The time-course of inflammatory cytokine secretion in a rat model of postoperative pain does not coincide with the onset of mechanical hyperalgesia. DOI: 10.1139/Y07-054
Bibliographic record
Abstract
Post-print copy of: Loram LC, Themistocleous AC, Fick LG, Kamerman PR. The time course of inflammatory cytokine secretion in a rat model of postoperative pain does not coincide with the onset of mechanical hyperalgesia. Canadian Journal of Physiology and Pharmacology 85: 613-620, 2007. DOI: 10.1139/Y07-054, PMID: 17823623 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".