Functional Recovery after Peripheral Nerve Injury is Dependent on the Pro-Inflammatory Cytokines IL-1β and TNF: Implications for Neuropathic Pain
Bibliographic record
Abstract
IL-1β and TNF are potential targets in the management of neuropathic pain after injury. However, the importance of the IL-1 and TNF systems for peripheral nerve regeneration and the mechanisms by which these cytokines mediate effects are to be fully elucidated. Here, we demonstrate that mRNA and protein levels of IL-1β and TNF are rapidly upregulated in the injured mouse sciatic nerve. Mice lacking both IL-1β and TNF, or both IL-1 type 1 receptor (IL-1R1) and TNF type 1 receptor (TNFR1), showed reduced nociceptive sensitivity (mechanical allodynia) compared with wild-type littermates after injury. Microinjecting recombinant IL-1β or TNF at the site of sciatic nerve injury in IL-1β- and TNF-knock-out mice restored mechanical pain thresholds back to levels observed in injured wild-type mice. Importantly, recovery of sciatic nerve function was impaired in IL-1β-, TNF-, and IL-1β/TNF-knock-out mice. Notably, the infiltration of neutrophils was almost completely prevented in the sciatic nerve distal stump of mice lacking both IL-1R1 and TNFR1. Systemic treatment of mice with an anti-Ly6G antibody to deplete neutrophils, cells that play an essential role in the genesis of neuropathic pain, did not affect recovery of neurological function and peripheral axon regeneration. Together, these results suggest that targeting specific IL-1β/TNF-dependent responses, such as neutrophil infiltration, is a better therapeutic strategy for treatment of neuropathic pain after peripheral nerve injury than complete blockage of cytokine production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".