Background/aim: This study compared TRPM2 and TRPM7 ion channel gene expression and irnmunohistochemical staining in endometrial hyperplasia and endometrium adenocarcinoma. Materials and methods: Sections were taken from paraffin blocks of 120 patients who were divided into 6 groups as follows: G1 (n = 20), proliferative endometrium (PE); G2 (n = 20), EH without atypia; G3 (n = 20), EH with atypia; G4 (n = 20), stage 1A, grade 1 EC; G5 (n = 20), stage 1A, grade 2 EC; and G6 (n = 20), stage 1A, grade 3 EC. TRPM2 and TRPM7 genes were analyzed with qRT-PCR in paraffin-embedded tissue samples. Under light microscopy, TRPM2 and TRPM7 immunostaining scores of the samples taken from polylysine slides were evaluated. Results: Compared to G1, TRPM2 mRNA gene expression was significantly downregulated in G3 and G5. TRPM2 immunoreactivity scores were similar in all groups. TRPM7 mRNA gene expression was significantly downregulated in G2, G3, and G6 when compared to G1. TRPM7 immunoreactivity scores were similar in G1, G2, and G3, but significantly decreased in G4, G5, and G6. Conclusion: Reduction in TRPM7 ion channel activity may be a progression marker for endometrial hyperplasia regardless of the atypical criteria.

Background/Objectives: The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. The proposed method transforms one-dimensional (1D) patient data—including features such as nuchal translucency (NT), human chorionic gonadotropin (hCG), and pregnancy-associated plasma protein A (PAPP-A)—into two-dimensional (2D) Aztec barcode images, enabling advanced feature extraction using transformer-based deep learning models. Methods: The dataset consists of 958 anonymous patient records. Each record includes four first trimester screening markers, hCG, PAPP-A, and NT, expressed as multiples of the median. The DS risk outcome was categorized into three classes: high, medium, and low. Three transformer architectures—DeiT3, MaxViT, and Swin—are employed to extract high-level features from the generated barcodes. The extracted features are combined into a unified set, and dimensionality reduction is performed using two feature selection techniques: minimum Redundancy Maximum Relevance (mRMR) and RelieF. Intersecting features from both selectors are retained to form a compact and informative feature subset. The final features are classified using machine learning algorithms, including Bagged Trees and Naive Bayes. Results: The proposed approach achieved up to 100% classification accuracy using the Naive Bayes classifier with 1250 features selected by RelieF and 527 intersecting features from mRMR. By selecting a smaller but more informative subset of features, the system significantly reduced hardware and processing demands while maintaining strong predictive performance. Conclusions: The results suggest that the proposed hybrid AI method offers a promising and resource-efficient solution for DS risk assessment in first trimester screening. However, further comparative studies are recommended to validate its performance in broader clinical contexts.

Artificial intelligence in prenatal diagnosis: Down syndrome risk assessment with the power of gradient boosting-based machine learning algorithms - Turkish Journal of Obstetrics and Gynecology

Iniencephaly is a rare and severe neural tube defect characterized by an occipital bone defect, partial or complete absence of the cervicothoracic vertebrae, and marked hyperextension of the fetal head. Its reported incidence ranges between 0.0001% and 0.01%, with a significantly higher prevalence in female fetuses. Iniencephaly is classified into two types: iniencephaly apertus and iniencephaly clausus.As a neural tube defect, preconceptional folic acid supplementation plays a crucial role in its prevention. Early prenatal diagnosis is essential for appropriate counseling and management, particularly given that the condition is generally incompatible with life. Timely identification allows consideration of pregnancy termination, thereby reducing potential maternal morbidity and psychological burden.Herein, we present two prenatally diagnosed cases: one of iniencephaly apertus detected in the first trimester and one of iniencephaly clausus diagnosed in the third trimester.

Objective: Fetal health classification is of great clinical importance as it allows the early detection and management of fetal health problems during pregnancy.This study aims to enhance fetal health classification by integrating Light Gradient Boosting Machine (LightGBM), synthetic minority oversampling technique (SMOTE), and Optuna-based hyperparameter tuning. The goal is to improve classification accuracy, address class imbalance, and optimize model performance in predicting normal, suspicious, and pathological fetal health conditions. Material and Methods: The study utilized the University of California, Irvine ''Cardiotocography Data Set'' which contains 2.126 fetal cardiotocography (CTG) records classified into 3 categories. To handle class imbalance, SMOTE is applied. Various machine learning models [Stochastic Gradient Descent Classifier, Gradient Boosting, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression (LG)] were compared, with LightGBM selected due to its efficiency in structured medical data processing. Optuna is used for hyperparameter tuning. The dataset was split into 80% training and 20% testing, and model performance was assessed using accuracy, precision, recall, and F1-score. Results: The optimized LightGBM+SMOTE+Optuna model achieved 99% accuracy, significantly outperforming the other classifiers. The recall and F1-score for the suspicious and pathological classes improved, reducing the misclassification rates. The confusion matrix confirmed a substantial decrease in errors, demonstrating the model's robustness and reliability. Conclusion: The proposed model successfully enhances fetal health classification accuracy by addressing class imbalance and optimizing hyperparameters. The integration of LightGBM, SMOTE, and Optuna improves model generalization, making it a valuable tool for fetal health assessment. Future studies could explore deep learning approaches to further refine classification performance and support clinical decision-making.

Aim: Obesity is a chronic condition that is increasingly prevalent globally and poses significant risks in the context of surgical procedures. Hysterectomy is among the most commonly performed surgeries in the field of gynecology. However, there is a paucity of studies in the existing literature that compare the various hysterectomy techniques in obese women. Consequently, this study aimed to elucidate the advantages and disadvantages of abdominal, laparoscopic, and vaginal hysterectomies performed on obese patients at our institution. Material and Method: This study is a retrospective cohort analysis involving 100 patients with a body mass index exceeding 30 kg/m² who underwent hysterectomy for benign indications at the Ondokuz Mayıs University Faculty of Medicine, Department of Gynecology, between 2016 and 2020. Among these patients, 40 underwent abdominal hysterectomy, 42 underwent laparoscopic hysterectomy, and 18 underwent vaginal hysterectomy. The intraoperative and postoperative outcomes of the 100 patients who underwent hysterectomy using these three different surgical techniques were compared. Results: Laparoscopy emerged as the surgical method associated with the lowest risk of postoperative wound infection and minimal blood loss. However, it is important to note that laparoscopic hysterectomy incurs higher costs and requires a longer operative time compared to other surgical approaches. Conversely, vaginal hysterectomy is characterized by the shortest hospital stay, the least requirement for postoperative intensive care, and the lowest overall cost. Notably, the highest blood loss was observed in patients who underwent vaginal hysterectomy. In terms of operative duration, abdominal hysterectomy was identified as the most efficient approach. Conclusion: For obese patients, less invasive hysterectomy techniques, vaginal and laparoscopic methods, may enhance postoperative outcomes.