Harnessing the Benefits of Neuroinflammation: Generation of Macrophages/Microglia with Prominent Remyelinating Properties
Bibliographic record
Abstract
Excessive inflammation within the CNS is injurious, but an immune response is also required for regeneration. Macrophages and microglia adopt different properties depending on their microenvironment, and exposure to IL4 and IL13 has been used to elicit repair. Unexpectedly, while LPS-exposed macrophages and microglia killed neural cells in culture, the addition of LPS to IL4/IL13-treated macrophages and microglia profoundly elevated IL10, repair metabolites, heparin binding epidermal growth factor trophic factor, antioxidants, and matrix-remodeling proteases. In C57BL/6 female mice, the generation of M(LPS/IL4/IL13) macrophages required TLR4 and MyD88 signaling, downstream activation of phosphatidylinositol-3 kinase/mTOR and MAP kinases, and convergence on phospho-CREB, STAT6, and NFE2. Following mouse spinal cord demyelination, local LPS/IL4/IL13 deposition markedly increased lesional phagocytic macrophages/microglia, lactate and heparin binding epidermal growth factor, matrix remodeling, oligodendrogenesis, and remyelination. Our data show that a prominent reparative state of macrophages/microglia is generated by the unexpected integration of pro- and anti-inflammatory activation cues. The results have translational potential, as the LPS/IL4/IL13 mixture could be locally applied to a focal CNS injury to enhance neural regeneration and recovery. SIGNIFICANCE STATEMENT The combination of LPS and regulatory IL4 and IL13 signaling in macrophages and microglia produces a previously unknown and particularly reparative phenotype devoid of pro-inflammatory neurotoxic features. The local administration of LPS/IL4/IL13 into spinal cord lesion elicits profound oligodendrogenesis and remyelination. The careful use of LPS and IL4/IL13 mixture could harness the known benefits of neuroinflammation to enable repair in neurologic insults.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".