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.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors， evaluate their modeling strategies， key predictors， and predictive performance， and provide evidence-based references for clinical decision-making and research. METHODS A literature search was conducted across 11 databases， including CNKI， Wanfang Data， and PubMed， for relevant studies published before April 2025. Two reviewers independently performed literature screening and data extraction， and the risk of bias and applicability of the models were evaluated using the PROBAST tool. RESULTS Ultimately， seven studies were selected， of which four were English articles and three were Chinese articles， involving 12 risk prediction models. Although model discrimination was good （AUC 0.748-0.981）， only two models underwent external validation； furthermore， calibration was inadequately reported in three studies. PROBAST indicated that all models exhibited a high risk of bias， with major issues including a predominance of retrospective designs， inadequate sample representativeness， and lack of blinding. However， in terms of applicability， all models received favorable evaluations. In terms of modeling methods， most studies employed traditional logistic regression approaches to construct models， while only a minority introduced machine learning algorithms and conducted systematic comparisons among multiple algorithms. Models developed using machine learning methods significantly outperformed those constructed with traditional statistical methods. CONCLUSIONS The existing risk prediction models for myelosuppression after chemotherapy in children with malignant tumors demonstrate potential in clinical risk early warning. However， they generally suffer from design and methodological limitations， such as a predominance of retrospective single-center designs， few events per variable， opaque handling of missing data， and inconsistent reporting of model coefficients. Future studies should adopt prospective designs， incorporate machine learning with key clinical predictors， and follow TRIPOD reporting guidelines to enhance scientific rigor and clinical utility.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

With the widespread application of information technology in the field of Traditional Chinese Medicine(TCM),the data in the TCM domain is becoming increasingly rich.The emergence of data-driven research in TCM has utilized knowledge graphs for the management,analysis,and presentation of TCM data,becoming a crucial method for the inheritance and innovation of Traditional Chinese Medicine.Therefore,reasoning based on TCM knowledge graphs has become a hot topic in contemporary TCM research.In order to better utilize knowledge graph reasoning technology to promote innovation and development in TCM,this article,after providing an overview of TCM knowledge graph reasoning,focuses on the development of knowledge graph reasoning in TCM and its applications from three perspectives:logic rule-based,distributed representation,and neural network-based methods.Finally,the article summarizes the advantages and disadvantages of knowledge reasoning methods and provides prospects for research and applications in TCM knowledge graph reasoning,including studies on interpretability,temporal knowledge graphs,and the fusion of multimodal information.

Background:Duodenal papillary adenoma is a benign tumor with relatively low incidence but significant carcinogenesis potential.Despite the minimal invasiveness and low complication rate,endoscopic papillectomy is associated with a definite risk of recurrence for duodenal papillary adenoma.Investigating the driver genes of duodenal papillary adenoma and establishing predictive models for recurrence and malignant progression could facilitate the precision medicine.Aims:To identify the key driver genes for tumor occurrence,carcinogenesis and recurrence in duodenal papillary adenoma by integrating multi-dimensional bioinformatics approaches based on transcriptomics data,and validate clinically.Methods:Expression profiles of duodenal papillary adenoma and adenocarcinoma were obtained from the GEO database(including data sets GSE189035,GSE94919,GSE111156,and GSE102208).Differentially expressed genes(DEGs)between adenomatous and normal tissues were screened.Weighted gene co-expression network analysis(WGCNA)and pseudo-time analysis were combined to identify the core genes exhibiting an"initial rise followed by decline"expression pattern during the dynamic progression from normal tissue to adenoma and adenocarcinoma.Functional annotation,immune microenvironment profiling,and protein-protein interaction network analysis were performed to explore the tumor-promoting mechanisms of these core genes.Clinical validation was conducted using immunohistochemistry to estimate the gene expression level and its relationship with tumor recurrence.Results:A total of 469 common DEGs were identified.WGCNA revealed that the blue module(including 1 051 genes)was associated with adenoma development and progression(Cor=-0.29,0.15,and 0.11 for normal tissue,adenoma,and adenocarcinoma,respectively).Intersection with DEGs pinpointed four key genes:SLC12A2,BEST4,SLC37A2,and SOAT2.Pseudo-time analysis demonstrated that only SLC12A2 maintained sustained high expression in both adenoma and adenocarcinoma tissues.KEGG enrichment analysis indicated that SLC12A2 was linked to various malignant pathways(e.g.,PD-1/PD-L1 signaling pathway),and its high expression correlated with the reduced immune cell infiltration(e.g.,γδ T cells,CD8+T cells,etc.).Clinical validation by immunohistochemistry confirmed the trend of initial upregulation and subsequent downregulation of SLC12A2 expression in normal,adenoma,and adenocarcinoma tissues.Patients with tumor recurrence showed higher SLC12A2 expression level(P=0.004);likewise,SLC12A2 high expression was associated with an elevated recurrence risk(P=0.034).Conclusions:SLC12A2 serves as a critical driver of tumorigenesis and progression for duodenal papillary adenoma,and might be a promising biomarker for recurrence prediction.

With the widespread application of information technology in the field of Traditional Chinese Medicine(TCM),the data in the TCM domain is becoming increasingly rich.The emergence of data-driven research in TCM has utilized knowledge graphs for the management,analysis,and presentation of TCM data,becoming a crucial method for the inheritance and innovation of Traditional Chinese Medicine.Therefore,reasoning based on TCM knowledge graphs has become a hot topic in contemporary TCM research.In order to better utilize knowledge graph reasoning technology to promote innovation and development in TCM,this article,after providing an overview of TCM knowledge graph reasoning,focuses on the development of knowledge graph reasoning in TCM and its applications from three perspectives:logic rule-based,distributed representation,and neural network-based methods.Finally,the article summarizes the advantages and disadvantages of knowledge reasoning methods and provides prospects for research and applications in TCM knowledge graph reasoning,including studies on interpretability,temporal knowledge graphs,and the fusion of multimodal information.

Objective:To evaluate the efficacy of lamb′s tripe extract and vitamin B 12 capsule (LTEVB 12C) on chronic atrophic gastritis (CAG) at different locations (antrum lesser curvature, antrum greater curvature, gastric angle, corpus lesser curvature, and corpus greater curvature). Methods:From August 2011 to January 2013, 715 patients with CAG in a multicenter, randomized, double-blind, placebo-controlled trial were enrolled from 16 tertiary first-class hospitals across the country, including the First Affiliated Hospital of Air Force Medical University, Nanfang Hospital of Southern Medical University, the First Hospital of Jilin University, West China Hospital of Sichuan University, etc., there were 476 cases in the LTEVB 12C group and 239 cases in the placebo group. The patients of the LTEVB 12C group received LTEVB 12C, and the patients of placebo group received LTEVB 12C mimetic, all the medications were taken 3 capsules each time and 3 times a day after meals, and the treatment course of 2 groups were both 6 months. The efficacy evaluation criteria included the effective rate (a decrease of ≥1 in histopathological score compared with baseline after 6 months of treatment) and the reversal rate (a decrease of ≥ 2 in histopathological score compared with baseline after 6 months of treatment in the patients with moderate to severe CAG). The impact of lesion sites on the therapeutic effects of LTEVB 12C was analyzed by logistic regression analysis. The two-way unordered Cochran-Mantel-Haenszel chi-square test considering the center effect and Pearson chi-square test were used for statistical analysis. Results:The effective rates of chronic inflammation at the antrum greater curvature and corpus greater curvature (23.3%, 110/473 vs. 13.0%, 31/239; 20.3%, 96/472 vs. 12.6%, 30/239), the effective rates of atrophy at the antrum lesser curvature, antrum greater curvature, gastric angle, corpus lesser curvature, and the corpus greater curvature (27.0%, 118/437 vs. 15.7%, 34/216; 29.2%, 126/432 vs. 18.5%, 38/205; 27.8%, 121/435 vs. 16.7%, 36/216; 32.5%, 127/391 vs. 19.8%, 37/187; 33.0%, 119/361 vs. 21.8%, 39/179), and the effective rates of intestinal metaplasia at the antrum lesser curvature, antrum greater curvature, gastric angle, and the corpus lesser curvature (45.0%, 112/249 vs. 29.8%, 31/104; 53.8%, 86/160 vs. 33.9%, 21/62; 45.8%, 103/225 vs. 24.0%, 25/104; 51.9%, 83/160 vs. 28.3%, 17/60) of the LTEVB 12C group were all higher than those of the placebo group, and the differences were statistically significant ( χ2=10.76, 6.39, 9.69, 7.91, 11.05, 9.62, 8.57, 5.20, 7.11, 12.45, and 6.73; all P<0.05). The reversal rates of chronic inflammation at the corpus lesser curvature and corpus greater curvature (5.2%, 12/231 vs. 0, 0/123; 4.7%, 8/170 vs. 0, 0/88), the reversal rates of atrophy at the antrum lesser curvature, antrum greater curvature, corpus lesser curvature, and the corpus greater curvature (6.8%, 22/323 vs. 1.3%, 2/151; 9.2%, 29/315 vs. 1.4%, 2/144; 14.2%, 38/267 vs. 2.5%, 3/121; 20.8%, 35/168 vs. 5.8%, 4/69), and the reversal rates of intestinal metaplasia at the antrum lesser curvature, antrum greater curvature, gastric angle, and the corpus lesser curvature (29.8%, 39/131 vs. 9.1%, 4/44; 41.0%, 32/78 vs. 12.5%, 3/24; 33.3%, 44/132 vs. 4.8%, 3/63; 50.0%, 37/74 vs. 8.7%, 2/23) of the LTEVB 12C group were all higher than those of the placebo group, and the differences were statistically significant ( χ2=6.58, 5.12, 5.60, 8.61, 11.43, 6.59, 7.30, 4.95, 15.92, 7.62; all P<0.05). There were no statistically significant differences in the effective rates and reversal rates of active inflammation at different locations between the LTEVB 12C group and the placebo group (all P>0.05). The results of logistic regression analysis (taking the antrum lesser curvature as the reference) further confirmed that the reversal rates of chronic inflammation ( OR=0.22, 95% confidence interval (95% CI): 0.07 to 0.67; OR=0.24, 95% CI: 0.07 to 0.80), atrophy ( OR=0.28, 95% CI: 0.16 to 0.49; OR=0.28, 95% CI: 0.16 to 0.49), and intestinal metaplasia ( OR=0.42, 95% CI: 0.24 to 0.77; OR=0.20, 95% CI: 0.08 to 0.52) at the corpus lesser curvature and corpus greater curvature were all higher than those at the antrum lesser curvature, and the differences were statistically significant (all P<0.05). There were no statistically siginificant differences in the reversal rates of the aforementioned pathological features between the antrum greater curvature, gastric angle, and the antrum lesser curvature (all P>0.05). Conclusion:LTEVB 12C can achieve good efficacy in the treatment of CAG, and the chronic inflammation, atrophy, and intestinal metaplasia at multiple locations are improved, especially at the corpus lesser curvature and the corpus greater curvature.

Objective:To analyze the research status of family management for children with cancer, explore research hotspots and development trends, so as to provide reference for subsequent research and clinical practice in family management for children with cancer.Methods:Literature related to family management for children with cancer was retrieved from the Web of Science Core Collection Database. The search period was from January 31, 2014 to December 31, 2024. CiteSpace 6.4.R1 was employed for bibliometric analysis.Results:A total of 647 articles were included. The annual publication volume showed an overall trend of first decreasing, then increasing, and fluctuating upward. The authors conducting the study on family management for children with cancer formed a distinct core team. The United States and the University of Pennsylvania were the top countries and institutions in terms of publications, with close collaboration and exchange among institutions. Research focuses on recurrence treatment, social support systems, and deepening precision interventions for Ewing sarcoma. The development of technology driven management tools, cross-cultural research, and the integration of policy and ethical studies represent a future trend.Conclusions:Research focuses on key areas such as recurrence treatment, social support systems, and precision interventions for Ewing sarcoma, emphasizing family management practices, building social support networks, and identifying therapeutic targets. In the future, technology driven management tools should be developed to achieve intelligence, cross-cultural research should be deepened, and a humanized management model should be constructed by combining policy and ethical research, in order to improve the efficiency and effectiveness of subsequent related research and clinical practice for family management for children with cancer.

To address the issue of reduced signal quality of photoplethysmography caused by local fluctuation,an approach called locally orthogonal weighted polynomial fitting(LOWPF)is proposed for signal smoothing.After determining the positions of the fluctuation sequences using the forward-backward difference XOR method,weighted polynomial fitting is applied to these sequences,and the fitted values are used to replace the fluctuation sequences to achieve signal smoothing.By constructing orthogonal basis functions,the condition number of the coefficient matrix is reduced,and the stability of the equation system solution for higher-order fitting is improved.Simulation results demonstrate that the smoothed signal's XOR smoothness of the proposed method surpasses that of the moving average algorithm and the empirical mode decomposition reconstruction algorithm.The smoothing results on 241 sets of measured PPG signals show that LOWPF achieves an efficiency of smoothness of 89.10%,significantly higher than the 78.05%of empirical mode decomposition and the 59.13%of the 5-point moving average algorithm.LOWPF has promising application prospects for smoothing signals with significant local fluctuations.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.