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 Fully Synthetic, Nature-Inspired Exosome-Mimetics for Melanoma Therapy(Elsevier B.V., 2026) Arda Ozturk N.Z.; Majchrzak O.B.; Ulivi G.; Kirmizibayrak P.B.; Borchard G.; Patrulea V.; Ozer O.; Ozturk, Nahide Zeren Arda; Borchard, Gerrit; Ozer, Ozgen; Patrulea, Viorica; Majchrzak, Oliwia Barbara; Arda Ozturk, Nahide Zeren; Kirmizibayrak, Petek Ballar; Ulivi, GianlucaFully synthetic exosome-mimetics (FSEMs) represent a nature-inspired drug delivery system designed to replicate the key physicochemical and biological properties of natural exosomes, while offering the potential to address limitations in scalability and reproducibility associated with natural exosomes. In this study, we prepared FSEMs at the laboratory scale. We loaded them with (–)-epigallocatechin-3-gallate (EGCG) and microRNA-23a (miR-23a), aiming to co-deliver therapeutic small molecules and nucleic acids for the treatment of melanoma. FSEMs were fabricated using three methods: thin-film hydration, ethanol injection, and microfluidics. They were surface-functionalized with either CD9, a tetraspanin involved in membrane fusion, or TSP-1, an adhesion protein promoting cellular interactions. Through physicochemical characterization via dynamic light scattering, we found that FSEMs were ∼ 100 nm in size, of low polydispersity (∼0.2) and showed a negative zeta potential (∼–55 mV). Both EGCG and miR-23a were efficiently encapsulated. SDS-PAGE analysis confirmed successful protein incorporation and correct positioning. In vitro release studies showed minimal premature leakage, supporting their suitability for cellular uptake-mediated delivery. When tested on melanoma cells (MDA-MB-435) and progenitor human dermal fibroblasts (FE002-SK2), FSEMs selectively killed melanoma cells while sparing fibroblasts. Importantly, EGCG within FSEMs was more effective than the free compound. Compared to conventional DOTAP-based liposomes, FSEMs were more selective and induced less off-target cytotoxicity. This study presents a proof-of-concept for fully synthetic, protein-functionalized FSEMs as dual carriers for both chemical and gene-based agents, offering a safer and potentially more effective alternative to traditional cationic liposomes. These results lay the groundwork for future in vivo validation and translational cancer research. © 2026 The Author(s)Article Effects of Combined Triflow, Deep Breathing and Coughing Exercises on Postoperative Pulmonary Function After Mitral Valve Replacement: A Randomized Controlled Trial(BMC, 2026) Akinci, Naile; Eren, EsraBackground Postoperative pulmonary complications remain a major cause of morbidity after cardiac valve surgery. Although incentive spirometry (Triflow) is routinely used in postoperative care, evidence regarding the additional benefits of combining Triflow with deep breathing and coughing exercises remains limited. This study aimed to evaluate the effects of combined Triflow, deep breathing, and coughing exercises on postoperative pulmonary function in adult patients undergoing mitral valve replacement. Methods This randomized controlled, single-blind trial was conducted between May and August 2025 in a private hospital in Istanbul. A total of 60 adult patients undergoing mitral valve replacement were randomly allocated to an experimental group (n = 30) or a control group (n = 30) using simple randomization. The experimental group performed Triflow combined with deep breathing and coughing exercises, while the control group performed Triflow alone. Results Postoperative SpO(2) levels were significantly higher in the experimental group at T1 (p = 0.009; 95% CI: 0.46-3.14), T2 (p < 0.001; 95% CI: 1.57-3.43), and T3 (p < 0.001; 95% CI: 2.72-4.54). The FEV1/FVC ratio increased significantly in the experimental group compared with the control group at discharge (p < 0.001; 95% CI: 4.46-6.41). Respiratory rate was significantly higher in the experimental group at T1 (p < 0.001; 95% CI: 1.68-4.45), T2 (p < 0.001; 95% CI: 3.34-6.26), and T3 (p < 0.001; 95% CI: 5.23-8.37). Hematocrit levels were significantly lower in the experimental group at T1 (p = 0.039; 95% CI: -8.32 to - 0.24), T2 (p = 0.007; 95% CI: -8.29 to - 1.40), and T3 (p = 0.034; 95% CI: -6.54 to - 0.28). Pain scores were significantly lower in the experimental group at T1 (p < 0.001; 95% CI: -2.82 to - 1.71) and T2 (p < 0.001; 95% CI: -1.98 to - 1.08). Time to first mobilization was significantly shorter in the experimental group (p < 0.001; 95% CI: -2.94 to - 1.26). No postoperative pulmonary complications were observed in either group. Conclusion The combined application of Triflow, deep breathing, and coughing exercises was associated with significant improvements in postoperative pulmonary function, oxygen saturation, pain reduction, and earlier mobilization compared with Triflow alone in patients undergoing mitral valve replacement. These findings suggest that a structured, combined respiratory exercise protocol may provide additional clinical benefits in the early postoperative period.Article The Relationship Between Nurses Anxiety and Attitudes Towards Artificial Intelligence and Examination of Influencing Factors(BMC, 2026) Nirgiz, Cansu; Sari, Merve Kiymac; Cayli, NazanAim to explore the relationships between nurses'anxiety and attitudes toward artificial intelligence (AI) and the factors associated with them. Background Although AI technologies are increasingly integrated into healthcare, research exploring nurses ' psychological readiness and emotional responses to AI remains limited. Existing studies have primarily focused on nursing students or general healthcare professionals, leaving a gap in understanding how practicing nurses perceive and emotionally adapt to AI within real clinical environments-particularly in T & uuml;rkiye, where digital transformation in healthcare is accelerating. Addressing this gap is essential, as nurses play a pivotal role in ensuring the safe and ethical implementation of AI-driven tools in patient care. Methods This descriptive and correlational study included 562 nurses from 14 branches of a private hospital chain across seven Turkish cities between November 2024 and January 2025.This sample was selected because it represents nurses actively engaged in clinical decision-making within healthcare systems that are rapidly adopting AI technologies. According to a power analysis performed in G*Power (rho = 0.25, alpha = 0.05, power = 0.95), the required sample size was 202 participants. Data were collected through an online questionnaire comprising a Descriptive Information Form, the AI Anxiety Scale, and the General Attitudes toward AI Scale. Descriptive statistics, independent samples t-tests, ANOVA (F), Tukey post hoc test, Pearson correlation, and multiple regression analyses were conducted. Ethical approval was obtained from the Fenerbah & ccedil;e University Ethics Committee, and informed consent was obtained digitally. Results Nurses reported moderate anxiety levels and generally positive attitudes toward AI. Male nurses showed an association with lower anxiety levels and higher positive attitude scores than female nurses. Single individuals and those with higher levels of education showed higher positive attitudes toward AI. Those with 0-3 years of experience in the profession were associated with lower anxiety and higher positive attitude scores. Nurses who used AI in practice, were knowledgeable about its use, or perceived it as reliable showed a relationship with lower anxiety and more positive attitudes. Regression analysis showed that each one-unit increase in the learning and AI configuration subscales of the AI Anxiety Scale was associated with a 0.740-and 0.716-point lower score in the total attitude score, respectively. Conclusion The findings suggest that lower levels of anxiety related to learning and AI configuration are associated with more positive attitudes toward AI. Addressing these specific anxiety domains may be related to the successful integration of AI technologies into clinical practice and could be linked to the digital transformation in healthcare.Article Mapping the Evolution of Stigmatization in Mental Disorders: A Bibliometric Analysis From 1974 to 2024(Springer Heidelberg, 2026) Goktas, Polat; Dikec, GulBackgroundThis bibliometric study scrutinizes the thematic evolution of research on stigma and discrimination in mental disorders, covering a span of five decades. It reflects on the shifting paradigms within the stigma-focused mental health research community from 1974 to 2024.MethodsA comprehensive bibliometric analysis was employed using the Bibliometrix R package and VOSviewer software, analyzing 1,892 articles from databases like Scopus, Web of Science, PubMed Central, and APA PsycInfo. Adherence to PRIBA guidelines ensured a holistic representation of the evolving research narrative.ResultsThe analysis outlined three distinct periods: the Genesis Period (1974 - 2007), focusing on foundational concepts of mental disorders and stigma; the Growth Period (2008 - 2015), which experienced a broadening into themes of discrimination and diagnostic refinement; and the Rapid Growth Period (2016 - 2024), characterized by a surge in research on child mental disorders and the impacts of posttraumatic stress disorder. Network analyses highlighted significant journals, key authors, and international collaborations that have shaped this field.ConclusionsThe study maps a significant transformation in stigma-focused mental health research themes over fifty years, highlighting the growing complexity and the need for ongoing research into stigma and discrimination. It calls for interdisciplinary approaches to tackle these enduring challenges effectively.Article Machine Learning Model for Predicting Multidrug Resistance in Clinical Klebsiella pneumoniae Isolates(MDPI, 2026) Akkaya, Yuksel; Aydin, Irfan; Tanyildizi-Kokkulunk, Handan; Erturk, Ayse; Kilic, Ibrahim HalilBackground/Objectives: Klebsiella pneumoniae is an opportunistic pathogen increasingly resistant to carbapenems and broad-spectrum antibiotics, complicating timely infection management. In critical cases like septic shock, where initiating effective antibiotics within 3 h improves survival, culture-based resistance testing is often too slow. This study evaluates machine learning (ML) algorithms for faster antimicrobial resistance prediction than conventional methods. Methods: In this retrospective study, antibiogram results of 607 Klebsiella pneumoniae isolates collected between 2017 and 2024 were combined with demographic and clinical information of the patients from whom the isolates were obtained. Four different ML algorithms, namely Decision Tree (DT), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN) and Random Forest (RF), were applied to classify the resistance status for 22 antibiotics. Model performances were evaluated using accuracy, precision, recall, F-score, AUC and feature importance metrics. Results: The RF model showed the highest overall performance in accurately predicting resistance to 22 antibiotics, achieving an average AUC value of 0.96. In particular, it predicted resistance to treatment-critical antibiotics such as Ertapenem (100%), Imipenem (93%) and Meropenem (95%) with high accuracy. Conclusions: ML models, especially RF, offer a powerful tool for rapid antibiotic resistance prediction, supporting accurate empirical treatment decisions and antimicrobial stewardship.Article Social Media and Financial Decisions: The Influence of Socio-Demographics and Financial Literacy(John Wiley and Sons Inc, 2026) Altinbas, H.This study investigates the predictors of individuals' reliance on social media for financial decision-making within the context of Türkiye's high-inflation environment and the associated surge in retail investor participation. Data were collected via an online survey utilizing the OECD's financial literacy toolkit. The results indicate that gender, family structure, high-risk asset preferences, and financial literacy predict social media usage for financial information. Specifically, males, individuals who invest in stocks or cryptocurrencies, and those with higher financial literacy demonstrate a greater propensity to access financial information on social media; conversely, households with children exhibit lower reliance on social media information. © 2026 American Association of Family and Consumer Sciences.Article In Vivo Antioxidant and Anti-Inflammatory Effects of Myrtus Communis Against Ionizing Radiation-Induced Gastrointestinal Injury: Trod-Grog Study(Kare Publishing, 2025) Kilic, Melisa Bagci; Varan, Melike Pekyurek; Atasoy, Ozum; Ozyilmaz, Nagehan; Pazarbasi, Seren Ede; Ertas, Busra; Atasoy, Beste Melek; Ercan, FerihaOBJECTIVE: This study aimed to investigate the in vivo radioprotective effects of Myrtus communis (MC) on the gastrointestinal system. METHODS: A total of 30 female rats were divided into four groups: i) Control; ii) irradiation (IR) only; iii) MC-pretreated; and iv) MC-treated. The rats received oral MC extract (100 mg/kg/day) for 4 days before exposure to 10 Gy IR or continued until sacrifice. On the fourth day of IR exposure, the rats were sacrificed, and histopathological and biochemical analyses were performed on the ileum, pancreas, and liver tissues. RESULTS: Malondialdehyde and myeloperoxidase levels decreased in both MC-treated groups, while glutathione levels and Na+-K+-ATPase activity increased (p<0.01), with significant histopathological improvements compared to the IR-only group. CONCLUSION: The results of this study demonstrate that MC significantly decreases ionizing radiation-induced oxidative and inflammatory damage in the gastrointestinal systems of rats. Therefore, it may be regarded as a new candidate with radioprotective potential for future clinical application.Article Early Detection of Lower Adherence to Long-Term E-Diary Recording: A Checkpoint to Target Early Educational Intervention in Seasonal Allergic Rhinitis(Wiley, 2026) Dramburg, S.; Hernandez Toro, C. J.; Grittner, U.; Tripodi, S.; Arasi, S.; Acar Sahin, A.; Matricardi, P. M.Background: Digital symptom monitoring via e-Diary apps can support the diagnosis and management of chronic diseases with trigger-induced exacerbations such as pollen allergies. Attrition is a major challenge for continuous e-Diary usage with an unsupervised approach. Objective: To investigate adherence to e-Diary reporting, its early determinants and predictors in a blended care setting among pollen allergic patients with heterogeneous cultural backgrounds. Methods: The @IT.2020 observational multicenter study recruited patients with diagnosed seasonal allergic rhinitis from seven Southern European/Mediterranean countries. Baseline characteristics were investigated through questionnaires, skin prick tests and serum specific IgE measurements. The study doctors asked patients to record their allergy symptoms via e-Diary (AllergyMonitor, TPS) daily during the clinically relevant season of pollination and increased mould concentrations. Results: Among 815 patients (467 adults, 348 children), the average prescribed e-Diary recording period was 106 (SD 47.1) days, with an average completion rate of 75.2% (SD 21.2%). Children (>= 10 years) filled 73.8% (95% CI 68.1-79.4) of prescribed days without parental support. We identified a stable 'higher' and a more variable 'lower' adherence cluster. Adherence was weakly associated with disease severity, but not with age, gender, country, education or digital literacy. Short-term (first 3 weeks) adherence was strongly associated with long-term adherence (partial R-2 = 0.387, p < 0.001), with 87.6% of lower adherent patients remaining poorly adherent beyond 3 weeks. Conclusion: In a blended care setting, adherence to e-Diary compilation among pollen allergic patients is high, irrespective of age and cultural background. Early identification of lower adherence is possible and might inform early interventions to improve patient adherence.Article Evaluation of Barriers to the Integration of Renewable Energy Technologies into Industries in Türkiye(MDPI, 2026) Caloglu Buyukselcuk, Elif; Turan, HakanThe transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with T & uuml;rkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems.