Stimulation of Monocytes, Macrophages, and Microglia by Amphotericin B and Macrophage Colony-Stimulating Factor Promotes Remyelination
Bibliographic record
Abstract
Approaches to stimulate remyelination may lead to recovery from demyelinating injuries and protect axons. One such strategy is the activation of immune cells with clinically used medications, since a properly directed inflammatory response can have healing properties through mechanisms such as the provision of growth factors and the removal of cellular debris. We previously reported that the antifungal medication amphotericin B is an activator of circulating monocytes, and their tissue-infiltrated counterparts and macrophages, and of microglia within the CNS. Here, we describe that amphotericin B activates these cells through engaging MyD88/TRIF signaling. When mice were subjected to lysolecithin-induced demyelination of the spinal cord, systemic injections of nontoxic doses of amphotericin B and another activator, macrophage colony-stimulating factor (MCSF), further elevated the representation of microglia/macrophages at the site of injury. Treatment with amphotericin B, particularly in combination with MCSF, increased the number of oligodendrocyte precursor cells and promoted remyelination within lesions; these pro-regenerative effects were mitigated in mice treated with clodronate liposomes to reduce circulating monocytes and tissue-infiltrated macrophages. Our results have identified candidates among currently used medications as potential therapies for the repair of myelin.
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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 | 0.000 | 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 | 0.000 |
| 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".