Testing the Benefits of Rat Tickling on a Rodent Model of Persistent Inflammatory Pain
Bibliographic record
Abstract
Chronic pain is a debilitating health problem that affects billions of people worldwide, requiring new, safe, and effective treatments. Current approaches to addressing this issue include nocifensive (pain-related) behaviour testing in animals and investigations of specific molecular determinants to observe how they influence pain behaviours in rodent models. Pain is a biopsychosocial phenomenon, even in rodents, and emotional/stressful states and basal experiences can modulate pain outputs, which can be a major confound in nocifensive behaviour testing. This project explored the effects of rat tickling (playful handling by the experimenter that mimics natural rough-and-tumble play) on the lab’s well-established persistent inflammatory pain model using von Frey filament (VFF) testing and recordings of 22- (alarm calls) and 50-kHz (indicative of positive affect) ultrasonic vocalizations (USVs). Adult male Sprague Dawley rats underwent tickle training or gentle handling followed by evoked somatosensory VFF testing. Baseline testing was followed by a subcutaneous left hind paw footpad injection of 0.3 mL of the pro-inflammatory substance Complete Freund’s Adjuvant (CFA) or vehicle. The animals then underwent VFF testing 24-, 48-, and 72-hours post-injection to verify that pain hypersensitivity was induced. Both tickled and gentle handled rats produced similar VFF testing results, but tickled rats produced more 50-kHz USVs than gentle handled rats. These results suggest both conditions are effective acclimation strategies, but that tickling seems to increase the production of 50-kHz USVs, suggesting improved welfare of the animals. We conclude that animal welfare is an important factor to consider when conducting VFF nocifensive behaviour testing.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".