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Related Experiment Video

Updated: Jul 26, 2025

Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice

Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice

Published on: October 13, 2023

Disease phenotype prediction in multiple sclerosis.

Stephanie Herman^1,2, Staffan Arvidsson McShane², Christina Zjukovskaja³

¹Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden.

|June 19, 2023

Summary

This summary is machine-generated.

Researchers identified cerebrospinal fluid biomarkers for early progressive multiple sclerosis (PMS) diagnosis. This method accurately predicted PMS in some patients and showed treatment effectiveness in a clinical trial.

Keywords:

Health technology Neuroscience

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Positron Emission Tomography Imaging for In Vivo Measuring of Myelin Content in the Lysolecithin Rat Model of Multiple Sclerosis

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Published on: February 28, 2021

Area of Science:

Neuroscience
Biomarker Discovery

Background:

Progressive multiple sclerosis (PMS) diagnosis is currently retrospective.
There is a need for early diagnostic tools and biomarkers for PMS.

Purpose of the Study:

To develop a biomarker-based methodology for early diagnosis of progressive multiple sclerosis (PMS).
To assess the utility of this methodology in predicting PMS onset and monitoring treatment response.

Main Methods:

Analysis of cerebrospinal fluid (CSF) metabolites using machine learning.
Application of conformal prediction to enhance classification confidence.
Evaluation of the methodology in an independent cohort and a clinical trial for intrathecal rituximab treatment.

Main Results:

A panel of 15 CSF metabolites differentiated PMS from its preceding phenotype with high accuracy (AUC=0.93).
Conformal prediction enabled confident PMS identification, predicting 3 of 8 patients who developed PMS within three years.
The methodology indicated that 68% of patients on intrathecal rituximab treatment showed decreased similarity to the PMS phenotype after one year.

Conclusions:

Inclusion of confidence predictors provides valuable information beyond traditional machine learning for disease diagnosis and monitoring.
This biomarker approach shows promise for early PMS detection and evaluating treatment efficacy.