Myelin disorders burden millions of people around the world, yet existing therapies are inadequate to cure them. Current remedies commonly treat the symptoms with minimal to no effect on the actual cause of the disorder. The basis and/or the mechanism of demyelination is not known for many of the disorders either. In recent years, stem cells of variable origin have been used in clinical trials as transplant agents to restore the defective biochemical process or the damaged tissue. We summarize the outcomes of these trials for demyelination disorders. The capability of reprograming mature cells into stem cells equips researchers with a new tool to replicate disease phenotypes in cell culture dishes for basic research and therapeutic screens. The applications of in vitro myelination disorder models are also discussed. The combined outcome of the discussed studies offers a promising future as stem cell transplantation generally results in decreased symptoms and improved quality of life. However, the mechanism of action of the interventions is not known and in cases of negative outcomes the reasons are usually obscure. Further basic science studies along with clinical interventions should close the knowledge gap and should help spread the positive results to a larger population.
Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca2+) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy.
Motivation Identifying and prioritizing disease-related proteins is an important scientific problem to develop proper treatments. Network science has become an important discipline to prioritize such proteins. Multiple sclerosis (MS), an autoimmune disease for which there is still no cure, is characterized by a damaging process called demyelination. Demyelination is the destruction of myelin, a structure facilitating fast transmission of neuron impulses, and oligodendrocytes, the cells producing myelin, by immune cells. Identifying the proteins that have special features on the network formed by the proteins of oligodendrocyte and immune cells can reveal useful information about the disease. Results We investigated the most significant protein pairs that we define as bridges among the proteins providing the interaction between the two cells in demyelination, in the networks formed by the oligodendrocyte and each type of two immune cells (i.e., macrophage and T-cell) using network analysis techniques and integer programming. The reason we investigated these specialized hubs was that a problem related to these proteins might impose a bigger damage in the system. We showed that 61% to 100% of the proteins our model detected, depending on parametrization, have already been associated with MS. We further observed the mRNA expression levels of several proteins we prioritized significantly decreased in human peripheral blood mononuclear cells (PBMCs) of MS patients. We therefore present a model, BriFin, which can be used for analyzing processes where interactions of two cell types play an important role. Availability BriFin is available at https://github.com/BilkentCompGen/brifin. Supplementary information Supplementary data are available at Bioinformatics online.
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