In multiple sclerosis (MS), there is a great need for treatment with the ability to suppress compartmentalized inflammation within the central nervous system (CNS) and to promote remyelination and regeneration. Mesenchymal stem cells (MSCs) represent a promising therapeutic option, as they have been shown to migrate to the site of CNS injury and exert neuroprotective properties, including immunomodulation, neurotrophic factor secretion, and endogenous neural stem cell stimulation. This review summarizes the current understanding of the underlying neuroprotective mechanisms and discusses the translation of MSC transplantation and their derivatives from pre-clinical demyelinating models to clinical trials with MS patients.

In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the gold standard segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.

Background: Image segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to segment the regions of interest for further processing. We propose a method for segmenting the nuclei and cytoplasms from white blood cells (WBCs).Methods: Initially, the method computes an initial value based on the minimum and maximum values of the input image. Then, a histogram of the input image is computed and approximated to obtain function values. The method searches for the first local maximum and local minimum from the approximated function values in the order of increasing of knots sequence. We approximate the required threshold from the first local minimum and the computed initial value based on defined conditions. The threshold is applied to the input image to binarize it, and then post-processing is performed to obtain the final segmented nucleus. We segment the whole WBC before segmenting the cytoplasm depending on the complexity of the objects in the image. For WBCs that are well separated from red blood cells (RBCs),nthresholds are generated and then producenthresholded images. Then, a standard Otsu method is used to binarize the average of the produced images. Morphological operations are applied on the binarized image, and then a single-pixel point from the segmented nucleus is used to segment the WBC. For images in which RBCs touch the WBCs, we segment the whole WBC using SLIC and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC.Results: The method is tested on two different public data sets and the results are compared to the state of art methods. The performance analysis shows that the proposed method segments the nucleus and cytoplasm well.Conclusion: We propose a method for nucleus and cytoplasm segmentation based on the local minima of the approximated function values from the image’s histogram. The method has demonstrated its utility in segmenting nuclei, WBCs, and cytoplasm, and the results are satisfactory.

Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.

Background and Objectives: Autologous hematopoietic stem cell transplantation (aHSCT) induces durable remission in treatment-refractory relapsing–remitting multiple sclerosis (RRMS). However, to what extent post-transplant immune recovery reflects restoration of pre-treatment state versus selective remodeling remains incompletely understood. We used high-dimensional whole-blood immune profiling to characterize lineage-specific remodeling in patients with RRMS following aHSCT and to compare trajectories with healthy reference profiles. Methods: Peripheral blood from 25 patients with RRMS treated with aHSCT in the RAM-MS trial was analyzed longitudinally at baseline, 100 days, and 6, 12, and 24 months post-transplant. Samples were compared with those from 9 age- and sex-matched healthy controls using a 41-marker mass cytometry panel. Unsupervised clustering and multi-level modeling were used to quantify lineage-level abundance changes and phenotypic remodeling across adaptive and innate compartments. Results: Immune recovery after aHSCT was characterized by selective and sustained reshaping of specific T-and B-cell subsets rather than uniform restoration of pre-treatment composition. Naïve CD4+ and CD8+ T cells gradually repopulated, whereas memory CD4+ T cells remained reduced at 24 months. Memory CD8+ T cells showed a transient early expansion before declining toward baseline levels. Naïve B cells expanded early and remained elevated, while memory B cells were durably reduced. B-cell reconstitution involved expansion of activation-associated naïve populations alongside contraction of selected memory subsets. Across T-cell compartments, CD161-expressing chemokine receptor–enriched subsets were selectively depleted. Several of these alterations persisted despite partial normalization of overall cell counts. These trajectories were consistent across patients, supporting a robust and reproducible effect of aHSCT on immune architecture. Discussion: aHSCT induces durable, lineage-specific reorganization of adaptive immunity along a shared trajectory across patients, characterized by contraction of inflammatory T-cell programs and hierarchical restructuring of B-cell maturation rather than simple immune restoration.