Mechanosensitive TRPV4 Channel-Induced Extracellular ATP Accumulation at the Acupoint Mediates Acupuncture Analgesia of Ankle Arthritis in Rats
Bibliographic record
Abstract
(1) Background: Acupuncture (AP) is a safe and effective analgesic therapy. Understanding how fine needles trigger biological signals can help us optimize needling manipulation to improve its efficiency. Adenosine accumulation in treated acupoints is a vital related event. Here, we hypothesized that extracellular ATP (eATP) mobilization preceded adenosine accumulation, which involved local activation of mechanosensitive channels, especially TRPV4 protein. (2) Methods: AP was applied at the injured-side Zusanli acupoint (ST36) of acute ankle arthritis rats. Pain thresholds were assessed in injured-side hindpaws. eATP in microdialysate from the acupoints was determined by luminescence assay. (3) Results: AP analgesic effect was significantly suppressed by pre-injection of GdCl3 or ruthenium red in ST36, the wide-spectrum inhibitors of mechanosensitive channels, or by HC067047, a specific antagonist of TRPV4 channels. Microdialysate determination revealed a needling-induced transient eATP accumulation that was significantly decreased by pre-injection of HC067047. Additionally, preventing eATP hydrolysis by pre-injection of ARL67156, a non-specific inhibitor of ecto-ATPases, led to the increase in eATP levels and the abolishment of AP analgesic effect. (4) Conclusions: These observations indicate that needling-induced transient accumulation of eATP, due to the activation of mechanosensitive TRPV4 channels and the activities of ecto-ATPases, is involved in the trigger mechanism of AP analgesia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".