To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveTo investigate the role of DEAD-box helicase 3 X-linked （DDX3X） in immune-mediated liver injury （ILI）， and to clarify its mechanism by regulating endoplasmic reticulum stress （ERS）-dependent apoptotic pathway and its association with the clinical progression of hepatitis B. MethodsMice were given injection of concanavalin A （ConA） via the caudal vein to establish a model of ILI， PBS （control group） and different concentrations of ConA were injected into the tail vein of hepatocyte-specific DDX3X-knockout mice （DDX3X^ΔHep and DDX3X-flox mice （DDX3X^fl/fl）， respectively.. The log-rank survival analysis， measurement of the serum levels of aspartate aminotransferase （AST） and alanine aminotransferase （ALT）， and HE staining of liver tissue were performed to assess liver injury， and qRT-PCR and Western Blot were used to measure the mRNA and protein expression levels of glucose-regulated protein 78 （GRP78）， CCAAT/enhancer-binding protein homologous protein （CHOP）， and DDX3X in liver tissue. Intraperitoneal injection of 4-phenylbutyric acid （4-PBA， 100 mg/kg） was performed to inhibit ERS. Serum samples （n=30） and liver tissue samples （n=6） were collected from healthy controls， chronic hepatitis B （CHB） patients， and hepatitis B virus-associated liver failure （HBV-LF） patients； ELISA was used to measure the serum level of DDX3X， and qRT-PCR/Western Blot was used to analyze the expression of targets in liver tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the control group of mice， the expression of DDX3X in the liver of mice induced by ConA was significantly increased after liver injury （P<0.05）， and hepatocyte-specific DDX3X knockout increased the 72-hour survival rate of mice by 55% （compared with 20% in the DDX3X^fl/fl group）， with significant reductions in the serum levels of ALT and AST （P<0.000 1） and the expression levels of the ERS markers GRP78 and CHOP （P<0.05）. After ERS was inhibited by 4-PBA， there was alleviation of liver injury （with reductions in ALT and AST， P <0.001） and a reduction in DDX3X expression （P<0.01）. The analysis of clinical samples showed that the mRNA and protein expression levels of liver DDX3X in CHB patients and HBV-LF patients were significantly higher than those in healthy controls （all P<0.01）， and there was a significant increase in the serum level of DDX3X in HBV-LF patients （P<0.000 1）. ConclusionDDX3X exacerbates ILI by regulating the ERS-dependent apoptotic pathway （GRP78/CHOP）， and its expression is associated with the progression of hepatitis B. Therefore， it can be used as a potential therapeutic target.

Background Depressive symptoms have become a significant factor affecting the physical and mental health of the occupational population, and workers in petroleum refining enterprises face multiple stressors in their work environment. Objective To explore the impact of occupational noise exposure on depressive symptoms among workers in a petroleum refining enterprise. Methods This cross-sectional study was conducted in July 2024 using a questionnaire survey among workers of a petroleum refining enterprise in Hainan Province. Basic information of the subjects was collected. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure depressive symptoms, the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality, and the Chinese version of the Effort-Reward Imbalance (ERI) scale was used to evaluate occupational stress. Chi-square test was employed to compare the differences in reporting depressive symptoms among populations with different characteristics. Binary logistic regression models were used to analyze the impact of occupational noise exposure and other factors on depressive symptoms. Results The overall positive rate of depressive symptoms in the study population was 42.7%. The results of the multifactor analysis indicated that compared with the control group, employees in both the low-exposure and high-exposure groups had elevated odds of depressive symptoms, with OR (95%CI) of 2.244 (1.131, 4.454) and 1.970 (1.009, 3.850), respectively. This association remained robust after adjusting for potential confounders, including gender, age, work tenure, and other occupational exposures. Additionally, female [OR (95%CI)=1.483 (1.039, 2.118)], exposure to benzene, toluene, or xylene [OR (95%CI)=1.621 (1.208, 2.174)], sleep disturbance [OR (95%CI)=3.772 (2.942, 4.838)], and occupational stress [OR (95%CI)=2.018 (1.575, 2.585)] were also significantly associated with higher odds of depressive symptoms. Conclusion The positive rate of depressive symptoms is relatively high among employees in this petrochemical enterprise, and occupational noise exposure may be a risk factor for depressive symptoms.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

Objective : To explore the predictive value of depression severity plasma thyroid-stimulating hormone(TSH) and brain-derived neurotrophic factor(BDNF) levels for agitated symptoms in patients with adolescent depressive disorder(MDD). Methods : Ninety-one patients with adolescent depressive disorder were enrolled, and the degree of agitation was assessed according to the modified outward aggressive behavior scale(MOAS); 24-item hamilton depression scale(HAMD24) was used to determine the severity of depression; chemiluminescence immunoassay(CLIA) was used to determine the plasma thyroid-stimulating hormone(TSH) level; and electrochemiluminescence immunoassay(ECL) was used to determine the plasma BDNF. SPSS 26.0 was used for statistical analysis of the data, Spearman correlation analysis was used to analyze the relationship between HAMD24and plasma TSH and BDNF levels and the degree of agitation, multiple linear regression analysis was used to analyze the factors influencing the degree of agitation in adolescents with MDD, and binary Logistic regression analysis and subjects′ work characteristic curves(ROC) were used to establish predictive models. Results: The degree of agitation in adolescent MDD patients was positively correlated with HAMD24total score(P<0.001); both HAMD24total score and plasma BDNF level were identified as risk factors for agitation severity(bothP<0.05); both HAMD24total score and plasma BDNF levels were risk factors for the degree of agitation(allP<0.05); HAMD24total score, plasma TSH, BDNF levels were all risk factors for concomitant agitation symptoms in adolescent MDD patients; ROC curve analysis showed that the three combined prediction models(AUC=0.889,P<0.001) had a higher predictive value than the single prediction model(P<0.01). Conclusion Concomitant agitation symptoms in adolescents with MDD are strongly associated with HAMD24total score and plasma TSH and BDNF levels, and the three combined models have good predictive power.

Objective:To evaluate the clinical efficacy, safety and treatment persistence of upadacitinib in Crohn's disease (CD) patients.Methods:The single-center retrospective cohort study was conducted. The patients with moderate-to-severe active CD initiating upadacitinib therapy from November 2023 to November 2024 in Nanjing Drum Tower Hospital were collected through searching the electronic medical records and paper-based patient databases. The primary outcome was the clinical remission rate at week 12. Secondary outcomes included the clinical response rate at week 12; clinical response and remission rates at weeks 4, 24 and 48; biomarker (fecal calprotectin or C-reactive protein) remission rates at all time points; as well as endoscopic remission and response rates, treatment persistence and safety evaluation.Results:A total of 44 CD patients were included, comprising 24 males (54.5%) and 20 females (45.5%). The median age was 33 (25, 40) years. The baseline Crohn's disease activity index (CDAI) score was 260.5 (225.9, 550.0) points. Patients had previously received a median of 2 (1, 2) biologic treatments. All 44 patients completed the 12-week induction therapy. With a median follow-up of 30.00 (16.25, 46.25) weeks, the clinical remission rate was 50.0% (22/44) at week 12. The clinical remission rate, clinical response rate, and biomarker remission rate were 52.3% (23/44), 88.6% (39/44) and 72.7% (32/44) respectively at week 4, and the clinical response rate and biomarker remission rate were 88.6% (39/44) and 77.2% (34/44) respectively at week 12. The clinical remission rates, clinical response rates and biomarker remission rates evolved to 43.3% (13/30), 86.7% (26/30) and 80.0% (24/30) at week 24, and further to 44.4% (4/9), 77.8% (7/9) and 77.8% (7/9) at week 48. During the follow-up period, 13 CD patients completing endoscopic evaluation, endoscopic remission and response rates were 30.8% and 23.1% respectively. CD-related surgery rate was 4.5% (2/44). Safety analysis demonstrated that the overall adverse events rate was 56.8% (25/44) including 7 patients with serious adverse events. A total of 8 patients discontinued treatment, among which 3 were due to primary loss of response, 1 due to secondary loss of response, 2 due to drug-related adverse events alone, and 2 due to concurrent primary loss of response and adverse events. The Kaplan-Meier curve for treatment persistence showed that among 39 CD patients who achieved clinical response at week 12, the continued treatment rates were 90.3% at week 12 and 85.3% at week 24 of follow-up. Two patients (5.6%) received dose escalation of upadacitinib, both of whom achieved clinical remission.Conclusion:Real-world research data demonstrate that upadacitinib exhibits significant clinical efficacy and a favorable safety profile in the treatment of moderate-to-severe active CD patients with prior biologic exposure, and no new unexpected adverse events are identified.

Objective:To select low-dose radiation-activated genes with intrinsic radiation protection by developing a model for adaptive responses to low-dose ionizing radiation, in order to explore the mechanisms behind the radiation resistance of the candidate genes.Methods:The cells were divided into adaptive response induction group and whole transcriptome sequencing group. The level of DNA damage was assessed using the γ-H2AX immunofluorescence assay. The low-dose radiation-activated candidate genes with radiation protection were selected through whole transcriptome sequencing and quantitative reverse transcription PCR (RT-qPCR)-based validation. The anti-radiation effect of candidate gene CCND1 was assessed based on CCK-8 cell proliferation and γ-H2AX immunofluorescence assay. After up- and down-regulation of CCND1 expression, the anti-radiation mechanism of CCND1 was preliminarily explored through transcriptome sequencing analysis.Results:A model for low-dose ionizing radiation-induced adaptive responses of lymphocytes was constructed. Using this model, six candidate genes with radiation protection, including CCND1, ZMAT3, MGAT3, DFFB, CYP4F2, ITGA6, were selected. Compared to the control group, overexpressed CCND1 led to significantly enhanced proliferation ability of AHH-1 cells ( t = 7.92-14.76, P < 0.05) and distinctly lowered level of DNA damage ( t = 2.79-9.68, P < 0.05) after 2 Gy of X-ray irradiation. Furthermore, compared to the control group, the CCND1 knockdown caused significantly decreased cell proliferation ability ( t = 13.58-26.25, P < 0.05) and notably elevated level of DNA damage of cells ( t = 2.87-7.61, P < 0.05). Transcriptome sequencing revealed that up- and down-regulation of CCND1 expression resulted in the activation of pathways related to cell growth, death, and damage repair. Conclusions:By selecting six low-dose-activated candidate genes with radiation protection and revealing the function of CCND1 in radiation protection, this study provides a new perspective for the development of radiation protection agents from the perspective of adaptive responses to low-dose radiation.

Objective To observe the value of machine learning model based on MR T2WI and diffusion weighted imaging(DWI)radiomics for predicting perineural invasion(PNI)of rectal cancer.Methods Totally 343 patients with rectal cancer were retrospectively collected and divided into training set(n=275,92 PNI[+]and 183 PNI[-])and test set(n=68,23 PNI[+]and 45 PNI[-])at the ratio of 8∶2.Univariate and multivariate logistic regression(LR)were used to analyze clinical data and screen the independent predictors of PNI in rectal cancer,so as to construct a clinical model.The best radiomics features were extracted and screened based on preoperative T2WI and DWI.Then extremely randomized trees,multilayer perceptron,light gradient boosting machine,extreme gradient boosting,support vector machine(SVM),LR,K-nearest neighbor and random forest algorithms were used to construct ML models,respectively,and the optimal ML model was selected to establish a clinical-radiomics ML model combined with clinical relevant independent predictors.The predictive efficacy and clinical value of each model were evaluated.Results Patients' age was the independent predictor of PNI of rectal cancer(OR=0.988,P＜0.001),and the area under the curve(AUC)of the clinical model constructed based on it was 0.435 and 0.458 in training and test sets,respectively.SVM model was the best one among 8 ML models,with AUC in training and test set of 0.887 and 0.854,respectively.The AUC of clinical-radiomics ML model in training and test sets was 0.887 and 0.860,respectively,not different with AUC of SVM model(both P＞0.05).Decision curve analysis showed that when the threshold value was 0.20-0.45,clinical net benefit of SVM model was higher than that of other models.Conclusion SVM model based on T2WI and DWI radiomics could effectively predict PNI of rectal cancer.

Objective:To construct a failure mode and effects analysis(FMEA)evaluation model for medical devices to manage devices,so as to enhance management efficiency for medical devices.Methods:The FMEA was adopted to construct FMEA evaluation model for medical devices,so as to conduct comprehensive lifecycle management for medical devices.The process of management for equipment was optimized,and the operational risk of medical devices was reduced through failure mode(FM)analysis and the construction of management system for equipment.A total of 47 medical devices in clinical use of The First People's Hospital of Neijiang from January to December 2023 were included.In them,23 devices received conventional management mode during January and June 2023,and 24 devices received FMEA evaluation model(model management mode)during July and December 2023.For each group,2,000 patients'medical records were selected.The control effectiveness of infectious indicators,effect of cleaning and disinfection,and quality scores of infectious control for medical devices were compared between different management modes.A self-designed questionnaire was adopted to investigate the recognition scores of engineers who used and managed devices,operators,physicians,and department administrators for two kinds of management modes.Results:In 2,000 patients'medical records,who adopted model management mode,the infection rate of patients,and infection rate of aseptic surgical incision were respectively 0.15%and 0.05%,both were significantly lower than those in the conventional management mode(x2=5.420,8.358,P<0.05).The cleaning rate,and disinfection qualification rate of 24 medical devices,which adopted model management mode,were respectively 83.33%and 87.50%,all of which were significantly higher than these of conventional management mode(x2=8.080,6.741,P<0.05).The scores of standardized operation,rational use,disinfection and cleaning,and emergency intervention for medical devices in adopting model management mode were significantly higher than those in adopting conventional management mode(t=14.435,16.014,13.049,12.537,P<0.05).The recognition scores of engineers who used and managed devices,operators,physicians,and department administrators for adopting model management mode were significantly higher than those for adopting conventional management mode,and the differences were significant(t=12.219,12.147,17.437,13.420,P<0.05).Conclusion:The FMEA evaluation model for medical devices can real-time monitor the entire management process for medical devices,and reduce clinical infections,and ensure normal operation of devices,and improve clinically operational quality of them,and increase satisfaction of staffs in clinical departments.

Objective:To determine the prevalence, risk factors, and longitudinal associations of loneliness during mid- to late pregnancy with anxiety and depressive symptoms in late pregnancy.Methods:In this prospective cohort study, 1 107 pregnant women at 24-28 weeks' gestation were enrolled between June 2021 and December 2022. Psychological status was assessed during mid-pregnancy (24-28 weeks) and late pregnancy (≥32 weeks) using standardized electronic questionnaires, including the Revised University of California Los Angeles Loneliness Scale (UCLA) Loneliness Scale-Short Form (Cronbach's α=0.82), Patient Health Questionnaire-9 ( α=0.86), and Generalized Anxiety Disorder-7 ( α=0.88). Multivariate logistic regression identified independent risk factors for loneliness. Cross-lagged path models analyzed the longitudinal predictions between loneliness and anxiety/depressive symptoms. Results:The prevalence of loneliness decreased significantly from 10.8% (120/1 107) in mid-pregnancy to 4.8% (37/777) in late pregnancy ( χ2=21.81, P<0.001). Multivariate analysis identified independent risk factors for loneliness: age <30 years ( OR=1.70, 95% CI: 1.15-2.50), annual household income <50 000 CNY ( OR=2.53, 95% CI: 1.28-5.02), unemployment during pregnancy ( OR=1.57, 95% CI: 1.03-2.39), history of alcohol consumption ( OR=1.63, 95% CI: 1.03-2.56), and the presence of mid-pregnancy depressive ( OR=2.76, 95% CI: 1.51-5.04) and anxiety symptoms ( OR=1.65, 95% CI: 1.01-2.71) (all P<0.05). Cross-lagged path models indicated bidirectional associations between loneliness and both anxiety ( β=0.32, P<0.01) and depressive symptoms ( β=0.28, P<0.01). However, the predictive effect of loneliness on subsequent depressive and anxiety symptoms ( β=0.28-0.32) was substantially stronger than the reverse prediction (mid-pregnancy anxiety on late-pregnancy loneliness: β=0.12; mid-pregnancy depression on late-pregnancy loneliness: β=0.11). Loneliness demonstrated high temporal stability (autoregressive effects β=0.29-0.32). Conclusion:Loneliness in mid-pregnancy exhibits a symmetric bidirectional association with anxiety and depressive symptoms in late pregnancy, suggesting it may be a core driver in the development of these emotional symptoms. Younger maternal age (<30 years), low household income (<50 000 CNY/year), unemployment during pregnancy, and a history of alcohol consumption were associated with a higher risk of loneliness and should be prioritized for psychological screening and intervention.