Parathyroidectomy has been shown to reduce cardiovascular risk factors in some studies, although findings on these parameters remain inconsistent. This study aimed to evaluate inflammatory and cardiovascular risk markers in patients with Primary Hyperparathyroidism (PHPT) before and one month after successful parathyroidectomy (PTX). We retrospectively analyzed PHPT patients who visited the outpatient clinic between 2015 and 2020. Patient demographics, hemogram data, calcium, parathormone (PTH), vitamin D, high-density lipoprotein (HDL), mean platelet volume (MPV), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-HDL ratio (MHR) were recorded and compared pre- and postoperatively. The analysis revealed significant postoperative increases in platelet, MPV, HDL, PLR, and vitamin D levels (p = 0.001, p = 0.001, p = 0.001, p = 0.024, p = 0.001, respectively). Conversely, PTH, calcium, NLR, and MHR levels significantly decreased (p = 0.001, p = 0.001, p = 0.011, p = 0.019, respective-ly). Correlation analysis demonstrated a negative association between postoperative PTH and vitamin D (p = 0.010, r = -0.292**) and a positive association between postoperative PTH and both calcium (p = 0.008, r = 0.309**) and NLR (p = 0.046, r = 0.227**). Multivariable regression analysis demonstrated that postoperative PTH levels were significantly associated with calcium (B = 39.82, Beta = 0.321, p = 0.0469), NLR (B = 110.02, Beta = 0.428, p = 0.0384), baseline comorbidity scores (B = -30.54, Beta = -0.287, p = 0.0361), and preoperative inflammation levels (B = 25.69, Beta = 0.311, p = 0.0386). Our findings highlight a potential link between PHPT and inflammatory-cardiovascular risk, with parathyroidectomy exerting a beneficial effect within the first month post-surgery. The study also suggests that these risk factors may be modifiable with timely surgical intervention. Not applicable.

Background/Aims: Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, which catalyzes the formation of reactive oxygen species (ROS) in phagocytic cells, has five subunits: p67phox (“phox”refers to “phagocyte oxidase”), p47phox, p40phox, p22phox, and gp91phox (catalytic subunit). Oxidative stress resulting from the accumulation of ROS and/or defective removal of ROS by antioxidants has detrimental effects on cellular functions and may contribute to chronic inflammation. Disruption of the colonic mucosa due to the dysregulation of antioxidants or transformation enzymes may play a role in the pathogenesis of ulcerative colitis (UC) and influence the clinical features of this disease. In this study, we examined the expression of the gene encoding NADPH oxidase subunit p22phox cytochrome b-245, alphapolypeptidein the colonic mucosa to test its possible contribution in the pathogenesis of UC. Materials and Methods: Expression levels of mRNA in the inflamed and non-inflamed colonic mucosa (determined using colonoscopy)of 22 patients with UC and in the normal mucosa of 22 healthy controls were analyzed using real-time polymerase chain reaction. Results: Expression levels of mRNA were not significantly different between patients with inflamed and non-inflamed colonic mucosa (p>0.05) and betweenpatients with inflamed colonicmucosa and healthy controls (p>0.05). Conclusion: Although our data suggest that expression of the gene encoding p22phox is not associated with chronic inflammation in patients with UC, other mechanisms can affect oxidative stress in these patients.

Graves’ disease is an autoimmune disorder characterized by hyperthyroidism and is often associated with diffuse goiter, Graves’ ophthalmopathy,

Background/Objectives: Preoperative differentiation between benign thyroid nodules and papillary thyroid carcinoma (PTC) remains clinically challenging, particularly after indeterminate cytology. This study evaluated routinely available complete blood count (CBC)-derived indices, erythrocyte parameters, thyroid function tests, and arithmetic thyroid hormone ratios. Methods: This retrospective, single-center case–control study reviewed thyroidectomy records from July 2020 to November 2024. Histopathology was re-adjudicated from the source reports; 158 adults with clearly benign nodules (n = 93) or PTC (n = 65) formed the final analytic cohort. Missing laboratory values were handled by variable-specific complete-case analysis. Benjamini–Hochberg false-discovery-rate (FDR) correction and an exploratory multivariable logistic model were applied. Results: Preoperative TSH and the conventional platelet-to-lymphocyte ratio (PLR) were higher in PTC (p = 0.002, q = 0.013; and p = 0.004, q = 0.023), whereas the fT4/TSH and fT3/TSH ratios were lower (both q = 0.013). The fT3 difference was nominal (p = 0.018, q = 0.076), and MCV was not significant (p = 0.076). In 126 complete cases, each doubling of TSH was associated with higher odds of PTC (adjusted odds ratio [aOR] 1.51, 95% CI 1.08–2.13), whereas higher fT3 was associated with lower odds (aOR 0.41, 95% CI 0.19–0.87). Conclusions: TSH and fT3 were independently associated with PTC in the exploratory model, while the PLR and TSH-normalized hormone ratios differed between groups but lacked validated diagnostic performance. These measures should not be used in isolation; prospective multicenter studies are needed to determine whether they add clinically meaningful information to ultrasonographic, cytological, and molecular assessments.

Background/Objectives: Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder associated with substantial long-term morbidity and mortality. Routinely collected anthropometric, biochemical, and hematological variables may contain useful discriminatory information for data-driven classification. This study aimed to compare the apparent classification performance of multiple machine learning algorithms for distinguishing individuals with and without T2DM using routinely obtained clinical parameters in a single-center dataset. Methods: This single-center observational study included 160 adults (95 females, 65 males) evaluated at the Endocrinology Outpatient Clinic of Gaziantep Islam Science and Technology University, Faculty of Medicine, Ersin Arslan Training and Research Hospital. The dataset comprised anthropometric measurements, biochemical markers, and complete blood count parameters. SMOTE was applied only within the training folds to address class imbalance and to avoid information leakage. Following fold-internal data preprocessing, which included imputing missing values and feature standardization where appropriate, the dataset was evaluated using stratified 5-fold cross-validation. SHAP analysis was performed to interpret the model predictions. A calibration curve was used to assess the model’s reliability. Eight supervised machine learning models were evaluated with and without HbA1c: Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Tree, Random Forest, Extra Trees, Gaussian Naive Bayes, and k-Nearest Neighbors. Model performance was evaluated using accuracy, sensitivity, specificity, and F1 score, and ROC curves were used as a diagnostic tool. Results: The models were evaluated in two different ways: with and without HbA1c. Random Forest demonstrated the best classification performance in the cross-validated evaluation; without HbA1c, it achieved 92.2% accuracy, 93.9% sensitivity, 97.9% specificity, and a 95.9% F1 score. When HbA1c was included, it achieved 98.0% accuracy, 97.9% sensitivity, 98.8% specificity, and a 99.0% F1 score. Decision Tree and Extra Trees demonstrated strong performance with accuracy rates of 87.6% and 92.8%, respectively, without HbA1c, and 90% and 93.5% when HbA1c was included; in contrast, KNN yielded the lowest accuracy rate (70.6%). Overall, tree-based models performed better than linear classifiers on this dataset. Conclusions: Machine learning models based on routine clinical and anthropometric variables demonstrated promising performance for T2DM classification in this single-center dataset; tree-based approaches yielded the most promising results. Including HbA1c improved the models’ ability to classify individuals with and without T2DM. However, since HbA1c was included both as a predictor and as part of the operational definition of the diabetes group, the findings should be interpreted with caution due to the risk of target leakage. Therefore, these results should be considered exploratory rather than evidence of clinically applicable predictive performance, and an independent external validation study should be conducted prior to clinical application.