The time course of inflammatory cytokine secretion in a rat model of postoperative pain does not coincide with the onset of mechanical hyperalgesia
Bibliographic record
Abstract
We characterized the time course of inflammatory cytokine release at the site of injury and in plasma after surgery on the rat tail. Anesthetized Sprague-Dawley rats had a 20 mm long incision made through the skin and fascia of their tails. Control rats were anesthetized, but no incision was made. Blood and tissue samples were taken 2 h and 1, 2, 4, and 8 days after surgery and analysed by ELISA for interleukin-1beta (IL-1beta), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), and cytokine-induced neutrophil chemoattractant-1 (CINC-1). In another group of rats, daily behavioral measurements were made of the rats' responses to a blunt noxious mechanical stimulus (4 Newtons) applied to their tails. Primary hyperalgesia developed within 2 h of surgery and lasted for 6 days. The tissue concentrations of IL-1beta, IL-6, and CINC-1 increased within 24 h of surgery, and TNF-alpha concentration increased within 48 h of surgery. Thereafter, cytokine concentrations remained elevated for 4 (IL-1beta and IL-6) to 8 days (CINC-1, TNF-alpha) after surgery. Control animals did not develop hyperalgesia and no changes in cytokines concentrations were detected. Thus, in our model of postoperative pain, secretion of inflammatory cytokines IL-1beta, IL-6, TNF-alpha, and CINC-1 was not essential for the initiation of postoperative hyperalgesia.
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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 | 0.000 |
| 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".