critical challenges including long-term biosafety and the establishment of standardized evaluation frameworks, aiming to bridge the gap between laboratory innovation and effective clinical deployment.

ObjectiveTo propose an X-Gait motor rehabilitation assessment system based on a perception-cognition decoupling architecture to jointly achieve high-accuracy gait recognition and clinical interpretability for low clinical credibility and opaque decision-making workflows in existing intelligent gait assessment models. MethodsIn the perception layer, YOLOv11-Pose and PoseFormerV2 were integrated for 2D-to-3D human pose reconstruction. A long short-term memory network was adopted for temporal classification of normal, abnormal and pathological gaits. The layer-wise relevance propagation algorithm was used to identify discriminative joints and automatically quantify biomechanical indices including trunk forward inclination angle, stance phase percentage and stride symmetry. In the cognition layer, rehabilitation-oriented prompt engineering and prior knowledge graphs were combined to drive large language models, converting quantitative biomechanical measurements into clinically reasonable structured reports. Eight subjects were enrolled for bedside clinical validation to evaluate classification performance and clinical consistency. ResultsThe overall gait classification accuracy of X-Gait reached 94.1%. The 2D pose detection achieved 100% valid detection rate with an average confidence of 0.819. The mean per-joint position error of 3D pose reconstruction was 105.97 mm. In clinical validation of eight cases, outputs generated by explainable artificial intelligence combined with large language models showed high consistency with manual assessments from experienced rehabilitation physicians. ConclusionThe proposed X-Gait system based on perception-cognition decoupling realizes high-precision gait identification and clinically interpretable outputs.

Objective To develop a quality of life(QoL)scale for Premature Ovarian Insufficiency(POI)with high reliability and validity in accordance with China's national conditions.Methods The theoretical framework of the scale was constructed under the guidance of traditional Chinese medicine theory with reference to the development method of the QoL scale.Literature analysis method,medical record review method,patient interview method,Delphi expert consultation method and clinical investigation were used to establish,improve and screen the scale items to form the final scale,and the characteristics and differentiation in terms of reliability and validity and feasibility of the final scale were evaluated.Results After screening the entries by the Delphi method and clinical surveys,the Chinese Premature Ovarian Insufficiency Quality of Life Scale was formed,which included three domains of physiology,psychology,and society,and eleven aspects such as menstruation,fertility,and physical symptoms,with 30 entries and two overall evaluations.The Cronbach's coefficient of this scale was 0.910,the fold-half coefficient was 0.956,and the re-test correlation coefficient was 0.868,which showed good internal consistency,fold-half and re-test reliabilities;the scale extracted 9 factors with characteristic root>1,and the cumulative variance contribution rate was 71.063%,the comparative fit index was 0.959,and the goodness-of-fit index was 0.919,and the degree of correlation was tested has showed that the content validity,structural validity and calibration correlation validity were good;the difference in the total scores of the scale between patients with POI and the healthy population was statistically significant,and the scale had a good degree of differentiation.Conclusion As confirmed by this study,the Chinese Premature Ovarian Insufficiency Quality of Life Scale has good reliability,validity,differentiation and feasibility,and can be used as a tool for evaluating the quality of life of POI patients in China as well as enriching the efficacy evaluation system of TCM in treating POI,and it is suitable for clinical popularization and application.

Autophagy is a conserved physiological phenomenon commonly found in all eukaryotic cells,which promotes cell survival by degrading intracellular proteins and organelles during starvation or nutrient deficiency.Dysfunc-tion autophagy is closely related to various diseases.Soluble N-elthylmaleimide-sensitive factor attachment protein recep-tor(SNARE)complex is a protein complex that mediates membrane fusion and plays a key role in the fusion process of au-tophagosomes and lysosomes.The aberrant assembly of the SNARE complex results in the arrest of autophagic flow,there-by leading to cellular dysfunction and the occurrence of diseases.In this review,we summarized the roles of SNARE pro-teins in the autophagosome-lysosome fusion and the latest research in related diseases,providing a reference for studying the mechanism of autophagy and diseases targeting the SNARE complex.

Objective To develop a quality of life(QoL)scale for Premature Ovarian Insufficiency(POI)with high reliability and validity in accordance with China's national conditions.Methods The theoretical framework of the scale was constructed under the guidance of traditional Chinese medicine theory with reference to the development method of the QoL scale.Literature analysis method,medical record review method,patient interview method,Delphi expert consultation method and clinical investigation were used to establish,improve and screen the scale items to form the final scale,and the characteristics and differentiation in terms of reliability and validity and feasibility of the final scale were evaluated.Results After screening the entries by the Delphi method and clinical surveys,the Chinese Premature Ovarian Insufficiency Quality of Life Scale was formed,which included three domains of physiology,psychology,and society,and eleven aspects such as menstruation,fertility,and physical symptoms,with 30 entries and two overall evaluations.The Cronbach's coefficient of this scale was 0.910,the fold-half coefficient was 0.956,and the re-test correlation coefficient was 0.868,which showed good internal consistency,fold-half and re-test reliabilities;the scale extracted 9 factors with characteristic root>1,and the cumulative variance contribution rate was 71.063%,the comparative fit index was 0.959,and the goodness-of-fit index was 0.919,and the degree of correlation was tested has showed that the content validity,structural validity and calibration correlation validity were good;the difference in the total scores of the scale between patients with POI and the healthy population was statistically significant,and the scale had a good degree of differentiation.Conclusion As confirmed by this study,the Chinese Premature Ovarian Insufficiency Quality of Life Scale has good reliability,validity,differentiation and feasibility,and can be used as a tool for evaluating the quality of life of POI patients in China as well as enriching the efficacy evaluation system of TCM in treating POI,and it is suitable for clinical popularization and application.

Objective To evaluate the effects of low-dose intrathecal morphine(ITM)combined with liposomal bupivacaine adductor canal block(LB-ACB)on postoperative analgesia and opioid-sparing efficacy in patients undergoing total knee arthroplasty(TKA).Methods In this randomized,double-blind,controlled trial,80 TKA patients were allocated to either an intervention group(ITM 0.1 mg+LB-ACB,n=40)or a control group(intrathecal saline+LB-ACB,n=40).Primary outcomes included resting/movement visual analog scale(VAS)scores at 6,12,24,48,and 72 hours postoperatively,48-hour morphine consumption,time to first rescue analgesia,and incidence of complications.Results(1)The intervention group showed significantly lower resting and movement VAS scores at 6,12,24,and 48 hours postoperatively compared with controls(all P<0.05),except at 72 hours(P>0.05).(2)The intervention group had a significant reduction in 48-hour morphine consumption(4.58±1.0 mg vs.9.34±4.8 mg,P=0.027),a significantly lower rescue analgesia rate(15.0%vs.47.5%,P=0.002),and a significantly prolonged time to first rescue analgesia(48.8±7.5 h vs.14.5±5.5 h,P<0.001).(3)The intervention group demonstrated a significant decrease in the incidence of nausea(from 15.0%to 35.0%,P=0.039)and vomiting(from 10.0%to 27.5%,P=0.045),but no significant differences were observed in the incidences of pruritus,urinary retention,or motor block(all P>0.05).Conclusion Low-dose ITM(0.1 mg)combined with LB-ACB significantly enhances early postoperative analgesia,reduces opioid consumption,and decreases nausea/vomiting risk,without increasing the risks of other complications.This regimen aligns with enhanced recovery after surgery(ERAS)principles.