Dorsal horn disinhibition and movement-induced behaviour in a rat model of inflammatory arthritis
Bibliographic record
Abstract
OBJECTIVES: Alterations beyond joint inflammation such as changes in dorsal horn (DH) excitability contribute to pain in inflammatory arthritis (IA). More complete understanding of specific underlying mechanisms will be important to define novel targets for the treatment of IA pain. Pre-clinical models are useful, but relevant pain assays are vital for successful clinical translation. For this purpose, a method is presented to assess movement-induced pain-related behaviour changes that was subsequently used to investigate DH disinhibition in IA. METHODS: IA was induced by intra-articular injection of complete Freund's adjuvant (CFA) in male rats, and weight distribution was assessed before and after walking on a treadmill. To confirm increased activity in nociception-related pathways, fos expression was assessed in the superficial DH, including in nociceptive neurons, identified by neurokinin 1 (NK1) immunoreactivity, and interneurons. Inhibitory terminal density onto NK1+ neurons was assessed and lastly, a cohort of animals was treated for 3 days with gabapentin. RESULTS: At 4 weeks post-CFA, walking reduced weight distribution to the affected joint and increased DH fos expression, including in NK1+ neurons. Neuronal activity in inhibitory cells and inhibitory terminal density on NK1+ neurons were decreased in CFA-treated animals compared with controls. Treatment with gabapentin led to recovered behaviour and DH neuronal activity pattern in CFA-treated animals. CONCLUSION: We describe an assay to assess movement-induced pain-related behaviour changes in a rodent IA model. Furthermore, our results suggest that disinhibition may contribute to pain related to movement in IA.
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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) | 0.002 | 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 | 0.001 |
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".