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    Chinese Journal of Current Advances in General Surgery

    Chinese Journal of Current Advances in General Surgery

    JournalISSN 1009-9905

    年发文量

    研究主题

    论文(5302)

    排序
    1Study of Learning Curve for Da Vinci Robotic Nipple Sparing Mastectomy and Immediate Breast Reconstruction with Gel Implant
    Kuo Chen, Pengwei Lu

    Objective: Exploring the learning curve of Da Vinci robotic nipple sparing mastectomy and immediate breast reconstruction with Gel implant(RNSMIBR). Methods: The clinical data of 129 breast cancer patients completed from September 2022 to June 2024 in a single institution were retrospectively analyzed. The duration of operation was evaluated by cumulative sum analysis (CUSUM) to fit a learning curve. According to the critical value of the learning curve, the learning curve of da Vinci robotic mastectomy was divided into different stages. Results: The patients' median age was 42.09 +/- 7.3 years. In addition, the median operative of RNSMIBR time was 118.52 +/- 65.3min. There was no conversion to laparoscopic or open surgery. Intraoperative complications were observed in 6.15 % of the procedures and the learning curve reached a level that would be considered acceptable in terms of failure rates. The optimal curve-fitting equation was: CUSUM = 0.002x3-0.773 x 2+58.491x+114 4.242. A tendency of decline in total operative time following the first 27 cases was found. The CUSUM graph for total operative time indicated the generation of two distinct performance phases, namely learning (n = 27) and proficiency (n = 102). Conclusions: The minimum number of operations required for surgeons to reach the competence and proficiency stages of the learning curve is 27. This learning curve can be used as a basis for performance guidance during the training of young surgeons in the future. (c) 2025 Asian Surgical Association and Taiwan Society of Coloproctology. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

    2024引用:15
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    2Holistic Integrated Medicine
    FAN Daiming

    Many people engagingin medical scienceasked me,"You’ve beenworking on the exploration of Holistic Integrated Medicine(HIM)for quite awhile.What is the progressyou have made in your research?Have you got your article published?May I beyour first reader?"

    2024引用:1
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    3Effect of LINC00467 on the proliferation and migration of gastric cancer cell HGC-27 by targeting miR-495-3p
    PAN Pan,GU Yong-juan,XU Li-xian,CHEN Ya-nan,SHEN Dong

    目的:探讨长基因间非编码RNA00467(LINC00467)是否靶向miR-495-3p调控胃癌细胞HGC-27的增殖和迁移.方法:采用实时定量PCR(RT-qPCR)技术检测HGC-27细胞以及人胃黏膜上皮细胞GES-1中LINC00467和miR-495-3p表达量.采用双荧光素酶报告实验和RT-qPCR确定LINC00467和miR-495-3p之间的靶向调控关系.将HGC-27细胞分为si-NC组、si-LINC00467组、si-LINC00467+anti-miR-NC组、si-LINC00467+anti-miR-495-3p组.采用CCK-8法、平板克隆实验检测细胞增殖;划痕愈合实验检测细胞迁移;免疫印迹法分析上皮细胞钙粘蛋白(E-cadherin)和神经钙黏素(N-cadherin)表达.结果:与GES-1细胞相比,HGC-27细胞中LINC00467表达量显著升高(P<0.05),miR-495-3p表达量显著降低(P<0.05).LINC00467与miR-495-3p直接结合.与si-NC组比较,si-LINC00467组HGC-27细胞存活率、克隆形成数、划痕愈合率、N-cadherin蛋白表达量均显著降低(P<0.05),E-cadherin蛋白表达量、miR-495-3p表达量显著升高(P<0.05).与si-LINC00467+an-ti-miR-NC组比较,si-LINC00467+anti-miR-495-3p组HGC-27细胞存活率、克隆形成数、划痕愈合率、N-cadherin蛋白表达量均显著升高(P<0.05),E-cadherin蛋白表达量显著降低(P<0.05).结论:干扰LINC00467通过上调miR-495-3p表达能够抑制胃癌细胞HGC-27增殖和迁移.

    2023
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    4胸骨切开术治疗巨大胸骨后甲状腺肿伴气管压迫1例

    胸骨后甲状腺肿(retrosternal goiter,RSG)是一种生长缓慢的肿块,较少见,目前对于胸骨后甲状腺肿定义尚缺乏统一定义,由于甲状腺肿病程进展缓慢,一般多无临床症状和体征,以往多在体检及出现症状时发现.RSG最常报告的症状是呼吸困难、吞咽困难和声音嘶哑,主要与气管和食管压迫有关[1].新疆医科大学第一附属医院2021年12月25日收治1例胸骨后甲状腺肿患者,手术方式为颈前胸骨上窝弧形切开术加胸骨切开术,报道如下.

    2023
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    5乳腺浸润性小叶癌与浸润性导管癌的临床和超声特征对比
    ,,

    通过对比分析浸润性小叶癌和浸润性导管癌的临床特点和超声表现,分析两者的异同,提高浸润性小叶癌的超声诊断的准确率.回顾性分析2016年12月—2021年12月在山东大学第二医院术前行超声检查并经术后病理证实为浸润性小叶癌的70个结节和浸润性导管癌的731个结节,通过单因素及多因素分析,评估两组间的异同,明确各超声征象的诊断价值.两组在年龄、绝经状态、厚径、长/厚、病变类型、触诊、导管异常、形态及边缘表现等方面差异有统计学意义(P<0.05).将上述因素作为自变量纳入多因素Logistic回归模型,结果显示,模型具有统计学意义(x2=7.383,P<0.001,R2=0.082).年龄较小]者(OR=1.025)、长/厚较大者倾向于浸润性小叶癌(OR=0.619).尽管多种临床和超声表现在诊断浸润性小叶癌时可以起到一定的作用,但病例年龄和病灶长/厚比值在单因素和多因素中都有重要意义,对浸润性小叶癌和导管癌的鉴别诊断有重要意义.

    2023
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    高被引作者

    作者引用发文
    Rong Ma39625
    Fisher Alexander V2971
    Sean Ronnekleiv-Kelly2971
    NanHai Shou24232
    Jun Niu23034
    XiaoPeng Wu20625
    FengJun Liu20018
    Daorong Wang18533
    YuXin Chen18026
    Xu-Ting Zhi17938

    高产作者

    作者引用发文
    KeSen Xu14138
    Xu-Ting Zhi17938
    Jun Niu23034
    Zaiping Jing8734
    Daorong Wang18533
    NanHai Shou24232
    YuXin Chen18026
    XiaoPeng Wu20625
    Rong Ma39625
    Yan Sun5124

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