Increased glutamate and deep brain atrophy can predict the severity of multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: In multiple sclerosis (MS), deep grey matter (DGM) atrophy has been recognised as a crucial component of the disease that presents early and it has been associated with disability. Although the precise mechanism underlying grey matter atrophy is unknown, several hypotheses have been postulated. Our previous research pointed to correlations of hypothalamic metabolic alterations with clinical outcomes of MS, therefore we decided to further test the relationship of these alterations with DGM atrophy. METHODS: H-MRS) of the hypothalamus to test its metabolites in 26 patients with RRMS and 22 healthy age-matched controls. DGM atrophy was evaluated by simple planimetry of third ventricular width on the hypothalamic level (3VW) in T1 weighted MRI pictures. Metabolite ratios of N-acetyl aspartate (NAA), choline (Cho), glutamate and glutamine (Glx), myo-inositol (mIns) and creatine (Cr) were correlated with Multiple Sclerosis Severity Scale (MSSS) and 3VW. RESULTS: Metabolite concentrations were compared between patients and controls using multiple regression models allowing for age, 3VW and metabolites. It revealed that the only relevant predictors of MSSS were 3VW and Glx/NAA. At a significance level of P<0.05, a unit increase of 3VW was associated with a 0.35 increase of MSSS, for a typical value of Glx/NAA; P value 0.0039. A unit increase of Glx/NAA was associated with a 0.93 increase of MSSS, for a typical value of atrophy; P value 0.090. There were significant linear correlations between Glx/Cr and MSSS, Glx/NAA and MSSS, and between mIns/NAA and 3VW. CONCLUSIONS: H-MRS parameters seem to be superior to other metabolites in determining disease burden, independently of otherwise powerful 3VW planimetry. Significantly increased mIns/NAA in MS patients compared to controls point to gliosis, which parallels the atrophy of hypothalamic DGM.
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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 | 0.000 | 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".