ARTICLE | doi:10.20944/preprints202302.0143.v1
Subject: Medicine & Pharmacology, Other Keywords: COVID-19 vaccines; demyelinating disorders; PEG
Online: 8 February 2023 (09:55:41 CET)
Introduction: The rapid emergence of COVID-19 as a global crisis has led to the approval of many vaccinations, which were unfortunately associated with high complication rates due to a lack of sufficient safety studies. Objectives: The following manuscript focuses on discussing the demyelinating disorders that were noticed after COVID vaccine administration. Methods: We conducted a retrospective study using anonymous medical records from the US vaccine adverse events reporting system, complications retrieved included Acute disseminated encephalomyelitis (ADEM), Guillain Barre syndrome (GBS), and Multiple sclerosis (MS), outcome parameters were age, sex and the dose after which this complication was observed. Patients younger than 18 years-old were excluded as some of the vaccines, namely Janssen (JNJ-78436735) is not yet approved below this age. Results: Our analysis showed that demyelinating disorders were more likely to occur in patients over the age of 50 compared to other age groups, regardless of the type of vaccination, except for MS and ADEM occurrences after the Jansen vaccine. In addition, demyelinating complications were more likely to occur after the first dose of vaccination. Conclusion: Further research and observation of demyelinating diseases in different vaccinations, as well as additional in vitro studies, are recommended to further explain the pathogenesis of demyelinating disorder occurrence.
ARTICLE | doi:10.20944/preprints202012.0705.v1
Subject: Medicine & Pharmacology, Clinical Neurology Keywords: Rituximab; Multiple Sclerosis; Neuromyelitis Optica; demyelinating diseases
Online: 28 December 2020 (13:22:04 CET)
Background: Rituximab is a monoclonal antibody widely used in the treatment of inflammatory and autoimmune disorders. Despite reports of its effectiveness in the treatment of demyelinating diseases of central nervous system (DDCNS), it is not yet approved for use in these disorders. The aim of this study was to investigate the effectiveness and safety of low dose rituximab in three different subgroups of DDCNS including relapsing-remitting multiple sclerosis (RRMS), secondary-progressive multiple sclerosis (SPMS) and neuromyelitis-optica-spectrum disorders (NMOSD).Methods: In a prospective cohort study, we monitored expanded-disability-status-scale (EDSS), relapses (new attacks) and serum-IgG levels to assess effectiveness and drug-adverse-events for safety in patients with RRMS, SPMS and NMOSD. These patients were candidates to receive rituximab according to our common practice protocol. We switched patients to rituximab if there was poor response to first line therapies. We follow a low dose protocol in our center (500 mg twice, two weeks apart, repeating every six months) and these patients treated in a 4-year period were assessed retrospectively for evaluation of our protocol’s safety and effectiveness. Results: 99 patients (42 RRMS, 43 SPMS and 14 NMOSD) received rituximab for a range of 12 to 40 months period. New attacks occurred in 8 RRMS (19%), 10 SPMS (23%) and 1 NMOSD (7%) patients. EDSS decreased in RRMS and NMOSD cases. Serum-IgG levels decremented in SPMS and NMOSD patients. Drug-adverse-events happened in two cases.Conclusion: In this study, low dose rituximab showed substantial effectiveness in preventing disease progression with a considerably good safety profile.
REVIEW | doi:10.20944/preprints202101.0426.v1
Subject: Mathematics & Computer Science, Algebra & Number Theory Keywords: deep learning; machine learning; ischemic stroke; demyelinating disease; image processing; computer aided diagnostics; brain MRI; CNN; White Matter Hyperintensities; VOSViewer
Online: 21 January 2021 (14:55:05 CET)
Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.