Background and Objectives: Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders in women of reproductive age. Women with PCOS often have metabolic disorders such as insulin resistance (IR), type 2 diabetes (T2DM), obesity, and metabolic syndrome (MetS). The assessment of visceral adiposity and dysfunctional adipose tissue is crucial for understanding the metabolic risks associated with PCOS. The visceral adiposity index (VAI) and the dysfunctional adiposity index (DAI) are two novel metabolic indices that more specifically assess adipose tissue dysfunction and visceral fat accumulation. This study aimed to evaluate the clinical utility of VAI and DAI as predictive markers for metabolic complications such as insulin resistance (IR) and metabolic syndrome (MetS) in women with PCOS. Materials and Methods: This cross-sectional study included 92 women diagnosed with PCOS based on the 2023 revised Rotterdam criteria, as well as 68 healthy controls. Anthropometric and biochemical parameters, including fasting glucose, insulin, lipid profile, and hormonal markers, were recorded. VAI and DAI were calculated using established formulas derived from previous validated studies. Results: The mean VAI in PCOS patients was 4.26 ± 3.23, compared to 2.61 ± 1.92 in controls (p = 0.003). The mean DAI in PCOS patients was 3.00 ± 1.86, while in controls it was 1.86 ± 1.22 (p = 0.003). Both VAI (Area Under the Curve [AUC] = 0.639) and DAI (AUC = 0.635) did not demonstrate statistically significant diagnostic performance for PCOS itself, but they were strongly associated with metabolic disturbances within the PCOS group. VAI and DAI values were significantly elevated in PCOS patients with IR (p < 0.001) and MetS (p < 0.001). For MetS in PCOS patients, VAI demonstrated the highest predictive ability, with an AUC of 0.87 and a cutoff of 4.73 (sensitivity 62%, specificity 92%), while DAI had an AUC of 0.86 with a cutoff of 2.44 (sensitivity 74%, specificity 80%). Regarding IR in PCOS patients, VAI had an AUC of 0.75 with a cutoff of 2.56 (sensitivity 82%, specificity 56%), while DAI had an AUC of 0.74 with a cutoff of 1.59, showing a sensitivity of 82% and a specificity of 55%. Conclusions: Although VAI and DAI are not suitable for diagnosing PCOS, they provide valuable insights into the metabolic risks associated with the condition. VAI and DAI can serve as promising biomarkers for identifying IR and MetS risk in women with PCOS. Their integration into clinical practice may facilitate the early detection of cardiometabolic complications, offering a more specific metabolic risk assessment compared to traditional anthropometric measures.

Background/Objectives: Endometrial polyps (EPs) are common benign endometrial lesions often linked to abnormal uterine bleeding and infertility. While hormonal factors play a key role in their development, recent studies suggest a potential inflammatory component. This retrospective study aimed to assess systemic inflammatory markers, including mean platelet volume (MPV), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), in EP patients. Methods: A total of 180 patients were classified into three groups: EP (n = 60), chronic endometritis (n = 60), and control (n = 60). Preoperative hematological parameters were retrieved from medical records. Group comparisons were performed using one-way ANOVA, with Tukey’s post hoc test applied when significant. Multinomial logistic regression was used to identify independent predictors of EPs. Results: MPV and PLR were significantly higher in the EP group compared to other groups (p = 0.014 and p = 0.015, respectively), while NLR differences were not statistically significant (p = 0.086). Logistic regression identified MPV (p = 0.004) and PLR (p = 0.045) as independent predictors of EPs. Conclusions: These findings suggest that systemic inflammation may contribute to EP development, with MPV and PLR serving as potential inflammatory biomarkers. Further prospective studies with histopathological validation are needed to clarify the role of inflammation in EP pathogenesis.

Objectives: This study aimed to evaluate the diagnostic utility of the triglyceride glucose (TyG), triglyceride glucose–body mass (TyG-BMI), and lipid accumulation product (LAP) indices for both screening polycystic ovary syndrome (PCOS) and diagnosing insulin resistance (IR) in women diagnosed with PCOS. Methods: Retrospective data from medical records of 124 women were analyzed, with 71 in the PCOS group and 53 in the non-PCOS group. The PCOS diagnosis followed the 2003 Rotterdam criteria. Basic clinical and biochemical parameters were compared. The TyG index was computed using the formula ln [triglyceride (TG) (mmol/L) × fasting plasma glukose (FPG) (mg/dL)/2]. TyG-BMI value was derived as TyG × BMI. LAP was calculated as (waist circumference (WC-58) × TG (mmol/L). IR was identified if Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) was >2.7. Results: TyG-BMI (AUC=0.62) and LAP indices (AUC=0.61) did not demonstrate statistically significant diagnostic performance for PCOS. Regarding IR in PCOS patients, the highest AUC was for TyG-BMI (0.84, 95 % CI: 0.73–0.93, p<0.001) with a cutoff at 116.15, showing 80 % sensitivity and 86 % specificity. LAP had an AUC of 0.86 with a cutoff of 30.21 (sensitivity 80 %, specificity 81 %), while TyG showed an AUC of 0.78 (95 % CI: 0.67–0.89, p<0.001) with a cutoff of 4.47, demonstrating a sensitivity of 70 % and specificity of 72 %. Conclusions: Numerous biochemical markers have been explored for PCOS detection, however, many are expensive, not universally available, and necessitate specific test kits. TyG, TyG-BMI, and LAP indices might not serve as reliable markers for PCOS screening but could offer utility in identifying IR in Turkish women diagnosed with PCOS.

This study aimed to compare the performance of three large language models—ChatGPT-3.5, Gemini, and ChatGPT-4.0—in generating responses to ten frequently asked pregnancy-related questions, as evaluated by obstetrics and gynecology specialists. Seventy-five specialists independently rated 30 anonymized AI-generated responses using a 5-point Likert scale across four domains: accuracy, reliability, patient-friendliness, and comprehensibility. All questions were standardized and presented verbatim to each model using identical zero-shot prompts. Data were analyzed using the Kruskal–Wallis test with Bonferroni-adjusted Mann–Whitney U post-hoc comparisons. Inter-rater consistency was assessed using Cronbach’s alpha. Spearman correlation was used to examine associations between clinical experience and evaluation patterns. ChatGPT-4.0 demonstrated the highest overall performance, particularly in accuracy (median 4.35; mean ± SD: 4.30 ± 0.48) and patient-friendliness (4.40; 4.35 ± 0.47). Gemini performed comparably to ChatGPT-4.0 in comprehensibility (3.70; 3.68 ± 0.54), while ChatGPT-3.5 consistently received the lowest scores. Significant differences were observed among the three models for accuracy, reliability, and patient-friendliness (all p < 0.001), but not for comprehensibility (p = 0.521). A modest positive correlation was found between clinical experience and reliability ratings (r = 0.261, p = 0.0238). Among the evaluated models, ChatGPT-4.0 provided the most clinically aligned and patient-friendly responses to common pregnancy questions. While AI tools may offer valuable support for patient education, expert oversight remains essential to ensure accuracy and safety. Further research should explore their real-world impact on patient comprehension, behavior, and clinical outcomes.

Objective: This study aims to investigate the potential role of vasorin as a novel biomarker in the pathogenesis of polycystic ovary syndrome (PCOS) by evaluating serum vasorin levels in women diagnosed with PCOS. Methods: A prospective study was conducted at Düzce University Faculty of Medicine between March and July 2024, including 92 women with PCOS, diagnosed based on the 2003 Rotterdam criteria, and 68 age- and BMI-matched healthy controls. Serum vasorin levels were measured using an enzyme-linked immunosorbent assay (ELISA) and compared between the two groups. Additionally, correlations between vasorin levels and metabolic, inflammatory, and hormonal parameters were analyzed. Results: Women with PCOS had significantly lower serum vasorin levels (median: 0.70 pg/mL) compared to the control group (median: 2.36 pg/mL, p < 0.001). No significant correlation was found between vasorin and metabolic or hormonal parameters in the PCOS group. However, a weak positive correlation with prolactin was observed in the control group (r = 0.264, p = 0.030). Although vasorin is involved in inflammatory and oxidative-stress pathways, its association with insulin resistance and lipid metabolism remains unclear based on this study. Receiver Operating Characteristic (ROC) curve analysis demonstrated a high diagnostic performance for vasorin in distinguishing PCOS from healthy individuals (AUC = 0.918, p < 0.001, 95% CI: 0.869–0.967). The optimal cutoff value for vasorin (1.285 pg/mL) yielded 92.6% sensitivity and 87.0% specificity. Conclusions: These findings suggest that vasorin may serve as a promising biomarker for PCOS, potentially linking hormonal dysregulation, inflammatory responses, and ovarian dysfunction. However, further validation is required through longitudinal studies, multi-center cohorts, and mechanistic investigations. Additionally, comparative assessments with established biomarkers such as anti-Müllerian hormone (AMH) and androgen levels are warranted to determine vasorin’s diagnostic and prognostic utility in clinical practice.