Molecular Tools for Candida auris Identification and Typing

Machine learning to identify clinically relevant Candida yeast species

Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organisms such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically important Candida species. Wet-mounted images were captured using bright field live-cell microscopy followed by separating single-cells, budding-cells, and cell-group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predict Candida species. Among the six algorithms tested, the InceptionV3 model performed best in predicting Candida species from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells of C. albicans, C. auris, C. glabrata (Nakaseomyces glabrata), and C. haemulonii in 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells of C. albicans, C. auris, C. glabrata, and C. haemulonii InceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were 77.1% and 92.4%, respectively. Overall, this study provides proof of the concept that microscopy images from wet-mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.

Increasing Number of Cases Due to Candida auris in North Italy, July 2019-December 2022

Candida auris is an emerging fungus that represents a serious health threat globally. In Italy, the first case was detected in July 2019. Then, one case was reported to the Ministry of Health (MoH) on January 2020. Nine months later, a huge number of cases were reported in northern Italy. Overall, 361 cases were detected in 17 healthcare facilities between July 2019 and December 2022 in the Liguria, Piedmont, Emilia-Romagna, and Veneto regions, including 146 (40.4%) deaths. The majority of cases (91.8%) were considered as colonised. Only one had a history of travel abroad. Microbiological data on seven isolates showed that all but one strain (85.7%) were resistant to fluconazole. All the environmental samples tested negative. Weekly screening of contacts was performed by the healthcare facilities. Infection prevention and control (IPC) measures were applied locally. The MoH nominated a National Reference Laboratory to characterise C. auris isolates and store the strains. In 2021, Italy posted two messages through the Epidemic Intelligence Information System (EPIS) to inform on the cases. On February 2022, a rapid risk assessment indicated a high risk for further spread within Italy, but a low risk of spread to other countries.