Objectives: The aim of this study was to investigate the potential of preoperative neopterin levels as a predictive marker for postoperative acute kidney injury (AKI) in patients undergoing on-pump cardiac surgery, in addition to other potential risk factors. Methods: This observational study included 91 patients who underwent elective cardiac surgery under cardiopulmonary bypass. Of these, 35 patients (38.46 %) experienced AKI following surgery, as outlined by the Kidney Disease Improving Global Outcomes (KDIGO) standards. The study participants were divided into two groups depending on whether they had developed AKI after the surgery or not. The study compared two groups and utilized logistic regression analysis to evaluate potential predictors. A receiver operating characteristic (ROC) analysis was conducted to determine the ability of preoperative neopterin levels to predict the occurrence of AKI. Results: A comparison of the baseline demographic, clinical, laboratory, and echocardiographic characteristics was conducted between patients who suffered from AKI and those who did not. The multivariate analysis demonstrated that EuroSCORE II (OR, 4.525; 95 % CI, 1.29–15.87; p=0.019), X-clamp time (OR, 1.157; 95 % CI, 1.01–1.326; p=0.035), and neopterin levels (OR, 22.952; 95 % CI, 3.14–167.763; p=0.002) were independently predicted the post-cardiac surgery AKI. ROC analysis identified a cut-off value of 9.65 nmol/L, which had a sensitivity of 91.4 % and a specificity of 91.1 % (area under the curve, 0.98; 95 % CI, 0.958–1; p<0.001). Conclusions: Our study emphasizes the potential of preoperative neopterin levels, EuroSCORE II, and X-clamp time as independent predictors of postoperative AKI, even in milder cases, in individuals undergoing on-pump cardiac surgery.

Intravenous leiomyomatosis (IVL) is a variant of leiomyoma characterised by intravascular proliferation and originates from the uterine mesoderm. The main pathogenesis of IVL has not been exactly a...

Background: Machine learning is increasingly being used to diagnose and treat various diseases, including cardiovascular diseases. Automatic image analysis can expedite tissue analysis and save time. However, using machine learning is limited among researchers due to the requirement of technical expertise. By offering extensible features through plugins and scripts, machine-learning platforms make these techniques more accessible to researchers with limited programming knowledge. The misuse of anabolic-androgenic steroids is prevalent, particularly among athletes and bodybuilders, and there is strong evidence of their detrimental effects on ventricular myocardial capillaries and muscle cells. However, most studies rely on qualitative data, which can lead to bias and limited reliability. We present a user-friendly approach using machine learning algorithms to measure the effects of exercise and anabolic-androgenic steroids on cardiac ventricular capillaries and myocytes in an experimental animal model.Method: Male Wistar rats were divided into four groups (n = 28): control, exercise-only, anabolic-androgenic steroid-alone, and exercise with anabolic-androgenic steroid. Histopathological analysis of heart tissue was conducted, with images processed and analyzed using the Trainable Weka Segmentation plugin in Fiji software. Machine learning classifiers were trained to segment capillary and myocyte nuclei structures, enabling quantitative morphological measurements.Results: Exercise significantly increased capillary density compared to other groups. However, in the exercise + anabolic-androgenic steroid group, steroid use counteracted this effect. Anabolic-androgenic steroid alone did not significantly impact capillary density compared to the control group. Additionally, the exercise group had a significantly shorter intercapillary distance than all other groups. Again, using steroids in the exercise + anabolic-androgenic steroid group diminished this positive effect.Conclusion: Despite limited programming skills, researchers can use artificial intelligence techniques to investigate the adverse effects of anabolic steroids on the heart's vascular network and muscle cells. By employing accessible tools like machine learning algorithms and image processing software, histopathological images of capillary and myocyte structures in heart tissues can be analyzed.

Background: Abdominal aortic aneurysms and peripheral artery disease pose significant health risks, ranking third after heart attacks and cerebral strokes. Surgical interventions often involve temporary aortic clamping, leading to ischemia–reperfusion injury and tissue damage. Colchicine and mesenchymal stem cells have shown promise, individually, in mitigating ischemia–reperfusion injury, but their combined effects remain understudied. Methods: This study utilized 42 male Wistar rats, divided into six groups: Control, Sham, Ischemia–Reperfusion, Colchicine, Mesenchymal stem cell, and Mix (colchicine and mesenchymal stem cell). The ischemia–reperfusion model involved clamping the abdominal aorta for 60 min, followed by 120 min of reperfusion. Colchicine and mesenchymal stem cell treatments were administered as pre- and post-ischemia interventions, respectively. Mesenchymal stem cells were cultured, characterized by flow cytometry, and verified for specific surface antigens. Blood and tissue samples were analyzed for oxidative stress markers, nitric oxide metabolites, and apoptosis using TUNEL. Results: There were significant differences between the groups in terms of the serum total antioxidant capacity (p < 0.001) and inflammation markers (ischemia-modified albumin, p = 0.020). The combined therapy group (Mix) exhibited the lowest inflammation levels. Arginine levels also showed significant variation (p = 0.028), confirming the ischemia–reperfusion injury model. In muscle tissues, the total antioxidant capacity (p = 0.022), symmetric dimethylarginine, and citrulline levels (p < 0.05) indicated nitric oxide metabolism. Apoptosis was notably high in the ischemia–reperfusion injury group as anticipated. It appeared to be reduced by colchicine, mesenchymal stem cells, and their combination, with the most significant decrease observed in the Mix group (p < 0.001). Conclusions: This study highlights the potential of using combined colchicine and mesenchymal stem cell therapy to reduce muscle damage caused by ischemia–reperfusion injury. Further research is needed to understand the underlying mechanisms and confirm the clinical significance of this approach in treating extremity ischemia–reperfusion injuries.