http://localhost:8080/xmlui/handle/123456789/11775 159-250 2026-04-08T23:14:10Z http://localhost:8080/xmlui/handle/123456789/11907 Title: Pre- and post-centrifugation stability of total and free prostate specific antigen samples at room temperature storage conditions Authors: Ilardo, Claudio; Hammer-Dedet, Florence Abstract: Objectives: Considering the recommendations of literature, it was important to note the potential for differences in pre- and post-analytical storage conditions at room temperature between total and free prostate-specific antigen. The aim of our study was to establish whether it would be appropriate to align the pre- and post-analytical times for the determination of free prostate-specific antigen (fPSA) with those for total prostate-specific antigen (tPSA). Methods: Two blood samples were taken from 48 male patients aged 60 to 84. One specimen was centrifuged within one hour of collection. Each sample was tested immediately for total and free PSA. The second blood sample was kept at room temperature for 12 hours before being tested and then reanalyzed 24 hours after blood sampling. Serum specimens were analyzed on the Roche Cobas E801. Results: There were no notable alterations in any PSA forms (p=0.866 and 0.971) or calculated ratios (Kappa=1) for the blood sample that was stored at room temperature for 12 hours prior to processing. Furthermore, all forms of PSA demonstrated stability (p=0.956 and 0.901), and fPSA/tPSA ratios showed good agreement in serum for up to 24 hours at room temperature (Kappa=1). Conclusion: It would be beneficial to extend the pre- and post-analysis times of fPSA to align them with those of tPSA. Following the elevated tPSA discovery, investigating fPSA could be more streamlined, offering an improved patient management solution. Keywords: fPSA/tPSA ratios, free prostate-specific antigen, in vitro stability, total prostate specific antigenstorage 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/11905 Title: Pre- and post-centrifugation stability of total and free prostate specific antigen samples at room temperature storage conditions Authors: Ilardo, Claudio Abstract: Objectives: Considering the recommendations of literature, it was important to note the potential for differences in pre- and post-analytical storage conditions at room temperature between total and free prostate-specific antigen. The aim of our study was to establish whether it would be appropriate to align the pre- and post-analytical times for the determination of free prostate-specific antigen (fPSA) with those for total prostate-specific antigen (tPSA). Methods: Two blood samples were taken from 48 male patients aged 60 to 84. One specimen was centrifuged within one hour of collection. Each sample was tested immediately for total and free PSA. The second blood sample was kept at room temperature for 12 hours before being tested and then reanalyzed 24 hours after blood sampling. Serum specimens were analyzed on the Roche Cobas E801. Results: There were no notable alterations in any PSA forms (p=0.866 and 0.971) or calculated ratios (Kappa=1) for the blood sample that was stored at room temperature for 12 hours prior to processing. Furthermore, all forms of PSA demonstrated stability (p=0.956 and 0.901), and fPSA/tPSA ratios showed good agreement in serum for up to 24 hours at room temperature (Kappa=1). Conclusion: It would be beneficial to extend the pre- and post-analysis times of fPSA to align them with those of tPSA. Following the elevated tPSA discovery, investigating fPSA could be more streamlined, offering an improved patient management solution. Keywords: fPSA/tPSA ratios, free prostate-specific antigen, in vitro stability, total prostate specific antigenstorage 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/11902 Title: The serum NF-κB and adiponectin levels in patients with acute pancreatitis Authors: Fevziye Mısırlıoglu, Naile Abstract: Objectives: This study aimed to evaluate whether serum nuclear factor-kappa B (NF-κB) and adiponectin levels can provide insight into disease progression and serve as potential biomarkers for predicting disease severity in patients with acute pancreatitis (AP). Methods: A total of 49 patients diagnosed with AP and admitted to the Emergency Department of Gaziosmanpaşa Training and Research Hospital were enrolled. An age-matched control group of 49 healthy individuals without AP was also included. Serum levels of NF-κB and adiponectin were measured and compared between groups. Results: Patients with AP exhibited significantly elevated serum NF-κB levels and reduced adiponectin levels compared to the control group (both p<0.001). A strong negative correlation was observed between adiponectin and NF- κB levels in the AP group (r=–0.865, p<0.05). Receiver operating characteristic (ROC) analysis determined the optimal cut-off value for adiponectin as 3.4, with a sensitivity and specificity of 1.000. The optimal cut-off for NF-κB was 1.8, with a sensitivity of 1.000 and specificity of 0.96. Conclusion: The findings suggest that serum NF-κB and adiponectin levels may be valuable biomarkers for assessing disease severity in AP. Their combined use or integration with existing scoring systems could enhance prognostic accuracy. Further experimental and clinical studies are necessary to evaluate the therapeutic potential of adiponectin and to validate these biomarkers for routine clinical use. Keywords: Acute pancreatitis, adiponectin, disease severity, inflammation, nuclear factor kappa B 2025-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/11900 Title: Machine learning-assisted prediction of positive urine cultures using urinalysis and hemogram data: A retrospective cohort study Authors: Demirci, Ferhat; Arikan, Yusuf Abstract: Objectives: Urinary tract infections (UTIs) are common and often lead to unnecessary urine culture testing, increasing costs and delaying treatment. This study aims to develop a machine learning (ML) model using urinalysis and hemogram data to predict urine culture positivity and reduce unnecessary testing. Methods: We retrospectively analyzed data from 12,433 patients who underwent urinalysis, urine culture, complete blood count, and CRP testing. After preprocessing and exclusion criteria, data were split into training, test, and validation sets. H2O AutoML was employed to develop and evaluate various ML algorithms. Results: The gradient boosting model demonstrated an AUC-ROC of 0.822 with high sensitivity (73.8%) and negative predictive value (90.4%), making it reliable in ruling out negative cases. Urinary leukocytes, nitrite, and bacterial count were identified as top predictors. Conclusion: ML-based models can improve diagnostic accuracy and reduce unnecessary urine cultures. These models have the potential to be integrated into clinical workflows to enhance cost-effectiveness and minimize empirical antibiotic use. Keywords: Artificial intelligence, diagnostic model, machine learning, predictive analytics, urinalysis, urinary tract infection, urine culture 2025-01-01T00:00:00Z