WeChat Mini Program
Old Version Features

线粒体DNA D-环区单核苷酸多态性与胃癌发病年龄的关系研究

Chinese General Practice(2022)

河北医科大学第四医院

Cited 0|Views8
Abstract
背景 胃癌发病率和死亡率长期居高不下,防控形势严峻,且发病年龄逐渐年轻化,但目前关于胃癌发病年龄预测因素的研究报道较少.目的 探讨线粒体DNA D-环区(D-Loop区)单核苷酸多态性(SNPs)与胃癌患者发病年龄之间的关系.方法 选取2007年7月至2008年12月在河北医科大学第四医院消化内科经胃镜病理证实的胃癌患者150例为研究对象,提取胃癌患者外周血中的线粒体DNA,采用聚合酶链反应(PCR)扩增目的片段,并进行线粒体DNA D-Loop区测序.采用Kaplan-Meier法绘制胃癌患者发病年龄的生存曲线,比较采用Log-rank检验;采用多因素Cox比例风险回归分析探究胃癌患者发病年龄的影响因素.结果 不同分化程度患者发病年龄比较,差异有统计学意义(P<0.05).Log-rank检验结果显示,153G基因型患者的发病年龄〔(48.0±5.3)岁〕早于153A基因型患者〔(60.1±0.8)岁〕(χ2=7.757,P=0.005).多因素Cox比例风险回归分析结果显示,线粒体DNA D-Loop区SNPs位点153A/G是预测胃癌患者发病年龄的影响因素〔HR=0.323,95%CI(0.140,0.745),P=0.008〕.结论 线粒体DNA D-Loop区SNPs位点153A/G或许可以作为胃癌发病年龄的新型预测指标,通过分析线粒体DNA D-Loop区的多态性,可以帮助识别早发的胃癌患者.
More
Translated text
Key words
stomach neoplasms|dna, mitochondrial|d-loop|polymorphism, single nucleotide|age of onset|sex factors
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined