Metabolites predict lesion formation and severity in relapsing-remitting multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis is characterized by white matter lesions, which are visualized with conventional T2-weighted magnetic resonance imaging (MRI). Little is known about local metabolic processes preceding the appearance and during the pathological development of new lesions. OBJECTIVE: To identify metabolite changes preceding white matter (WM) lesions and pathological severity of lesions over time. METHODS: A total of 59 relapsing-remitting multiple sclerosis (MS) patients were scanned four times, with 6-month intervals. Imaging included short-TE magnetic resonance spectroscopic imaging (MRSI) and diffusion tensor imaging (DTI). RESULTS: A total of 16 new lesions appeared within the MRSI slab in 12 patients. Glutamate increased (+1.0 mM (+19%), p = 0.039) 12 and 6 months before new lesions appeared. In these areas, the increase in creatine and choline 6 months before until lesion appearance was negatively correlated with radial diffusivity (ρ = -0.73, p = 0.002 and ρ = -0.72, p = 0.002). Increase in creatine also correlated with the increase of axial diffusivity in the same period (ρ = -0.53, p = 0.034). When splitting the lesions into "mild" and "severe" based on radial diffusivity, only mild lesions showed an increase in creatine and choline during lesion formation ( p = 0.039 and p = 0.008, respectively). CONCLUSION: Increased glutamate heralded the appearance of new T2-visible WM lesions. In pathologically "mild" lesions, an increase in creatine and choline was found during lesion formation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".