Central nervous system manifestations in acute and chronic graft-versus-host disease.

Despite the growing evidence supporting the existence of CNS involvement in acute and chronic graft-versus-host disease (CNS-GvHD), the characteristics and course of the disease are still largely unknown. In this multicenter retrospective study, we analyzed the clinical, biological, radiological, and histopathological characteristics, as well as the clinical course of 66 patients diagnosed with possible CNS-GvHD (pCNS-GvHD), selected by predetermined diagnostic criteria. Results were then contrasted depending on whether pCNS-GvHD occurred before or after day 100 following allogeneic hematopoietic stem cell transplantation.

Is paravertebral muscles edema a consequence of neurogenic changes in MuSK-positive myasthenia gravis?

Anti-MuSK myasthenia gravis (Anti-MuSK MG) is a chronic autoimmune disease caused by complement-independent dysfunction of the agrin-MuSK-Lrp4 complex, accompanied by the development of the pathological muscle fatigue and sometimes muscle atrophy. Fatty replacement of the tongue, mimic, masticatory and paravertebral muscles, revealed by muscle MRI and proton magnetic resonance spectroscopy (MRS), is considered to be a consequence of the myogenic process in anti-MuSK antibody MG in the patients with a plenty long course of the disease. However, in most experimental studies on animal models with anti-MuSK MG, complex presynaptic and postsynaptic changes are revealed, accompanied by the functional denervation of masticatory and paravertebral muscles predominantly.

Application of Machine Learning Methods for Epilepsy Risk Ranking in Patients with Hematopoietic Malignancies Using.

Machine learning methods to predict the risk of epilepsy, including vascular epilepsy, in oncohematological patients are currently considered promising. These methods are used in research to predict pharmacoresistant epilepsy and surgical treatment outcomes in order to determine the epileptogenic zone and functional neural systems in patients with epilepsy, as well as to develop new approaches to classification and perform other tasks. This paper presents the results of applying machine learning to analyzing data and developing diagnostic models of epilepsy in oncohematological and cardiovascular patients.