The extracellular matrix as modifier of neuroinflammation and remyelination in multiple sclerosis
Bibliographic record
Abstract
Remyelination failure contributes to axonal loss and progression of disability in multiple sclerosis. The failed repair process could be due to ongoing toxic neuroinflammation and to an inhibitory lesion microenvironment that prevents recruitment and/or differentiation of oligodendrocyte progenitor cells into myelin-forming oligodendrocytes. The extracellular matrix molecules deposited into lesions provide both an altered microenvironment that inhibits oligodendrocyte progenitor cells, and a fuel that exacerbates inflammatory responses within lesions. In this review, we discuss the extracellular matrix and where its molecules are normally distributed in an uninjured adult brain, specifically at the basement membranes of cerebral vessels, in perineuronal nets that surround the soma of certain populations of neurons, and in interstitial matrix between neural cells. We then highlight the deposition of different extracellular matrix members in multiple sclerosis lesions, including chondroitin sulphate proteoglycans, collagens, laminins, fibronectin, fibrinogen, thrombospondin and others. We consider reasons behind changes in extracellular matrix components in multiple sclerosis lesions, mainly due to deposition by cells such as reactive astrocytes and microglia/macrophages. We next discuss the consequences of an altered extracellular matrix in multiple sclerosis lesions. Besides impairing oligodendrocyte recruitment, many of the extracellular matrix components elevated in multiple sclerosis lesions are pro-inflammatory and they enhance inflammatory processes through several mechanisms. However, molecules such as thrombospondin-1 may counter inflammatory processes, and laminins appear to favour repair. Overall, we emphasize the crosstalk between the extracellular matrix, immune responses and remyelination in modulating lesions for recovery or worsening. Finally, we review potential therapeutic approaches to target extracellular matrix components to reduce detrimental neuroinflammation and to promote recruitment and maturation of oligodendrocyte lineage cells to enhance remyelination.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".