Balkan Medical Journal最新文献

Background: Ischemic heart damage reduces the pumping efficiency of the heart by affecting the left ventricular ejection fraction (LVEF) and causing wall motion abnormality (WMA). In daily clinical practice, these parameters are interpreted by physicians using two dimensional transthoracic echocardiography (2D-TTE). Because 2D-TTE reports rely on visual evaluations, they are subject to experience-based limitations and exhibit low reproducibility.

Aims: To develop an artificial intelligence algorithm composed of two modules that enable automatic LVEF calculation and WMA detection for analyzing 2D-TTE images.

Study design: Diagnostic accuracy study.

Methods: A total of 600 adult patients were retrospectively included. The model combined static frame segmentation with dynamic tracking using a hybrid Simpson's method applied to apical 2- and 4-chamber views. Model performance was assessed against cardiologist measurements using Bland-Altman analysis. The YOLOv8 and ResNet50 models were employed for the wall motion module. Performance metrics, including accuracy, precision, F1 score, and area under the curve, were evaluated.

Results: In the Bland-Altman analysis, the mean bias between the LVEF module and cardiologist measurements was -4, with limits of agreement ranging from -15 to -3. Regression analysis demonstrated a strong correlation between the LVEF module and cardiologist measurements (r = 0.71, p < 0.001). In the wall motion module, the YOLOv8 segmentation model exhibited high accuracy, while ResNet50 achieved superior performance with an accuracy of 95%. The algorithm's color coding contributed to standardized interpretation among operators, enhancing consistency.

Conclusion: This is the first study to integrate automated EF calculation and WMA detection within a single workflow. SafeHeart offers accurate, reproducible, and rapid analysis, with the potential to support routine echocardiography practice. Color-coded region segmentation can facilitate more standardized and reliable results. Sidem Gül1, Reşit Taşdemir2, Beyza Açıkgöz2, Hakan Duman3, Hamza Hodzic4, Sena Köker5, Nazlı Erdemir6, Mehmet Kıvrak7 Corresponding.

Background: Sepsis-associated encephalopathy is a prevalent complication in the sepsis population, especially in patients in the intensive care unit (ICU). The relationship between the hemoglobin glycation index (HGI) and delirium in sepsis patients in the ICU is not yet clearly established.

Aims: To investigate the relationship between HGI and delirium risk in sepsis patients admitted to the ICU.

Study design: Retrospective cohort study.

Methods: The data were extracted from the Medical Information Mart for Intensive Care IV 3.1 for the sepsis population in the ICU. The primary outcome was delirium occurrence in the ICU, whereas the secondary outcome was 30-day all-cause mortality (ACM) after ICU admission. The patients were stratified into tertiles according to HGI levels: T1 (HGI < -0.612), T2 (-0.612 ≤ HGI < 0.008), and T3 (HGI ≥ 0.008). The link of HGI to clinical outcomes in ICU patients was examined through logistic regression (LR), Cox proportional hazard models, and restricted cubic spline (RCS) and threshold effect analyses. The robustness of our findings was rated through subgroup analyses and interaction tests.

Results: In total, 3,744 patients were encompassed in the final analysis. The LR model showed that delirium risk in the T1 group was 67.7% higher than that in the T2 group [odds ratio (OR) = 1.677, 95% confidence interval (CI): 1.414, 1.992], while that in the T3 group was 24.8% higher than that in the T2 group (OR = 1.248, 95% CI: 1.048, 1.487). The Cox proportional hazard model indicated a 36.2% higher risk of 30-day ACM in T1 compared to T2 (hazard ratio = 1.362; 95% CI: 1.041-1.782). The RCS curve demonstrated an approximately U-shaped relation of HGI to delirium risk. The threshold effect analysis revealed an inflection point at HGI = -0.34. When HGI ≤ -0.34, each one-unit increase in HGI lowered the delirium risk by 36.2% (95% CI: 0.527-0.768).

Conclusion: This study suggested an independent association between HGI and both delirium risk and short-term prognosis in particularly in patients admitted to the ICU. HGI may be used as a prognostic risk stratification biomarker.

Familial Mediterranean Fever (FMF) is the first described and most prevalent monogenic autoinflammatory periodic fever syndrome worldwide. The disease is caused by pathogenic variants in the MEFV (Mediterranean fever) gene, which lead to dysregulated innate immune responses and a persistent hyperinflammatory state. Despite extensive genetic characterization, the molecular mechanisms linking MEFV mutations to aberrant inflammation remain incompletely understood. Moreover, substantial clinical heterogeneity-manifested as incomplete penetrance, variable expressivity, and modulation by additional autoinflammatory genes-indicates that FMF pathogenesis extends beyond classical Mendelian inheritance. Emerging evidence suggests that epigenetic mechanisms, including DNA methylation, histone modifications, and microRNA regulation, may contribute to phenotypic variability, disease severity, and therapeutic response; however, available data are limited and occasionally conflicting. This review provides a comprehensive and up-to-date overview of the genetic, molecular, and epigenetic factors implicated in FMF, highlights unresolved controversies, and proposes future research priorities aimed at elucidating disease mechanisms and improving clinical management.