ObjectiveTo systematically evaluate the effect of intermittent theta burst stimulation (iTBS) on lower extremity motor function and balance function in patients with stroke. MethodsA systematic literature search was conducted in the Cochrane Library, Embase, PubMed, Web of Science, CNKI, SinoMed, Wanfang data and VIP from inception to February 19, 2025. Randomized controlled trials comparing iTBS with conventional rehabilitation or sham iTBS in patients with post-stroke lower extremity motor and balance dysfunction were included. The methodological quality of the included studies was assessed using the Cochrane Risk of Bias Tool. Meta-analysis was performed using RevMan 5.4 and Stata 14.0. ResultsA total of 16 articles involving 647 patients were included. Meta-analysis showed that iTBS improved the Fugl-Meyer Assessment-Lower Extremities score (MD = 2.58, 95%CI 1.61 to 3.55, P < 0.001), Berg Balance Scale score (MD = 4.11, 95%CI 2.43 to 5.79, P < 0.001), Barthel Index score (MD = 4.95, 95%CI 0.97 to 8.92, P = 0.010) and motor-evoked potential (MEP) latency (MD = -1.42, 95%CI -2.54 to -0.30, P = 0.010). Subgroup analyses suggested that at subacute stage, cerebellar iTBS, more than ten treatment sessions, and 1 200 pulses per day may be more effective. ConclusioniTBS may improve lower extremity motor function and activities of daily living in patients with stroke, and shorten MEP latency, and it may also confer potential benefits in improving balance function in these patients.

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

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.

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*

Objective: To screen and identify the key active molecules, signaling pathways, and therapeutic targets of Shuxuening (SXN) injection for treating liver cirrhosis (LC) and to evaluate its therapeutic potential using a mouse model. Methods: Target genes of SXN and LC were retrieved from public databases, and enrichment analysis was performed. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and hub genes were identified using Molecular Complex Detection (MCODE). LC was induced in rats and mice via intraperitoneal injections of diethylnitrosamine and carbon tetrachloride (CCl4) for 12 weeks. Starting at week 7, SXN was administered intraperitoneally to the mice in the treatment group. Serum and liver tissues of the mice were collected for the detection of indicators, pathological staining, and expression analysis of hub targets using quantitative real-time polymerase chain reaction (qRT-PCR). Results: We identified 368 overlapping genes (OLGs) between SXN and LC targets. These OLGs were subsequently used to build a PPI network and to screen for hub genes. Enrichment analysis showed that these genes were associated with cancer-related pathways, including phosphoinositide-3-kinase/Akt and mitogen-activated protein kinase signaling and various cellular processes, such as responses to chemicals and metabolic regulation. In vivo experiments demonstrated that SXN treatment significantly improved liver function and pathology in CCl4-induced LC mice by reducing inflammation and collagen deposition. Furthermore, qRT-PCR demonstrated that SXN regulated the expression of MAPK8, AR and CASP3 in the livers of LC mice. Conclusion This study highlighted the therapeutic effects of SXN in alleviating LC using both bioinformatics and experimental methods. The observed effect was associated with modulation of hub gene expression, particularly MAPK8, and CASP3.

BACKGROUND:Impaired postural control is an important risk factor for falls and secondary damage in patients with idiopathic normal pressure hydrocephalus.Most of the existing studies have analyzed the gait parameters of patients during straight-line walking,but few have analyzed the postural stability characteristics of patients during static and dynamic activities. OBJECTIVE:To analyze the characteristics of postural stability in elderly patients with idiopathic normal pressure hydrocephalus. METHODS:Twenty-two patients clinically diagnosed with idiopathic normal pressure hydrocephalus at the Department of Neurosurgery,Huadong Hospital Affiliated to Fudan University,Shanghai,China,from September 2022 to February 2023 were selected as the patient group,and 18 healthy accompanying family members were selected as the healthy control group.The postural stability characteristics of the subjects were assessed using the Timed Up-and-Go Test,Multi-Directional Reach Test,Berg Balance Scale,and Static Balance Function Test(reaction time,speed of movement,directional control,maximum offset distance,and endpoint travel). RESULTS AND CONCLUSION:The time required to complete the Timed Up-and-Go Test was significantly longer in the patient group than in the healthy control group(P＜0.05).The results of the stretching test in the four directions of anterior,posterior,leftand right were significantly lower in the patient group than in the healthy control group(P＜0.05).The Berg Balance Scale scores in the patient group were lower than those in the healthy control group(P＜0.05).In the Static Balance Function Test,the results of reaction,movement speed,directional control,maximum offset distance and endpoint travel index were smaller in the patient group than the healthy control group(P＜0.05).To conclude,patients with idiopathic normal pressure hydrocephalus exhibit overall postural control deficits,and impaired reaction and execution abilities make these patients unable to make timely and accurate motor responses in the face of disturbances from internal or external sources,resulting in postural instability and increasing the risk of falls.

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.

Chronic atrophic gastritis(CAG)is a chronic gastric disorder characterized by recurrent damage to the gastric mucosal epithelium,resulting in the reduction of intrinsic glands,with or without concurrent intestinal metaplasia.The"hyperactive stomach qi"theory,derived from Huang Di Nei Jing Su Wen Ji Zhu,proposes that the core pathogenesis of CAG lies in excessive stomach qi activity,grounded in the physiological principle of"strong yang qi in earth and weak yin qi in earth".This theory synthesizes the clinical manifestations and pathological progression of CAG,asserting that its development often involves intertwined pathological factors such as stagnation,dryness-heat,phlegm-dampness and stasis-toxicity.A triphasic therapeutic framework is proposed:the spleen qi deficiency phase,marked by impaired spleen transport function and dysregulated qi-fluid distribution,requiring spleen fortification and qi-fluid regulation;the hyperactive stomach qi phase,characterized by intensified stomach qi activity coupled with dryness-damp stagnation,necessitating stagnation resolution,dampness elimination and yin nourishment;the decline and disorder of middle qi phase,characterized by the deficiency of the middle qi,with phlegm,blood stasis and toxins forming the terminal stage.Treatment should focus on reinforcing the middle and restoring balance,detoxifying and dissipating accumulation.By exploring CAG pathogenesis and treatment through the lens of"hyperactive stomach qi",this study aimed to provide novel theoretical insights and therapeutic strategies for TCM in the prevention and treatment of CAG.

Eye-tracking technology monitors eye movement trajectories to reveal the cognitive mechanisms underlying visual behavior.With advantages like objectivity and real-time capability,it is increasingly applied in nursing.However,research and application in China are still in the early stages.This article reviews the development,measurement metrics,methods,and impact of eye-tracking in nursing,analyzes current challenges,and suggests solutions to aid its development in the field of nursing in China.

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.