Chronic prostaglandin E2 treatment induces the synthesis of the pain‐related peptide substance P and calcitonin gene‐related peptide in cultured sensory ganglion explants
Bibliographic record
Abstract
Prostaglandin E2 (PGE2) is a well known pain and pro-inflammatory mediator abundantly produced in inflamed tissue. It causes pain by directly exciting nociceptive primary sensory neurons (nociceptors) and indirectly stimulating the release of pain-related peptide substance P (SP) and calcitonin gene-related peptide (CGRP). In an ex vivo culture of sensory ganglion explants, we tested the hypothesis that PGE2 could induce the synthesis of SP and CGRP in nociceptors. A stabilized PGE2 analog, 16,16-dimethyl PGE2, in a concentration- and time-dependent manner, significantly increased mRNA and peptide levels of SP and CGRP. The agonists of EP1 and EP4 receptors also significantly increased SP and CGRP levels. Moreover, 16,16-dimethyl PGE2-induced SP and CGRP were blocked by EP1 and EP4 antagonists as well as the inhibitors of both protein kinase A and protein kinase C. Nerve growth factor was partially involved in PGE2-induced SP and CGRP synthesis. Taken together, these results indicate that PGE2 contributes to the synthesis of SP and CGRP in nociceptors, an event mediated by EP1 and EP4 receptors, nerve growth factor and protein kinase A and protein kinase C signalling pathways. We thus conclude that facilitating the synthesis of pain-related peptides in nociceptors is a novel mechanism underlying the role of PGE2 in nociception and chronic pain states.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".