Comparative Pain Expression and Its Association to Intestinal Microbiota Through the MI-RAT© Osteoarthritis Model Induced in LOU/C/Jall and Sprague-Dawley Aged Rats
Bibliographic record
Abstract
To investigate the involvement of gut–brain axis in musculoskeletal chronic pain in the elderly, this preclinical study aimed to compare osteoarthritis (OA) pain expression, cognitive function and gut microbiota composition in two aging rat strains (11–15 months). A validated surgically induced OA model was used in Sprague-Dawley (SD; n = 12), as standard group, and in LOU/C/Jall rats (LOU; n = 8), a healthy aging model. The OA pain response was assessed longitudinally (60 days) through quantitative sensory testing (mechanical sensitization and endogenous inhibitory control functionality), spatial memory, and gut microbiota. At sacrifice, joint structural alterations and spinal neuropeptides concentrations were quantified. After OA induction, higher mechanical hypersensitivity in LOU than in SD was also associated with higher endogenous inhibitory control (p < 0.05). Expression of pro-/anti-nociceptive spinal neuropeptides, cognitive function and joint alterations were similar in both groups. Gut microbiota composition was different (p < 0.001) and different taxa were associated with each strain (e.g., Akkermansia spp. with LOU vs. Lactobacillus spp. with SD). This study suggests healthy aging to be associated with more efficient endogenous pain control and expression of specific intestinal bacteria. This research questions the implication of the intestinal microbiota in aging and chronic pain control.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".