Molecular Nerve Repair : The molecular properties of the injured peripheral nerve and the application of viral vectors to enhance regeneration

Verhaagen, J. [Promotor]Malessy, M.J.A. [Copromotor]Boer, G.J. [Copromotor

Fatigue in patients with myasthenia gravis. a systematic review of the literature

Myasthenia Gravis (MG) is a chronic autoimmune disease affecting the neuromuscular junction. Although a hallmark of MG is muscle fatigability due to dysfunction of the neuromuscular junction (peripheral fatigue), a large number of MG patients also report symptoms of central fatigue, defined as an experienced lack of energy, physically and/or mentally. We systematically reviewed the literature on all aspects of central fatigue in MG. Results were categorized in 5 domains: prevalence, diagnosis, pathophysiology, treatment or impact. The prevalence of patient-reported fatigue varies between 42 and 82%, which is significantly higher than in control subjects. Fatigue severity is usually assessed with standardized questionnaires, but the choice of questionnaire varies widely between studies. The pathophysiology of fatigue is unknown, but it is strongly associated with depressive symptoms, female gender and disease severity. Fatigue is also highly prevalent in ocular MG and patients in remission, suggesting a multifactorial origin. Fatigued MG patients have a lower quality of life. Pharmacological treatment of MG is associated with improvement of fatigue and promising results have been found with physical and psychological training programs. Fatigue is a highly prevalent symptom of MG with a severe negative impact on quality of life. Physicians treating patients with MG should be aware of this symptom, as it may be treatable with physical or psychological training programs. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )Neurological Motor Disorder

Prevalence and associated factors of fatigue in autoimmune myasthenia gravis

Fatigue is usually defined as a subjective perception of lacking energy, mentally or physically, with a difficulty sustaining voluntary activities. It is a common symptom of many diseases and most likely has a multifactorial cause. In myasthenia gravis (MG), fatigue has a high prevalence and is correlated with female sex and disease severity. However, no large scale studies have been performed. Therefore, we aimed to evaluate fatigue in the Dutch participants (n = 420) of the Dutch-Belgian Myasthenia Patient Registry using an online survey. Additional information was obtained on mood, sleep, coping, quality of life, disease severity, physical activities and medication. Severe fatigue was present in 62% with a mean score of 37.1 +/- 13.2 points. Fatigue severity and prevalence increased significantly with disease severity. A positive correlation was found for female gender, BMI, disease severity and depressive symptoms. A negative correlation was found for strenuous physical activities and older age. The strong association with disease severity suggests that fatigue should be recognized as an element of the symptomatology of MG. The observed association between strenuous activity and fatigue and differences in coping style between fatigued and non-fatigued patients warrant future clinical trials on exercise and cognitive behavioral therapy. (C) 2021 The Authors. Published by Elsevier B.V.Neurological Motor Disorder

Lowering the cutoff value for increment increases the sensitivity for the diagnosis of Lambert-Eaton myasthenic syndrome

Background: Increment of compound muscle action potential amplitude is a diagnostic hallmark of Lambert-Eaton myasthenic syndrome (LEMS). Making a diagnosis can be challenging, therefore, a proper cutoff for abnormal increment is highly relevant for improved recognition

Walking Behavior Change Detector for a “Smart” Walker

AbstractThis study investigates the design of a novel real-time system to detect walking behavior changes using an accelerometer on a rollator. No sensor is required on the user. We propose a new non-invasive approach to detect walking behavior based on the motion transfer by the user on the walker. Our method has two main steps; the first is to extract a gait feature vector by analyzing the three-axis accelerometer data in terms of magnitude, gait cycle and frequency. The second is to classify gait with the use of a decision tree of multilayer perceptrons. To assess the performance of our technique, we evaluated different sampling window lengths of 1, 3 an 5seconds and four different Neural Network architectures. The results revealed that the algorithm can distinguish walking behavior such as normal, slow and fast with an accuracy of about 86%. This research study is part of a project aiming at providing a simple and non-invasive walking behavior detector for elderly who use rollators

Measuring CMAPs in addition to MEPs can distinguish peripheral ischemia from spinal cord ischemia during endovascular aortic repair

Objective: Spinal cord injury is a devastating complication after endovascular thoracic and thoracoabdominal aneurysm repair (EVAR). Motor evoked potentials (MEPs) can be monitored to detect spinal cord injury, but may also be affected by peripheral ischemia caused by femoral artery sheaths. We aimed to determine the incidence of peripheral ischemia during EVAR, and whether central and peripheral ischemia can be distinguished using compound muscle action potentials (CMAPs).Methods: We retrospectively analyzed all EVAR procedures between March 1st 2015 and January 1st 2020 during which MEPs were monitored. Peripheral ischemia was defined as both a reduction in MEP amplitudes reversed by removing the femoral sheaths and no clinical signs of immediate post-procedural paraparesis. All other MEP decreases were defined as central ischemia.Results: A significant MEP decrease occurred in 14/27 (52%) of all procedures. Simultaneous CMAP amplitude reduction was observed in 7/8 (88%) of procedures where peripheral ischemia occurred, and never in procedures with central ischemia.Conclusions: MEP reductions due to peripheral ischemia are common during EVAR. A MEP-reduction without a CMAP decrease indicates central ischemia.Significance: CMAP measurements can help to distinguish central from peripheral ischemia, potentially reducing the chance of misinterpreting of MEP amplitude declines as centrally mediated, without affecting sensitivity. (C) 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Cardiovascular Aspects of Radiolog

Accuracy of patient-reported data for an online patient registry of autoimmune myasthenia gravis and Lambert-Eaton myasthenic syndrome

Disorders of the neuromuscular junction (NMJ) comprise a spectrum of rare diseases causing muscle fatigability and weakness, leading to life-long effects on quality of life. We established the Dutch-Belgian registry for NMJ disorders, based on a unique combination of patient -and physician-reported information. Information on natural course, disease burden, prevalence of complications and comorbidity is collected through patient-reported standardized questionnaires and verified using medical documentation. Currently, the registry contains information of 565 Myasthenia Gravis (MG) patients and 38 Lambert-Eaton myasthenic syndrome (LEMS) patients, constituting approximately 25% (MG) and 80% (LEMS) of patients in the Netherlands. This is a very large registry, with the highest participation rate per capita. In addition to confirming many disease characteristics previously described in the literature, this registry provides several novel insights. The reported rate of potentially corticosteroid-related comorbidity, including hypertension, heart disease, osteoporosis and type 2 diabetes was high, emphasizing the need to commence corticosteroid-sparing immune suppressive treatment as soon as possible. The reported rate of other auto-immune diseases is far higher than previously expected: 27% of MG and 38% of LEMS patients, and a surprisingly high number of MG patients (47%) is unaware of their antibody status. In conclusion, this registry provides a valuable collection of information regarding MG and LEMS disease course. Continuous collection of annual follow-up data will provide further longitudinal insights in disease burden, course and treatment effect. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )Neurological Motor Disorder

Deriving reference values for nerve conduction studies from existing data using mixture model clustering

Objective: to obtain locally valid reference values (RVs) from existing nerve conduction study (NCS) data.Methods: we used age, sex, height and limb temperature-based mixture model clustering (MMC) to identify normal and abnormal measurements on NCS data from two university hospitals. We compared MMC-derived RVs to published data; examined the effect of using different variables; validated MMC-derived RVs using independent data from 26 healthy control subjects and investigated their clinical applicability for the diagnosis of polyneuropathy.Results: MMC-derived RVs were similar to published RVs. Clustering can be achieved using only sex and age as variables. MMC is likely to yield reliable results with fewer abnormal than normal measurements and when the total number of measurements is at least 300. Measurements from healthy controls fell within the 95% MMC-derived prediction interval in 97.4% of cases.Conclusions: MMC can be used to obtain RVs from existing data, providing a locally valid, accurate reflection of the (ab)normality of an NCS result.Significance: MMC can be used to generate locally valid RVs for any test for which sufficient data are available.(1) (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Development and application of statistical models for medical scientific researc

Eye muscle MRI in myasthenia gravis and other neuromuscular disorders

Introduction:MRI of extra-ocular muscles (EOM) in patients with myasthenia gravis (MG) could aid in diagnosis and provide insights in therapy-resistant ophthalmoplegia. We used quantitative MRI to study the EOM in MG, healthy and disease controls, including Graves’ ophthalmopathy (GO), oculopharyngeal muscular dystrophy (OPMD) and chronic progressive external ophthalmoplegia (CPEO).Methods:Twenty recently diagnosed MG (59±19yrs), nineteen chronic MG (51±16yrs), fourteen seronegative MG (57±9yrs) and sixteen healthy controls (54±13yrs) were included. Six CPEO (49±14yrs), OPMD (62±10yrs) and GO patients (44±12yrs) served as disease controls. We quantified muscle fat fraction (FF), T2water and volume. Eye ductions and gaze deviations were assessed by synoptophore and Hess-charting.Results:Chronic, but not recent onset, MG patients showed volume increases (e.g. superior rectus and levator palpebrae [SR+LPS] 985±155 mm3 compared to 884±269 mm3 for healthy controls, p 3, p 3, p p water were found.Interpretation:We observed small increases in EOM volume and FF in chronic MG compared to healthy controls. Surprisingly, we found no atrophy in MG, even in patients with long-term ophthalmoplegia. This implies that even long-term ophthalmoplegia in MG does not lead to secondary structural myopathic changes precluding functional recovery.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Machine learning for automated EEG-based biomarkers of cognitive impairment during Deep Brain Stimulation screening in patients with Parkinson's Disease

Objective: A downside of Deep Brain Stimulation (DBS) for Parkinson's Disease (PD) is that cognitive function may deteriorate postoperatively. Electroencephalography (EEG) was explored as biomarker of cognition using a Machine Learning (ML) pipeline.Methods: A fully automated ML pipeline was applied to 112 PD patients, taking EEG time-series as input and predicted class-labels as output. The most extreme cognitive scores were selected for class differentiation, i.e. best vs. worst cognitive performance (n = 20 per group). 16,674 features were extracted per patient; feature-selection was performed using a Boruta algorithm. A random forest classifier was modelled; 10-fold cross-validation with Bayesian optimization was performed to ensure generalizability. The predicted class-probabilities of the entire cohort were compared to actual cognitive performance.Results: Both groups were differentiated with a mean accuracy of 0.92; using only occipital peak frequency yielded an accuracy of 0.67. Class-probabilities and actual cognitive performance were negatively linearly correlated (b =-0.23 (95% confidence interval (-0.29,-0.18))).Conclusions: Particularly high accuracies were achieved using a compound of automatically extracted EEG biomarkers to classify PD patients according to cognition, rather than a single spectral EEG feature.Significance: Automated EEG assessment may have utility for cognitive profiling of PD patients during the DBS screening. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Neurological Motor Disorder