Tibial post fracture pain is reduced in kinin receptors deficient mice and blunted by kinin receptor antagonists
Bibliographic record
Abstract
BACKGROUND: Tibial fracture is associated with inflammatory reaction leading to severe pain syndrome. Bradykinin receptor activation is involved in inflammatory reactions, but has never been investigated in fracture pain. METHODS: This study aims at defining the role of B1 and B2-kinin receptors (B1R and B2R) in a closed tibial fracture pain model by using knockout mice for B1R (B1KO) or B2R (B2KO) and wild-type (WT) mice treated with antagonists for B1R (SSR 240612 and R954) and B2R (HOE140) or vehicle. A cyclooxygenase (COX) inhibitor (ketoprofen) and an antagonist (SB366791) of Transient Receptor Potential Vaniloid1 (TRPV1) were also investigated since these pathways are associated with BK-induced pain in other models. The impact on mechanical and thermal hyperalgesia and locomotion was assessed by behavior tests. Gene expression of B1R and B2R and spinal cord expression of c-Fos were measured by RT-PCR and immunohistochemistry, respectively. RESULTS: B1KO and B2KO mice demonstrated a reduction in post-fracture pain sensitivity compared to WT mice that was associated with decreased c-Fos expression in the ipsilateral spinal dorsal horn in B2KO. B1R and B2R mRNA and protein levels were markedly enhanced at the fracture site. B1R and B2R antagonists and inhibition of COX and TRPV1 pathways reduced pain in WT. However, the analgesic effect of the COX-1/COX-2 inhibitor disappeared in B1KO and B2KO. In contrast, the analgesic effect of the TRPV1 antagonist persisted after gene deletion of either receptor. CONCLUSIONS: It is suggested that B1R and B2R activation contributes significantly to tibial fracture pain through COX. Hence, B1R and B2R antagonists appear potential therapeutic agents to manage post fracture pain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".