Iron Heterogeneity in Early Active Multiple Sclerosis Lesions
Bibliographic record
Abstract
OBJECTIVE: Multiple sclerosis (MS) is a heterogeneous inflammatory demyelinating disease. Iron distribution is altered in MS patients' brains, suggesting iron liberation within active lesions amplifies demyelination and neurodegeneration. Whether the amount and distribution of iron are similar or different among different MS immunopatterns is currently unknown. METHODS: We used synchrotron X-ray fluorescence imaging, histology, and immunohistochemistry to compare the iron quantity and distribution between immunopattern II and III early active MS lesions. We analyzed archival autopsy and biopsy tissue from 21 MS patients. RESULTS: Immunopattern II early active lesions contain 64% more iron (95% confidence interval [CI] = 17-127%, p = 0.004) than immunopattern III lesions, and 30% more iron than the surrounding periplaque white matter (95% CI = 3-64%, p = 0.03). Iron in immunopattern III lesions is 28% lower than in the periplaque white matter (95% CI = -40 to -14%, p < 0.001). When normalizing the iron content of early active lesions to that of surrounding periplaque white matter, the ratio is significantly higher in immunopattern II (p < 0.001). Microfocused X-ray fluorescence imaging shows that iron in immunopattern II lesions localizes to macrophages, whereas macrophages in immunopattern III lesions contain little iron. INTERPRETATION: Iron distribution and content are heterogeneous in early active MS lesions. Iron accumulates in macrophages in immunopattern II, but not immunopattern III lesions. This heterogeneity in the two most common MS immunopatterns may be explained by different macrophage polarization, origin, or different demyelination mechanisms, and paves the way for developing new or using existing iron-sensitive magnetic resonance imaging techniques to differentiate among immunopatterns in the general nonbiopsied MS patient population. ANN NEUROL 2021;89:498-510.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".