WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
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Article Comprehensive Proteomic and Metabolomic Analysis of Novel Substituted Fluoroquinolone Derivatives in Escherichia Coli Isolates(John Wiley and Sons Ltd, 2026) Nigiz Ş.; Kulabaş N.; Türe A.; Kablan S.; Koçak E.; Özkul C.; Küçükgüzel İ.; Koçak, Engin; Nigiz, Şeyma; Kablan, Sevilay Erdoğan; Özkul, Ceren; Kulabaş, Necla; Küçükgüzel, İlkay; Türe, AslıAntimicrobial resistance is one of the most important global problems, and new antibiotic requirements have been emerging as a key point in this issue. In the present work, we focused on the efficiency of two novel promising fluoroquinolone derivatives on resistant Escherichia coli isolates at the molecular level. Their mode of action and adaptation process were evaluated by using proteomics and metabolomics analysis. Proteomics analysis showed that two compounds have an effect mainly on the ribosomal process and energy metabolism. Moreover, we observed compounds that affect various important antimicrobial targets, such as ribosomal subunits, phosphotransacetylase, and chaperone proteins. In metabolomics analysis, we found that compounds altered bacterial metabolism directly. Pathway analysis showed that cofactor biosynthesis and energy metabolism were affected mainly by undertreated groups. Our experiments demonstrated that novel fluoroquinolone derivatives have promising results at the molecular level and results will contribute to further studies. © 2026 John Wiley & Sons Ltd.Article Machine Learning Model for Predicting Multidrug Resistance in Clinical Escherichia Coli Isolates: A Retrospective General Surgery Study(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Tolan, H.K.; Aydın, İ.; Tanyildizi-Kökkülünk, H.; Karakuş, M.; Akkaya, Y.; Kaya, O.; Işman, F.K.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 β-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. © 2025 by the authors.