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.
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 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) | 0.001 | 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".