ObjectiveTaking classical herbal pair compatibility research as the entry point， this study aimed to deeply investigate the material basis and compatibility rules underlying the antidepressant effects of the traditional Chinese medicine （TCM） formula Zhizi Houpotang， and to elucidate its antidepressant mechanism， with a particular focus on its regulation of neuroinflammatory responses mediated by the NOD-like receptor protein 3 （NLRP3）/gasdermin D （GSDMD） signaling pathway and the consequent improvement of neuronal synaptic plasticity. MethodsC57BL/6J mice were randomly divided into a blank control group， a chronic unpredictable mild stress （CUMS） depression model group， a Zhizi Houpotang full-formula group （6 g·kg^-1·d^-1）， a Magnoliae Officinalis Cortex （MOC）-Aurantii Fructus Immaturus （AFI） herbal pair group （4.2 g·kg^-1·d^-1）， a Gardeniae Fructus （GF）-MOC herbal pair group （4.2 g·kg^-1·d^-1）， a GF-AFI herbal pair group （3.6 g·kg^-1·d^-1）， and a positive drug group （fluoxetine， 12 mg·kg^-1·d^-1）. Depressive-like behaviors in mice were evaluated using behavioral tests. Immunofluorescence staining was used to label and quantify the expression of the microglial marker ionized calcium-binding adaptor molecule 1 （Ibal） and the purinergic receptor P2X ligand-gated ion channel 7 （P2RX7） in the prefrontal cortex （PFC）. Enzyme-linked immunosorbent assay （ELISA） was applied to detect the levels of inflammatory cytokines interleukin-1β （IL-1β） and interleukin-18 （IL-18） in serum and PFC tissues. Western blot was employed to determine the expression of pannexin 1 （Panx1）， P2RX7， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， Caspase-1， GSDMD， postsynaptic density protein 95 （PSD95）， and the presynaptic protein Synapsin 1 in PFC tissues. Golgi staining was used to assess dendritic spine density of neurons in the PFC. ResultsCompared with the blank control group， the depression model group exhibited significant depressive-like behaviors. In addition， the immunofluorescence areas of Ibal and P2RX7 in the PFC were significantly increased （P<0.01）， the levels of IL-1β and IL-18 in serum and the PFC were significantly elevated （P<0.01）， and the protein expression levels of Panx1， P2RX7， NLRP3， ASC， Caspase-1， and GSDMD in the PFC were significantly upregulated （P<0.01）. In contrast， the protein expression levels of PSD95 and Synapsin 1 were significantly downregulated （P<0.01）， and neuronal dendritic spine density was significantly reduced （P<0.01）. Compared with the model group， the Zhizi Houpotang full-formula group and the GF-MOC herbal pair group showed significant improvement in all the above indicators （P<0.01）. The GF-AFI herbal pair group improved all the above indicators except P2RX7， Caspase-1， GSDMD， and PSD95 （P<0.05， P<0.01）. In contrast， the MOC-AFI herbal pair group showed no statistically significant improvement in any of the above indicators compared with the model group. ConclusionZhizi Houpotang and its key herbal pair， GF-MOC， can effectively ameliorate CUMS-induced depressive-like behaviors in mice. Its core antidepressant mechanism may involve inhibition of P2RX7/Panx1 signaling， thereby blocking the NLRP3/GSDMD-mediated pyroptosis pathway and significantly reducing the release of inflammatory cytokines IL-1β and IL-18. Simultaneously， it upregulates the expression of synapse-related proteins PSD95 and Synapsin 1 and increases dendritic spine density， promoting the recovery of synaptic plasticity. These results suggest that GF plays a key role in the antidepressant effects of this formula， and that the compatibility of GF with MOC may represent the principal herbal pair combination responsible for its core therapeutic action.

ObjectiveTo evaluate the therapeutic effect of Shenji Guben prescription （SJGB） on chronic obstructive pulmonary disease （COPD） in mice and to explore its potential therapeutic mechanism via transcriptomics. MethodsA mouse model of COPD was established by combined exposure to lipopolysaccharide （LPS） and cigarette smoke. C57BL/6J mice were randomly allocated into a control group， a model group， an acetylcysteine （78 mg·kg^-1·d^-1） group， and low- （7.5 g·kg^-1·d^-1）， medium- （15 g·kg^-1·d^-1）， and high-dose （30 g·kg^-1·d^-1） SJGB groups. After 13 weeks of modeling， oral administration was initiated and continued for 28 days. The therapeutic effects of SJGB were assessed based on body weight， lung function， lung tissue pathology， and inflammatory factors in bronchoalveolar lavage fluid （BALF）. Ultra-high performance liquid chromatography-tandem mass spectrometry （UHPLC-MS/MS） was employed to identify the active ingredients in SJGB. Transcriptomic profiling of lung tissue was performed by RNA-seq to identify differentially expressed genes （DEGs）， followed by gene ontology （GO） enrichment analysis， Kyotoencyclopedia of Genes and Genomes （KEGG） enrichment analysis， Gene Set Enrichment Analysis （GSEA）， and protein-protein interaction （PPI） network construction. Key genes were further validated by Western blot and Real‑time polymerase chain reaction （Real-time PCR）. ResultsAfter drug intervention， the final body weights of mice in medium- and high-dose SJGB groups were higher than those in the model group （P<0.05）. Compared with the model group， SJGB groups demonstrated a significant decrease in expiratory time （Te）， along with increases in peak expiratory flow （PEF）， 50% expiratory flow （EF50）， tidal volume （TV）， and expiratory volume （EV） （P<0.05）. High-dose SJGB ameliorated pathological changes in the lung tissue， reduced peribronchial inflammation scores， and decreased mean linear intercept （MLI） （P<0.01）. Compared with the model group， medium- and high-dose SJGB groups showed reductions in interleukin-6 （IL-6） and interleukin-1β （IL-1β） levels （P<0.01）. UHPLC-MS/MS revealed that the five most abundant components in SJGB were （S）-actinidine， ephedrine， pyroglutamic acid， methylephedrine， and coumarin. Transcriptomic analysis revealed 219 DEGs regulated by SJGB， suggesting that the anti-COPD effects of SJGB may be mediated through key genes such as cluster of differentiation 68 （CD68）， integrin subunit alpha X （ITGAX）， and cathepsin S （CTSS）， primarily involved in the lysosomal pathway. Western blot and Real-time PCR results confirmed that SJGB inhibited the expression of CD68， ITGAX， and CTSS compared with the model group （P<0.05）. ConclusionSJGB effectively improved the lung function and attenuated lung tissue inflammation in the mouse model of COPD by modulating the lysosomal pathway and related genes including CD68， ITGAX， and CTSS.

Objective To explore the advantages of combining small private online courses(SPOC)with artificial intelligence(AI)in radiology nursing teaching,in order to compensate for the shortcomings of traditional teaching models.Methods Eighty nursing students interning in the radiology department were randomly selected as research subjects and divided into an experimental group(SPOC+flipped classroom+AI-assisted teaching mode)and a control group(traditional teaching mode),with 40 students in each group.The effectiveness of the SPOC+flipped classroom+AI-assisted teaching mode was evaluated by comparing theoretical tests,nursing skills tests,self-learning ability assessments,and satisfaction with teaching modes between the two groups.Results The average scores of chapter tests,month-end assessments and graduation examinations in the experimental group were higher than those in the control group(P<0.001);The average scores of indwelling needle embedding,contrast agent injection,and contrast agent allergy treatment tests in the experimental group were higher than those in the control group(P<0.001);The online learning time,homework completion rate,and online test scores of the experimental group were higher than those in the control group(P<0.001);The overall satisfaction with the teaching mode was higher in the experimental group than in the control group,with statistically significant differences(P<0.001).Conclusion The SPOC+flipped classroom+AI-assisted teaching model possesses important advantages in the instruction of nursing the department of radiology,and provides strong support for the innovation and development of nursing education in the field.

Objective: To investigate the association between screen time (ST) during leisure time and anxiety-depression symptoms among middle school students, so as to provide a basis for formulating relevant intervention measures. Methods: From November to December 2023, a stratified cluster random sampling method was used to select 2 542 students from junior and senior high school in Taiyuan City. A self designed questionnaire, the Generalized Anxiety Disorder Scale (GAD-7), and the Patient Health Questionnaire (PHQ-9) were used to investigate ST and anxiety/depression symptoms among middle school students. The Logistic regression model was used to explore the association of ST with symptoms of anxiety and depression, as well as with anxiety and depression comorbiditles (CAD). Results: The detection rates of anxiety symptoms, depression symptoms, and CAD were 13.69%, 15.77%, and 10.11%, respectively. The median ST was 2.00 h/d [interquartile range ( IQR =2.38) for weekly averages], with 0.33 h/d ( IQR =1.67) on work days and 5.00 h/d ( IQR=5.50) on rest days. Logistic regression analysis indicated that the total ST of mobile phones during rest days ( OR =1.07, 1.10, 1.11) and the averages ST of mobile phones over a week ( OR = 1.20 , 1.22, 1.29), as well as the total ST of all screen types during rest days ( OR =1.04, 1.04, 1.05) and the averages ST of all screen types over a week ( OR =1.08, 1.09, 1.21) were positively correlated with anxiety symptoms, depression symptoms, and CAD (all P <0.01). Conclusions Among middle school students in Taiyuan City, screen time is positively correlated with symptoms of anxiety or depression and the comorbidity of anxiety and depression, especially smartphone screen time and weekend screen use. Therefore, measures should be implemented to reduce unnecessary screen time among middle school students, especially the use of mobile phones, in order to mitigate the occurrence of anxiety and depression.

Objective:To investigate the clinical efficacy of a self-detachable single-J internal stent drainage tube in the laparoscopic primary suture of the common bile duct for hepatolithiasis.Methods:Clinical data of 36 patients with hepatobiliary duct stones who underwent laparoscopic common bile duct primary suture combined with self-detached single J-type internal stent drainage at the First Affiliated Hospital of Xiamen University from April 2022 to April 2025 were retrospectively analyzed, including 17 males and 19 females, aged (54.3±8.7) years. All 36 patients underwent choledochoscopic stone extraction, primary common bile duct suture, and drainage with the self-expelling single-J internal stent. Total bilirubin, aspartate transaminase, and alanine transaminase before and 3 days after operation, as well as operation time, intraoperative blood loss, postoperative hospital stay, postoperative complications (bile leakage, cholangitis, intestinal obstruction, and stent retention), and stent expulsion time were collected.Results:All 36 patients successfully underwent the operation. Total bilirubin, aspartate transaminase, and alanine transaminase 3 days after operation showed significant improvement compared to preoperative levels (all P<0.05). The operation time was (86.5±22.6) min, intraoperative blood loss was 34.2 (13.7, 56.8) ml, and the postoperative hospital stay was (6.6±1.8) days. All single-J internal stents were spontaneously expelled via the anus between 6 and 21 days postoperatively, with the expulsion time of (10.7±2.1) days. No single J-type internal stent drainage tube was displaced into the bile duct in all cases, and there were no complications such as intestinal obstruction, bile leakage, cholangitis, or residual internal stent drainage tube. Conclusion:The self-detachable single-J internal stent drainage tube has been applied in the laparoscopic primary suture of the common bile duct for patients with hepatolithiasis, which demonstrated a good safety and effectiveness.

Drug development remains a critical issue in the field of biomedicine.With the rapid advancement of information technologies such as artificial intelligence(AI)and the advent of the big data era,AI-assisted drug development has become a new trend,particularly in predicting drug-target associations.To address the challenge of drug-target prediction,AI-driven models have emerged as powerful tools,of-fering innovative solutions by effectively extracting features from complex biological data,accurately modeling molecular interactions,and precisely predicting potential drug-target outcomes.Traditional machine learning(ML),network-based,and advanced deep learning architectures such as convolutional neural networks(CNNs),graph convolutional networks(GCNs),and transformers play a pivotal role.This review systematically compiles and evaluates AI algorithms for drug-and drug combination-target predictions,highlighting their theoretical frameworks,strengths,and limitations.CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions.GCNs provide deep insights into molecular interactions via relational data,whereas transformers increase prediction accu-racy by capturing complex dependencies within biological sequences.Network-based models offer a systematic perspective by integrating diverse data sources,and traditional ML efficiently handles large datasets to improve overall predictive accuracy.Collectively,these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy.This review summa-rizes the application of AI in drug development,particularly in drug-target prediction,and offers rec-ommendations on models and algorithms for researchers engaged in biomedical research.It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

Traditional Chinese medicine (TCM) is characterized by complex, multicomponent herbal formulations that challenge the conventional“one drug, one target” paradigm. Network pharmacology, through the construction of multilayered drug-target-disease networks, provides a systematic framework for unraveling TCM’s multitarget and multipathway mechanisms. Recent advancements in artificial intelligence, particularly large language models (LLMs), further enhance data integration, target identification, and clinical decision-making. This review synthesizes current progress in the application of network pharmacology and LLMs in TCM, highlighting their potential to deepen mechanistic insights and optimize drug discovery. By bridging traditional medical wisdom with modern computational tools, this integrative approach aims to advance the scientific validation of TCM and foster innovative healthcare solutions.

Objective To analyze the clinical efficacy and safety of Huoxue Xiaoyi decoction combined with exercise in the treatment of ovarian endometriosis.Methods 36 patients with ovarian endometriosis treated in Guang'anmen Hospital,China Academy of Chinese Medical Sciences from May 2022 to November 2022 were selected and treated with Huoxue Xiaoyi decoction combined with exercise for 3 months.The size of ovarian ectopic cyst,dysmenorrhea VAS score,PBAC score,female hormones,serum CA125 and CA199,catecholamines and cytokines levels were observed to evaluate the clinical efficacy and safety.Results After treatment,the maximum diameter of ovarian ectopic cyst,dysmenorrhea VAS score,PBAC score,cytokines(serum IL-1β,IL-2,IL-4,IL-6,IL-8,IL-10)levels were significantly lower than those before treatment(P<0.05).The levels of SHBG and adrenaline were significantly higher than those before treatment(P<0.05).During the treatment,the patient had no adverse drug reactions,vital signs,liver,and kidney function indexes were normal.Conclusion Huoxue Xiaoyi decoction combined with exercise therapy can reduce the size of ovarian ectopic cyst,relieve dysmenorrhea,regulate menstrual volume,increase the level of adrenaline,increase the level of sex hormone binding globulin,reduce the level of cytokines,and has fewer adverse reactions and good safety.

In recent years, the ongoing development of transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) has demonstrated significant potential in the treatment and rehabilitation of various brain diseases. In particular, the combined application of TES and TMS has shown considerable clinical value due to their potential synergistic effects. This paper first systematically reviews the mechanisms underlying TES and TMS, highlighting their respective advantages and limitations. Subsequently, the potential mechanisms of transcranial electromagnetic combined stimulation are explored, with a particular focus on three combined stimulation protocols: Repetitive TMS (rTMS) with transcranial direct current stimulation (tDCS), rTMS with transcranial alternating current stimulation (tACS), and theta burst TMS (TBS) with tACS, as well as their clinical applications in brain diseases. Finally, the paper analyzes the key challenges in transcranial electromagnetic combined stimulation research and outlines its future development directions. The aim of this paper is to provide a reference for the optimization and application of transcranial electromagnetic combined stimulation schemes in the treatment and rehabilitation of brain diseases.
Humans ; Transcranial Magnetic Stimulation/methods* ; Transcranial Direct Current Stimulation/methods* ; Brain Diseases/therapy*

Traditional Chinese medicine (TCM) demonstrates distinctive advantages in disease prevention and treatment. However, analyzing its biological mechanisms through the modern medical research paradigm of "single drug, single target" presents significant challenges due to its holistic approach. Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks, overcoming the limitations of reductionist research models and showing considerable value in TCM research. Recent integration of network target computational and experimental methods with artificial intelligence (AI) and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology. The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles. This review, centered on network targets, examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships, alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae, syndromes, and toxicity. Looking forward, network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics, potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
Artificial Intelligence ; Medicine, Chinese Traditional ; Humans ; Network Pharmacology/methods* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Multiomics