Browsing by Author "Akkaya, Yuksel"

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Detection of Candida (Candidozyma) Auris by Molecular Methods and Investigation of Clinical Symptoms of Patients in a Tertiary Hospital in Istanbul
(K Faisal Spec Hosp Res Centre, 2025) Akkaya, Yuksel; Erdin, Begum Nalca; Aydin, Irfan; Ozel, Ayse Serra; Yilmaz, Ahmet Munir; Cilkiz, Mustafa; Toraman, Zulal Asci
BACKGROUND: Candidozyma auris is resistant to many antifungals, spreads rapidly and causes deaths in patient groups with comorbid factors. OBJECTIVES: The aim of this study was to determine the virulence of C. auris, antifungal resistance genes and clinical characteristics of the patients. DESIGN: Retrospective cohort SETTING: Single-center MATERIAL AND METHODS: This study was conducted between August 2022 and December 2023 at & Uuml;mraniye Training and Research Hospital. ITS1-5.8S-ITS2 and ITS1-ITS4 gene regions of the rDNA gene of C. auris isolates identified by VITEK MS v.3.2 were amplified by polymerase chain reaction (PCR) method. These regions were partially sequenced using the Sanger method. The presence of C. aurisspecific CDR1, ERG11, MDR1, ACT1, SAP5, HYR3, ALS5, IFF4, FUR1, PLB3, PGA26 and PGA52 gene regions were determined by PCR. Antifungal susceptibility testing of C. auris was performed with VITEK 2 Compact AST YS08 and SYO. MAIN OUTCOME MEASURES: Variations in C. auris isolates, antifungal resistance and clinical characteristics of patients SAMPLE SIZE: Forty-four isolates from 31 patients RESULTS: According to gene regions, nine different variations were identified in our hospital, with VAR-1 being the most common. Twenty-five (80.6%) of the patients died and isolation of the causative agent was between days 1-30 in 13 (41.9%) patients. Antibiotic use, ICU admission rate, and central venous catheter use in patients were 29 (93.6%), 28 (90.3%), and 21 (67.7%), respectively. Hypertension, diabetes mellitus (DM) and septic shock were found in 14 (45.2%), 13 (41.9%) and 10 (32.3%) patients, respectively. Antifungal resistance rates of the isolates were determined as 97.7% and 84.1% for amphotericin B and fluconazole, respectively. No resistance to micafungin and caspofungin was detected. The survival rate with echinocandin use was 22% (4 patients). CONCLUSION: Identification of gene regions is valuable in determining the pathogenicity of C. auris. . Due to the presence of comorbidi- ties in patients with C. auris, , it is not possible to determine the exact proportion of deaths attributable to C. auris alone. LIMITATIONS: Single center setting; gene regions could not be ex- pressed.
Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study
(MDPI, 2025) Tolan, Huseyin Kerem; Aydin, Irfan; Tanyildizi-Kokkulunk, Handan; Karakus, Mehmet; Akkaya, Yuksel; Kaya, Osman; Isman, Ferruh Kemal
Background/Objectives: Escherichia coli is one of the leading causes of surgical site infections (SSIs) and poses a growing public health concern due to its increasing antimicrobial resistance. High rates of extended-spectrum beta-lactamase (ESBL) production among E. coli strains complicate treatment outcomes and emphasize the need for effective surveillance and control strategies. Methods: A total of 691 E. coli isolates from general surgery clinics (2020-2025) were identified using MALDI-TOF MS. Antibiotic susceptibility data and patient variables were cleaned, encoded, and used to predict resistance using the Random Forest, CatBoost, and Naive Bayes algorithms. SMOTE addressed class imbalance, and model performance was assessed through various validation methods. Results: Among the three machine learning models tested, Random Forest (RF) showed the best performance in predicting antibiotic resistance of E. coli, achieving median accuracy, precision, recall, and F1-scores of 0.90 and AUC values up to 0.99 for key antibiotics. CatBoost performed similarly but was less stable with imbalanced data, while Naive Bayes showed lower accuracy. Feature importance analysis highlighted strong inter-antibiotic resistance links, especially among beta-lactams, and some influence of demographic factors. Conclusions: This study highlights the potential of simple, high-performing models using structured clinical data to predict antimicrobial resistance, especially in resource-limited clinical settings. By incorporating machine learning into antimicrobial resistance (AMR) surveillance systems, our goal is to support the advancement of rapid diagnostics and targeted antimicrobial stewardship approaches, which are essential in addressing the growing challenge of multidrug resistance.