Objective: To explore the impact of adverse childhood experiences (ACEs) on health risk behaviors (HRBs) among college students and the mediating role of psychological symptoms, so as to provide a basis for developing intervention strategies. Methods: From March to April 2023, a convenience cluster sample of 1 801 students from 12 universities in Nanning, Liuzhou, Guilin, Wuzhou of Guangxi completed an online survey. A self designed questionnaire, Adverse Childhood Experiences-International Questionnaire (ACE-IQ) and Symptom Checklist-90 (SCL-90) were used for evaluation tools. Binary Logistic regression, structural equation modeling (SEM) and Bootstrap methods were used to analyze the associations and mediating effects. Results: Overall, 71.2% of college students experienced at least one type of ACE, with emotional neglect (40.3%) and emotional abuse ( 25.2 %) having the highest detection rates. The top three HRBs were unhealthy diet (77.8%), physical inactivity (54.1%), and smoking/alcohol use (18.5%). Logistic regression showed that poor family functioning, abuse, and extra familial violence were each associated with an increased risk of smoking/alcohol use ( OR =1.14, 1.11, 1.18) and deliberate self harm ( OR =1.26, 1.19,1.30) (all P <0.05). Experience of abuse increased the risk of high risk sexual behavior and family dysfunction increaded the risk of physical inactivity, respectively ( OR = 1.07 , 1.04, both P <0.05). Mediation analysis revealed that anxiety ( β =0.20) and depression ( β = 0.09 ) partially mediated the pathway from poor family functioning to deliberate self harm; paranoia ( β =0.02) partially mediated the pathway from abuse to high risk sexual behavior; and obsessive-compulsive symptoms ( β =0.26) and depression ( β =0.10) partially mediated the pathway from extra familial violence to deliberate self harm (all P <0.05). Conclusion Psychological symptoms play a mediating role in the association between ACEs and HRBs, and mental health interventions may reduce the risk of HRBs among college students.

This paper summarizes Professor WANG Xixing's clinical experience in treating immune checkpoint inhibitor-related pneumonitis （CIP） based on the theory of "cough attributed to the five zang （脏） organs". Cough is a common predominant symptom of CIP. According to the theory of "cough attributed to the five zang organs"， drug toxicity triggers cancer toxin， leading to disharmony among the five zang organs， and then lung failing to diffuse and govern descent as the core pathogenesis. Therefore， treatment should focus on harmonizing the five zang organs to restore the normal function of lung qi to diffuse and govern descent. In clinical practice， CIP can be classified into four syndrome patterns， including lung yin depletion， deficiency of both the lung and the spleen with phlegm-dampness， liver fire harassing the lung， and lung-kidney yin deficiency. Correspondingly， Chaimai Jinluo Runfei Decoction （柴麦金络润肺汤） is used to nourish yin and moisten the lung； Qigui Peitu Huayin Decoction （芪桂培土化饮汤） is used to fortify the spleen and tonify the lung， resolve dampness and dispel phlegm； Chaidan Shuyu Runjin Decoction （柴丹疏郁润金汤） is used to drain liver and clear the lung； and Dimai Jinshui Xiangsheng Decoction （地脉金水相生汤） is used to nourish the kidney and moisten the lung.

ObjectiveTo investigate the efficacy of Xingpi capsules （XPC） in treating diarrhea-predominant irritable bowel syndrome （IBS-D） with spleen deficiency and elucidate its potential molecular mechanisms. MethodsA rat model of IBS-D with spleen deficiency was established by administering senna leaf in combination with restrained stress and swimming fatigue for 14 d. Ten specific pathogen free （SPF）-grade healthy rats were used as the normal control group. After successful modeling， SPF-grade rats were randomly divided into a model group， a pinaverium bromide group （1.5 mg·kg^-1）， and low- and high-dose XPC groups （0.135 and 0.54 g·kg^-1）， with 10 rats in each group. Rats in the normal control group and the model group were given distilled water by gavage， while the remaining groups were administered corresponding drug solutions by gavage once a day for 14 consecutive days. The rat body weights and fecal condition were observed every day， and the Bristol score was recorded. Enzyme-linked immunosorbent assay （ELISA） was used to determine the levels of 5-hydroxytryptamine （5-HT） in serum and colon tissue. Transmission electron microscopy was used to observe the microvilli and tight junctions in the colon. The integrity of the colonic barrier， intestinal motility， and expression of related pathway proteins were evaluated by hematoxylin-eosin （HE） staining， immunohistochemistry， and Western blot. ResultsCompared with those in the normal control group， rats in the model group showed a significantly decreased body weight and increased diarrhea rate， diarrhea grade， and Bristol score （P<0.01）. HE staining revealed incomplete colonic mucosa in the model group， with evident congestion and edema observed. Electron microscopy results indicated decreased density and integrity of the colonic barrier， shedding and disappearance of microvilli， and significant widening of tight junctions. The expression levels of colonic tight junction proteins Occludin and Claudin-5 were downregulated （P<0.01）， and the levels of 5-HT in serum and colon tissue were elevated （P<0.01）. The small intestine propulsion rate significantly increased （P<0.01）， and the expression of contractile proteins Ras homolog family member A （RhoA） and Rho-associated coiled-coil containing protein kinase 2 （ROCK2） in colon and phosphorylation of myosin light chain （MLC₂₀） were upregulated （P<0.01）. Compared with the model group， the treatment groups showed alleviated diarrhea， diarrhea-associated symptoms， and pathological manifestations of colon tissue to varying degrees. Specifically， high-dose XPC exhibited effectively relieved diarrhea， promoted recovery of colonic mucosal structure， significantly reduced congestion and edema， upregulated expression of Occludin and Claudin-5 （P<0.01）， decreased levels of 5-HT in serum and colon tissue （P<0.05，P<0.01）， significantly slowed small intestine propulsion rate （P<0.01）， and significantly downregulated expression of contractile proteins RhoA and ROCK2 in colon and phosphorylation of MLC₂₀ （P<0.05，P<0.01）. ConclusionXPC effectively alleviates symptoms of spleen deficiency and diarrhea and regulates the secretion of brain-gut peptide. The characteristics of XPC are mainly manifested in alleviating IBS-D with spleen deficiency from the aspects of protecting intestinal mucosa and inhibiting smooth muscle contraction， and the mechanism is closely related to the regulation of the 5-HT-RhoA/ROCK2 pathway expression.

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 potential protective effect of Shexiang Tongxin dropping pills (STDP) on ischemia-reperfusion injury and its underlying mechanisms in improving endothelial cell function in coronary microvascular disease (CMVD). Methods: A rat model of myocardial ischemia-reperfusion injury with CMVD was established using ligation and reperfusion of the left anterior descending artery. The effect of STDP (21.6 mg/kg) on cardiac function was evaluated using echocardiography, hematoxylin-eosin staining, and Evans blue staining. The effects of STDP on the microvascular endothelial barrier were assessed based on nitric oxide production, endothelial nitric oxide synthase expression, structural variety of tight junctions (TJs), and the expression of zonula occludens-1 (ZO-1), claudin-5, occludin, and vascular endothelial (VE)-cadherin proteins. The mechanisms of STDP (50 and 100 ng/mL) were evaluated by examining the expression of sphingosine 1-phosphate receptor 2 (S1PR2), Ras Homolog family member A (RhoA), and Rho-associated coiled-coil-containing protein kinase (ROCK) proteins and the distribution of ZO-1, VE-cadherin, and F-actin proteins in an oxygen and glucose deprivation/reoxygenation model. Results: The administration of STDP on CMVD rat model significantly improved cardiac and microvascular endothelial cell barrier functions (all P < .05). STDP enhanced the structural integrity of coronary microvascular positioning and distribution by clarifying and completing TJs and increasing the expression of ZO-1, occludin, claudin-5, and VE-cadherin in vivo (all P < .05). The S1PR2/RhoA/ROCK pathway was inhibited by STDP in vitro, leading to the regulation of endothelial cell TJs, adhesion junctions, and cytoskeletal morphology. Conclusion STDP showed protective effects on cardiac impairment and microvascular endothelial barrier injury in CMVD model rats induced by myocardial ischemia-reperfusion injury through the modulation of the S1PR2/RhoA/ROCK pathway.

Renal cancer is a common and increasingly prevalent malignancy with a complex pathogenesis influenced by genetics,smoking,and obesity.Current treatment mainly involves surgery with adjunctive chemotherapy,radiation,and immunotherapy,but high rates of recurrence and metastasis indicate its limited effectiveness,emphasizing the need for better therapeutic targets.Growing evidence indicates that epigenetic modifications,particularly RNA modifications,play a critical role in renal cancer development and progression.This review highlights recent advances in renal cancer epigenetics,focusing on RNA modifications such as N6-methyladenosine(m6 A),N7-methylguanosine(m7G),5-methylcytosine(m5C),N1-methyladenosine(m1A),adenosine-to-inosine(A-to-I),N6,2'-O-dimethyladenosine(m6Am),and N4-acetylcytidine(ac4C),along with their regulatory factors.It also explores the diagnostic and therapeutic potential of targeting RNA modifications and associated proteins.

A ketogenic diet(KD)refers to an eating pattern designed to achieve a low-calorie content,minimum carbohydrate intake,high-fat consumption,and standard protein levels.A ketogenic diet is used in clinical practice to treat conditions including heart disease,diabetes,obesity,autism,glioblastoma,and other cancers.Although a ketogenic diet has not been recommended for any neurological disorders except epilepsy,extensive recent research suggests that such a diet may have a neuroprotective effect and may thus represent a new dietary therapy for the treatment of Parkinson's disease(PD).In this review,we discuss in detail the mechanisms responsible for the neuroprotective effects of a ketogenic diet in Parkinson's disease,with the aim of providing references for future clinical and experimental studies.

Objective:To explore the correlation between weekend moderate-to-vigorous physical activity(MVPA), weekday sedentary behavior(SB)and the risk of frailty in the elderly population monitored by wearable devices, and to provide a scientific basis for lifestyle interventions for frailty in the elderly.Methods:This study was based on the data of the UK Biobank from 2013 to 2015.A cross-sectional study design was adopted, and 33, 212 elderly people aged 60 and above with complete physical activity monitoring data were selected.The Frailty Index(FI)constructed by the deficit accumulation method was used to assess the frailty status.The correlation between the combined effect of weekday SB and weekend MVPA and the frailty status was analyzed, and the differences between genders were explored.Results:There were significant differences in physical activity indicators among the elderly with different frailty statuses.As the degree of frailty increased, the MVPA-related indicators showed a downward trend, while the weekday SB time gradually increased.There were sex differences in physical activity patterns and frailties.Compared with women, men had longer SB time on weekdays, lower metabolic equivalent of weekly MVPA consumption, and higher MVPA time on weekends, but the frailties index of women was slightly higher than that of men.After adjusting for confounding factors, the frailty risks for men and women in the subgroup with the lowest weekday SB and the highest weekend MVPA duration decreased by 46.9% and 59.8%, respectively( P<0.001)when compared to the highest-risk group. Conclusions:Based on the monitoring data from wearable devices, elderly individuals who reduced their SB time during weekdays and increased their MVPA time on weekends were associated with a lower risk of frailty, especially among women; which providing a new perspective for lifestyle-based intervention strategies for frailty among the elderly.

Myocardial fibrosis (MF) is a common pathological hallmark of cardiovascular diseases, reflecting shared mechanisms in their progression. However, the lack of reliable MF models that accurately mimic its pathogenesis has hindered drug discovery, highlighting the urgent need for more effective therapeutic agents. Herein, a novel contractile three-dimensional (3D) myocardial tissue model integrating cardiomyocytes, cardiac-fibroblasts, and bone marrow-derived macrophages in collagen hydrogel was developed to simulate the fibrotic changes of cardiovascular disease, and facilitate the screening of anti-MF compounds. The 3D myocardial tissue model exhibited precise, visualizable, and quantifiable contractile characteristics under hypoxia and drug interventions. 76 compounds extracted from the resins of Toxicodendron vernicifluum, a traditional Chinese medicine with clear clinical benefits for fibrotic diseases, were screened for anti-fibrotic activity. Using an in vitro 3D oxygen-glucose deprivation (OGD)-treated myocardial tissue model instead of a two-dimensional transforming growth factor-β treated cardiac-fibroblasts model, two candidates including LQ-40 and SQ-3 exert impressive anti-MF activity, which was further validated in left anterior descending coronary artery ligation-induced MF mouse model. The current results demonstrate the feasibility and advantage of the novel contractile 3D tissue model with multi-cell types in discovering candidates for MF, further stressing the great potential of regulating macrophages in the treatment of MF.

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.