The purpose of this study was to evaluate whether 50 Hz extremely low frequency electromagnetic fields (ELF-EMFs) affect the amount of orthodontic tooth movement in rats. The experiments were performed on 18 male Sprague-Dawley rats. The rats were randomly divided into three groups (n = 6): cage-control (Cg-Cnt) group (n = 6); sinusoidal electromagnetic field (SEMF) group (n = 6); and pulsed electromagnetic field (PEMF) group (n = 6). In SEMF and PEMF groups, rats were subjected to 1.5 mT EMF exposure eight hours per day for eight days. In order to obtain tooth movement, holes were drilled on the right and left maxillary central incisors of the rats at a distance 1.5–2 mm away from the gingiva and 20 g of orthodontic forces were applied to the teeth. Generated linear model for repeated measures and Bonferroni tests were used to evaluate the differences between the groups. Interactions among groups by days were found by using Pillai's trace multivariate test. The results showed that significant differences were present among the groups (F = 5.035; p = 0.03) according to the extent of tooth movement. Significant differences between the amount of tooth movements were determined especially after the fifth day and the following days six, seven and eight (p < 0.001). Within the limitations, according to the results of the present study, the application of ELF-EMF accelerated the orthodontic tooth movement in rats.

Background. Osteoporosis is characterised by low bone mass and structural deterioration of bone tissue. In this study, the role of long-term extremely low frequency magnetic field (ELFMF) on osteoporosis was evaluated. Methods. The experiments were performed on 45 female Sprague-Dawley rats. The rats were divided into three groups (n = 15): Group I (ovariectomy (OVX) + ELFMF exposure), Group II (ovariectomised rats did not receive any treatment) and Group III (cage-control). Six months, 50 Hz, 1.5 mT magnetic field (MF) was used on Group I and Group II. Total body images of the animals were obtained with dual-energy X-ray absorptiometry. Results. Bone mineral content (BMC) and bone mineral density values were increased significantly in ELFMF group, decreased in the group of OVX and not changed in cage-control. At the end of the 6 months after exposure with ELFMF, alteration in studied biochemical markers were detected significant. Bone specific alkaline phosphatase (BAP) levels were increased in ELFMF and decreased in OVX groups when compared with cage-control group. N-telopeptide levels in OVX group were significantly higher than other groups. Testosterone and cortisol levels in OVX group were significantly higher and estradiol was lower than other groups. Conclusions. Our study suggests that ELFMF may be useful in the prevention and treatment of osteoporosis.

Accurate segmentation of cellular structures in whole slide images (WSIs) is essential for quantitative analysis in computational pathology. However, the complexity and scale of WSIs present significant challenges for conventional segmentation methods. In this study, we propose a novel hybrid deep learning framework that integrates three complementary approaches, YOLOv11, StarDist, and Segment Anything Model v2 (SAM2), to achieve robust and precise cell segmentation. The proposed pipeline utilizes YOLOv11 as an object detector to localize regions of interest, generating bounding boxes or preliminary masks that are subsequently used either as prompts to guide SAM2 or to filter segmentation outputs. StarDist is employed to model cell and nuclear boundaries with high geometric precision using star-convex polygon representations, which are particularly effective in densely packed cellular regions. The framework was evaluated on a unique WSI dataset comprising 256 × 256 image tiles annotated with high-resolution cell-level masks. Quantitative evaluations using the Dice coefficient, intersection over union (IoU), F1-score, precision, and recall demonstrated that the proposed method significantly outperformed individual baseline models. The integration of object detection and prompt-based segmentation led to enhanced boundary accuracy, improved localization, and greater robustness across varied tissue types. This work contributes a scalable and modular solution for advancing automated histopathological image analysis.

Accurate and automated segmentation of white blood cells (WBCs) in whole slide images (WSIs) is a critical step in computational pathology. This study presents a comprehensive evaluation and enhancement of the StarDist algorithm, leveraging its star-convex polygonal modeling to improve segmentation precision in complex WSI datasets. Our pipeline integrates tailored preprocessing, expert annotations from QuPath, and adaptive learning strategies for model training. Comparative analysis with U-Net and Mask R-CNN demonstrates StarDist’s superiority across multiple performance metrics, including Dice coefficient (0.89), precision (0.99), and IoU (0.95). Visual evaluations further highlight its robustness in handling overlapping cells and staining inconsistencies. The study establishes StarDist as a reliable tool for digital pathology, with potential integration into clinical decision-support systems. In addition to Dice and IoU, metrics such as Aggregated Jaccard Index and Boundary F1-Score are gaining popularity for biomedical segmentation. Preprocessing techniques like Macenko stain normalization and adaptive histogram equalization can further improve generalizability. QuPath, an open-source digital pathology platform, was utilized to perform accurate WBC annotations prior to training and evaluation.

Background: Accurate segmentation of white blood cells (WBCs) in peripheral blood smear images is a fundamental step in computational hematology, enabling downstream tasks such as classification, morphological assessment, and quantitative analysis. However, reliable segmentation remains challenging due to staining variability, complex cellular morphology, overlapping structures, and limited availability of high-quality annotations. Aim and Methods: The aim of this study is to develop a robust and fully automated segmentation framework for white blood cells (WBCs) in microscopic blood smear images, providing a reliable foundation for subsequent computational analysis. We propose TransNet–SAM2, a hybrid deep learning architecture that integrates hierarchical transformer-based feature extraction with a foundation-model-based decoder for prompt-free segmentation. Specifically, a Swin Transformer backbone is employed to capture multi-scale contextual representations, which are subsequently aligned and fused through a feature adaptation module. The fused features are directly injected into the SAM2 mask decoder, replacing conventional prompt-based conditioning and enabling fully automatic segmentation. In addition, a weakly supervised self-training strategy is incorporated to utilize partially annotated data, improving model generalization while reducing annotation requirements. The proposed framework is evaluated using a clinically curated dataset from Dicle University, the publicly available Raabin-WBC dataset, and an additional external leukemic blast validation dataset (ALL-IDB) to assess robustness under both routine and atypical hematological conditions. Results: TransNet-SAM2 achieved a Dice coefficient of 0.95 ± 0.01 and IoU of 0.90 on internal testing, significantly outperforming U-Net (0.89), Mask R-CNN (0.90), and SAM2 (0.92) (p < 0.05). In cross-dataset evaluation (Dicle training, Raabin-WBC testing), the framework maintained strong performance (Dice: 0.91, IoU: 0.84), demonstrating robustness to domain shifts. Ablation studies confirmed each component’s contribution, with the full model improving Dice by 6% over a CNN baseline. Qualitative analysis showed accurate boundary delineation even with cell overlap and background clutter. Conclusions: These findings indicate that the proposed framework provides a promising and scalable framework for WBC segmentation. While the current study focuses on segmentation, future work will investigate integration with classification and radiomics pipelines, as well as validation on more diverse clinical datasets, including bone marrow and leukemia samples.