Macrophage populations in most mammalian organs consist of cells of different origin. Resident macrophages originate from erythromyeloid precursors of the yolk sac wall; maintenance of the numbers of such macrophages in postnatal ontogenesis is practically independent of bone marrow haematopoiesis. The largest populations of the resident macrophages of embryonic origin are found in the central nervous system (microglia) and liver (Kupffer cells). In contrast, skin dermis and mucous membranes become predominantly colonized by bone marrow-derived monocytes that show pronounced functional and phenotypic plasticity. In the present study, we compared Kupffer cells and monocytes using the immunophenotype, gene expression profile, proteome, and pool of microRNA. The observed differences did not consider the resident liver macrophages as purely M2 macrophages or state that monocytes have purely M1 features. Monocytes show signs of high plasticity and sensitivity to pathogen-associated molecular patterns (e.g., high levels of transcription for Tlr 2, 4, 7, and 8). In contrast, the resident liver macrophages were clearly involved in the regulation of specific organ functions (nitrogen metabolism, complement system protein synthesis).

We present EndoNuke, an open dataset consisting of tiles from endometrium immunohistochemistry slides with the nuclei annotated as keypoints. Several experts with various experience have annotated the dataset. Apart from gathering the data and creating the annotation, we have performed an agreement study and analyzed the distribution of nuclei staining intensity.

Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder–decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out.

H-score is a semi-quantitative method used to assess the presence and distribution of proteins in tissue samples by combining the intensity of staining and the percentage of stained nuclei. It is widely used but time-consuming and can be limited in terms of accuracy and precision. Computer-aided methods may help overcome these limitations and improve the efficiency of pathologists’ workflows. In this work, we developed a model EndoNet for automatic H-score calculation on histological slides. Our proposed method uses neural networks and consists of two main parts. The first is a detection model which predicts the keypoints of centers of nuclei. The second is an H-score module that calculates the value of the H-score using mean pixel values of predicted keypoints. Our model was trained and validated on 1780 annotated tiles with a shape of 100 × 100 µm and we achieved 0.77 mAP on a test dataset. We obtained our best results in H-score calculation; these results proved superior to QuPath predictions. Moreover, the model can be adjusted to a specific specialist or whole laboratory to reproduce the manner of calculating the H-score. Thus, EndoNet is effective and robust in the analysis of histology slides, which can improve and significantly accelerate the work of pathologists.

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care.

Neurodegenerative diseases pose a significant challenge to modern medicine. Despite significant advances in neurology, current therapeutic approaches often prove insufficient to treat such disorders. This study investigates the neuroprotective effect of extracellular vesicles derived from glial derivates of human-induced pluripotent stem cells. The extracellular vesicle’s cargo was characterised by proteomic analysis. The neuroprotective effect was assessed using a model of glutamate excitotoxicity performed on a primary culture of cortical neuroglial cells. The viability of cells was estimated using the MTT test and morphometric analyses. A comprehensive methodology was applied to investigate intracellular mechanisms, integrating assessments of intracellular calcium concentrations, mitochondrial membrane potential, and targeted inhibition of the PI3K-Akt pathway. Transcriptomic analysis of neuroglial cultures was used to validate the role of obtained mechanisms of extracellular vesicle’s neuroprotective effect. The obtaining results demonstrated the improvement of neuronal survival by reducing intracellular calcium levels and stabilising mitochondrial membrane potential under glutamate-induced excitotoxicity via PI3K-Akt signalling pathway activation. Moreover, the vesicles contained proteins that contribute to preventing apoptotic processes, activating regeneration of the nervous system, and modulating calcium ion transport and are associated with redox processes. Further transcriptomic analyses of neuroglial cultures treated with EVs showed an up-regulation of genes associated with regeneration, inhibition of calcium ion transport, regulation of membrane depolarisation, and negative regulation of apoptotic pathways.