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.

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.

Objective:To investigate the efficacy of self-prescribed Huoxue Shengjing Prescription as adjuvant therapy for varicocele-induced infertility and its impact on the outcomes of in vitro fertilization-embryo transfer/intracytoplasmic sperm injection (IVF-ET/ICSI).Methods:A randomized controlled trial was conducted. A total of 99 patients with varicocele-induced infertility in our hospital from January 2022 to July 2023 were selected as observation subjects and divided into three groups using a random number table method, with 33 patients in each group. The low ligation group received low ligation of varicocele under a microscope, the low ligation + conventional Western medicine treatment group received low ligation + conventional Western medicine therapy, and the combined group received low ligation + conventional Western medicine therapy + a self-prescribed Huoxue Shengjing Prescription. Among them, the low ligation of varicocele under a microscope was followed by IVF-ET/ICSI assisted reproductive technology 3 months after surgery; the conventional Western medicine therapy involved continuous administration of L-carnitine oral solution for 3 months; the self-prescribed Huoxue Shengjing Prescription was started on the first day after surgery and continued for 3 months. TCM syndrome scores were assessed before and after treatment, and semen routine analysis was performed using an automated semen quality analyzer. Mitochondrial activity of granulosa cells was measured using the Hrudka extraction method, and sperm nuclear DNA integrity was assessed using a modified alkaline single-cell gel electrophoresis method. Follow-up was conducted for 1 year to observe and record the outcomes of IVF-ET/ICSI and evaluate the clinical efficacy.Results:The total effective rate was 93.9% (31/33) in the combined group, 69.7% (23/33) in the low ligation group, and 75.8% (25/33) in the low ligation + conventional Western medicine treatment group, with statistical significance ( χ 2=6.52, P=0.039). After treatment, the scores for mild abdominal pain, testicular heavy pain, impotence, mental fatigue, and the total score in the combined group were lower than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 89.29, 97.51, 136.36, 155.06, and 311.13, respectively, P<0.001). The sperm survival rate, sperm concentration, normal morphology rate, and progressive motility rate in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 19.23, 11.85, 35.97, and 52.21, respectively, P<0.001). Mitochondrial grade I cell activity of granulosa cells was higher than that of the low ligation + conventional treatment group and low ligation group ( F=23.23, P<0.001), and grade Ⅲ cell activity was lower than that of the low ligation + conventional treatment group and low ligation group ( F=20.28, P<0.001). After treatment, the detection of grade Ⅰ and Ⅱ sperm nuclear DNA integrity in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 17.73 and 18.39, respectively, P<0.001), while grades Ⅲ and Ⅳ were lower than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( F values were 29.07 and 10.36, respectively, P<0.001). During follow-up, the excellent embryo rate and the spouse's clinical pregnancy rate in the combined group were higher than those in the low ligation + conventional Western medicine treatment group and the low ligation group ( χ2 values were 14.92 and 8.38, respectively; P values were 0.001 and 0.015, respectively). Conclusion:The adjuvant treatment with a self-prescribed Huoxue Shengjing Prescription can enhance sperm quality in patients with varicocele-related infertility, maintain DNA integrity, regulate seminal plasma mitochondrial function, increase the rate of high-quality embryos, and improve the spouse's pregnancy outcomes.

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.

BACKGROUND: The proportion of patients with non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutations is relatively high in China. However, these patients currently lack significant benefits from available neoadjuvant treatment options. This study aims to explore the potential application value of neoadjuvant targeted therapy by evaluating its efficacy and safety in patients with EGFR-mutant resectable lung adenocarcinoma. METHODS: A multicenter retrospective study was used to analyze the treatment effect of patients with stage IIA-IIIB EGFR-mutant lung adenocarcinoma who underwent surgical resection after receiving neoadjuvant targeted therapy from July 2019 to October 2024. RESULTS: A total of 24 patients with EGFR-mutant lung adenocarcinoma from three centers were included in this study. All patients successfully underwent surgery and achieved R0 resection of 100.0%. The objective response rate (ORR) was 83.3% (20/24) . The major pathologic response (MPR) rate was 37.5% (9/24), with 2 patients (8.3%) achieving pathological complete response (pCR). During neoadjuvant therapy, 13 out of 24 patients (54.2%) experienced adverse events of grade 1-2, with no occurrences of ≥ grade 3. The most common treatment-related adverse events were rash (n=4, 16.7%), mouth sores (n=2, 8.3%), and diarrhea (n=2, 8.3%). The median follow-up time was 33.0 months, no deaths occurred in all patients, and the overall survival (OS) rate was 100.0%. The 1-year disease-free survival (DFS) rate was 91.1%, and the 2-year DFS rate remained at 86.2%. CONCLUSIONS The application of neoadjuvant targeted therapy in patients with EGFR-mutant resectable lung adenocarcinoma is safe and feasible, and is expected to become a highly promising neoadjuvant treatment option for the patients with EGFR-mutant lung adenocarcinoma.
Humans ; ErbB Receptors/metabolism* ; Male ; Female ; Middle Aged ; Adenocarcinoma of Lung/surgery* ; Neoadjuvant Therapy ; Lung Neoplasms/surgery* ; Aged ; Retrospective Studies ; Mutation ; Adult