双镜联合治疗老年胆道结石对患者T细胞亚群及肝功能的影响
Chinese Journal of Gerontology(2016)
Abstract
目的:研究腹腔镜联合胆道镜(双镜)治疗老年胆道结石患者围术期T细胞亚群及肝功能的变化及意义。方法回顾性分析28例双镜联合术前术后肝功能指标变化及T细胞亚群指标的变化。结果围术期无手术死亡病例;28例术前术后第3天 T细胞亚群比较无明显变化(P>0.05),术第3天肝功能有多样指标变化,分别为谷草转氨酶(AST,t=2.67)、谷丙转氨酶(ALT,t=2.53)、γ-谷氨酸转肽酶(γ-GT,t=28.23)、碱性磷酸酶(AKP,t=13.69),均有统计学意义(P<0.01);治疗后第8天肝功能AST及ALT指标恢复明显(P>0.05)。结论腹腔镜联合胆道镜术后对肝功能及T细胞亚群的指标影响不大,双镜技术尤其适应老年胆石病患者。
More求助PDF
上传PDF
View via Publisher
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