Özlem Güler

Background: Despite continuous advancements in diagnostic imaging and targeted antimicrobial protocols for infective endocarditis (IE), global epidemiological data show that both disease burden and incidence continue to rise. This study aimed to evaluate real-world diagnostic and treatment approaches in accordance with the 2023 European Society of Cardiology (ESC) IE Guidelines and to identify independent predictors of mortality in a contemporary patient cohort. Methods: This multicenter, retrospective cohort study analyzed data from 470 consecutive adult patients diagnosed with IE across 27 academic reference centers between 1 September 2023 and 1 June 2025. Patients were managed and adjudicated according to the 2023 ESC diagnostic criteria. Demographic data, clinical characteristics, microbiological profiles, imaging findings, empirical antibiotic therapy patterns, and clinical outcomes were analyzed. Independent predictors of mortality were determined using multivariable Cox proportional hazards regression models. Results: The mean age of the overall study population was 58.08 ± 16.81 years, with a male predominance (60.6%, n = 285). Staphylococcus aureus was the most frequently isolated pathogen (27.9%), with a high proportion of methicillin resistance (38.9%). Hospital-acquired IE was identified in 41.1% of cases. Surgical treatment was performed in 40.6% (n = 191) of the cohort. The 28-day mortality rate was 11.9%, and the overall mortality during follow-up reached 26.4%. Multivariable Cox regression analysis identified hospital-acquired IE (HR = 2.247; 95% CI: 1.216–4.167; p = 0.010) and the presence of major clinical complications (HR = 4.207; 95% CI: 1.556–11.378; p = 0.005) as the strongest independent predictors of mortality. Conclusions: Contemporary infective endocarditis is a highly complex and fatal disease characterized by a prominent healthcare-associated burden and a predominant staphylococcal etiology. The occurrence of major clinical complications and hospital-acquired infections were the primary independent predictors of mortality.

Artificial intelligence (AI) is increasingly influencing healthcare, with growing relevance in infectious diseases and clinical microbiology. This review summarizes current applications of machine learning (ML), deep learning (DL), and large language models (LLMs) in these areas while critically evaluating their strengths and limitations. Although AI has shown promising performance in image analysis, molecular diagnostics, risk prediction, and clinical decision support, its routine implementation remains constrained by limited external validation, heterogeneous datasets, algorithmic bias, and challenges in generalizability and explainability. The review also discusses AI’s emerging role in scientific writing and the ethical and practical considerations associated with its clinical adoption. Overall, current evidence suggests that AI should be regarded as a complementary decision-support technology whose safe and effective integration depends on rigorous validation, high-quality data, and appropriate clinical oversight.

Background: Cryptococcal infections remain life-threatening fungal infections affecting both people living with HIV (PLWH) and non-HIV populations. Data from real-world cohorts in middle-income settings, particularly those including both groups, remain limited. This study aimed to evaluate the clinical characteristics and identify predictors of one-year mortality in patients with cryptococcal infections. Methods: In this retrospective multicenter cohort study, adult patients diagnosed with cryptococcal infection between 2013 and 2025 were included and followed for one year after diagnosis or until death. Demographic, clinical, laboratory, and treatment data were collected. Survival was analyzed using Kaplan–Meier methods and compared with the log-rank test. Cox proportional hazards regression was used to identify factors independently associated with mortality. Subgroup analyses were performed according to HIV status. Results: A total of 92 patients with an average age of 46 ± 16 years were included; 56.5% were PLWH, and 87% were immunocompromised. Central nervous system involvement was the most common presentation (64.1%). Overall one-year mortality was 46.7%. In the multivariable Cox regression analysis, altered consciousness (HR 3.110, 95% CI 1.624–5.956, p < 0.001) and disseminated infection (HR 2.155, 95% CI 1.156–4.016, p = 0.016) were independently associated with increased mortality, whereas higher serum albumin levels were independently associated with improved survival (HR 0.532, 95% CI 0.328–0.861, p = 0.010). Age remained independently associated with mortality (HR 1.025 per year, 95% CI 1.006–1.045, p = 0.010). Kaplan–Meier analysis showed significantly lower one-year survival among patients with altered consciousness and disseminated infection. Although CNS involvement was more frequent among PLWH (78.8% vs. 45%, p < 0.001), mortality rates were comparable between PLWH and non-HIV patients. Conclusions: Cryptococcal infections are associated with high mortality regardless of HIV status. Altered consciousness and disseminated infection were independently associated with one-year mortality, whereas higher albumin levels were associated with improved survival. Simple clinical parameters may contribute to early clinical risk assessment, particularly in resource-limited settings.

Tuberculosis has affected humanity for centuries, shaping societies and appearing in literary works, and it remains one of the most persistent infectious diseases worldwide. Despite remarkable advances in diagnosis, treatment

1. Effective combination antiretroviral therapy (cART) has extended the life expectancy of individuals living with HIV and significantly improved the clinical course of the disease. However, signif...

Background and Objective: Multidrug and carbapenem resistant gram-negative bacilli bloodstream infections cause high mortality in intensive care units (ICUs). Predicting mortality can improve treatment and support end-of-life decisions. This study aimed to develop a machine learning model to predict mortality in ICU patients with these infections. Methods: This retrospective cohort study was conducted at a tertiary care medical center between 2017 and 2023. Adult ICU patients with bloodstream infections caused by multidrug and carbapenem resistant Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii were included. Demographic, clinical, and laboratory data were collected. Mann-Whitney U and Chi-square tests were used to compare the groups. Multivariable analysis with binary logistic regression was used to identify mortality risk factors. Ten machine learning classifiers were evaluated using stratified 5-fold cross-validation, and model predictions were interpreted with SHapley Additive exPlanations (SHAP). Results: 197 patients were included, with a 15-day mortality rate of 48%. The Light Gradient Boosting Machine (LightGBM) classifier showed the best performance, with an AUROC of 0.94, AUPRC of 0.952, accuracy of 0.868, precision of 0.906, recall of 0.822, F1 score of 0.855, Matthews Correlation Coefficient (MCC) of 0.744, and Brier score of 0.131. SHAP analysis revealed coagulopathy, rapid access to antibiotics, septic shock, SOFA score, platelet count, C-reactive protein (CRP) level, and time-related parameters as the most important predictive features. Conclusion: The LightGBM model showed promising results in predicting mortality in ICU patients. This model may support early intervention and assist in complex end-of-life decisions. This study was registered at ClinicalTrials.gov( https://clinicaltrials.gov/ct2/show/NCT06167083 )

Background/Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is used in oncology but has limited specificity due to uptake in infectious and inflammatory conditions. This study evaluated the diagnostic value of 18F-FDG PET/CT in patients with infectious, inflammatory, and malignant lesions and its role in guiding histopathological biopsy decisions in an infectious disease setting. Methods: A retrospective cohort study included 186 adult patients who underwent 18F-FDG PET/CT between 2018 and 2023 for diagnostic evaluation at a tertiary care hospital. Clinical indications were fever of unknown origin (FUO), inflammation of unknown origin (IUO), lymphadenopathy of unknown origin, and suspected solid mass. Diagnostic yield, biopsy decisions, and factors associated with biopsy were analyzed. Results: The diagnostic yield of 18F-FDG PET/CT was 58.6%, with higher in malignant conditions (hematologic malignancy 85.7%, solid organ malignancy 88.9%) and lower in autoimmune/inflammatory diseases (20.8%) and mycobacterial infections. PET/CT showed moderate sensitivity (59.8%) and high specificity (98.7%) for infection detection, improving to 67.8% sensitivity after excluding mycobacterial infections. Biopsy was performed more in patients with lymphadenopathy, higher SUVmax (>7.4), and PET/CT findings not suggestive of infection. Analysis identified lymphadenopathy (aOR = 2.77), PET/CT not suggestive of infection (aOR = 4.73), and SUVmax > 7.4 (aOR = 4.98) as predictors of biopsy. Conclusions: 18F-FDG PET/CT provides moderate diagnostic value across infectious, inflammatory, and malignant diseases and guides biopsies effectively, particularly in patients with lymphadenopathy, elevated SUVmax, and non-infectious findings. Its limited performance in mycobacterial and autoimmune diseases requires cautious interpretation. Overall, 18F-FDG PET/CT supports clinical decisions in complex diagnostic scenarios.

Background: In this study, we aimed to explore the relationship between quantitative indices derived from computed tomography (CT) attenuation histograms and disease prognosis in patients with pneumonia and acute respiratory failure. We also sought to assess the effectiveness of these parameters as clinical prognostic markers. Methods: CT images of patients with pneumonia and acute respiratory failure were analyzed using Vitrea^® Advanced Visualization software. The analyzed quantitative CT (qCT) indices included mean lung Hounsfield unit (HU) and density-based volume measurements, specifically low-, medium-, and high-density volume (LDV, MDV, and HDV). Comparative analyses were performed to examine the differences in the volume density between the lungs bilaterally; these were accompanied by regional analyses and density indices. All indices were calculated using previously defined and validated Hounsfield unit (HU) thresholds, which helped to ensure accurate and consistent quantitative measurements and facilitated a more robust evaluation of the prognostic potential of qCT parameters. Results: Quantitative CT indices proved to have significant prognostic value in predicting mortality. In multivariable analysis, Difference for Lung HDV > 193 mL emerged as an independent risk factor (aOR: 4.29, p = 0.041). The prognostic significance was especially evident in patients with unilateral dominant pneumonia, where Difference for Lung MDV >219 mL (aOR: 9.30, p = 0.03) and Difference for Lung HDV > 193 mL (aOR: 10.85, p = 0.02) emerged as strong independent predictors of mortality. In this subgroup, lung volume differences demonstrated the strongest diagnostic performance (AUC: 0.808, 95% CI: 0.667–0.908, p < 0.001). Conclusions: Clinical outcomes are associated with quantitative CT-derived lung volume and density difference indices. Inter-lung differences in Lung MDV and Lung HDV are linked to mortality and may provide additional prognostic information beyond conventional imaging methods. Prospective studies should be conducted to validate these findings, and caution should be exercised during their interpretation.

Cytomegalovirus (CMV) colitis is a significant entity in hospitalized patients with ulcerative colitis, particularly during immunosuppressive therapy. The factors associated with antiviral treatment duration remain incompletely defined. This retrospective cohort study included hospitalized adult patients with ulcerative colitis and immunohistochemically confirmed CMV colitis. Baseline demographic, clinical, endoscopic, and laboratory characteristics were evaluated for the cohort and stratified by antiviral treatment duration of ≤14 days and >14 days. Correlation analyses were performed between tissue CMV polymerase chain reaction (PCR) viral load and laboratory parameters. Receiver operating characteristic analysis identified a tissue CMV PCR cut-off associated with prolonged antiviral therapy. The study included 52 patients (median age, 41.5 years; 65.4% male). Fourteen patients received biologic therapy and were younger and had higher C-reactive protein levels than those who did not receive biologics. Tissue CMV PCR viral load was higher in patients who received antiviral therapy for >14 days. The analysis identified a tissue CMV PCR cut-off value of 162,000 IU/mg, with an area under the curve of 0.69, sensitivity of 70.4%, and specificity of 76.0%. Tissue CMV PCR viral load showed a weak negative correlation with serum albumin levels (Spearman ’s r = −0.34, p < 0.05). Tissue CMV PCR viral load is associated with antiviral treatment duration and may help identify patients with ulcerative colitis–associated CMV colitis who require prolonged therapy.

Purpose: Mucormycosis is a rapidly progressive angioinvasive fungal infection with limited large-scale outcome data. We aimed to describe the epidemiology, clinical spectrum, treatment strategies, and predictors of outcomes in adult patients with proven mucormycosis in Türkiye. Methods: We conducted a nationwide, retrospective study of 280 adults diagnosed with proven mucormycosis between 2004 and 2024 across 27 tertiary centers. Case definitions were applied according to the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clinical characteristics, antifungal management, surgical interventions, and outcomes were systematically analyzed. Results: The median age of the cohort was 60 years, and diabetes mellitus, frequently complicated by ketoacidosis, was the most common underlying condition. Rhino-orbital-cerebral disease represented the predominant clinical form, whereas pulmonary, gastrointestinal, and disseminated presentations were less frequent but carried less favorable prognoses. Liposomal amphotericin B was the main first-line therapy, often combined with surgery, although antifungal treatment was initiated at a median of five days after symptom onset. Ninety-day mortality was 42.1% by Kaplan–Meier estimation (crude 49.3%) and reached 49.3% (crude 55.0%) at 365 days. Crude mortality was lowest in patients who did not undergo surgery; however, this association was not significant in the time-dependent Cox regression analysis, suggesting confounding by baseline severity. Poor outcomes were associated with corticosteroid exposure, prolonged neutropenia, and neurological manifestations, while a history of sinusitis appeared to be associated with improved survival. Conclusion: In this multicenter study, survival was mainly determined by host-related factors rather than the choice of antifungal agent. Corticosteroid exposure, prolonged neutropenia, and neurological involvement independently predicted poor outcomes, whereas a prior history of sinusitis was linked to improved survival, likely reflecting earlier diagnosis and intervention. These findings reinforce the need for heightened clinical awareness, rapid recognition, and prompt multidisciplinary management to improve outcomes in patients with mucormycosis.