Nociceptive tolerance is improved by bradykinin receptor B1 antagonism and joint morphology is protected by both endothelin type A and bradykinin receptor B1 antagonism in a surgical model of osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: Endothelin-1, a vasoconstrictor peptide, influences cartilage metabolism mainly via endothelin receptor type A (ETA). Along with the inflammatory nonapeptide vasodilator bradykinin (BK), which acts via bradykinin receptor B1 (BKB1) in chronic inflammatory conditions, these vasoactive factors potentiate joint pain and inflammation. We describe a preclinical study of the efficacy of treatment of surgically induced osteoarthritis with ETA and/or BKB1 specific peptide antagonists. We hypothesize that antagonism of both receptors will diminish osteoarthritis progress and articular nociception in a synergistic manner. METHODS: Osteoarthritis was surgically induced in male rats by transection of the right anterior cruciate ligament. Animals were subsequently treated with weekly intra-articular injections of specific peptide antagonists of ETA and/or BKB1. Hind limb nociception was measured by static weight bearing biweekly for two months post-operatively. Post-mortem, right knee joints were analyzed radiologically by X-ray and magnetic resonance, and histologically by the OARSI histopathology assessment system. RESULTS: Single local BKB1 antagonist treatment diminished overall hind limb nociception, and accelerated post-operative recovery after disease induction. Both ETA and/or BKB1 antagonist treatments protected joint radiomorphology and histomorphology. Dual ETA/BKB1 antagonism was slightly more protective, as measured by radiology and histology. CONCLUSIONS: BKB1 antagonism improves nociceptive tolerance, and both ETA and/or BKB1 antagonism prevents joint cartilage degradation in a surgical model of osteoarthritis. Therefore, they represent a novel therapeutic strategy: specific receptor antagonism may prove beneficial in disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".