Gut Microbiome Influences on Anastomotic Leak and Recurrence Rates Following Colorectal Cancer Surgery
British Journal of Surgery(2018)SCI 1区
Abstract
Background: The pathogenesis of colorectal cancer recurrence after a curative resection remains poorly understood. A yet-to-be accounted for variable is the composition and function of the microbiome adjacent to the tumour and its influence on the margins of resection following surgery. Methods: PubMed was searched for historical as well as current manuscripts dated between 1970 and 2017 using the following keywords: 'colorectal cancer recurrence', 'microbiome', 'anastomotic leak', 'anastomotic failure' and 'mechanical bowel preparation'. Results: There is a substantial and growing body of literature to demonstrate the various mechanisms by which environmental factors act on the microbiome to alter its composition and function with the net result of adversely affecting oncological outcomes following surgery. Some of these environmental factors include diet, antibiotic use, the methods used to prepare the colon for surgery and the physiological stress of the operation itself. Conclusion: Interrogating the intestinal microbiome using next-generation sequencing technology has the potential to influence cancer outcomes following colonic resection.
MoreTranslated text
Key words
Anastomotic Leakage
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
Related Papers
1998
被引用131 | 浏览
2004
被引用119 | 浏览
2010
被引用157 | 浏览
1999
被引用334 | 浏览
2009
被引用36 | 浏览
2013
被引用180 | 浏览
2013
被引用1801 | 浏览
2014
被引用179 | 浏览
2015
被引用835 | 浏览
2015
被引用218 | 浏览
2016
被引用74 | 浏览
2017
被引用1733 | 浏览
2017
被引用1262 | 浏览
2016
被引用95 | 浏览
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
GPU is busy, summary generation fails
Rerequest