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    Journal of Modern Medicine & Health

    Journal of Modern Medicine & Health

    JournalISSN 1009-5519

    年发文量

    研究主题

    论文(51317)

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    1Establishment and Validation of Lung Adenocarcinoma Aging-Related Genes Prognostic Model Based on TCGA Database
    WU Shuangli, WU Tiecheng, YU Guang,XU Jingxuan, LI Baojian, XING Long

    Objective To explore autophagy-related genes(ARGs)in lung adenocarcinoma and con-struct a prognostic model for lung adenocarcinoma based on ARGs.Methods RNA high-throughput tran-scriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas(TCGA)database and HADb database to acquire ARGs.A prognostic model for lung adenocarcinoma was constructed and validated based on differentially expressed ARGs,followed by the construction of column line graphs and calibration curves to explore the clinical application value of the model.Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on differentially expressed ARGs.Lasso regression analysis was conducted on differentially expressed ARGs with prognostic significance to construct the prognostic model for lung adenocarcinoma.Kaplan-Meier survival curves were plotted.Results A total of 31 differentially expressed ARGs were obtained,and 10 differentially expressed ARGs with prognostic signifi-cance were selected.Patients in the high-risk group were significantly associated with poorer overall survival,with statistical significance(P<0.05).T stage,N stage and risk score was an independent prognostic factor for patients with lung adenocarcinoma.The global consistency of the calibration curve column line graph was 0.710,indi-cating a high level of agreement between the model's predicted results and actual outcomes.Conclusion The risk model constructed based on differentially expressed ARGs can serve as a prognostic feature for patients with lung ade-nocarcinoma or provide a reference for individualized treatment for patients with lung adenocarcinoma.

    2024
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    2尿液中肿瘤标志物检测在膀胱癌诊断中的应用研究进展

    膀胱癌是一种十分容易复发的恶性肿瘤,治疗后需严密随访.目前膀胱镜检查及可疑病变处活检仍是膀胱癌诊断和随访的金标准[1].

    2023
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    3Research progress on the relationship between gamma-glutamyltransferase and atherosclerosis
    WU Xuan,TANG Yiyue, CHEN Mingxing

    γ-谷氨酰转移酶(GGT)是肝酶谱检测指标之一,其检测成本低而灵敏度较高,因而被广泛用于临床检测肝胆疾病及酒精蓄积.近年来,GGT已被引入作为新的氧化应激血清标志物,同时,也有研究表明,GGT水平升高预示心血管疾病发生率增加,可将GGT视为检测动脉粥样硬化的新型血清学指标.

    2023
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    4SPOC教学模式在临床医学外科手术教学中的应用
    ,,,

    目的 探索小规模的限制性在线课程(SPOC)教学模式在临床医学外科手术教学中的应用效果.方法 选取2019级临床医学八年制64名学生作为试验组,采用SPOC教学模式授课;选取同期2020级临床医学五年制130名学生作为对照组,采用传统教学模式授课.以2组学生平均成绩评价教学模式的教学效果.结果 试验组学生总成绩[(89.3±5.7)分]显著优于对照组[(82.7±6.2)分],差异有统计学意义(P<0.001).结论 SPOC模式可有效提高学生的理论成绩和临床技能成绩,提升学生的操作能力及团队协作能力.

    2023
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    5Comparison of dose parameters of three different intensity modulated radiotherapy techniques in local advanced NSCLC radical radiotherapy
    GUAN Mingli,LEI Yanan, LI He

    目的 比较固定野调强放射治疗(放疗)(IMRT)、容积弧形调强放疗(VMAT)和螺旋断层放疗系统(TOMO)3种不同调强放疗技术在局部晚期非小细胞肺癌(NSCLC)根治性放疗中的剂量参数.方法 选取2020年9月至2022年9月该院收治的96例局部晚期NSCLC患者为研究对象,为每一例患者设计以上三套不同调强放疗计划.结果 计划靶区(PTV)剂量参数对比显示,与IMRT和TOMO比较,VMAT的靶区剂量适形度指数(CI)显著更高,与TOMO比较,VMAT的不均匀度指数(HI)显著更低,差异均有统计学意义(P<0.05);与TOMO比较,IMRT的HI显著更低,差异有统计学意义(P<0.05);与IMRT和TOMO比较,VMAT的平均剂量(Dmean)显著更低,差异有统计学意义(P<0.05);在高剂量区(D1、D2)和低剂量区(D98、D99)及V95、V105等方面,TOMO较IMRT、VMAT显著更低,差异均有统计学意义(P<0.05).危及器官剂量对比显示,3种不同调强放疗技术下的全肺Dmean、V5、V10、V40及V50比较,差异均无统计学意义(P>0.05);与IMRT比较,TOMO的全肺V20及V30显著更低,差异均有统计学意义(P<0.05);与TOMO比较,IMRT的心脏Dmean、V5、V10及V20显著更低,差异均有统计学意义(P<0.05);3种不同调强放疗技术下的脊髓、食管最大剂量和Dmean比较,差异均无统计学意义(P>0.05).在右肺NSCLC患者中,与IMRT和TOMO比较,VMAT的CI和HI明显更优,差异均有统计学意义(P<0.05);与IMRT比较,TOMO的正常肺组织Dmean、心脏V5及V10均显著更高,差异均有统计学意义(P<0.05).在纳入大靶区的患者中,VMAT的CI明显优于IMRT,HI明显优于TOMO,差异均有统计学意义(P<0.05);与TOMO比较,IMRT的心脏Dmean、V5、V10、V20显著更低,差异均有统计学意义(P<0.05).在中央型NSCLC患者中,与IMRT比较,TOMO的V20显著更低,但V5却显著更高,差异均有统计学意义(P<0.05).结论 VMAT可能最适合局部晚期NSCLC患者(尤其对中央型、大靶区和右侧肺癌)放疗治疗的照射计划.

    2023
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    高被引作者

    作者引用发文
    HuaiAn Ma2813
    Liang Feng27813
    LiLi Xiong2642
    WenNing Wei2631
    QingBi Zhang23716
    Wei Zhang2355
    XuDong Zhao2355
    Huaiwu Jiang23429
    ShiFeng Lou20217
    Li Chen17416

    高产作者

    作者引用发文
    BangFu Rao17257
    Yang Xie8938
    WanBiao Tang11736
    Huaiwu Jiang23429
    HuiMin Du4929
    Yuzhang Wu9327
    MaoJin Xu6826
    XiaoYan Fu14924
    BingYuan Wang7924
    Yun Zhang6723

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