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.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Objective: To analyze respiratory syncytial virus(RSV) outbreaks surveillance results and the epidemiological characteristics in kindergarten and school in Shenzhen during 2017-2023 , so as to provide a scientific reference for control and prevention of RSV. Methods: Epidemiological data and surveillance results of RSV outbreaks in kindergarten and school from 2017 to 2023 were collected for descriptive analyses. Results: A total of 31 RSV outbreaks were identified in kindergarten and school in 2017-2023 in Shenzhen, 346 cases were reported, the average incidence rate was 22.02%. The most annual RSV outbreaks were reported in 2020 with 14 outbreaks, followed by 8 outbreaks in 2023. A total of 64.52% of RSV outbreaks were identified in kindergarten with rest occurring in primary school or middle school. The greatest monthly count of outbreak was 18 (58.06%) in September, followed by 3 outbreaks (9.68%) in March and October. A total of 244 swab samples were collected, 169 samples were positive for respiratory viruses, the positive rate was 69.26%, 121 samples were positive for RSV,from 31 respiratory syncytical virus outbreaks 57 and samples were positive for other respiratory viruses(9 samples were positive for two respiratory viruses). A toral of 14(45.16%) outbreaks are caused by RSV alone, 17 outbreaks (54.84%) were caused by RSV and other respiratory viruses. Conclusions Most RSV outbreaks in kindergarten and school are reported after 2020 in Shenzhen, most RSV outbreaks occur in kindergarten, peak seasons of RSV outbreaks are autumn and spring.

[摘 要] 目的：探究钾钙激活通道亚家族N成员4（KCNN4）在胰腺癌组织中的表达及其对胰腺癌进展的影响，解析KCNN4在胰腺癌临床诊断及预后判断中的作用。方法：利用GEPIA2数据分析平台，结合TCGA和GTEx数据库的数据分析KCNN4在胰腺癌组织中的表达水平及其与患者预后的关系。收集24例海军军医大学长海医院手术切除的胰腺癌患者的癌及癌旁组织标本，通过qPCR、WB法和免疫组化染色技术验证KCNN4在胰腺癌组织中的表达水平。利用shRNA敲低人胰腺癌细胞中BXPC3和PANC-1中KCNN4的表达，通过CCK-8和克隆形成实验检测细胞增殖与生长情况。利用小鼠胰腺癌KPC细胞构建胰腺癌原位成瘤模型，观察敲低KCNN4对胰腺原位成瘤的影响，统计小鼠生存期（OS）。结果：整合TCGA和GTEx数据库数据分析结果发现，KCNN4在胰腺癌组织中高表达（P < 0.05），且与患者OS和DFS缩短相关（均P < 0.05）。胰腺癌组织中KCNN4 mRNA和蛋白表达量均显著高于癌旁组织（均P < 0.01）。KCNN4敲低后，胰腺癌细胞生长速率显著减慢、克隆形成数量显著减少（均P < 0.01）。小鼠胰腺原位荷瘤实验结果表明，KCNN4敲低可抑制肿瘤细胞在胰腺原位的生长并延长小鼠OS。结论：KCNN4在胰腺癌组织中高表达，其能促进胰腺癌细胞增殖和胰腺癌进展，与患者预后密切相关，有望作为胰腺癌临床诊断及预后评估的靶点。

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: 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 an evaluation index system of cervical cancer prevention and control literacy, so as to provide an assessment tool for cervical cancer prevention and control literacy. Methods: The preliminary framework for cervical cancer prevention and control literacy was designed based on literature review. Twenty-one experts with both theoretical and practical experience in cervical cancer prevention and control were invited to participate in two rounds of Delphi consultation. The weights of indicators were determined by the percentage weighting method and product method, and the response rate, authority level, opinion concentration degree, and coordination degree of experts were evaluated. Results: Twenty-one experts participated in the consultation, including 3 males and 18 females. There were 11 experts with a doctor's degree, 7 with a master's degree and 3 with a bachelor's degree. All of them had senior professional titles and had more than 10 years of working experience. The recovery rates of the two rounds of consultations were 100.00% and 95.24%, the authority coefficients were 0.963 and 0.948, and Kendall's coefficients of concordance were 0.175 and 0.148 (both P<0.05), respectively. The final cervical cancer prevention and control literacy evaluation index system included 3 primary indicators (basic knowledge and concepts: 0.334; healthy lifestyle and behaviors: 0.338; basic skills: 0.328), 12 secondary indicators, with "capability to accurately acquire, comprehend, evaluate and apply health information" having the highest weight (0.166), and 51 tertiary indicators, with "HPV vaccination" (0.086), "consulting on relevant issues" (0.082), and "expressing personal perspectives" (0.080) having relatively higher weights. Conclusion The evaluation index system of cervical cancer prevention and control literacy serves as a valid assessment tool for women of appropriate age, providing the reference for developing targeted health education to enhance cervical cancer prevention and control literacy.

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: 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: 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.