Role of Tachykinin 1 and 4 Gene-Derived Neuropeptides and the Neurokinin 1 Receptor in Adjuvant-Induced Chronic Arthritis of the Mouse
Bibliographic record
Abstract
OBJECTIVE: Substance P, encoded by the Tac1 gene, is involved in neurogenic inflammation and hyperalgesia via neurokinin 1 (NK1) receptor activation. Its non-neuronal counterpart, hemokinin-1, which is derived from the Tac4 gene, is also a potent NK1 agonist. Although hemokinin-1 has been described as a tachykinin of distinct origin and function compared to SP, its role in inflammatory and pain processes has not yet been elucidated in such detail. In this study, we analysed the involvement of tachykinins derived from the Tac1 and Tac4 genes, as well as the NK1 receptor in chronic arthritis of the mouse. METHODS: Complete Freund's Adjuvant was injected intraplantarly and into the tail of Tac1(-/-), Tac4(-/-), Tacr1(-/-) (NK1 receptor deficient) and Tac1(-/-/)Tac4(-/-) mice. Paw volume was measured by plethysmometry and mechanosensitivity using dynamic plantar aesthesiometry over a time period of 21 days. Semiquantitative histopathological scoring and ELISA measurement of IL-1β concentrations of the tibiotarsal joints were performed. RESULTS: Mechanical hyperalgesia was significantly reduced from day 11 in Tac4(-/-) and Tacr1(-/-) animals, while paw swelling was not altered in any strain. Inflammatory histopathological alterations (synovial swelling, leukocyte infiltration, cartilage destruction, bone damage) and IL-1β concentration in the joint homogenates were significantly smaller in Tac4(-/-) and Tac1(-/-/)Tac4(-/-) mice. CONCLUSIONS: Hemokinin-1, but not substance P increases inflammation and hyperalgesia in the late phase of adjuvant-induced arthritis. While NK1 receptors mediate its antihyperalgesic actions, the involvement of another receptor in histopathological changes and IL-1β production is suggested.
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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) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".