The epidermal growth factor receptor inhibitor gefitinib enhances in vitro and in vivo sensory axon regeneration and functional recovery following transection in a mouse median nerve injury model
Bibliographic record
Abstract
INTRODUCTION: The epidermal growth factor receptor (EGFR; ErbB1), a membrane bound receptor tyrosine kinase, is hypothesized to have an inhibitory influence on peripheral nerve regeneration. This study examines the impact of EGFR inhibition on nerve regeneration using the commercially available small molecule inhibitor gefitinib. METHOD: In vitro assays included neurite outgrowth of cultured dorsal root ganglion (DRG) neurons from adult C57Bl/6 wildtype mice on immobilized chondroitin sulfate proteoglycans (CSPG). Following forelimb median nerve injury, EGFR expression, number of regenerated neurons (using retrograde labeling) and myelination of motor and sensory neurons were compared between mice that received either gefitinib or vehicle. Functional recovery was assessed using grip strength. RESULTS: EGFR expression on DRG and spinal motor neurons was confirmed. Gefitinib significantly increased neurite outgrowth in medium sized (30-50 μm) DRG neurons, resulting in longer neurites (183 ± 36 μm) compared with CSPG alone (49 ± 9 μm). After median nerve injury, significantly greater numbers of sensory neurons (638 ± 112 vs. 301 ± 81), but not motor neurons (31 ± 12 vs. 42 ± 13) regenerated in animals treated with gefitinib compared with controls. Regenerated axons in gefitinib treated animals displayed significantly greater diameter and increased g-ratio compared with controls. Grip strength recovered more quickly in animals receiving gefitinib compared with controls (27.6 vs. 19.1 g 18 days post-injury). DISCUSSION: This study provides data supporting the role of EGFR as a negative regulator of sensory but not motor neuron regeneration. Further, it demonstrates versatile potential uses of existing pharmaceuticals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".