The role of TLR2 in nerve injury‐induced neuropathic pain is essentially mediated through macrophages in peripheral inflammatory response
Bibliographic record
Abstract
Activation of macrophages/microglia via toll-like receptors (TLRs) plays an important role in inflammation and host defense against pathogens. Pathogen-associated molecular patterns bind TLRs, thereby triggering NF-κB signaling and production of proinflammatory cytokines. Recent data suggest that nonpathogenic molecules resulting from trauma can also trigger inflammation via TLRs. We sought to determine whether peripheral nerve injury could induce the expression of TLR2 on the site of injury-damaged nerves and/or in the central nervous system and to investigate whether TLR2 is necessary for the development of nerve injury-induced neuropathic pain. We observed a significant increase in TLR2, IκB-α, and TNF-α mRNAs in damaged nerves. Increased inflammation-related molecules were found essentially on ED1(+) macrophages. Expression of both IκB-α and TNF-α in peripheral injured nerves was reduced in TLR2 deficient mice where the recruitment of ED1(+) cells is significantly impaired. Although after peripheral nerve injury, spinal microglia became highly activated showing an increase in Iba-1 immunoreactivity and an enlargement of their cell bodies, neither TLR2 mRNA nor IκB-α mRNA was detected in activated microglia. Nerve injury-evoked spinal microglial activation was not significantly altered in TLR2 KO mice. Paw withdrawal threshold and latency in response to mechanical and heat stimuli, respectively, decreased shortly after nerve lesion in wild type mice. In TLR2 KO mice, nerve injury-induced thermal hyperalgesia was completely abolished contrary to that seen in wild-type mice, whereas mechanical allodynia was partially reduced. We suggest that TLR2 is necessary for the development of neuropathic pain and its contribution is more important in thermal hypersensitivity than that of mechanical allodynia.
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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 | 0.003 | 0.001 |
| 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".