Sirolimus is an immunosuppressive drug used to prevent graft rejection. Therapeutic drug monitoring is required as with other immunosuppressive drugs. Previous studies have shown the interactions between sirolimus and drugs that affect the activity of cytochrome P450 3A4 and P-glycoprotein. There is an increasing tendency for the use of herbal remedies in many countries. Medicinal herbs are rich sources of natural bioactive compounds that could interact with drugs. Parsley, Petroselinum crispum, is a food, spice, and also a medicinal herb. We report a case of a renal transplant recipient who had a supratherapeutic blood level of sirolimus due to consuming excessive parsley to highlight a possible herb–drug interaction. This is the first case report describing sirolimus–parsley interaction. Herb–drug interactions are especially important for drugs with a narrow therapeutic window. For this reason, healthcare professionals should question all patients, especially transplant patients, about the use of herbs or herbal products and report interactions. Plain Language Summary: Parsley, a commonly consumed food, affects the level of an important drug in a renal transplant recipient: A case report Sirolimus is a drug that suppresses the immune response used to prevent organ rejection in people who have had kidney transplants. In order to reach the optimum balance between therapeutic efficacy and adverse effects, sirolimus blood levels should be closely monitored. Previous studies have shown the interactions between sirolimus and drugs that affect the activities of metabolizing enzymes and transporter proteins. Parsley is a food, spice, and also a medicinal herb. Medicinal herbs are rich sources of natural bioactive compounds that could interact with a prescription drug. We report a case of a renal transplant recipient who had a rise in the blood level of sirolimus due to the ingestion of an excessive amount of parsley to highlight possible herb–drug interaction.

Background: Chronic kidney disease is a growing public health problem worldwide, and the number of patients requiring renal replacement therapy is steadily increasing. Türkiye has experienced a similar rise in both the incidence and prevalence of renal replacement therapy over the past decades; however, national‐level projections of future renal replacement therapy demand are lacking. Aims: This study aimed to forecast the future burden of renal replacement therapy in Türkiye by modeling historical incidence and prevalence trends using national registry data and demographic projections. Methods: National renal replacement therapy registry data from 2012 to 2024 were analyzed together with demographic projections from the Turkish Statistical Institute extending to 2035. Four models were constructed separately for incidence and prevalence. Model performance was compared using the coefficient of determination, root mean square error, and Akaike information criterion. Leave‐one‐out cross‐validation was used to assess model robustness. The modeling was further validated on data from Japan and the United Kingdom. Results: All models indicated a consistent upward trend in renal replacement therapy incidence in Türkiye over the next decade. By 2035, the prevalence of renal replacement therapy in Türkiye is projected to reach approximately 1150–1250 per million population. The models demonstrated strong goodness of fit, and cross‐validation supported good internal consistency, although the machine‐learning model showed some overfitting for prevalence. Conclusion: The national demand for renal replacement therapy in Türkiye is projected to continue increasing through 2035. Incorporating predictive modeling into national registry surveillance can help guide proactive capacity planning and fair resource distribution for dialysis and transplantation services. At the same time, public health strategies aimed at preventing and slowing the progression of chronic kidney disease remain essential to mitigate the growing burden of end‐stage renal disease.

Purpose: This study aimed to evaluate the predictive role of the C-reactive protein-albumin-lymphocyte (CALLY) index, which integrates systemic inflammation, nutritional status, and immune response for contrast-associated acute kidney injury in patients undergoing elective coronary angiography. Material and Methods: This retrospective, single-center study included 945 patients who underwent elective coronary angiography for stable angina pectoris between 2020 and 2024. Patients with an ejection fraction <50%, those who required intra-aortic balloon pump support were excluded. Contrast-associated acute kidney injury was defined as an increase in serum creatinine ≥25% or ≥0.5 mg/dL within 72 hours after contrast exposure. Clinical characteristics, laboratory parameters, and the CALLY index were analyzed. Results: Among 945 patients, 332 (35.1%) were female, median age was 58 years. Contrast-associated acute kidney injury developed in 150 patients (15.9%). In the multivariate CALLY-based model, advanced age, diabetes, lower albumin, lower bicarbonate, higher mean corpuscular volume, higher contrast volume and lower CALLY index were independent predictors, while C-reactive protein and uric acid lost significance. Conclusion: The CALLY index independently predicted contrast-associated acute kidney injury but showed modest diagnostic strength. It may serve as a complementary immunonutritional marker rather than a standalone tool. Bicarbonate exhibited the highest diagnostic accuracy, supporting the role of metabolic acidosis in contrast-associated acute kidney injury.

Background and Objectives: Contrast-associated acute kidney injury (CA-AKI) is a frequent and clinically significant complication in patients with acute myocardial infarction (AMI) undergoing coronary angiography. Early and accurate risk stratification remains challenging with conventional models that rely on linear assumptions and limited variable integration. This study aimed to evaluate and compare the predictive performance of multiple machine learning (ML) algorithms with traditional logistic regression and the Mehran risk score for CA-AKI prediction and to explore key determinants of risk using explainable artificial intelligence methods. Materials and Methods: This retrospective, single-center study included 1741 patients with AMI who underwent coronary angiography. CA-AKI was defined according to KDIGO criteria. Multiple ML models, including gradient boosting machine (GBM), random forest (RF), XGBoost, support vector machine, elastic net, and standard logistic regression were developed using routinely available clinical and laboratory variables. A weighted ensemble model combining the best-performing algorithms was constructed. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model interpretability was evaluated using feature importance and SHapley Additive exPlanations (SHAP). Results: CA-AKI occurred in 356 patients (20.4%). In multivariable logistic regression, lower left ventricular ejection fraction, higher contrast volume, lower sodium, lower hemoglobin, and higher neutrophil-to-lymphocyte ratio (NLR) were independently associated with CA-AKI. Among ML approaches, the weighted ensemble model demonstrated the highest discriminative performance (AUC 0.721), outperforming logistic regression and the Mehran risk score (AUC 0.608). Importantly, the ensemble model achieved a consistently high NPV (0.942), enabling reliable identification of low-risk patients. Explainability analyses revealed that inflammatory markers, particularly NLR, along with sodium, uric acid, baseline renal indices, and contrast burden, were the most influential predictors across models. Conclusions: In patients with AMI undergoing coronary angiography, interpretable ML models, especially ensemble and gradient boosting-based approaches, provide superior risk stratification for CA-AKI compared with conventional methods. The high negative predictive value highlights their clinical utility in safely identifying low-risk patients and supporting individualized, risk-adapted preventive strategies.