Pharmacological validation of early and late phase of rat mono‐iodoacetate model using the <scp>T</scp>ekscan system
Bibliographic record
Abstract
BACKGROUND: Previous pharmacological validations of the rat mono-iodoacetate (MIA)-induced chronic joint pain model were mostly performed by measuring weight-bearing (WB) deficit with an incapacitance tester. However, conventional incapacitance testers have several drawbacks including restrain stress on animal and sole use of hind limbs WB. OBJECTIVES: The aim of the present study was to compare pharmacological sensitivity of the early (up to 1 week after MIA) versus late (between 2 and 4 weeks after MIA) phase of the rat MIA model using a highly sensitive tactile pressure measurement system (Tekscan(®)), which can measure weight borne by all four limbs and the tail in a non-restrained animal. METHODS: The Tekscan(®) WB measurement system was used in MIA rats to examine the acute and chronic dosing effects of drugs that targeted different mechanisms. Electrophysiological recordings from joint afferents and biochemical analysis of synovial fluid were also performed. RESULTS: Dexamethasone, duloxetine and morphine significantly alleviated WB deficits in the Tekscan(®) system during both early and late phase of the MIA model while celecoxib and naproxen alleviated WB deficit only during the early phase. Similarly, naproxen was able to inhibit spontaneous neuronal activity from MIA joint afferents only during the early phase. Finally, concentrations of prostaglandin E(2) in synovial fluid were elevated only during the early phase of the rat MIA model. CONCLUSIONS: Our pharmacological validation studies using the Tekscan(®) system along with electrophysiological and biochemical results suggest different mechanisms for early and late phase of MIA-induced chronic joint pain in rat.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".