RAGE modulates peripheral nerve regeneration via recruitment of both inflammatory and axonal outgrowth pathways
Bibliographic record
Abstract
Axotomy of peripheral nerve stimulates events in multiple cell types that initiate a limited inflammatory response to axonal degeneration and simultaneous outgrowth of neurites into the distal segments after injury. We found that pharmacological blockade of RAGE impaired peripheral nerve regeneration in mice subjected to RAGE blockade and acute crush of the sciatic nerve. As our studies revealed that RAGE was expressed in axons and in infiltrating mononuclear phagocytes upon injury, we tested the role of RAGE in these distinct cell types on nerve regeneration. Transgenic mice expressing signal transduction‐deficient RAGE in mononuclear phagocytes or peripheral neurons were generated and subjected to unilateral crush injury to the sciatic nerve. Transgenic mice displayed decreased functional and morphological recovery compared with littermate controls, as assessed by motor and sensory conduction velocities;and myelinated fiber density. In double transgenic mice expressing signal transduction deficient RAGE in both mononuclear phagocytes and peripheral neurons, regeneration was even further impaired, suggesting the critical interplay between RAGE‐modulated inflammation and neurite outgrowth in nerve repair. These findings suggest that RAGE signaling in inflammatory cells and peripheral neurons plays an important role in plasticity of the peripheral nervous system.—Rong, L. L., Yan, S.‐F., Wendt, T., Hans, D., Pachydaki, S., Bucciarelli, L. G., Adebayo, A., Qu, W., Lu, Y., Kostov, K., Lalla, E., Yan, S. D., Gooch, C., Szabolcs, M., Trojaborg, W., Hays, A. P.,Schmidt, A. M. RAGE modulates peripheral nerve regeneration via recruitment of both inflammatory and axonal outgrowth pathways. FASEB J. 18, 1818–1825 (2004)
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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) | 0.001 | 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".