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.

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

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.

The formation of portal vein thrombosis (PVT) is one of the complications of liver cirrhosis, which is easily overlooked but is not uncommon. With the deepening research on PVT in liver cirrhosis, evidence regarding the assessment of PVT formation, treatment effectiveness, and prognostic outcomes is continually being updated. This article summarizes recent studies on predicting the formation, anticoagulation efficacy, and survival models; analyzes and evaluates the rationality, standardization, and practicality of prediction models; and compares their strengths and weaknesses to provide a reference for clinicians in the individualized management and treatment of PVT in patients with liver cirrhosis.

Objective:To investigate the co-occurrence patterns of psychosocial and behavioral problems among children and to identify associated influencing factors.Methods:A multicenter cross-sectional survey was conducted in 2023. A cluster random sample of 19 176 children aged 6-16 years was recruited from middle-income areas across 10 provincial capitals and municipalities in China. Psychological and behavioral problems, including anxiety, compulsive behavior, social withdrawal, depression, somatic complaints, social problems, schizoid, delinquent behaviors, hyperactivity, sexual issues, and aggression, were assessed using the Achenbach Child Behavior Checklist parent version. Co-occurrence was defined as ≥2 concurrent problems. Children were divided into 4 groups by gender and age: boys aged 6-11 years, girls aged 6-11 years, boys aged 12-16 years, and girls aged 12-16 years. Those children who had psychosocial and behavioral problems were further categorized into the single-problem group, and the co-occurrence group based on assessment results. High-frequency co-occurrence phenotypes of children′s psychosocial and behavioral problems were identified. Demographic factors, such as parental employment, education, as well as psychosocial factors like parent-child relationship, screen time and outdoor activity, were investigated. χ 2 test was used to analyze differences between groups. Multivariate Logistic regression modeling was conducted to identify potential factors. Results:Among 14 711 children (7 501 boys, 7 210 girls) who provided effective questionnaires, the detection rates of single problem in the boys aged 6-11 years, girls aged 6-11 years, boys aged 12-16 years, and girls aged 12-16 years groups were 4.9% (171/3 461), 6.2% (193/3 120), 3.9% (158/4 040), and 5.1% (208/4 090), respectively; the detection rates of co-occurrence were 7.6% (262/3 461), 7.7% (241/3 120), 4.9% (199/4 040), and 5.7% (234/4 090), respectively. The overall detection rates of co-occurrence was higher than that of single problem ( χ2=25.47, P<0.001). Among children with co-occurrence, there were varied manifestations: in the boys aged 6-11 years group, the detection rates of social withdrawal (69.8% (183/262)), schizoid-like behavior (68.3% (179/262)), and compulsive behavior (67.6% (177/262)) were relatively high; in the girls aged 6-11 years group, the detection rates of schizoid-compulsive behavior (69.3% (167/241)), delinquent behavior (65.6% (158/241)), and hyperactivity (58.9% (142/241)) were relatively high; in the boys aged 12-16 years group, the detection rates of hyperactivity (78.9% (157/199)), compulsive behavior (67.3% (134/199)), and immature behavior (57.3% (114/199)) were relatively high; in the girls aged 12-16 years group, the detection rates of schizoid-like behavior (89.7% (210/234)), immature behavior (59.0% (138/234)), and cruelty (57.7% (135/234)) were relatively high. Maternal bachelor′s degree or higher ( OR=0.78, 95% CI 0.61-0.99, P=0.038) served as co-occurrence protective factors, whereas having 1 or more siblings, increased parent-child conflict and decreased parent-child interaction time ( OR=1.24, 1.41, 1.36; 95% CI 1.02-1.52, 1.15-1.73, 1.02-1.82, all P<0.05) were co-occurrence risk factors. Conclusions:Children exhibit strong co-occurrence tendencies in psychosocial and behavioral problems. Compulsive and schizoid traits are the predominant co-occurring phenotypes for childhood and girls respectively. ?Familial environment plays a critical role, necessitating ?multidimensional clinical assessments and ?family-centered interventions.

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*

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, offering 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 accuracy 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 summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations 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.

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.

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.