The Use of Proteomic Analyses to Identify Potential Cerebrospinal Fluid Biomarkers in Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MScl) remains a highly unpredictable disease. Many hope that fluid biomarkers may contribute to better stratification of disease, aiding the personalization of treatment decisions, ultimately improving patient outcomes. The objective of this study was to evaluate the predictive value of cerebrospinal fluid (CSF) brain-specific proteins from early in the disease course of MScl on long term clinical outcomes. In this prospective longitudinal study, 34 MScl patients had their CSF collected and stored within 5 years of disease onset and were then followed clinically for at least 15 years. CSF concentrations of 64 brain-specific proteins were analyzed in the 34 patient CSF, as well as 19 age and sex-matched controls, using a targeted liquid-chromatography tandem mass spectrometry approach. We identified six CSF brain-specific proteins that significantly differentiated MScl from controls (p<0.05) and several proteins that could predict disease course over the next decade. Calcium/Calmodulin Dependent Protein Kinase II Alpha (CAMK2A) emerged as a biomarker candidate that could discriminate between MScl and controls and could predict long-term disease progression. Targeted approaches to identify and quantify biomarkers associated with MScl in the CSF may inform on long term MScl outcomes. CAMK2A may be one of several candidates, warranting further exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".