摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx记者来自巴黎Franc E的新闻报道，研究表明，“艰难梭状芽胞杆菌(CD)感染由毒素A(TcdA)和B(TcdB)以及二元毒素(CDT)定义。20年前，‘剧毒’(Hv)菌株PR 027和PR 176和181的出现重塑了欧洲CD感染的流行病学。”本研究采用MALDI-TOF质谱(MALDI-TOF MS)结合Ma Chine Learning(ML)和Deep Learning(DL)对产毒菌株(包括TcdA、TcdB和CDT)和Hv菌株进行鉴定,共分析201株CD菌株,包括151株产毒菌株(24株ToxABCDT)。22株ToxABCDT Hv和105株ToxABCDT)和50株非产毒(ToxAB)菌株，基于DL的分类器对排除ToxABCDT菌株的预测值为0.95，表明该菌株分类的准确性，正确识别ToxABCDT菌株的敏感性为0.68～0.91.。所有分类器均表现出较高的特异性(>0.96),各分类器检测Hv株的性能与较高的特异性(0.96)相关,本研究强调ML技术增强的MALDI-TOF MS是一种快速、经济有效的检测CD株毒力因子的工具。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Paris, Franc e, by NewsRx correspondents, research stated, "Clostridioides difficile (CD) inf ections are defined by toxins A (TcdA) and B (TcdB) along with the binary toxin (CDT). The emergence of the 'hypervirulent' (Hv) strain PR 027, along with PR 17 6 and 181, two decades ago, reshaped CD infection epidemiology in Europe." Our news editors obtained a quote from the research from Universite Paris Cite, "This study assessed MALDI-TOF mass spectrometry (MALDI-TOF MS) combined with ma chine learning (ML) and Deep Learning (DL) to identify toxigenic strains (produc ing TcdA, TcdB with or without CDT) and Hv strains. In total, 201 CD strains wer e analysed, comprising 151 toxigenic (24 ToxABCDT, 22 ToxABCDT Hv and 105 ToxABC DT) and 50 non-toxigenic (ToxAB) strains. The DL-based classifier exhibited a 0. 95 negative predictive value for excluding ToxAB strains, showcasing accuracy in identifying this strain category. Sensitivity in correctly identifying ToxABCDT strains ranged from 0.68 to 0.91. Additionally, all classifiers consistently de monstrated high specificity (>0.96) in detecting ToxABCD T strains. The classifiers' performances for Hv strain detection were linked to high specificity ( 0.96). This study highlights MALDI-TOF MS enhanced by ML tech niques as a rapid and cost-effective tool for identifying CD strain virulence fa ctors."
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