Increased calcitonin gene‐related peptide in neuroma and invading macrophages is involved in the up‐regulation of interleukin‐6 and thermal hyperalgesia in a rat model of mononeuropathy
Bibliographic record
Abstract
The pain related peptide, calcitonin gene-related peptide (CGRP), plays an important role in inflammatory pain and immune responses. However, its role in neuropathic pain is not established. Following nerve injury, CGRP and pro-inflammatory interleukin-6 (IL-6) are increased in injured nerves. The aim of this study was to determine if CGRP in injured nerves is involved in the up-regulation of IL-6 and in the maintenance of neuropathic pain. Perineural injection of a neutralizing IL-6 antiserum or CGRP receptor antagonists (CGRP8-37 and BIBN4096BS) effectively attenuated thermal hyperalgesia 4 weeks after partial sciatic nerve ligation. Perineural CGRP antagonists also dramatically reduced IL-6 level in injured nerves. CGRP release from injured sites was dramatically increased and CGRP immunoreactivity was localized in both neuroma and invading macrophages. CGRP receptor markers (CRLR and RAMP1) were expressed in invading macrophages. Both CGRP antagonists significantly reduced IL-6 release from injured nerve explants. In cell cultures derived from injured nerves, CGRP concentration-dependently increased IL-6 release, an effect also blocked by CGRP antagonists. Taken together, these data show that increased levels of CGRP in injured neuroma and invading macrophages are involved in the up-regulation of IL-6 in macrophages as well as in the maintenance of neuropathic pain.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".