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.

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.

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.

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

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.

Astragali Radix (AR) and Notoginseng Radix et Rhizoma (NR) are frequently employed in cardiovascular disease treatment. However, the efficacy of the AR-NR medicine pair (AN) in improving cardiac remodeling and its underlying mechanism remains unclear. This study aimed to evaluate AN's cardioprotective effect and potential mechanism on cardiac remodeling using transverse aortic constriction (TAC) in mice and angiotensin II (Ang II)-induced neonatal rat cardiomyocytes (NRCMs) and fibroblasts in vitro. High-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (HPLC-Q-TOF-MS/MS) characterized 23 main components of AN. AN significantly improved cardiac function in the TAC-induced mice. Furthermore, AN considerably reduced the serum levels of N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin T (CTn-T), and interleukin-6 (IL-6) and mitigated inflammatory cell infiltration. Post-AN treatment, TAC-induced heart size approached normal. AN decreased cardiomyocyte cross-sectional area and attenuated the upregulation of cardiac hypertrophy marker genes (ANP, BNP, and MYH7) in vivo and in vitro. Concurrently, AN alleviated collagen deposition in TAC-induced mice. AN also reduced the expression of fibrosis-related indicators (COL1A1 and COL3A1) and inhibited the activation of the transforming growth factor-β1 (TGF-β1)/mothers against decapentaplegic homolog 3 (Smad3) pathway. Thus, AN improved TAC-induced cardiac remodeling. Moreover, AN downregulated p-dynamin-related protein (Drp1) (Ser616) expression and upregulated mitogen 2 (MFN-2) and optic atrophy 1 (OPA1) expression in vivo and in vitro, thereby restoring mitochondrial fusion and fission balance. In conclusion, AN improves cardiac remodeling by regulating mitochondrial dynamic balance, providing experimental data for the rational application of Chinese medicine prescriptions with AN as the main component in clinical practice.
Animals ; Drugs, Chinese Herbal/pharmacology* ; Myocytes, Cardiac/metabolism* ; Mice ; Rats ; Male ; Mitochondrial Dynamics/drug effects* ; Ventricular Remodeling/drug effects* ; Astragalus Plant/chemistry* ; Mice, Inbred C57BL ; Rhizome/chemistry* ; Panax notoginseng/chemistry* ; Rats, Sprague-Dawley ; Natriuretic Peptide, Brain/genetics* ; Humans ; Angiotensin II ; Astragalus propinquus

Objective:To elucidate the expression of angiopoietin-like protein 4 (ANGPTL4) in LPS-induced septic cardiomyopathy tissue and cardiomyocyte, and to explore the mechanism of ANGPTL4 in septic cardiomyopathy.Methods:Fifty C57BL/6 mice, aged 8 weeks, were randomly(random number) divided into a treatment group (LPS) and a control group ( n = 25 each). The mice in the treatment group were intraperitoneally injected with LPS (10 mg/kg) to establish a sepsis model. After 24 h, the myocardial tissues of the mice in the sepsis group and the control group, which were caused by LPS, were collected for RNA sequencing to pick out the differentially expressed gene of ANGPTL4.Ventricular myocytes of neonatal mice were taken, and the silencing and overexpression vectors of ANGPTL4 were transfected. After 48 hours of transfection, the cells were collected for subsequent detection. Western blot method was used to detect the expression of apoptotic factors Bax, Bcl-2, and Caspase 3 in mouse ventricular myocytes; CCK8 method was used to detect the activity of ventricular myocytes; using the Annexin V-FITC and PI double staining method, the apoptosis of ventricular myocytes was detected. Results:RNA-seq analysis revealed a statistically significant upregulation of ANGPTL4 expression at both transcriptional and translational levels in the ventricular tissue of septic mice, as compared to the control group ( P<0.05). The results of qRT-PCR and Western blot indicated that the mRNA and protein levels of ANGPTL4 in the ventricular tissues and cardiomyocytes of mice treated with LPS were significantly increased ( P<0.05). After transfection of the silencing and overexpression vectors of ANGPTL4 in cardiomyocytes, it was found that compared with NC, the mRNA and protein expression levels of ANGPTL4 in the si-ANGPTL4 group significantly decreased ( P<0.05), the vitality of ventricular myocytes increased ( P<0.05), the expressions of apoptosis-related factors Bax and Caspase 3 significantly decreased ( P<0.05), and the expression of Bcl-2 significantly increased ( P<0.05), and the number of apoptotic cells significantly decreased ( P<0.05); while the transfection of the overexpression vector of ANGPTL4 showed an opposite trend. Conclusions:In septic myocardial tissue and cardiomyocyte, the expression of ANGPTL4 is elevated, resulting in the inhibition of ventricular myocyte viability and the promotion of cardiomyocyte apoptosis.

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.

Objective To investigate the reliability and validity of the assessment tools of Brief ICF Core Sets for Arthroplasty of Knee Osteoarthritis in Perioperative Period(ICSAKOPP). Methods From May,2022 to April,2023,320 patients undergoing knee arthroplasty were selected in Peking University Third Hospital,China-Japan Friendship Hospital,Peking University First Hospital and Chinese PLA General Hospital.Trained assessors used Brief ICSAKOPP to evaluate all enrolled patients before arthroplasty,three days(±one day)after arthroplasty,three weeks(±one week)after arthroplasty,and three months(±one month)after ar-throplasty.Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)scores were recorded at the same time.Five professionals were asked to score all the items of Brief ICSAKOPP,and the content validity index(CVI)was caculated. Results A total of 64 cases were dropped down.CVI of all the items of the Brief ICSAKOPP were above 0.8,with a av-erage CVI of the scale of 0.938.The Cronbach's α coefficient of the Brief ICSAKOPP was 0.813.There was a moderate correlation(r=0.681,P<0.001)between the overall Brief ICSAKOPP and WOMAC scores,as well as body functional dimension score(r=0.668,P<0.001)and activities and participation dimension score(r=0.657,P<0.001). Conclusion Brief ICSAKOPP is good in content validity,internal consistency reliability and criterion validity.