Hepatitis C virus (HCV) has been associated with several extrahepatic disorders including mixed cryoglobulinemia (MC), autoimmune thyroiditis, Sjogren's syndrome. Such associations have led to the suggestion that HCV may participate in the development of various immunmediated disorders. Recently, it has been hypothesised that HCV might act as a trigger for the development of monoclonal B-cell disorders such as non-Hodgkin's lymphoma (NHL). Discordant data have been reported in different geographic regions of the world. The aim of this prospective case-control study was to detect the prevalence of HCV in patients with NHL in southeastern Anatolia region of Turkey. In this study, HCV antibody prevalence and cryoglobulinemia were investigated in 119 patients with histologically diagnosed NHL. The control group consisted of 117 patients who visited the outpatient clinic of internal medicine. None of the patients had HCV antibody positive (0%) with the enzyme immunoassay and reverse transcriptase polymerase chain reaction (RT-PCR). One of the control patients had positive HCV antibody (0.9%). Our data does not support the association between HCV infection and NHL in southeastern Anatolia region of Turkey.

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.

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.

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.

This study aimed to investigate the clinical manifestations, treatment patterns and clinical outcomes in patients with iTTP across Türkiye via an interim analysis of the Turkish iTTP registry. A total of 215 patients with iTTP (median age at diagnosis 41 years, 58.6% female) diagnosed between 2001 and 2023 were retrospectively analyzed in the interim analysis of the prospective noninterventional observational multicenter iTTP registry study conducted at 19 tertiary hematology centers. Data on patient demographics, disease characteristics on initial admission, treatment characteristics, and responses, exacerbations/relapses, and the survival outcome were registered via electronic case report forms. Infection (15.0%), new drug initiation (9.7%), and pregnancy/postpartum (6.3%) within 3 weeks before diagnosis were the most prevalent potential triggers. Patients presented more commonly with systemic/constitutional (fatigue 68.8%; fever 18.1%) and neurologic (headache 40.0%, vertigo 32.1%), followed by hemorrhagic, gastrointestinal, renal, and cardiovascular manifestations. Based on the PLASMIC risk scoring, 77.8% of patients were initially at high risk for TTP. The initial treatment was commenced within the first 48 hours of hospital admission in 64.1% of patients (36.2% on the day of admission). Treatment was mainly based on TPE exchange (92.1%) and steroids (63.7%), while rituximab was used in 15.8% of patients. The clinical response rate was 79.9%, and clinical remission was achieved in 68.2% of patients. As for the ADAMTS13 levels, partial and complete responses were achieved in 17.7% and 14.6%, respectively. During a median of 30 months (range, 0.1 to 262.4 months) follow-up, 35 patients experienced exacerbations/relapses. Mortality occurred in 11 (5.5%) patients and was found to be disease-related in 6 (3.0%). This interim analysis of the nationwide Turkish iTTP registry study provided valuable data on the real-world clinical practice in the diagnosis and management of iTTP at different hematology clinics across the country.