Tissue preconditioning may explain concentric lesions in Baló's type of multiple sclerosis
Bibliographic record
Abstract
Lesions of Baló's concentric sclerosis are characterized by alternating layers of myelinated and demyelinated tissue. The reason for concentric demyelination in this variant of multiple sclerosis is unclear. In the present study we investigated the immunopathology in autopsy tissue of 14 patients with acute multiple sclerosis or fulminant exacerbations of chronic multiple sclerosis with Baló-type lesions in the CNS, focusing on the patterns of tissue injury in actively demyelinating lesions. We found that all active concentric lesions followed a pattern of demyelination that bears resemblances to hypoxia-like tissue injury. This was associated with high expression of inducible nitric oxide synthase in macrophages and microglia. At the edge of active lesions and, less consistently, in the outermost layer of preserved myelin, proteins involved in tissue preconditioning, such as hypoxia-inducible factor 1alpha and heat-shock protein 70, were expressed mainly in oligodendrocytes and to a lesser degree also in astrocytes and macrophages. Due to their neuroprotective effects, the rim of periplaque tissue, where these proteins are expressed, may be resistant to further damage in an expanding lesion and may therefore remain as a layer of preserved myelinated tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".