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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".