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 investigate the correlation between neutrophil-to-lymphocyte ratio （NLR）， platelet-to-lymphocyte ratio （PLR）， systemic immune-inflammation index （SII） and the severity of intrauterine adhesions （IUA）. MethodsThe retrospective study included 380 patients who underwent transcervical resection of adhesions （TCRA） from December 2019 to March 2025. Based on the American Fertility Society （AFS） classification， patients were divided into mild （n=61）， moderate （n=225）， and severe （n=94） groups. NLR， PLR， and SII were calculated from preoperative blood tests. Statistical analyses included Kruskal-Wallis test and ordinal Logistic regression. ResultsNLR， PLR， and SII were significantly higher in the severe IUA group compared to the mild group （P<0.05）， with SII showing the strongest predictive ability （OR=1.004， P=0.001）. The number of intrauterine procedures was an independent risk factor （OR=1.27/level， P=0.016）. The predictive model ［Logit（P）=-0.676+0.241×operation times+0.004×SII］ effectively identified severe IUA cases. ConclusionInflammatory markers （particularly SII） are correlated with IUA severity and may serve as non-invasive tools for clinical assessment.

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 explore the characteristics and influential factors of pharmaceutical clinic service demands， providing evidence for optimizing pharmaceutical service models and facilitating pharmaceutical service models of pharmacist role transformation. METHODS A cross-sectional survey design was adopted， and 410 outpatient participants were selected from Fudan University Shanghai Cancer Center through convenience sampling for questionnaire administration from February to May 2025. Kano model was applied to analyze the demand attributes of 25 pharmaceutical services， while questionnaires were used to assess patients’ awareness and demand status. Subgroup analyses were conducted based on key demographic variables such as gender， age， educational attainment， and economic burdens， to SACA- systematically examine the differences in Kano attribute classification among patients in each subgroup. RESULTS The awareness rate of pharmaceutical outpatient services among patients was only 14.63%， yet those who were aware demonstrated a significantly higher demand rate for such services compared to those who were unaware （P＜0.001）. The demand for pharmaceutical clinic services exhibited a hierarchical characteristic： twelve items were identified as attractive attributes （e. g.， providing suggestions for more affordable treatment options， offering online consultation services， etc.）， five items as expected attributes （e.g.， having a good attitude and being able to patiently answer your questions， etc.）， three items as must-have attributes （e.g.， providing guidance on medication dosage and usage， providing guidance on medication precautions， etc.）， five items as indifferent attributes （e.g.， providing treatment plan recommendations based on the patient’s condition）. There were zero items classified as reverse attribute. Subgroup analysis revealed that female patients showed greater concern for “neat and clean attire of medical staff” than male patients （P＜0.001）； patients under 60 years of age demonstrated stronger demand for “providing treatment plan recommendations based on patients’ conditions” compared to patients aged 60 or above （P＝0.016）； those with below high school education placed greater emphasis on “providing guidance on medication precautions” compared to those with a high school education or above （P＝0.011）； patients with lower economic burdens exhibited stronger preferences for “neat and clean attire of medical staff ” （P＝0.002）. CONCLUSIONS The public awareness rate of pharmaceutical clinic services is considerably low； however， those who are aware of such services demonstrate significantly higher demand. The medication safety-related services and convenience-oriented demands should be prioritized in the development of pharmaceutical clinics. Moreover， the study also revealed that factors such as gender， age， educational level， and economic burdens exert significant influences on patients’ service demands.

OBJECTIVE: To investigate the anti-inflammatory effect of rutaecarpine (RUT) on monosodium urate crystal (MSU)-induced murine peritonitis in mice and further explored the underlying mechanism of RUT in lipopolysaccharide (LPS)/MSU-induced gout model in vitro. METHODS: In MSU-induced mice, 36 male C57BL/6 mice were randomly divided into 6 groups of 8 mice each group, including the control group, model group, RUT low-, medium-, and high-doses groups, and prednisone acetate group. The mice in each group were orally administered the corresponding drugs or vehicle once a day for 7 consecutive days. The gout inflammation model was established by intraperitoneal injection of MSU to evaluate the anti-gout inflammatory effects of RUT. Then the proinflammatory cytokines were measured by enzyme-linked immunosorbent assay (ELISA) and the proportions of infiltrating neutrophils cytokines were detected by flow cytometry. In LPS/MSU-treated or untreated THP-1 macrophages, cell viability was observed by cell counting kit 8 and proinflammatory cytokines were measured by ELISA. The percentage of pyroptotic cells were detected by flow cytometry. Respectively, the mRNA and protein levels were measured by real-time quantitative polymerase chain reaction (qRT-PCR) and Western blot, the nuclear translocation of nuclear factor κB (NF-κB) p65 was observed by laser confocal imaging. Additionally, surface plasmon resonance (SPR) and molecular docking were applied to validate the binding ability of RUT components to tumor necrosis factor α (TNF-α) targets. RESULTS: RUT reduced the levels of infiltrating neutrophils and monocytes and decreased the levels of the proinflammatory cytokines interleukin 1β (IL-1β) and interleukin 6 (IL-6, all P<0.01). In vitro, RUT reduced the production of IL-1β, IL-6 and TNF-α. In addition, RT-PCR revealed the inhibitory effects of RUT on the mRNA levels of IL-1β, IL-6, cyclooxygenase-2 and TNF-α (P<0.05 or P<0.01). Mechanistically, RUT markedly reduced protein expressions of tumor necrosis factor receptor (TNFR), phospho-mitogen-activated protein kinase (p-MAPK), phospho-extracellular signal-regulated kinase, phospho-c-Jun N-terminal kinase, phospho-NF-κB, phospho-kinase α/β, NOD-like receptor thermal protein domain associated protein 3 (NLRPS), cleaved-cysteinyl aspartate specific proteinase-1 and cleaved-gasdermin D in macrophages (P<0.05 or P<0.01). Molecularly, SPR revealed that RUT bound to TNF-α with a calculated equilibrium dissociation constant of 31.7 µmol/L. Molecular docking further confirmed that RUT could interact directly with the TNF-α protein via hydrogen bonding, van der Waals interactions, and carbon-hydrogen bonding. CONCLUSION RUT alleviated MSU-induced peritonitis and inhibited the TNFR1-MAPK/NF-κB and NLRP3 inflammasome signaling pathway to attenuate gouty inflammation induced by LPS/MSU in THP-1 macrophages, suggesting that RUT could be a potential therapeutic candidate for gout.
Animals ; NF-kappa B/metabolism* ; Male ; Indole Alkaloids/therapeutic use* ; Signal Transduction/drug effects* ; Mice, Inbred C57BL ; Inflammation/complications* ; Uric Acid ; Quinazolines/therapeutic use* ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Humans ; Gout/chemically induced* ; Inflammasomes/metabolism* ; Cytokines/metabolism* ; THP-1 Cells ; Mitogen-Activated Protein Kinases/metabolism* ; Mice ; Molecular Docking Simulation ; Lipopolysaccharides ; Quinazolinones

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.

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 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.