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56. Foretinib Enhances Myelination of Neurons in Vitro

2023· article· en· W4377104417 on OpenAlexaff
Kaveh Mirmoeini, Kiana Tajdaran, Konstantin Feinberg, Gregory H. Borschel

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRemyelinationSchwann cellIn vitroRegeneration (biology)Ascorbic acidNeuroscienceMyelinNerve injuryPeripheral nerve injuryMedicineBiologyPathologyCell biologyCentral nervous systemBiochemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.307
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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