Proteoglycans as a double-edged sword in multiple sclerosis: Implications for future approaches to immunomodulatory therapy
Bibliographic record
Abstract
Proteoglycans are components of the extracellular matrix that have been identified as barriers to endogenous remyelination.Surfen (bis 2-methyl, 4-amino, 6-quinolyl amide) is a small molecule proteoglycan antagonist.We have previously reported that surfen reduces T cell proliferation in vivo and in vitro while also decreasing the production of chemotactic and pro-inflammatory factors produced by macrophages.Here we extend these studies to clinically relevant mouse models of chronic neuroinflammation (experimental autoimmune encephalomyelitis; EAE) and focal demyelination (lysolecithin).In the EAE model, surfen treated mice displayed a reduced disease severity that was associated with decreased percentages of CD4+CD45+ T cells and CD11b/F480 myeloid populations in the spinal cord.The chemokines RANTES, CCL2, and CCL3 were reduced in the spinal cords of surfen treated mice, resembling previous in vitro macrophage results and implicating a chemotactic mechanism that reduces cell infiltration.By contrast, when surfen was administered into a developing brain lesion using the lysolecithin model of demyelination it produced significantly larger lesions.The opposing effects of surfen observed in EAE and the lysolecithin model suggests that distinct proteoglycan families influence inflammation and remyelination differently depending on the stage of repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".