Toll-Like Receptor Signaling Is Critical for Wallerian Degeneration and Functional Recovery after Peripheral Nerve Injury
Bibliographic record
Abstract
Toll-like receptors (TLRs) bind specific components conserved among microorganisms as well as endogenous ligands produced by necrotic cells, injured axons, and the extracellular matrix. Here, we investigated whether TLRs are involved in regulating the immune response, Wallerian degeneration (WD), and nerve regeneration after sciatic nerve lesion. Early expression of interleukin-1beta and monocyte chemoattractant protein-1 was compromised in the sciatic nerve distal stump of mice deficient in TLR signaling. In addition, significantly fewer macrophages were recruited and/or activated in the sciatic nerve distal stump of TLR2-, TLR4-, and MyD88-deficient mice compared with wild-type littermates, whereas WD, axonal regeneration, and recovery of locomotor function were impaired. In contrast, animals that received a single microinjection of TLR2 and TLR4 ligands at the site of sciatic nerve lesion had faster clearance of the degenerating myelin and recovered earlier than saline-injected control rats. Finally, rats that had altered innate immune response through dexamethasone treatment exhibited three times more myelin debris in their sciatic nerve distal stump and a significant delay in recovery of locomotor function. Our results provide strong evidence that TLR signaling plays a critical role in orchestrating the innate immune response leading to efficient and rapid clearance of inhibitory myelin debris and nerve regeneration.
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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 | 0.001 |
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".