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.

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.

Septic acute lung injury(ALI),a life-threatening complication of sepsis,has garnered significant attention due to its high mortality rate.Despite advances in Western medicine,including anti-infective therapy and lung-protective ventilation strategies,managing inflammatory storm and alveolar-capillary barrier repair remain critical challenges with Western medicine alone,leading to suboptimal patient outcomes.Traditional Chinese medicine,with its emphasis on holistic regulation,has unique advantages in inhibiting excessive inflammation,protecting lung function,alleviating clinical symptoms,and improving the quality of life of patients,and integrated Chinese and Western integrative therapy has demonstrated improved clinical outcomes.This article systematically reviews recent research on Traditional Chinese medicine for septic ALI,focusing on single herbal medicines,traditional Chinese medicine injections,compound formulas,acupuncture,and herbal enemas.It also analyzes research gaps,aiming to inform clinical practice and promote the standardization of integrated Chinese and Western integrative therapy approaches,thus offering new therapeutic strategies for patients with septic ALI.

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.

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.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

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.

N7-methylguanosine(m7G)modification occurs at the 5'cap of mRNA in eukaryotes,and is also found at specific sites on tRNA and rRNA,showing wide conservation across various biological organisms.Aberrant m7G modification is involved in the dysregulation of gene expression and serves as a biomarker for multiple cancers,with significant potential for applications in tumor diagnosis and therapy.This review summarizes the biological functions and regulatory mechanisms of m7G modification,and outlines its potential clinical applications.It also highlights the oncogenic roles of aberrant m7G modification and its association with prognosis,providing a detailed discussion of the role and molecular mechanisms of abnormal m7G modification in regulating drug resistance in various cancers.