Nuclear Factor-κB Activation in Axons and Schwann Cells in Experimental Sciatic Nerve Injury and Its Role in Modulating Axon Regeneration: Studies With Etanercept
Bibliographic record
Abstract
Early inflammatory events may inhibit functional recovery after injury in both the peripheral and central nervous systems. We investigated the role of the inflammatory tumor necrosis factor/nuclear factor-kappaB (NF-kappaB) axis on events subsequent to sciatic nerve crush injury in adult rats. Electrophoretic mobility shift assays revealed that within 6 hours after crush, NF-kappaB DNA-binding activity increased significantly in a 1-cm section around the crush site. By immunofluorescence staining, there was increased nuclear localization of the NF-kappaB subunits p50 but not p65 or c-Rel in Schwann cells but no obvious inflammatory cell infiltration. In rats injected subcutaneously with etanercept, a tumor necrosis factor receptor chimera that binds free cytokine, the injury-induced rise in NF-kappaB DNA-binding activity was inhibited, and nuclear localization of p50 in Schwann cells was lowered after the injury. Axonal growth 3 days after nerve crush assessed with immunofluorescence for GAP43 demonstrated that the regeneration distance of leading axons from the site of nerve crush was greater in etanercept-treated animals than in saline-treated controls. These data indicate that tumor necrosis factor mediates rapid activation of injury-induced NF-kappaB DNA binding in Schwann cells and that these events are associated with inhibition of postinjury axonal sprouting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".