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2,738 results for “Multiple Sclerosis”
Multiple Sclerosis Smart Belt Dataset - ALAMEDA
<p>The dataset contains 17,135 samples collected from patients with Multiple sclerosis (MS). The data was gathered using a belt sensor with accelerometer and gyroscope sensors. Two belts were used to record the physical activities of the patients, where Belt 4 (B4) comprised 6,981 samples, and Belt 6 (B6) contained 10,154 samples. Each belt was attached at specific body locations around the waist, including the left, right, and middle hip. The variables generated from the sensors were recorded at a frequency of 100Hz and are described as follows:</p> <ol> <li> <p><strong>Packet ID</strong>: distinguishes data packets that change with time.</p> </li> <li> <p><strong>Timestamp:</strong> the time that a reading was taken on a sensor</p> </li> <li> <p><strong>Timer:</strong> Timer of the sensor</p> </li> <li> <p><strong>Acceleration X:</strong> Linear acceleration in the X-axis</p> </li> <li> <p><strong>Acceleration Y:</strong> Linear accelerations in the y-axis</p> </li> <li> <p><strong>Acceleration Z:</strong> Linear acceleration in the z-axis</p> </li> <li> <p><strong>Gyroscope X:</strong> Angular acceleration in the x-axis</p> </li> <li> <p><strong>Gyroscope Y:</strong> Angular acceleration in the y-axis</p> </li> <li> <p><strong>Gyroscope Z:</strong> Angular acceleration in the z-axis</p> </li> <li> <p><strong>Temperature:</strong> internal temperature for signal compensation</p> </li> </ol> <p>After acquiring raw sensor signals, a series of minor preprocessing tasks were performed on this dataset. These include reformatting the data with a suitable format, removing null values, and preparing the data for analysis.</p>
All Tasks- Multiple Sclerosis - ALAMEDA Bracelet Data
<p>The tasks_all_total.csv file contains accelerometer data and task labels of the pilot stroke patients that are given in folder datasets/bracelet/<strong>stroke</strong>. It includes 11 patients that are annotated and consists of 7 columns. Those columns are:</p> <ol> <li> <p>x, y, z, which represent the accelerometer values of the bracelets sensors used on either left or right wrist of the patients</p> </li> <li> <p>T and time, which represent the timestamp of the activity (time) and the period (T).</p> </li> <li> <p>Patient ID column, which is the number id of the patients.</p> </li> <li> <p>Task column, which represents the task performed by the patient.</p> </li> </ol> <p>This file contains the tasks that are described below. Inside of each parenthesis, is given the name of each task, based on the annotations that were provided on datasets/annotations/raw_medical_tracking/stroke/<strong>intense-monitoring-clean.xlsx</strong> file and on the accelerometer data that were available on stroke pilot folder mentioned above. Those tasks are:</p> <ol> <ol> <li> <p>cane_above_head (Cane above the head)</p> </li> <li> <p>standing_on_forefeet (Standing on the forefeet)</p> </li> <li> <p>lateral_steps (Lateral steps)</p> </li> <li> <p>rotation_cane (Rotations using a cane)</p> </li> <li> <p>cane_to_chest (Cane-to-chest)</p> </li> <li> <p>lateral_movement (Lateral movements with cane)</p> </li> <li> <p>hands_on_cane (Hands on the cane)</p> </li> <li> <p>lifting_knees (Lifting the knees)</p> </li> <li> <p>normal_walk (Normal walking)</p> </li> <li> <p>tandem_walk (Tandem walking)</p> </li> <li> <p>bicycle_walk (Bycicle walking)</p> </li> <li> <p>walk_with_knees_raised (Walking with the knees raised)</p> </li> <li> <p>rowing_movement (Rowing movements)</p> </li> <li> <p>flexion_extension_knees (Flexion and extension of the knees)</p> </li> </ol> </ol> <p>The features used to recognize activities in stroke patients are x,y,z and Task.</p>
Socioeconomic status affects the incidence of COVID-19 in Chilean multiple sclerosis patients
<p><span><b><span>Objective: </span></b>To investigate the frequency of coronavirus disease (COVID-19) in patients with multiple sclerosis (pwMSs) living in a high socioeconomic vulnerability area in Chile.<b> </b></span></p> <p><span><b><span>Methods:</span></b> In this prospective cohort study, we compared the frequency of COVID-19 in 52 Chilean pwMSs on disease-modifying treatments (DMTs), living in urban municipalities with low-income/high-poverty levels, with that previously reported in pwMSs living in municipalities with high-income/low-poverty rates in Santiago, Chile. Demographic and clinical features of the pwMSs were obtained from their last consultation between March 3, 2020, and August 29, 2020.<b> </b></span></p> <p><span><b><span>Results:</span></b> In the low-income pwMSs, the mean patient age was 34 years, 69% were women, mean disease duration was 3 years, and mean Expanded Disability Status Scale score was 1.6. Of these, 61.5% pwMSs (32/52) underwent quarantine during the study period. <span>COVID-19 diagnosis was confirmed by reverse transcriptase polymerase chain reaction in five patients (10%): two were on glatiramer acetate, one was on fingolimod, and two were on alemtuzumab. All pwMSs with COVID-19 recovered fully. </span>The previously reported frequency of confirmed COVID-19 in middle‒upper income pwMSs living in Santiago was 1%. The frequency of COVID-19 among pwMSs in the low- and middle‒high income inhabitants of Santiago differed significantly (z = -4.3235, p < 0.00001; one-tailed Fisher exact test, p < 0.01).</span></p> <p><span><b>Conclusion: </b>The frequency of COVID-19 in the low-income/high-poverty cohort in Santiago, Chile, was markedly high. Accordingly, high socioeconomic vulnerability should be considered as an important risk factor for COVID-19 in pwMSs.</span></p>
Coloc summary results for "Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"
<p>Text files containing coloc results between MS GWAS loci and CD4 T and B cell cis-eQTLs from DICE. These results accompany the paper "<strong>Dissection of multiple sclerosis genetics identifies B and CD4+ T cells as driver cell subsets"</strong></p>
The role of teriflunamide in multiple sclerosis patient: an observational study
<p>Teriflunomide is a drug with immunosuppressive and selective immunomodulatory action, characterized by anti-inflammatory and antiproliferative properties. Several clinical studies have demonstrated the efficacy and safety of this drug in Multiple Sclerosis, estimating a significant improvement in cognitive performance.</p> <p>The aim of our study is to evaluate the effects of teriflunomide by analysing the correlation between brain atrophy and the general cognitive profile and evaluating long-term changes. The effect of teriflunomide was studied in 30 patients with multiple sclerosis and 30 control subjects. Patients underwent a full cognitive profile assessment using the Brief Repeatable Battery of Neuropsychological Tests and a neuroimaging examination with a 3.0 T working scanner.</p> <p>Our results suggested that treatment with teriflunomide could potentially not only slow down the accumulation of microstructural tissue damage in Grey Matter and With Matter, but also better preserve the cognitive profile, particularly by highlighting the benefits in the memory domain. Thanks to drug therapy, brain volume in our patients has remained constant, leading to improvements in memory, indicating teriflunomide as a neuroprotective potential and further strengthening the evidence of a link between loss of brain volume and cognitive impairment.</p>
Evolution of retinal degeneration and prediction of disease activity in relapsing and progressive multiple sclerosis
<p><span>Retinal optical coherence tomography has been identified as biomarker for disease progression in relapsing-remitting multiple sclerosis (RRMS), while the dynamics of retinal atrophy in progressive MS are less clear. We investigated retinal layer thickness changes in RRMS, </span><span>primary and secondary progressive MS (PPMS, SPMS)</span><span>, and their prognostic value for disease activity. Here, we analyzed 2651 OCT measurements of 195 RRMS, 87 SPMS, 125 PPMS patients, and 98 controls from five German MS centers after quality control. Peripapillary and macular retinal nerve fiber layer (pRNFL, mRNFL) thickness </span><span>predicted</span><span> future relapses in all MS and RRMS patients while mRNFL</span><span> and </span><span>ganglion cell-inner plexiform layer (GCIPL) </span><span>thickness predicted </span><span>future </span><span>MRI activity </span><span>in RRMS (mRNFL, GCIPL) and PPMS (GCIPL). mRNFL thickness </span><span>predicted </span><span>future disability progression </span><span>in PPMS.</span><span> </span><span>However, thickness change rates were subject to considerable amounts of measurement variability. In conclusion, retinal degeneration, most pronounced of pRNFL and GCIPL, occurs in all subtypes. Using the current state of technology, longitudinal assessments of retinal thickness may not be suitable on a single patient level.</span></p>
Objective evaluation of Nintendo Wii Fit plus balance program training on postural stability in Multiple Sclerosis patients
<p>The use of the Nintendo Wii system has become a common tool for balance rehabilitation in patients with multiple sclerosis, but few studies verified the effectiveness of such an approach using quantitative measures of postural control. We aimed to evaluate the impact of rehabilitation treatment using the Nintendo Wii Fit Plus balance program on objective stabilometric parameters in multiple sclerosis patients. We enrolled 36 multiple sclerosis patients, with mild-moderate disability, referring to the multiple sclerosis Centre of the University of Catania from September 2013 to June 2014. Twenty participants underwent 20 individual sessions of balance exercise using the Nintendo Wii Fit Plus. They were assessed at baseline (T0) and at the end of rehabilitation program (T1) by Neurocom Balance Manager. Functional independence measure, Barthel index, and Berg balance scale were also administered. At T1, we found a significant improvement in total path length-open eyes, sway area-open eyes, and mean sway velocity-open eyes. Patients showed significant improvement in functional independence measure motor score, Barthel index, and in Berg balance scale. No significant differences between T0 and T1 in closed eyes trials were found. A significant correlation between delta values between T0 and T1 of sway area-open eyes and the Berg balance scale (r = -0.76; P < 0.0001) was found. This study confirmed that balance rehabilitation training performed using the Nintendo Wii with balance board significantly reduced some postural sway parameters in multiple sclerosis patients. It could be a good support to standard rehabilitation program in multiple sclerosis patients.</p>
Not all roads lead to the immune system: The genetic basis of multiple sclerosis severity
<p>Multiple sclerosis is a leading cause of neurological disability in adults. Heterogeneity in multiple sclerosis clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international multiple sclerosis Registry, MSBase. We assembled a cohort of deeply phenotyped individuals of European ancestry with relapse-onset multiple sclerosis. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined multiple sclerosis severity phenotypes in 1,813 individuals. Our primary analyses did not identify any genetic variants of moderate to large effect sizes that met genome-wide significance thresholds. The strongest signal was associated with rs7289446 (β=-0.4882, P = 2.73 × 10−7), intronic to SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in relapse-onset multiple sclerosis are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62,000 variants together with clinical and demographic variables available at multiple sclerosis disease onset, we could predict severity with an area under the receiver operator curve of 0.84 (95% CI 0.79–0.88). Our machine learning algorithm achieved positive predictive value for outcome assignation of 80% and negative predictive value of 88%. This outperformed our machine learning algorithm that contained clinical and demographic variables alone (area under the receiver operator curve 0.54, 95% CI 0.48–0.60). Secondary, sex-stratified analyses identified two genetic loci that met genome-wide significance thresholds. One in females (rs10967273; βfemale =0.8289, P = 3.52 × 10<sup>-8</sup>), the other in males (rs698805; βmale = -1.5395, P = 4.35 × 10<sup>-8</sup>), providing some evidence for sex dimorphism in multiple sclerosis severity. Tissue enrichment and pathway analyses identified an overrepresentation of genes expressed in central nervous system compartments generally, and specifically in the cerebellum (P = 0.023). These involved mitochondrial function, synaptic plasticity, oligodendroglial biology, cellular senescence, calcium and g-protein receptor signalling pathways. We further identified six variants with strong evidence for regulating clinical outcomes, the strongest signal again intronic to SEZ6L (adjusted hazard ratio 0.72, P = 4.85 × 10<sup>-4</sup>). Here we report a milestone in our progress towards understanding the clinical heterogeneity of multiple sclerosis outcomes, implicating functionally distinct mechanisms to multiple sclerosis risk. Importantly, we demonstrate that machine learning using common single nucleotide variant clusters, together with clinical variables readily available at diagnosis can improve prognostic capabilities at diagnosis, and with further validation has the potential to translate to meaningful clinical practice change.</p>
The role of teriflunomide in Multiple Sclerosis patient: an observational study
<p>Teriflunomide is a drug with immunosuppressive and selective immunomodulatory action, characterized by anti-inflammatory and antiproliferative properties. Several clinical studies have demonstrated the efficacy and safety of this drug in Multiple Sclerosis, estimating a significant improvement in cognitive performance.The aim of our study is to evaluate the effects of teriflunomide by analysing the correlation between brain atrophy and the general cognitive profile and evaluating long-term changes. The effect of teriflunomide was studied in 30 patients with multiple sclerosis and 30 control subjects. Patients underwent a full cognitive profile assessment using the Brief Repeatable Battery of Neuropsychological Tests and a neuroimaging examination with a 3.0 T working scanner.Our results suggested that treatment with teriflunomide could potentially not only slow down the accumulation of microstructural tissue damage in Grey Matter and With Matter, but also better preserve the cognitive profile, particularly by highlighting the benefits in the memory domain. Thanks to drug therapy, brain volume in our patients has remained constant, leading to improvements in memory, indicating teriflunomide as a neuroprotective potential and further strengthening the evidence of a link between loss of brain volume and cognitive impairment.</p>
Quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis and healthy controls
<p>This dataset provides access to radiomic features of brain MR susceptibility-based images (QSM). Specifically, a cohort of 151 subjects, mixed of patients with multiple sclerosis (121) and healthy controls (30) was analysed, studying the Normal Appearing White Matter (NAWM) and NAWM tracts (e.g. corticospinal tract and optic radiation). Robustness analysis of those imaging descriptors can be found in Fiscone et al., <em>Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis. </em></p> <p>In the .zip folder, instructions about the organization of the dataset can be found. Together with the data, the code used to assess the reliability of those features is available. </p>
A Cooperative Clinical Study of Abatacept in Multiple Sclerosis
ClinicalTrials.gov study NCT01116427. IPD Sharing: YES. Countries: 2. Publications: 2.
An Investigation of Delta-9-tetrahydrocannabinol (THC) and Cannabidiol (CBD) in Multiple Sclerosis Patients
ClinicalTrials.gov study NCT01610700. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Long-term Follow-up Study Of Multiple Sclerosis Patients Who Participated In Genzyme-sponsored Studies of GZ402668
ClinicalTrials.gov study NCT02313285. IPD Sharing: YES. Countries: 1. Publications: 1.
Safety and Efficacy Study of OnabotulinumtoxinA for the Treatment of Urinary Incontinence Due to Neurogenic Detrusor Overactivity (NDO) in Non-Catheterizing Patients With Multiple Sclerosis (MS)
ClinicalTrials.gov study NCT01600716. IPD Sharing: Not stated. Countries: 8. Publications: 1.
A Study of Ocrelizumab in Participants With Primary Progressive Multiple Sclerosis
ClinicalTrials.gov study NCT01194570. IPD Sharing: YES. Countries: 29. Publications: 11.
Prospective Randomized Endovascular Therapy in Multiple Sclerosis - PREMiSE
ClinicalTrials.gov study NCT01450072. IPD Sharing: Not stated. Countries: 1. Publications: 1.
BG00012 Phase 2 Combination Study in Participants With Multiple Sclerosis
ClinicalTrials.gov study NCT01156311. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of Ocrelizumab in Participants With Relapsing Remitting Multiple Sclerosis (RRMS) Who Have Had a Suboptimal Response to an Adequate Course of Disease-Modifying Treatment (DMT)
ClinicalTrials.gov study NCT02861014. IPD Sharing: Not stated. Countries: 17. Publications: 1.
Study and Treatment of Visual Dysfunction and Motor Fatigue in Multiple Sclerosis
ClinicalTrials.gov study NCT02391961. IPD Sharing: NO. Countries: 1. Publications: 1.
A Multicentre Study of the Efficacy and Safety of Supplementary Treatment With Cholecalciferol in Patients With Relapsing Multiple Sclerosis Treated With Subcutaneous Interferon Beta-1a 44 µg 3 Times
ClinicalTrials.gov study NCT01198132. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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