Identification of markers of relapse in EAE using high-resolution quantitative mass spectrometry
Bibliographic record
Abstract
Abstract Approximately 85 percent of individuals newly diagnosed with multiple sclerosis have the relapsing-remitting form of the disease characterized by attacks of neurologic symptoms that are unpredictable in occurrence and duration. Currently there are no clinically available biomarkers predictive of relapse. To address this need, we investigated CNS proteome changes over the disease course of relapsing-remitting experimental autoimmune encephalomyelitis (EAE) in SJL mice as a preclinical model of the disease. Using a high-throughput quantitative preparation technique and high-resolution Bruker timsTOF mass spectrometry, we were able to identify thousands of unique proteins at each disease timepoint. Principal component analysis of protein expression confirmed that the remission and relapse phases of disease cluster independently, indicating distinguishable variation in protein expression profiles between the two disease states. Importantly, statistical testing identified proteins with differential expression in the CNS at different stages of disease, several of which are CNS specific. We are seeking to detect corollary changes in these CNS-specific proteins in the serum, pointing to a minimally invasive means of monitoring disease progress and measuring drug efficacy. Our study will validate homologous human biomarkers to guide treatment in individual patients and allow for proactive therapeutic intervention. Furthermore, our results may provide insights into mechanisms that contribute to disease pathology and offer novel therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".