通过使用mNGS在一个中国人群中鉴定病原体构成,以iatrogenic和本地脊椎骨质炎
Qile Gao1,2, Qianfei Liu1,2, Guang Zhang1,2
1Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China.
Annals of medicine
|April 9, 2024
概括
这项研究使用元基因组测序区分了iatrogenic (IVO) 和原生 (NVO) 脊椎骨质炎中的病原体. 机器学习识别了血清生物标志物和名谱,以指导脊椎骨髓炎的早期抗菌治疗.
科学领域:
- 微生物学与传染病的研究
- 基因组学和生物信息学
- 临床诊断 临床诊断 临床诊断
背景情况:
- 脊椎骨质炎的早期诊断对于患者的预后至关重要.
- 鉴定脊椎骨髓炎的致病原体仍然是一个重大的临床挑战.
- 区分iatrogenic (IVO) 和本地 (NVO) 脊椎骨髓炎对于向治疗至关重要.
研究的目的:
- 为了区分IVO与NVO中的病原体类型,使用元基因组下一代测序 (mNGS).
- 确定血清生物标志物,以区分IVO和NVO.
- 开发一种用于脊椎骨髓炎早期抗微生物治疗指导的预测模型.
主要方法:
- 使用mNGS对145名脊柱感染患者的分析.
- 通过mNGS检测到的病原体在IVO和NVO组中的分类.
- 应用机器学习 (LASSO回归,随机森林) 来选择血清生物标志物并构建一个名录模型.
主要成果:
- 细菌族群比例的显著差异:Actinobacteria在NVO中更高,Firmicutes在IVO中更高.
- 葡萄球菌被确定为IVO中占主导地位的病原体,而Mycobacterium则在NVO中占主导地位.
- 确定了五种血清生物标志物 (BASO%,Mono%,PCT,G,APTT),从而产生了一个用于准确IVO/NVO区分的诺莫格拉姆模型.
结论:
- 该研究提供了一种方法,使用mNGS和血清生物标志物来区分IVO和NVO.
- 开发的诺莫格拉姆模型有助于临床医生进行差异诊断.
- 这些发现支持改善脊椎骨髓炎的早期抗菌药物策略.
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