In the last few decades, statistical methods and machine learning (ML) algorithms have become efficient in medical decision-making. Coronary artery disease (CAD) is a common type of cardiovascular disease that causes many deaths each year. In this study, two CAD datasets from different countries (TRNC and Iran) are tested to understand the classification efficiency of different supervised machine learning algorithms. The Z-Alizadeh Sani dataset contained 303 individuals (216 patient, 87 control), while the Near East University (NEU) Hospital dataset contained 475 individuals (305 patients, 170 control). This study was conducted in three stages: (1) Each dataset, as well as their merged version, was subject to review separately with a random sampling method to obtain train-test subsets. (2) The NEU Hospital dataset was assigned as the training data, while the Z-Alizadeh Sani dataset was the test data. (3) The Z-Alizadeh Sani dataset was assigned as the training data, while the NEU hospital dataset was the test data. Among all ML algorithms, the Random Forest showed successful results for its classification performance at each stage. The least successful ML method was kNN which underperformed at all pitches. Other methods, including logistic regression, have varying classification performances at every step.

The objective of the present study was to determine the accuracy of machine learning (ML) models in the detection of mesiobuccal (MB2) canals in axial cone-beam computed tomography (CBCT) sections. A total of 2500 CBCT scans from the oral radiology department of University Dental Hospital, Sharjah were screened to obtain 277 high-resolution, small field-of-view CBCT scans with maxillary molars. Among the 277 scans, 160 of them showed the presence of MB2 orifice and the rest (117) did not. Two-dimensional axial images of these scans were then cropped. The images were classified and labelled as N (absence of MB2) and M (presence of MB2) by 2 examiners. The images were embedded using Google's Inception V3 and transferred to the ML classification model. Six different ML models (logistic regression [LR], naïve Bayes [NB], support vector machine [SVM], K-nearest neighbours [Knn], random forest [RF], neural network [NN]) were then tested on their ability to classify the images into M and N. The classification metrics (area under curve [AUC], accuracy, F1-score, precision) of the models were assessed in 3 steps. NN (0.896), LR (0.893), and SVM (0.886) showed the highest values of AUC with specified target variables (steps 2 and 3). The highest accuracy was exhibited by LR (0.849) and NN (0.848) with specified target variables. The highest precision (86.8%) and recall (92.5%) was observed with the SVM model. The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. It was also observed that when the target variable was specified, significant success rates such as 86.8% in precision and 92.5% in recall were achieved. The present study showed promising results in the ML-based detection of MB2 canal using axial CBCT slices.

Background: Candidozyma auris is an emerging multidrug-resistant fungal pathogen responsible for nosocomial outbreaks worldwide. In addition to antifungal resistance, its ability to persist in the hospital environment and tolerate commonly used biocides presents a critical challenge for infection control. However, the relationship between biocide tolerance, antifungal resistance, and virulence traits in C. auris remains poorly understood. Methods: In this study, 47 C. auris Clade 1 isolates were evaluated using phenotypic and genotypic methods. ITS region sequencing was performed using Oxford Nanopore technology. Susceptibility testing was conducted for seven antifungal agents and four biocides using the CLSI reference microdilution method. Virulence factors including biofilm formation, secreted aspartyl proteinase, esterase, caseinase, phospholipase, and hemolysis were assessed. Results: All isolates were identified as Clade 1. MIC values for antifungals ranged from 0.015 to 64 µg/mL, and for biocides from 0.0078 to 128 mg/L. Fluconazole resistance was found in 31% of isolates, while amphotericin B resistance was 4%; no echinocandin resistance was observed. Biofilm and SAP activity were detected in all isolates; esterase was positive in 87%, and caseinase in 4%. Statistically significant correlations were observed between amphotericin B and isavuconazole MICs (ρ = 0.32, p = 0.028), amphotericin B and triclosan MICs (ρ = 0.35, p = 0.018), and amphotericin B MICs and caseinase activity (ρ = 0.31, p = 0.035). These findings suggest potential phenotypic links between antifungal resistance and specific virulence traits. Conclusions: This is the first study from Türkiye to evaluate the antifungal and biocide susceptibility alongside virulence characteristics of Clade 1 C. auris. While statistically significant associations were observed, we acknowledge that resistance mechanisms and pathogenicity pathways are distinct. Therefore, these correlations should be interpreted cautiously and warrant further investigation at the molecular level.

Candidozyma (Candida) auris is a multidrug‐resistant yeast capable of causing persistent outbreaks in healthcare settings. Its reduced susceptibility to both antifungals and biocides poses a direct threat to infection control and hospital hygiene, as biocides remain the cornerstone of surface disinfection and outbreak containment. This study aimed to construct a decision tree to predict biocide susceptibility and identify key predictive parameters using machine learning. Virulence factors were evaluated in 55 C. auris strains isolated from a state hospital in Türkiye that were identified by MALDI‐TOF and verified by DNA sequencing. CLSI guidelines were followed in determining the antifungal MICs. The z ‐score method was used to standardize and numerically code phenotypic data. The Random Forest Regressor was used to analyze feature importance. Resistance thresholds were defined as triclosan ≥ 0.5 µg/mL, benzalkonium ≥ 150 µg/mL, chlorhexidine ≥ 1.0 µg/mL, and chlorine ≥ 0.03 µg/mL. As all strains were benzalkonium‐resistant, these data were excluded. Anidulafungin MIC was the strongest predictor of biocide sensitivity, followed by amphotericin B, flucytosine, and isavuconazole, while other virulence factors showed little or no value. This proof‐of‐concept study demonstrates, for the first time, that a decision tree model trained on antifungal MIC profiles can predict the susceptibility of C. auris strains to triclosan, chlorine, and chlorhexidine. Although biocide susceptibility testing was performed to establish reference thresholds, the final predictive framework relied solely on anidulafungin MIC values, suggesting that such models may reduce the need for routine biocide testing in future surveillance studies.