ErbB2 blockade with Herceptin (trastuzumab) enhances peripheral nerve regeneration after repair of acute or chronic peripheral nerve injury
Bibliographic record
Abstract
OBJECTIVE: Attenuation of the growth supportive environment within the distal nerve stump after delayed peripheral nerve repair profoundly limits nerve regeneration. Levels of the potent Schwann cell mitogen neuregulin and its receptor ErbB2 decline during this period, but the regenerative impact of this change is not completely understood. Herein, the ErbB2 receptor pathway is inhibited with the selective monoclonal antibody Herceptin (trastuzumab) to determine its significance in regulating acute and chronic regeneration in a rat hindlimb. METHODS: The common peroneal nerve of Sprague-Dawley rats was transected and repaired immediately or after 4 months of chronic denervation, followed by administration of Herceptin or saline solution. Regenerated motor and sensory neurons were counted using a retrograde tracer 1, 2, or 4, weeks after repair. Distal myelinated axon outgrowth after 4 weeks was quantified using histomorphometry. Immunofluorescent imaging was used to evaluate Schwann cell proliferation and epidermal growth factor receptor (EGFR) activation in the regenerating nerves. RESULTS: Herceptin administration increased the rate of motor and sensory neuron regeneration and the number of proliferating Schwann cells in the distal stump after the first week. Herceptin also increased the number of myelinated axons that regenerated 4 weeks after immediate and delayed repair. Reduced EGFR activation was observed using immunofluorescent imaging. INTERPRETATION: Inhibition of the ErbB2 receptor with Herceptin unexpectedly enhances nerve regeneration after acute and delayed nerve repair. This finding raises the possibility of using targeted molecular therapies to improve outcomes of peripheral nerve injuries. The mechanism may involve a novel inhibitory association between ErbB2 and EGFR. Ann Neurol 2016;80:112-126.
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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".