Role of prostaglandin E2 in the synthesis of the pro‐inflammatory cytokine interleukin‐6 in primary sensory neurons: an <i>in vivo</i> and <i>in vitro</i> study
Bibliographic record
Abstract
Following various types of nerve injury, cyclooxygenase 2 and prostaglandin E2 (PGE2) are universally and chronically up-regulated in injured nerves and contribute to the genesis of neuropathic pain. Persistent high levels of PGE2 likely exert chronic effects on nociceptive dorsal root ganglion (DRG) neurons. In the present study, we tested the hypothesis that injured nerve-derived PGE2 contributes to the up-regulation of the pro-inflammatory cytokine interleukin-6 (IL-6) in DRG neurons following partial sciatic nerve ligation. In naive adult rats, IL-6 was expressed in only a few small size DRG neurons which all co-expressed EP4 receptors. Partial sciatic nerve ligation increased and shifted IL-6 expression from small to medium and large size damaged DRG neurons. Perineural injection of a selective cyclooxygenase 2 inhibitor or a selective EP4 receptor antagonist significantly suppressed the up-regulation of IL-6 in DRG, suggesting that injured nerve derived PGE2 contributes to the de novo synthesis of IL-6 in DRG neurons through EP4 receptors. In cultured sensory ganglion explants, a stabilized PGE2 analog increased IL-6 mRNA and protein levels through the activation of EP4, protein kinase A, protein kinase C, extracellular regulated protein kinase/MAPK, cAMP response element binding protein and NFκB signalling pathways. Taken together, these data indicate that facilitating the de novo synthesis of pain-related cytokines in injured medium and large size DRG neurons is a novel mechanism underlying the role of injured nerve derived PGE2 in the genesis of neuropathic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".