Candida viswanathii has been sporadically reported in Asia and South America but not in Europe. This study reports the first European outbreak of C. viswanathii in a paediatric hospital, outlining diagnostic challenges and containment measures. Fifteen C. viswanathii isolates were recovered from blood cultures of consecutive pediatric patients admitted to intensive care units between April and August 2025. Identification was performed using MALDI-TOF MS, chromogenic media, Fourier-transform infrared (FT-IR) spectroscopy, and whole-genome sequencing (WGS). Antifungal susceptibility testing was performed by broth microdilution. All isolates were initially misidentified as C. tropicalis by MALDI-TOF MS and undetected by the FilmArray BCID2 panel. WGS confirmed C. viswanathii, and FT-IR analysis revealed clonally related strains, indicating an outbreak. Colonies displayed a distinct deep-blue color on chromogenic CHROMagar™ medium. Elevated fluconazole minimum inhibitory concentrations were observed, while isolates remained susceptible to echinocandins and amphotericin B. A multidisciplinary infection-control response halted transmission within four weeks. This investigation documents the first C. viswanathii outbreak in Europe, highlighting diagnostic limitations of current commercial tools and the need for updated databases. Integration of FT-IR spectroscopy and WGS facilitated outbreak detection and containment, underscoring the importance of advanced diagnostics and surveillance for emerging fungal pathogens.

Background: Since 2022, invasive Group A Streptococcus (GAS) infections have increased, mainly due to the spread of specific emm-types, such as emm1. As therapy may depend on the emm-type, rapid and cost-effective identification is crucial. Fourier-transform infrared spectroscopy (FTIR) has emerged as a promising alternative to sequencing for GAS typing. We applied machine learning (ML) to FTIR spectra to build a predictive model for emm-type identification. Methods: Twenty-four GAS strains were analyzed by whole-genome sequencing and FTIR. The model was trained on twenty-one strains (emm-types: 1, 3, 4, and 6), using leave-one-out cross validation (LOOCV). To test the model’s ability to avoid false positive results, the model was also tested with three strains belonging to emm-types not included in the training of the model (emm-types: 12, 89, and 75). Results: An artificial neural network trained for 400 cycles achieved the highest accuracy (90.7%) out of the thirteen different models tested. When the three strains belonging to emm-types not included in the model were predicted with this model, it produced low score values, confirming its ability to avoid false positive results. Conclusions: We developed a preliminary and proof-of-concept model capable of accurately predicting the four most-prevalent emm-types in our setting, including the highly virulent emm1. These findings support FTIR combined with ML as a rapid, low-cost tool for GAS typing, with potential for real-time clinical applications to guide timely treatment decisions. However, as a proof-of-concept study, the relatively small sample size and limited emm-type diversity underline the need for further validation with larger and more diverse datasets.

Chronic Pseudomonas aeruginosa colonisation leads to lung deterioration and poor prognosis in people with cystic fibrosis (pwCF). Early and aggressive therapies can achieve P. aeruginosa eradication, which may recur later. Therefore, determining whether it has resisted therapy or has been newly acquired may guide subsequent treatment(s). This information is crucial also for patients treated with CFTR modulators representing P. aeruginosa after prolonged negativity and to confirm chronic infections. We evaluated the ability of Fourier-transform infrared (FT-IR) spectroscopy to determine intra-patient isoclonality for 103 P. aeruginosa strains isolated from 36 pwCF. Two were chronically and two intermittently colonised; twelve were on modulators with a past P. aeruginosa colonisation; and twenty received eradication therapy, ten of whom were also treated with modulators. FT-IR data were validated by Whole Genome Sequencing (WGS) identification of Sequence Type. FT-IR identified persistence of P. aeruginosa in 24 patients, with a WGS-confirmed positive predictive value of 100% and diagnostic accuracy of 94%. The eradication therapy success rate was 45%, and the time to P. aeruginosa reappearance was similar in both patients with failed eradication treatment and those who initially cleared the infection but later acquired a new strain. Nine patients in modulators showed persistent infections. FT-IR can rapidly determine the clonality of P. aeruginosa isolates, allowing discrimination of recurring versus new infections both in patients with established colonisations and those subjected to eradication therapy representing P. aeruginosa. By overcoming the time-based criteria used to define new infections and by providing the actual success rate of eradication therapies, FT-IR can effectively contribute to the development of efficient therapeutic strategies.