Sigma‐1 receptors control neuropathic pain and macrophage infiltration into the dorsal root ganglion after peripheral nerve injury
Bibliographic record
Abstract
Neuron-immune interaction in the dorsal root ganglia (DRG) plays a pivotal role in the neuropathic pain development after nerve injury. Sigma-1 receptor (Sig-1R) is expressed by DRG neurons but its role in neuropathic pain is not fully understood. We investigated the effect of peripheral Sig-1R on neuroinflammation in the DRG after spared (sciatic) nerve injury (SNI) in mice. Nerve injury induced a decrease in NeuN staining along with the nuclear eccentricity and ATF3 expression in the injured DRG. Sig-1R was present in all DRG neurons examined, and after SNI this receptor translocated to the periphery of the soma and the vicinity of the nucleus, especially in injured ATF3 + neurons. In WT mice, injured DRG produced the chemokine CCL2, and this was followed by massive infiltration of macrophages/monocytes, which clustered mainly around sensory neurons with translocated Sig-1R, accompanied by robust IL-6 increase and mechanical allodynia. In contrast, Sig-1R knockout (Sig-1R-KO) mice showed reduced levels of CCL2, decreased macrophage/monocyte infiltration into DRG, and less IL-6 and neuropathic mechanical allodynia after SNI. Our findings point to an important role of peripheral Sig-1R in sensory neuron-macrophage/monocyte communication in the DRG after peripheral nerve injury; thus, these receptors may contribute to the neuropathic pain phenotype.
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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) | 0.001 | 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".