Pathogenic implications of distinct patterns of iron and zinc in chronic MS lesions
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system (CNS) in which oligodendrocytes, the CNS cells that stain most robustly for iron and myelin are the targets of injury. Metals are essential for normal CNS functioning, and metal imbalances have been linked to demyelination and neurodegeneration. Using a multidisciplinary approach involving synchrotron techniques, iron histochemistry and immunohistochemistry, we compared the distribution and quantification of iron and zinc in MS lesions to the surrounding normal appearing and periplaque white matter, and assessed the involvement of these metals in MS lesion pathogenesis. We found that the distribution of iron and zinc is heterogeneous in MS plaques, and with few remarkable exceptions they do not accumulate in chronic MS lesions. We show that brain iron tends to decrease with increasing age and disease duration of MS patients; reactive astrocytes organized in large astrogliotic areas in a subset of smoldering and inactive plaques accumulate iron and safely store it in ferritin; a subset of smoldering lesions do not contain a rim of iron-loaded macrophages/microglia; and the iron content of shadow plaques varies with the stage of remyelination. Zinc in MS lesions was generally decreased, paralleling myelin loss. Iron accumulates concentrically in a subset of chronic inactive lesions suggesting that not all iron rims around MS lesions equate with smoldering plaques. Upon degeneration of iron-loaded microglia/macrophages, astrocytes may form an additional protective barrier that may prevent iron-induced oxidative damage.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".