ObjectiveTo observe the changes in the levels of different subtypes of epidermal growth factor receptor （ErbB）， namely ErbB1， ErbB2， ErbB3， and ErbB4， in the spinal dorsal horn of inflammatory pain model rats， and to explore their mechanism of mediating hyperalgesia as well as the intervention mechanism of electroacupuncture at "Zusanli （ST 36）" and "Kunlun （BL 60）". MethodsThe study was divided into five parts. In experiment 1， 14 Sprague Dawley （SD） rats were randomly divided into control and inflammatory pain group （7 rats each group） to observe the pain behavior and the protein expression of different ErbB receptor subtypes in the spinal dorsal horn. In experiment 2， 30 rats were randomly divided into control group 1， inflammatory pain group 1， and low-， medium-， and high-concentration TX1-85-1 groups， with 6 rats in each group， to observe the effect of inhibiting spinal ErbB3 on inflammatory pain. In experiment 3， 12 rats were randomly divided into control virus group and ErbB3 knockdown virus group， with 6 rats in each group， to observe the effect of knocking down ErbB3 in the spinal dorsal horn on inflammatory pain. In experiment 4， 44 rats were randomly divided into control group 2， inflammatory pain group 2， electroacupuncture group， and sham electroacupuncture group， with 11 rats in each group， to observe the effect of electroacupuncture. In experiment 5， 40 rats were randomly divided into control group 3， inflammatory pain group 3， electroacupuncture group 1， and electroacupuncture + NRG1 group， with 10 rats in each group， to observe the effect of activating ErbB3 on electroacupuncture. A rat model of inflammatory pain was established by subcutaneous injection of 100 μl of complete Freund's adjuvant into the sole of the unilateral hind foot of SD rats. Rats in the low-， medium-， and high-concentration TX1-85-1 groups were intrathecally injected with ErbB3 inhibitor TX1-85-1 on day 5 to day 7 after modeling. Rats in the ErbB3 knockdown virus group were injected with ErbB3 knockdown virus packaged with adenovirus vector-based short hairpin RNA （shRNA） into the spinal dorsal horn in situ 3 weeks before modeling. Rats in each electroacupuncture group received electroacupuncture at bilateral "Zusanli （ST 36）" and "Kunlun （BL 60）" from day 1 to day 7 after modeling， with dense-sparse waves at a frequency of 2 Hz/100 Hz and a current of 0.5-1.5 mA for 30 minutes once a day. Rats in the electroacupuncture + NRG1 group were intrathecally injected with ErbB3 ligand recombinant human neuregulin-1 （NRG1） after electroacupuncture intervention from day 5 to day 7 after modeling. The mechanical withdrawal threshold and thermal withdrawal latency of rats were measured on day 1， 3， 5， and 7 after modeling to evaluate behavior， and Western Blot was used to detect the protein and phosphorylation levels of each ErbB subtype in the spinal dorsal horn. ResultsCompared with the control group， rats in the inflammatory pain group showed decreased mechanical withdrawal threshold and thermal withdrawal latency of rats， and increased expression of phosphorylated ErbB3 （p-ErbB3） protein in the spinal dorsal horn on days 1， 3， 5， and 7 after modeling （P＜0.01）. On day 5 and day 7 after modeling， compared with the inflammatory pain group 1， the mecha-nical withdrawal threshold and thermal withdrawal latency of rats in the medium- and high-concentration TX1-85-1 groups increased， and the expression of p-ErbB3 protein decreased （P＜0.05）. On day 1， 3， 5， and 7 after modeling， compared with the control virus group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the ErbB3 knockdown virus group increased （P＜0.05）. On day 5 and day 7 after modeling， compared with the inflammatory pain group 2 and the sham electroacupuncture group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the electroacupuncture group increased， and the expression of p-ErbB3 protein decreased （P＜0.05）. On day 5 and day 7 after modeling， compared with the electroacupuncture + NRG1 group， the mechanical withdrawal threshold and thermal withdrawal latency of rats in the electroacupuncture group 1 increased （P＜0.05）. ConclusionThe p-ErbB3 in the spinal dorsal horn involved in hyperalgesia in rats with inflammatory pain， and electroacupuncture at "Zusanli （ST 36）" and "Kunlun （BL 60）" can alleviate inflammatory pain by inhibiting the expression of p-ErbB3 protein in the spinal dorsal horn of rats.

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.

Objective: To investigate the factors influencing the co-prevalence of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia, so as to provide a data foundation and theoretical basis for developing targeted intervention measures. Methods: In September and October 2024, a stratified cluster random sampling method was employed to select 139 102 students from 539 schools across 12 leagues/cities and 103 banners/counties in Inner Mongolia Autonomous Region. Participants who were diagnosed with allergic rhinitis by a doctor at least once within one year and had a body mass index ≥ 28 kg/m 2 were considered to have comorbid conditions. Results: The coprevalence rate of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia was 6.4% (8 931 cases). Lasso-Logistic regression revealed that nonboarding status, higher maternal education, consuming high protein foods ≥1 time daily, occasionally or never eating breakfast, engaging in moderate to vigorous physical activity for ≥60 minutes on fewer than half of holidays, and having been exposed to second hand smoke in person within the past seven days were associated with higher odds ratios for co-prevalence of allergic rhinitis and obesity( OR = 1.23 , 1.22-1.63, 1.20, 1.19, 1.38, 1.35); being female, higher grade level, residence in flag/county/district areas, non only child status, never having consumed a full glass of alcohol, non hypertensive status, and households without pets were associated with lower co-prevalence risks ( OR =0.65, 0.67-0.77, 0.81, 0.87, 0.73, 0.41, 0.68) (all P <0.05). The ROC curve indicated an area under the curve of 0.64 for the predictive model, demonstrating satisfactory discriminatory ability. The calibration curve showed consistency between predicted and actual occurrence probabilities. Conclusions The co-prevalence of allergic rhinitis and obesity among primary and secondary school students in Inner Mongolia is closely associated with demographic characteristics, dietary behaviours, and lifestyle habits. Future prevention and control strategies should prioritize these factors to implement targeted interventions.

Objective: To analyze the dental plaque microecological characteristics and the levels of salivary interleukin (IL)-5, IL-12, and IL-16 in school aged children with mixed dentition, providing a theoretical basis for the prevention and control of childhood dental caries. Methods: A total of 199 school aged children admitted aged 6-12 years to Tianjin Stomatological Hospital from January 2023 to January 2025 were selected. According to the decayed missing and filled teeth(DMFT) index, they were divided into high caries group ( n =68, DMFT index≥4), low caries group ( n =65, 1≤DMFT index≤3) and caries free group ( n =66, DMFT index=0). The microbial composition of dental plaque was analyzed by 16S rDNA high throughput sequencing. Salivary levels of IL-5, IL-12 and IL-16 were detected by enzymelinked immunosorbent assay kit. Pearson correlation test was used to analyze the correlation between levels of salivary IL-5, IL-12 and IL-16 and the DMFT index in children with caries. Results: Species richness and evenness (Shannon index) and uniformity (Shannonevenness index) in the high caries group were (3.28±0.64) and (0.61±0.13), respectively, which were lower than (3.66±0.52) and (0.74±0.15) in low caries group, and ( 3.60± 0.49) and (0.72±0.16) in caries free group ( F =9.08, 15.20, both P <0.05). The relative abundances of Firmicutes and Actinobacteria in the high caries group were higher than those in the low caries and caries free groups, while the relative abundances of Bacteroidetes, Fusobacteria, and Candidate phylum TM7 were lower than those in the low caries and caries free groups. The relative abundances of Streptococcus, Leptotrichia, Veillonella, Prevotella, and Actinomycesin in the high caries group were higher than those in the low caries and caries free groups, while the relative abundances of Capnocytophagaand Porphyromonas were lower ( F = 47.55- 1 969.24 , all P <0.05). The levels of salivary IL-5, IL-12, and IL-16 in the high caries group [(12.57±3.21, 28.73±6.79, 45.31 ±11.14)pg/mL] were higher than those in the low caries [(8.63±2.39, 18.16±4.62, 32.60±9.46)pg/mL] and caries free groups [(7.84±2.17, 14.94±3.83, 29.55±8.21)pg/mL] ( F =62.08, 141.23, 49.35, all P <0.05). Pearson correlation test results showed that salivary IL-5, IL-12, and IL-16 levels were positively correlated with the DMFT index in children with caries ( r = 0.73 , 0.68, 0.65, all P <0.05). Conclusions The microecological structure of dental plaque in school aged children with mixed dentition caries is significantly altered, the levels of pro inflammatory factors are significantly elevated, and these levels are positively correlated with the severity of caries.Oral microbial dysbiosis and local inflammatory responses may play a key role in the development and progression of dental caries.

Background: Diabetic nephropathy (DN) is the most common and serious complication of diabetes mellitus. Shionone (SH), an important triterpenoid compound in the root extract of Aster, might exert a protective effect in DN mice and high glucose cultivated glomerular podocytes. The current study aimed to unravel the underlying mechanism by which SH mitigates DN. We postulate that SH stimulates the expression of sestrin-2 (SESN2), a pivotal stress-inducible protein in the anti-inflammasome machinery. Methods: We utilized high-fat diet combined with streptozotocin (55 mg/kg intraperitoneal) for DN mice model, and high glucose (30 mM, 48 hours) cultured glomerular podocytes for DN cell model to evaluate the effect of SH. We also preformed experimentation on SESN2 deficiency models (SESN2 knockout mice and SESN2 siRNA in cells) to further prove our hypothesis. Results: The results demonstrated that SH effectively suppressed glomerular fibrosis, induced adenosine monophosphate-activated protein kinase (AMPK) phosphorylation, and inhibited NLR family pyrin domain containing 3 (NLRP3) activation. Furthermore, our findings revealed that SH exerted its anti-inflammatory effect through Sesn2-dependent nuclear factor erythroid 2-related factor 2 (Nrf2) nuclear translocation and subsequent activation of its downstream target heme oxygenase-1 (HO-1). Conclusion In summary, our findings suggest that SH serves as a promising therapeutic agent for the treatment of DN-related glomerular fibrosis. SH enhances the expression of SESN2, attenuates α-smooth muscle actin accumulation, and suppresses NLRP3-related inflammation through the Nrf2/HO-1 signaling pathway.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE To construct three-class （insufficient， normal， excessive） and two-class （insufficient， normal） models for predicting plasma concentration of valproic acid （VPA）， and compare the performance of these two models， with the aim of providing a reference for formulating clinical medication strategies. METHODS The clinical data of 480 patients who received VPA treatment and underwent blood concentration test at the Xi’an International Medical Center Hospital were collected from November 2022 to September 2024 （a total of 695 sets of data）. In this study， predictive models were constructed for target variables of three-class and two-class models. Feature ranking and selection were carried out using XGBoost scores. Twelve different machine learning algorithms were used for training and validation， and the performance of the models was evaluated using three indexes: accuracy， F1 score， and the area under the working characteristic curve of the subject （AUC）. RESULTS XGBoost feature importance scores revealed that in the three-class model， the importance ranking of kidney disease and electrolyte disorders was higher. However， in the two-class model， the importance ranking of these features significantly decreased， suggesting a close association with the excessive blood concentration of VPA. In the three-class model， Random Forest method performed best， with F1 score of 0.704 0 and AUC of 0.519 3 on the test set； while in the two-class model， CatBoost method performed optimally， with F1 score of 0.785 7 and AUC of 0.819 5 on the test set. CONCLUSIONS The constructed three-class model has the ability to predict excessive VPA blood concentration， but its prediction and model generalization abilities are poor； the constructed two-class model can only perform classification prediction for insufficient and normal blood concentration cases， but its model performance is stronger.

In February 2025， the American Association for the Study of Liver Diseases （AASLD） published online “Critical Update： AASLD Practice Guidance on Prevention， Diagnosis， and Treatment of Hepatocellular Carcinoma”. This update mainly focuses on the latest analysis results of the IMbrave050 study and performs corresponding updates and adjustments to recommendations based on the issues in clinical practice. As for the postoperative adjuvant therapeutic strategies for hepatocellular carcinoma （HCC） based on immune checkpoint inhibitors， the AASLD has re-evaluated and updated the practice guidance. The update revises related texts and recommendations for adjuvant therapy and the management algorithms for HCC recurrence during or after adjuvant therapy. Furthermore， the AASLD emphasizes that even for HCC patients at a high risk of recurrence after resection or local ablation， close monitoring for recurrence remains the current standard treatment regimen. Our team makes an excerpt of the update， systematically introduces the background and specific contents of the update， and discuss the adjuvant therapy for HCC， in order to provide a reference for readers.

Objective: To explore the relationship between related factors of non-suicidal self-injury behavior (NSSI) among middle school students in Guizhou Province, so as to provide the evidence for preventing high risk behaviors in adolescents. Methods: A stratified cluster random sampling method was used to select 1 034 junior and senior middle school students from Zunyi City, Qiannan Prefecture and Tongren City in Guizhou Province from April to October in 2023. Questionnaire survey was conducted to collect information including Adolescent Self injury Scale and Family Assessment Device. The R 4.4.1 software was employed for network analysis visualization, centrality indicators, and result stability assessment. Results: The detection rate of NSSI behavior among middle school students in Guizhou province was 29.6%, with a detection rate of 25.5% for boys and 33.1% for girls, showing a statistically significant difference ( χ 2=7.07, P <0.05). There were statistically significant differences in scores of emotional communication, egoism, family rules, positive communication, problem solving, expression of positive emotions and management of negative emotions self-efficacy, and bullying victimization in various dimensions between middle school students with and without NSSI ( Z =-13.66 to -7.05, P <0.01). NSSI among middle school students was positively correlated with social/relational bullying, depression and anxiety, and there were relatively close connections in the network ( r =0.35, 0.43, 0.42, P <0.01). Centrality indicators showed that the highest in strength and closeness centrality were stress ( Z =1.29, 1.58), the highest in betweenness centrality was for emotional communication ( Z =1.91), and the highest in expected influence index was for physical bullying ( Z =1.44)( P < 0.05). Conclusions Stress, emotional communication and physical bullying have significant impacts in the network of factors related to NSSI. Social/relational bullying, depression and anxiety have strong direct correlations with NSSI behavior among middle school students.

ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.