Sürdürülebilir Kalkınma Amaçları

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• Editöre MektupAçık Erişim2026/r/gumushane/16316
  Reply to the Letter to the Editor: Enhancing Risk Stratification in Coronary Artery Ectasia: The Synergy of Inflammation and Metabolic Scores

  Tunca, Cagatay; Özkan, Mehmet Taha; Ergin, Berin Nur; Gök, Saner Bahadir; Tas, Alperen; Kürklü, Haci Ali; Akbuga, Kursat; Tanik, Veysel Ozan; Ars, Kardiyol Dern

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• MakaleAçık Erişim2026/r/gumushane/16324
  Pan-Immune-Inflammation Value as an Independent Indicator of Isolated Coronary Artery Ectasia

  Tunca, Cagatay; Özkan, Mehmet Taha; Ergin, Berin Nur; Gök, Saner Bahadir; Tas, Alperen; Kürklü, Haci Ali; Akbuga, Kursat; Tanik, Veysel Ozan; Özlek, Bulent

  Objective: Coronary artery ectasia (CAE) is increasingly recognized as an active inflammatory vascular disorder rather than a benign anatomical variant. The pan-immune-inflammation value (PIV) is a novel biomarker integrating neutrophil, monocyte, platelet, and lymphocyte counts, providing a comprehensive measure of systemic inflammation. This study aimed to evaluate the association between PIV and CAE and to compare their diagnostic performance with that of conventional inflammatory indices. Method: In this retrospective case-control study, 17,538 patients who underwent elective coronary angiography between 2018 and 2024 were screened. A total of 228 patients with isolated CAE and 296 age-, sex-, and Body Mass Index (BMI)-matched controls with normal coronary arteries were included. Hematologic and biochemical parameters were analyzed, and inflammatory indices were calculated. Logistic regression and receiver operating characteristic (ROC) analyses were performed to identify independent predictors and assess diagnostic performance. Results: Patients with CAE had significantly higher PIV levels compared to controls (801.6 [504.4-1301.8] vs. 491.8 [302.4-872.7], P < 0.001). In multivariable logistic regression, log-transformed PIV remained independently associated with CAE (odds ratio [OR]: 1.987, 95% confidence interval [CI]: 1.057-3.737, P = 0.033), along with hypertension, triglycerides, high-density lipoprotein (HDL) cholesterol, and serum creatinine. PIV demonstrated the highest discriminative ability among all inflammatory indices (area under the curve [AUC]: 0.674, 95% CI: 0.623-0.722), and correlated strongly with the Systemic Immune-Inflammation Index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and Systemic Inflammation Response Index (SIRI) (P = 0.75-0.94). Conclusion: Elevated PIV levels are independently associated with CAE, reflecting the pivotal role of systemic inflammation in its pathogenesis. Given its simplicity and availability, PIV may serve as a practical adjunctive marker for identifying patients at risk of CAE, warranting validation in larger prospective studies.

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• Editöre MektupAçık Erişim2026/r/gumushane/13730
  Reply to the Letter to the Editor: Assessing the Predictive Value of Kolmogorov–Arnold Networks for the No–Reflow Phenomenon in ST–Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study; [Editöre Mektup Yanıtı: ST Segment Yükselmesi Miyokard Enfarktüsünde No–Reflow Fenomeni için Kolmogorov–Arnold Ağlarının Öngörü Değerinin Değerlendirilmesi: Karşılaştırmalı Makine Öğrenimi Çalışması]

  Taşolar, Hakan; Bayramoğlu, Adil; Günen, Mehmet Akif; Levent, Sümeyye; Güral, Yunus; Halisdemir, Nurhan

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• MakaleAçık Erişim2026/r/gumushane/12234
  Assessing the Predictive Value of Kolmogorov–Arnold Networks for the No–Reflow Phenomenon in ST–Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study; [ST Segment Yükselmeli Miyokard Enfarktüsünde No–Reflow Fenomeni için Kolmogorov–Arnold Ağlarının Öngörü Değerinin Değerlendirilmesi: Karşılaştırmalı Makine Öğrenimi Çalışması]

  Taşolar, Hakan; Bayramoğlu, Adil; Günen, Mehmet Akif; Levent, Sümeyye; Güral, Yunus; Halisdemir, Nurhan

  Objective: The no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) is a significant clinical issue associated with poor cardiovascular outcomes. This study aimed to develop and compare multiple supervised machine learning algorithms, including the recently introduced Kolmogorov-Arnold Network (KAN), to predict the occurrence of the no-reflow phenomenon in patients with STEMI undergoing primary percutaneous coronary intervention (PCI). Method: This prospective, single-center study included 890 consecutive STEMI patients undergoing primary PCI. The Synthetic Minority Over-sampling Technique (SMOTE) was utilized to address class imbalance during training. Feature selection using analysis of variance (ANOVA) F-statistics and validation of feature independence (Variance Inflation Factor [VIF] < 5) identified ejection fraction (EF), baseline troponin level, stent length, B-type natriuretic peptide (BNP) level, and total ischemic time as the most influential predictors. Results: The KAN and Extreme Gradient Boosting (XGBoost) models achieved the highest predictive accuracy (area under the curve > 0.98, F1 > 0.95), outperforming traditional models such as logistic regression and decision tree classifiers (DeLong test, P < 0.001). Feature selection improved efficiency and reduced runtime by 20–40%, while Shapley Additive exPlanations-based (SHAP-based) explainability confirmed that the predictions were physiologically consistent: higher EF and lower BNP reduced the probability of no-reflow, whereas longer stent length and ischemic time increased it. The superior performance of KAN and XGBoost underscores the importance of modeling nonlinear relationships and multidimensional interactions among clinical, laboratory, and procedural variables. Conclusion: These findings suggest that KAN may serve as a reliable analytical framework for exploring complex cardiovascular outcomes. However, further multicenter and externally validated studies are needed to confirm its generalizability and potential role in clinical risk assessment.

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• EditoryalAçık Erişim2018/r/gumushane/16272
  A rare coincidence of hypoplasia of the posterior mitral leaflet and the bicuspid aortic valve

  Demirel, Muhittin; Acar, Emrah; Sivri, Serkan; Sökmen, Erdogan; Özbek, Sinan Cemgil

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