《流行病学》作为公共卫生与预防医学的主干课程,对于培养高质量公共卫生人才至关重要。在大数据、人工智能和互联网技术迅猛发展的时代背景下,传统的单一教学模式已无法满足现代医学教育的需求。线上线下混合教学模式逐渐成为高等医学教育的主要教学方式。这种模式结合了线上资源的丰富性和线下课堂的互动性,能够拓展教学内容,提高学生的自主学习能力。通过大数据分析和人工智能技术,可以提供个性化学习体验和实时反馈,优化教学效果。然而,这一模式在实际应用中仍面临诸如资源整合不够、师生互动不足等挑战。文章以广州医科大学为例,分析了《流行病学》课程中混合教学模式的优势与不足,并提出了针对性的改进建议。通过这些建议,旨在提升混合教学模式的有效性,为未来教学改革提供新的思路和参考。

Epidemiology,as a core course in public health and preventive medicine,is crucial for training high-quality public health professionals．With the rapid development of big data,artificial intelligence,and internet technologies,traditional single-mode teaching methods no longer meet the demands of modern education．The blended learning model,combining online and offline teaching,has gradually become a primary method in higher medical education．This model integrates the richness of online resources with the interactivity of offline classes,expanding instructional content and enhancing students' self-directed learning abilities．By leveraging big data analysis and artificial intelligence,personalized learning experiences and real-time feedback can be provided to optimize teaching effectiveness．However,this model still faces challenges such as inadequate resource integration and insufficient teacher-student interaction in practical application．This study uses a medical university as a case study to analyze the advantages and limitations of the blended learning model in epidemiology courses and proposes targeted improvement suggestions．The aim is to enhance the effectiveness of blended learning and provide new insights and references for future teaching reforms．

嵌合基因是指由两个或多个原本不连续的基因片段重组而成的新基因,它们可以通过基因组重排、转录诱导等机制产生。嵌合基因在正常生理和发育过程中具有重要的功能和调控作用。嵌合基因可以改变原有基因的表达水平、编码蛋白质的结构和功能、信号通路的激活和抑制等,从而促进肿瘤细胞的增殖、侵袭、转移和耐药性。近年来,随着高通量测序技术的发展和应用,越来越多的嵌合基因被发现和鉴定,它们在不同类型的肿瘤中具有不同的表达模式和功能作用,为肿瘤的分子诊断、预后评估和靶向治疗提供了新的机会和挑战。本文旨在对嵌合基因产生的机制、检测方法和在肿瘤中的功能和应用等方面进行综述,为进一步认识嵌合基因在肿瘤进展中的功能机制及其精准化治疗提供参考。

Chimeric genes refer to novel genes formed by the recombination of two or more originally non-contiguous gene fragments through mechanisms like genomic rearrangement and transcriptional induction．They play important roles in physiological and developmental regulation．Chimeric genes can alter the expression,structure and function of original genes,modulate signaling pathway activation and inhibition,and thereby promote tumor cell proliferation,invasion,metastasis and drug resistance．In recent years,with the development and application of high-throughput sequencing technologies,increasing numbers of chimeric genes have been discovered and identified．They demonstrate different expression patterns and functional roles in various tumor types,providing new opportunities and challenges for molecular diagnosis,prognostic assessment and targeted therapy of cancers．This review summarizes the mechanisms of chimeric gene formation,detection methods and their functions and applications in tumors,to provide insights into the functional mechanisms of chimeric genes in tumor progression and their implications for precision treatment．

目的 探究m6A甲基化基因与卵巢癌生存预后的关系,为卵巢癌的靶向治疗、预后评估提供科学依据。方法 从TCGA及GTEx数据库中下载卵巢癌组织与正常组织mRNA表达数据进行组间差异分析,通过LASSO回归筛选与卵巢癌生存相关基因,进一步使用逐步Cox回归分析构建风险评分预测模型,根据风险评分中位数将患者分为高风险组和低风险组并使用ROC曲线下面积评价模型的预测能力。相关性分析构建与m6A基因的共表达调控网络,GO功能富集和KEGG通路分析初步探讨潜在的生物作用机制。结果 在癌组织与正常组织中发现20个m6A甲基化基因差异表达,逐步Cox回归分析筛选出3个基因(HNRNPA2B1,ZC3H13,WTAP)用于构建风险评分模型,高风险组患者的生存期较低风险组患者明显缩短(P=0.001 9),死亡风险显著增加(HR=2.643, P＜0.01),风险评分模型结合患者年龄、临床分级和分期后,1、3、5年的AUC为0.74、0.64、0.64。生物信息学分析结果提示m6A相关基因参与RNA的剪接、定位、转运、代谢调控、蛋白水解、细胞周期、核糖体合成等生物学过程。结论 成功构建卵巢癌m6A甲基化基因预后风险评估模型且该模型具备一定的预测效能。

Objective To explore the relationship between m6A methylated genes and prognosis of ovarian cancer, so as to provide scientific basis for targeted therapy and prognosis assessment of ovarian cancer. Methods The mRNA expression data of ovarian cancer tissues and normal tissues were downloaded from TCGA and GTEx databases for difference analysis between two groups. The genes related to ovarian cancer survival were screened by LASSO regression, and the risk score prediction model was further constructed by step Cox regression analysis. The patients were divided into high-risk group and low-risk group according to the median risk score, and the ROC was used for analysis. Correlation analysis was performed to construct an expression regulatory network with m6A genes, and GO function enrichment and KEGG pathway analysis were performed to preliminarily explore the potential biological mechanism. Results 20 m6A methylation genes were found in differential expression between cancer tissue and normal tissue, three genes (HNRNPA2B1, ZC3H13, WTAP) were used to construct the model through step Cox regression analysis. Patients' survivals of high-risk group were shortened than that of the low-risk group obviously (P=0.001 9), the risk of death significantly was increased (HR=2.643, P<0.01). After risk score model combined with patient age, clinical classification and stage, the AUC of 1, 3, 5 years was 0.74, 0.64 and 0.64. Bioinformatics analysis indicated that those m6A genes were involved in RNA splicing, localization, transport, metabolic regulation, proteolysis, cell cycle, ribosome synthesis and other biological processes. Conclusion The prognostic risk assessment model of m6A methylated genes for ovarian cancer was successfully constructed and the model had certain predictive efficacy.