病原高通量测序在中枢神经系统感染患者中的应用效果
Chinese Journal of Medical Device（2023）
Abstract
目的 探究病原高通量测序与传统检验方法在中枢神经系统(CNS)感染患者中的应用效果.方法 选取2020年10月至2022年9月医院收治的80例CNS感染患者资料进行回顾性分析,根据病原微生物检测方法分为对照组和试验组,各40例.对照组采用形态学检查、生化检查、培养分离、免疫学等常规病原微生物检查,试验组采用病原高通量测序.观察并记录两组病原微生物检出率、症状改善天数(意识障碍、头痛、呕吐、发热等)、住院总天数及治愈率.结果 利用高通量测序检测出阳性结果28例,阳性检出率为70.00％,传统方法检测出阳性结果7例,阳性检出率为17.50％,试验组高于对照组,差异有统计学意义(P<0.05);试验组症状改善天数和住院总天数明显短于对照组,差异有统计学意义(P<0.05);试验组治愈率高于对照组,差异有统计学意义(P<0.05).结论 病原高通量测序在CNS感染患者检测中的阳性检出率较高,有利于临床医师及时干预治疗,提高治愈率,缩短症状改善时间及住院时间.
More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