Hepatitis B virus （HBV） infection is the primary cause of viral hepatitis and represents a substantial disease burden in China. However， effective and safe agents capable of completely eliminating HBV DNA are still lacking. In modern medicine， anti-HBV strategies mainly target covalently closed circular DNA （cccDNA）， among other mechanisms， and multiple novel drugs are currently under clinical investigation. Traditional medicine has been shown to exert anti-HBV effects through direct pathways， such as blocking viral entry， as well as indirect pathways， including the regulation of programmed cell death. Studies have confirmed that the integration of traditional Chinese medicine （TCM） and Western medicine in treating HBV infection and its related complications offers complementary advantages， particularly in enhancing HBV clearance rates， improving liver function， preventing various complications， and delaying the progression from hepatic fibrosis to hepatocellular carcinoma. This review focuses on advances in anti-HBV research involving TCM， Western medicine， and their integrated application， aiming to provide a basis for integrated HBV therapy and new drug development.

ObjectiveTo explore the effect and mechanism of Jianpi Xiaoai prescription （JPXA） in ameliorating the 5-fluorouracil （5-FU） resistance of colon cancer. MethodsA HCT116/5-FU resistant cell line was established. Different concentrations （10%， 15%， 20%） of JPXA-containing serum and drug-free serum were used for intervention， and 10% fetal bovine serum （10% FBS）， fibroblast growth factor receptor （FGFR） inhibitor （AZD4547）， and recombinant fibroblast growth factor 2 （FGF2） were set as the control groups. Sensitive HCT116 cells were used in the FGF2 group， while HCT116/5-FU cells were used in other groups. Drug resistance， the level of FGF2 in the cell culture medium， the mRNA level of FGF2 in cells， and the protein levels of FGF2/FGFR and phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） were determined. The drug-resistant cells were transplanted into the axilla of nude mice to establish a tumor model. The modeled mice were allocated into model， JPXA （15 g·kg^-1）， 5-FU （0.02 g·kg^-1）， JPXA+5-FU （15 g·kg^-1+0.02 g·kg^-1）， AZD4547 （0.012 5 g·kg^-1）， and AZD4547+5-FU （0.012 5 g·kg^-1+0.02 g·kg^-1） groups. The tumor growth and the protein levels of FGF/FGFR and PI3K/Akt in each group were observed. ResultsThe survival rate of HCT116/5-FU cells decreased in all the JPXA groups with different concentrations. The cell survival rate was decreased most obviously in the 20% JPXA group. The level of FGF2 in the cell culture medium and the mRNA level of FGF2 in cells of each JXPA group decreased， and the decrease was the most significant in the 20% group （P<0.01）. HCT116/5-FU cells showed up-regulated protein levels of FGF2 and phosphorylated fibroblast growth factor receptor 1 （p-FGFR1）， but down-regulated protein level of FGFR1 （P<0.01）. JPXA down-regulated the expression of FGF2 and p-FGFR1 and up-regulated the expression of FGFR1 （P<0.05）. In addition， JPXA down-regulated the expression levels of phosphorylated protein kinase B （p-Akt） and phosphorylated mammalian target of rapamycin （p-mTOR）， while up-regulating the expression levels of Akt and Bcl-2-asociated death promoter （Bad） （P<0.05）. Animal experiments showed that the JPXA combined with 5-FU significantly inhibited the growth of drug-resistant tumors， reduced the protein levels of FGF2， p-FGFR1， phosphorylated phosphatidylinositol-3-kinase （p-PI3K）， p-Akt， and p-mTOR， and increased the expression of Bad. It indicated that JPXA can inhibit the FGF2/FGFR1 signaling in colon cancer and regulate PI3K/Akt and downstream signaling pathways. ConclusionJPXA can ameliorate the chemotherapy resistance of colon cancer through down-regulating FGF2 expression and inhibiting the activation of the PI3K/Akt signaling pathway.

Objective To explore scientific and accurate prediction methods for the incidence of hand, foot, and mouth disease in the post-pandemic era, and to address modeling challenges caused by abnormal fluctuations in case numbers from 2020 to 2023. Methods The seasonal index was used to pre-process the data. The traditional seasonal autoregressive integrated moving average (SARIMA) model, singular spectrum analysis (SSA)-ARIMA model, ARIMA-Long short-term memory (LSTM) model, and SSA-ARIMA-LSTM model were used to fit the incidence from 2013 to 2023, and the incidence of hand, foot and mouth disease in 2024 was predicted. The real data collected in 2024 were used as the test set to compare the prediction performance of the models. Results The fitting performance of the constructed models was as follows: the ARIMA model had MAE=107.50 and RMSE=144.53, the SSA-ARIMA model showed MAE=2.84 and RMSE=4.33, the ARIMA-LSTM model achieved MAE=99.46 and RMSE=131.59, and the SSA-ARIMA-LSTM model had MAE=96.35 and RMSE=132.13. In terms of prediction performance, the ARIMA model resulted in MAE=151.64 and RMSE=146.70, the SSA-ARIMA model demonstrated MAE=41.22 and RMSE=57.01, the ARIMA-LSTM model yielded MAE=220.75 and RMSE=257.89, and the SSA-ARIMA-LSTM model recorded MAE=58.83 and RMSE=72.06. Conclusion The SSA-ARIMA model has the best fitting degree and the highest prediction accuracy, and is suitable for predicting the incidence trend of hand, foot and mouth disease.

BackgroundThe middle school stage represents a crucial period for the development of borderline personality features. Negative parenting styles and emotional regulation strategies are associated with the formation of borderline personality features. However， the moderating role of emotional regulation strategies between negative parenting styles and borderline personality features among middle school students remains unclear. ObjectiveTo explore the moderating influence of emotional regulation strategies in the relationship between negative parenting styles and borderline personality features among middle school students， and to provide references for the intervention of borderline personality features. MethodsIn October 2023， a total of 5 965 middle school students from three middle schools in Nanning， Guangxi Zhuang Autonomous Region were selected by cluster sampling， and assessed by the Borderline Personality Features Scale for Children （BPFS-C）， the Egna Minnen Barndoms Uppfostran （EMBU）， and the Emotion Regulation Questionnaire-Chinese Revised Version （ERQ-CRV）. Pearson correlation analysis was used to test the correlation between the scores of each scale， and the model 1 of the Process macro program was used to conduct the moderating effect test. ResultsA total of 5 572 middle school students （93.41%） completed this study， and 1 388 of them （24.91%） were identified as having high borderline personality features. The BPFS-C score of middle school students was positively correlated with the score of the negative parenting style dimension of EMBU （r=0.367， P<0.01）， negatively correlated with the score of the cognitive reappraisal dimension of ERQ-CRV （r=-0.168， P<0.01）， and positively correlated with the score of the expression inhibition dimension of ERQ-CRV （r=0.344， P<0.01）. Cognitive reappraisal played a negative moderating effect between negative parenting styles and borderline personality features （β=-0.072， 95% CI： -0.104–-0.041， P<0.01）， while expressive suppression played a positive moderating effect （β=0.076， 95% CI： 0.055–0.097， P<0.01）. ConclusionCognitive reappraisal strategy may help mitigate the negative influence of negative parenting styles on middle school students' borderline personality features， while expressive suppression may exacerbate the harm of negative parenting styles to the borderline personality features of middle school students.

Esophageal cancer is a prevalent malignant tumor of the digestive tract in China, and radical surgery remains the cornerstone of its comprehensive treatment. However, multifactorial challenges such as postoperative gastrointestinal tract reconstruction, traumatic stress, and tumor-related metabolic disturbances render esophageal cancer patients highly susceptible to malnutrition. Perioperative nutritional support therapy plays a crucial role in enhancing surgical safety, improving clinical outcomes, and elevating patients' quality of life by regulating metabolic homeostasis, preserving organ function, and optimizing the immune microenvironment. This article reviews the mechanisms underlying malnutrition in esophageal cancer, methods for nutritional status assessment, and precision intervention pathways based on multi-omics evaluations. The aim is to strengthen clinicians' awareness of standardized perioperative nutritional management for esophageal cancer patients and promote its clinical implementation, thereby facilitating postoperative recovery and improving long-term quality of life.

Patients with Takayasu arteritis combined with aortic valve disease often have a poor prognosis following surgical valve replacement, frequently encountering complications such as perivalvular leakage, valve detachment, and anastomotic aneurysm. This article presents a high-risk case wherein severe aortic valve insufficiency associated with Takayasu arteritis was successfully managed through transcatheter aortic valve implantation via the transapical approach. The patient had satisfactory valve function with no complications observed during the six-month postoperative follow-up. This case provides a minimally invasive and feasible alternative for the clinical management of such high-risk patients.

Objective To explore the application of the GPT-4 large language model in simplifying lung cancer radiology reports to enhance patient comprehension and doctor–patient communication efficiency. Methods A total of 362 radiology reports of non-small cell lung cancer (NSCLC) patients were collected from two hospitals between September and December 2024. Interpretive radiology reports (IRRs) were generated using GPT-4. Original reports (ORRs) and IRRs were compared through radiologist consistency evaluation and volunteer-based assessments of reading time, comprehension scores, and simulated communication duration. Results The average word count of ORRs was (459.83±55.76) words, compared with (625.42±41.59) words for IRRs (P<0.001). No significant differences were observed in expert consistency scores between ORRs and IRRs across dimensions of image interpretation accuracy, report detail completeness, explanatory depth and insight, and clinical practicality. Compared with reading ORRs, volunteers (simulated patient) read IRRs with shorter time [(346.88±29.15) s versus (409.01 ±102.40) s], with higher comprehension scores [(7.83±1.04) points versus (5.53±0.94) points] and shorter doctor-patient communication times [(317.31±57.81) s versus (714.20±56.67) s]. All differences were statistically significant (all P<0.001). Conclusion GPT-4 generated IRRs significantly improve patient comprehension and shorten communication time while maintaining medical accuracy. These findings suggest a new approach to optimizing radiology report management and enhancing healthcare service quality.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

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.