Clostridioides difficile (CD) infections 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 176 and 181, two decades ago, reshaped CD infection epidemiology in Europe. This study assessed MALDI-TOF mass spectrometry (MALDI-TOF MS) combined with machine learning (ML) and Deep Learning (DL) to identify toxigenic strains (producing TcdA, TcdB with or without CDT) and Hv strains. In total, 201 CD strains were analysed, comprising 151 toxigenic (24 ToxA(+)B(+)CDT(+), 22 ToxA(+)B(+)CDT(+) Hv(+) and 105 ToxA(+)B(+)CDT(-)) and 50 non-toxigenic (ToxA(-)B(-)) strains. The DL-based classifier exhibited a 0.95 negative predictive value for excluding ToxA(-)B(-) strains, showcasing accuracy in identifying this strain category. Sensitivity in correctly identifying ToxA(+)B(+)CDT(-) strains ranged from 0.68 to 0.91. Additionally, all classifiers consistently demonstrated high specificity (>0.96) in detecting ToxA(+)B(+)CDT(+) strains. The classifiers' performances for Hv strain detection were linked to high specificity (>= 0.96). This study highlights MALDI-TOF MS enhanced by ML techniques as a rapid and cost-effective tool for identifying CD strain virulence factors. Our results brought a proof-of-concept concerning the ability of MALDI-TOF MS coupled with ML techniques to detect virulence factor and potentially improve the outbreak's management.

Gene panel sequencing (NGS) offers the possibility to analyze rare forms of monogenic diabetes (MgD). To that end, 18 genes were analyzed in 1676 patients referred for MODY genetic testing. Among the 307 patients with a molecular diagnosis of MgD, 55 (17.9%) were mutated in a gene associated with a genetic syndrome. Eight percent (n=25) of the patients with mutations carried the m.3243A>G variant associated with MIDD (Maternally inherited diabetes and deafness). At time of referral very little had reported hearing loss or any other element of the typical syndromic presentation. Six percent of the patients were mutated in HNF1B even though the typical extra-pancreatic features were not known at time of referral. Surprisingly the third most prominent etiology in these rare forms was the WFS1 gene accounting for 2.9% of the patients with pathogenic mutations (n=9). None of them depicted a Wolfram syndrome presentation even though some features were reported in 6/9 patients. Restricting the analysis of certain genes to patients with the respective specific phenotypes would miss out those with partial presentations. These results therefore underlie the undisputable benefit of NGS strategies even though the situation implies cascade consequences both for the molecular biologist and the clinician.