Educational Outcomes in Pediatric Multiple Sclerosis

2017 ◽  
Author(s):  
Wendy Vargas
Keyword(s):  
Multiple Sclerosis ◽  
Educational Outcomes ◽  
Pediatric Multiple Sclerosis

Neurocognitive Sequelae in African American and Caucasian Pediatric Multiple Sclerosis Patients

2010 ◽  
Author(s):  
Kelly A. Ross ◽  
Joseph Ackerson ◽  
John Rinker ◽  
David C. Schwebel
Keyword(s):  
Multiple Sclerosis ◽  
African American ◽  
Pediatric Multiple Sclerosis ◽  
Multiple Sclerosis Patients

Ongoing improvement in mobility, activity and cognition of a teenage girl with pediatric multiple sclerosis after 3 years of natalizumab treatment – a case report

2011 ◽  
Vol 42 (S 01) ◽  
Author(s):  
K Lengnick ◽  
P Waibel ◽  
U Heiniger ◽  
H Spescha ◽  
M Käppeli ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Case Report ◽  
Teenage Girl ◽  
Pediatric Multiple Sclerosis ◽  
Natalizumab Treatment

VITAMIN D LEVELS, VITAMIN D INTAKE AND SUNLIGHT EXPOSURE IN PEDIATRIC MULTIPLE SCLEROSIS

2006 ◽  
Vol 37 (S 1) ◽  
Author(s):  
R Hung ◽  
R Vieth ◽  
R Goldman ◽  
E Sochett ◽  
B Banwell
Keyword(s):  
Multiple Sclerosis ◽  
Vitamin D ◽  
Sunlight Exposure ◽  
Pediatric Multiple Sclerosis ◽  
Vitamin D Intake ◽  
Vitamin D Levels

scholarly journals MicroRNA expression classification for pediatric multiple sclerosis identification

2021 ◽  
Author(s):  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Arianna Consiglio ◽  
Nicoletta Nuzziello ◽  
Gennaro Vessio
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Healthy Children ◽  
Multifactorial Diseases ◽  
Hyperactivity Disorder ◽  
Pediatric Multiple Sclerosis ◽  
Mirna Expression Profiles ◽  
Selection Algorithms ◽  
Expression Classification

Abstract MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

scholarly journals Interval Timing in Pediatric Multiple Sclerosis: Impaired in the Subsecond Range but Unimpaired in the One-Second Range

2020 ◽  
Vol 11 ◽  
Author(s):  
Stefan J. Troche ◽  
Tugba Kapanci ◽  
Thomas H. Rammsayer ◽  
Carl P. A. Kesseler ◽  
Martin Georg Häusler ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Interval Timing ◽  
Pediatric Multiple Sclerosis ◽  
The One

Disease progression in pediatric multiple sclerosis: disparities between physical and neurocognitive outcomes

2011 ◽  
Vol 11 (3) ◽  
pp. 433-440 ◽  
Author(s):  
Eluen Ann Yeh ◽  
Joy B Parrish ◽  
Bianca Weinstock-Guttman
Keyword(s):  
Multiple Sclerosis ◽  
Disease Progression ◽  
Pediatric Multiple Sclerosis ◽  
Neurocognitive Outcomes
Sign in / Sign up

Export Citation Format

Share Document