Diffusely Abnormal White Matter in Multiple Sclerosis: Further Histologic Studies Provide Evidence for a Primary Lipid Abnormality With Neurodegeneration
Bibliographic record
Abstract
Although multiple sclerosis (MS) lesions have been studied extensively using histology and magnetic resonance imaging (MRI), little is known about diffusely abnormal white matter (DAWM). Diffusely abnormal white matter, regions with reduced mild MRI hyperintensity and ill-defined boundaries, show reduced myelin water fraction, and decreased Luxol fast blue staining of myelin phospholipids, with relative preservation of myelin basic protein and 2',3'-cyclic-nucleotide 3'-phosphohydrolase. Because DAWM may be important in MS disability and progression, further histologic characterization is warranted. The MRI data were collected on 14 formalin-fixed MS brain samples that were then stained for myelin phospholipids, myelin proteins, astrocytes and axons. Diffusely abnormal white matter showed reduced myelin water fraction (-30%, p < 0.05 for 13 samples). Myelin phospholipids showed the most dramatic and consistent histologic reductions in staining optical density (-29% Luxol fast blue and -24% Weil's, p < 0.05 for 13 and 14 samples,respectively) with lesser myelin protein involvement (-11% myelin-associated glycoprotein, -10% myelin basic protein, -8% myelin-oligodendrocyte glycoprotein, -7% proteolipid protein, -5% 2',3'-cyclic-nucleotide 3'-phosphohydrolase, p < 0.05 for 3, 3, 1, 2, and 3 samples, respectively). Axonal involvement was intermediate. Diffusely abnormal white matter lipid and protein reductions occurred independently. These findings suggest a primary lipid abnormality in DAWM that exceeds protein loss and is accompanied by axonal degeneration. These phenomena may be important in MS pathogenesis and disease progression, which is prominent in individuals with DAWM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".