通过MALDI-TOF质谱和机器学习来区分Clostridioides difficile的高病毒性和非高病毒性核型
Ahmed Mohamed Mostafa Abdrabou1,2,3, Issa Sy4, Markus Bischoff4,5
1Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421, Homburg, Saar, Germany. ahmed.mostafa@uks.eu.
概括
机器学习与MALDI-TOF质谱学相结合,可以准确识别高病毒性Clostridioides difficile核型 (HVRT). 这种方法超过了直接的光谱比较,在欧洲临床隔离中区分HVRT和非HVRT.
科学领域:
- 微生物学 微生物学
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 复杂 Clostridioides 的高病毒性核型 (HVRT),如核型 (RT) 027,带来了重大的流行病学挑战.
- 准确和快速识别HVRT对于有效的感染控制和治疗策略至关重要.
研究的目的:
- 评估MALDI-TOF质谱 (MS) 结合机器学习 (ML) 算法的能力,以区分C. difficile的HVRT和非HVRT.
- 评估在欧洲临床隔离物中对特定HVRT进行子集群的潜力.
主要方法:
- 用MALDI-TOFMS分析了代表欧洲常见的HVRT和非HVRT的临床C. difficile分离物 (培训为157个,验证为83个).
- 用各种ML模型处理了Spectra数据,以确定HVRT和非HVRT组之间的歧视特征.
- 进行了MALDI-TOF光谱的直接比较,以评估其独立的歧视力.
主要成果:
- 仅仅对MALDI-TOF光谱进行直接比较,就无法可靠地区分HVRT和非HVRT.
- 使用MALDI-TOF光谱的ML模型在区分HVRT与非HVRT方面实现了超过95%的区分精度.
- 通过ML方法,成功地分组了三个主要的HVRT组:RT027/RT176,RT023和RT045/078/126/127.
结论:
- 当与ML集成时,MALDI-TOF MS提供了一种强大而快速的方法来识别欧洲主要的HVRT.
- 这种综合方法显著增强了对C. difficile类型的传统MALDI-TOFMS分析的歧视力.
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