Targeting extracellular matrix components to attenuate microglia neuroinflammation: A study of fibulin-2 and CSPGs in a model of multiple sclerosis
Bibliographic record
Abstract
The extracellular matrix (ECM) plays an important role in the central nervous system (CNS), shaping tissue structure and functions as well as contributing to the pathology of chronic diseases such as multiple sclerosis (MS). ECM components, including fibulin-2 (FBLN2) and chondroitin sulfate proteoglycans (CSPGs), may impact neuroinflammation and remyelination. We investigated the capacity of FBLN2 to modulate immune responses and evaluated its interaction with CSPGs in experimental autoimmune encephalomyelitis (EAE), a common model for MS. We show that FBLN2 deficiency in EAE mice reduced microglial pro-inflammatory activity, while effects on monocyte-derived macrophages and border-associated macrophages were less pronounced. Targeting FBLN2 and CSPGs individually, using FBLN2 −/− mice and the CSPG-synthesis inhibitor difluorosamine (DIF), respectively, enhanced recovery of disability and reduced neuroinflammation in EAE mice. However, their combined targeting did not result in additive therapeutic effects beyond either alone. This study underscores the complex regulatory roles of ECM components on neuroinflammation and provides insights into potential therapeutic strategies for neuroinflammatory diseases.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".