Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos

AbstractGiven a Lie superalgebra $${\mathfrak {g}}$$ g with a subalgebra $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 , and a finite-dimensional irreducible $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 -module F, the induced $${\mathfrak {g}}$$ g -module $$M(F)={\mathcal {U}}({\mathfrak {g}})\otimes _{{\mathcal {U}}({\mathfrak {g}}_{\ge 0})}F$$ M ( F ) = U ( g ) ⊗ U ( g ≥ 0 ) F is called a finite Verma module. In the present paper we classify the non-irreducible finite Verma modules over the largest exceptional linearly compact Lie superalgebra $${\mathfrak {g}}=E(5,10)$$ g = E ( 5 , 10 ) with the subalgebra $${\mathfrak {g}}_{\ge 0}$$ g ≥ 0 of minimal codimension. This is done via classification of all singular vectors in the modules M(F). Besides known singular vectors of degree 1,2,3,4 and 5, we discover two new singular vectors, of degrees 7 and 11. We show that the corresponding morphisms of finite Verma modules of degree 1,4,7, and 11 can be arranged in an infinite number of bilateral infinite complexes, which may be viewed as “exceptional” de Rham complexes for E(5, 10).

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The International Classification of Functioning, Disability, and Health (ICF) as a community participation model for people with multiple sclerosis: A hierarchical regression analysis

Australian Journal of Rehabilitation Counselling ◽

10.1017/jrc.2020.15 ◽

2020 ◽

Vol 26 (2) ◽

pp. 55-72

Author(s):

Joseph Pfaller ◽

Fong Chan ◽

Kanako Iwanaga ◽

Jia-Rung Wu ◽

Stuart Rumrill ◽

...

Keyword(s):

Multiple Sclerosis ◽

Regression Analysis ◽

Community Participation ◽

International Classification Of Functioning ◽

Self Management ◽

Hierarchical Regression ◽

Personal Factors ◽

Disability And Health ◽

The Relationship

AbstractMultiple sclerosis (MS) is a central nervous system disorder that impacts more than 400,000 people in the U.S. The disease results in multiple functional impairments that are diverse and varied across individuals. Additonally, MS has a profound impact on community participation which, like other rehabilitation outcomes, cannot be explained on the basis of functional limitations alone. The purpose of this study was to develop and evaluate a model of community participation for people living with MS using the World Health Organization (WHO) International Classification of Functioning, Disability, and Health (ICF) framework. The model focused on the roles that personal factors have as predictors of community participation, while also serving as mediators and moderators for the relationship between activity limitation and participation. Results from the hierarchical regression analysis indicated that demographic characteristics (i.e. MS type), personal factors (i.e. core self-evaluations (CSE), MS self-management, resilience, and social skills), and activity limitations accounted for 64% of the variance in participation. Further, mediation analysis indicated that CSE mediated the relationship between activity limitation and community participation. Finally, moderation analysis indicated an interaction effect between educational attainment and MS self-management. Implications for future research in rehabilitation and clinical application are discussed.

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Perceived and actual arm performance in multiple sclerosis: relationship with clinical tests according to hand dominance

Multiple Sclerosis Journal ◽

10.1177/1352458513475832 ◽

2013 ◽

Vol 19 (10) ◽

pp. 1341-1348 ◽

Cited By ~ 29

Author(s):

Ilse Lamers ◽

Lore Kerkhofs ◽

Joke Raats ◽

Daphne Kos ◽

Bart Van Wijmeersch ◽

...

Keyword(s):

Multiple Sclerosis ◽

Handgrip Strength ◽

Real Life ◽

Activity Level ◽

International Classification Of Functioning ◽

Fine Motor ◽

Control Group ◽

Hand Dominance ◽

Clinical Tests

Background: The real-life relevance of frequently applied clinical arm tests is not well known in multiple sclerosis (MS). Objective: This study aimed to determine the relation between real-life arm performance and clinical tests in MS. Methods: Thirty wheelchair-bound MS patients and 30 healthy controls were included. Actual and perceived real-life arm performance was measured by using accelerometry and a self-reported measure (Motor Activity Log). Clinical tests on ‘body functions & structures’ (JAMAR handgrip strength, Motricity Index (MI), Fugl Meyer (FM)) and ‘activity’ level (Nine Hole Peg Test (NHPT), Action Research Arm test) of the International Classification of Functioning were conducted. Statistical analyses were performed separately for current dominant and non-dominant arm. Results: For all outcome measures, MS patients scored with both arms significantly lower than the control group. Higher correlations between actual arm performance and clinical tests were found for the non-dominant arm (0.63–0.80). The FM (55%) was a good predictor of actual arm performance, while the MI (46%) and NHPT (55%) were good predictors of perceived arm performance. Conclusions: Real-life arm performance is decreased in wheelchair-bound MS patients and can be best predicted by measures on ‘body functions & structures’ level and fine motor control. Hand dominance influenced the magnitude of relationships.

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Discrepancies in the interpretation of clinical symptoms and signs in the diagnosis of multiple sclerosis. A proposal for standardization

Multiple Sclerosis Journal ◽

10.1191/1352458505ms1149oa ◽

2005 ◽

Vol 11 (2) ◽

pp. 227-231 ◽

Cited By ~ 23

Author(s):

Bernard MJ Uitdehaag ◽

Ludwig Kappos ◽

Lars Bauer ◽

Mark S Freedman ◽

David Miller ◽

...

Keyword(s):

Multiple Sclerosis ◽

Demyelinating Disease ◽

Clinical Symptoms ◽

Clinical Findings ◽

Large Trial ◽

Mri Findings ◽

Mcdonald Criteria ◽

Magnetic Resonance Imaging Mri ◽

Eligibility Assessment

The new McDonald diagnostic criteria for multiple sclerosis (MS) incorporate detailed criteria for the interpretation and classification of magnetic resonance imaging (MRI) findings, but, in contrast, provide no instructions for the interpretation of clinical findings. Because MS according to the McDonald criteria is one of the primary endpoints in a large trial enrolling patients after the first manifestation suggestive for a demyelinating disease (BENEFIT study), it was decided to organize a centralized eligibility assessment for this trial. During this eligibility assessment it was observed that there were marked inconsistencies in the decisions of participating neurologists with respect to the classification of clinical symptoms as being caused by one or more lesions provoking discussions in about one in every five patients. This paper describes these inconsistencies and their sources, and recommends a systematic approach that attempts to reduce the variability in interpreting clinical findings.

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Gastroparesis

10.2310/gastro.5442 ◽

2015 ◽

Author(s):

Braden Kuo ◽

Laurence Guay

Keyword(s):

Multiple Sclerosis ◽

Nervous System ◽

Gastric Emptying ◽

Clinical Manifestations ◽

Gastrointestinal Dysmotility ◽

Inflammatory Conditions ◽

Gastric Physiology ◽

Neuromuscular Apparatus ◽

Pharmacologic Treatments

The gastric phase of digestion requires a tightly coordinated neuromuscular apparatus to permit appropriate timing for each step. Dysregulations in this apparatus may be related to the Cajal cells, the intrinsic enteric nervous system, the extrinsic nervous system, the muscle cells, or any combination of structures and may lead to abnormal gastric emptying, referred to as gastroparesis. Gastroparesis is idiopathic in 35 to 49.4% of cases but may also be related to diabetes, autoimmune and inflammatory conditions, multiple sclerosis, use of certain medications, and infections, among other factors. This review describes the epidemiology, etiology, pathophysiology, clinical manifestations, diagnosis, treatment, and prognosis of gastroparesis. The figure shows symptom mechanisms related to gastric physiology in gastroparesis patients. Tables list the physiopathologic mechanisms and symptoms associated with defective gastric physiologic phenomena, the classification of gastroparesis according to affected structures, and pharmacologic treatments for gastrointestinal dysmotility. This review contains 1 highly rendered figure, 3 tables, and 56 references.

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How much do periventricular lesions assist in distinguishing migraine with aura from CIS?

Neurology ◽

10.1212/wnl.0000000000007266 ◽

2019 ◽

Vol 92 (15) ◽

pp. e1739-e1744 ◽

Cited By ~ 3

Author(s):

Caterina Lapucci ◽

Laura Saitta ◽

Giulia Bommarito ◽

Maria Pia Sormani ◽

Matteo Pardini ◽

...

Keyword(s):

Multiple Sclerosis ◽

Migraine With Aura ◽

Diagnostic Performance ◽

White Matter Hyperintensities ◽

Clinically Isolated Syndrome ◽

Lower Sensitivity ◽

Resonance Imaging ◽

Future Studies ◽

Periventricular Lesions

ObjectiveTo evaluate in clinically isolated syndrome (CIS) and migraine with aura (MA) how the number of periventricular lesions (PVLs) detected at MRI influences diagnostic performance when the Magnetic Resonance Imaging in Multiple Sclerosis (MAGNIMS) or the 2017 revised criteria are applied.MethodsIn this retrospective study, white matter hyperintensities (WMH) of 84 patients with MA and 79 patients with CIS were assessed using manual segmentation technique. Lesion probability maps (LPMs) and voxel-wise analysis of lesion distribution by diagnosis were obtained. Furthermore, we performed a logistic regression analysis based on lesion locations and volumes.ResultsCompared to patients with MA, patients with CIS showed a significant overall higher T2 WMH mean number and volume (17.9 ± 16.9 vs 6.2 ± 11.9 and 3.1 ± 4.2 vs 0.3 ± 0.6 mL; p < 0.0001) and a significantly higher T2 WMH mean number in infratentorial, periventricular, and juxtacortical areas (p < 0.0001). LPMs identified the periventricular regions as the sites with the highest probability of detecting T2 WMH in patients with CIS. Voxel-wise analysis of lesion distribution by diagnosis revealed a statistically significant association exclusively between the diagnosis of CIS and the PVLs. MAGNIMS criteria demonstrated the highest specificity in differentiating patients with CIS from patients with MA (100% vs 87%) against a predictable lower sensitivity (63% vs 72%).ConclusionsPVLs play a key role in the differential diagnosis between MA and CIS, particularly when there are more than 3. Future studies on multiple sclerosis criteria might reconsider the 3 PVLs to minimize the risk of misdiagnosis.Classification of evidenceThis study provides Class IV evidence that the presence at least 3 PVLs increases the specificity in distinguishing MA from CIS.

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Accurate classification of secondary progression in multiple sclerosis using a decision tree

Multiple Sclerosis Journal ◽

10.1177/1352458520975323 ◽

2020 ◽

pp. 135245852097532

Author(s):

Ryan Ramanujam ◽

Feng Zhu ◽

Katharina Fink ◽

Virginija Danylaitė Karrenbauer ◽

Johannes Lorscheider ◽

...

Keyword(s):

Multiple Sclerosis ◽

Decision Tree ◽

Clinical Judgment ◽

Clinical Information ◽

Disease Classification ◽

Additional Information ◽

History Of ◽

Phenotype Transition ◽

Independent Cohort

Background: The absence of reliable imaging or biological markers of phenotype transition in multiple sclerosis (MS) makes assignment of current phenotype status difficult. Objective: The authors sought to determine whether clinical information can be used to accurately assign current disease phenotypes. Methods: Data from the clinical visits of 14,387 MS patients in Sweden were collected. Classifying algorithms based on several demographic and clinical factors were examined. Results obtained from the best classifier when predicting neurologist recorded disease classification were replicated in an independent cohort from British Columbia and were compared to a previously published algorithm and clinical judgment of three neurologists. Results: A decision tree (the classifier) containing only most recently available expanded disability scale status score and age obtained 89.3% (95% confidence intervals (CIs): 88.8–89.8) classification accuracy, defined as concordance with the latest reported status. Validation in the independent cohort resulted in 82.0% (95% CI: 81.0–83.1) accuracy. A previously published classification algorithm with slight modifications achieved 77.8% (95% CI: 77.1–78.4) accuracy. With complete patient history of 100 patients, three neurologists obtained 84.3% accuracy compared with 85% for the classifier using the same data. Conclusion: The classifier can be used to standardize definitions of disease phenotype across different cohorts. Clinically, this model could assist neurologists by providing additional information.

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