56. Foretinib Enhances Myelination of Neurons in Vitro
Bibliographic record
Abstract
BACKGROUND: Nerve injuries cause significant functional impairment. Despite advanced diagnostic methods and microsurgical procedures, recovery after peripheral nerve repair is often disappointing. Therefore, new therapeutics are needed to promote recovery after nerve injury. Schwann cells play a crucial role in nerve repair and regeneration, including remyelination post-injury. Our group previously discovered that Foretinib, a pan-kinase inhibitor, is neuroprotective (Feinberg et al., 2017). In the present study, we ask whether foretinib affects myelination in vitro. METHODS: To test the effect of foretinib on myelination in vitro, we extracted dorsal root ganglia from E14 rats and co-cultured them with Schwann cells. After three days, we induced myelination with ascorbic acid or ascorbic acid plus foretinib. After ten days in culture, we performed immunohistochemistry using the myelination marker myelin basic protein (MBP) to assess the extent of myelination. RESULTS: Co-cultures with neuronal cells and Schwann cells ten days after inducing myelination demonstrated a significant increase (more than six-fold) in myelination with foretinib in vitro CONCLUSION: Foretinib increases myelination in vitro, possibly by virtue of the mechanism of mitochondrial preservation as noted in our previous studies, or perhaps by a new, as yet unidentified molecular pathway. Given these intriguing findings, our group is interested in conducting studies of this agent in vivo as a potential agent for future clinical application.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".