首页|Universite Paris Cite Reports Findings in Machine Learning (Contribution of MALD I-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains)
Universite Paris Cite Reports Findings in Machine Learning (Contribution of MALD I-TOF mass spectrometry and machine learning including deep learning techniques for the detection of virulence factors of Clostridioides difficile strains)
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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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