KCNQ3 activation by the naturally occurring phenol, eugenol
Bibliographic record
Abstract
Pharmacological targeting of ion channels represents a crucial avenue for pain management. The KCNQ family of ion channels plays a significant role in controlling neuronal excitability and the generation and propagation of pain-related nerve impulses, mitigating excessive electrical signaling and limiting the transmission of pain signals. Eugenol has a variety of biological activities, including analgesic and anti-inflammatory properties. When used in conjunction with the anti-inflammatory drug diclofenac, eugenol demonstrates enhanced analgesic efficacy in animal models. We investigated the effects of eugenol on KCNQ ion channels. Eugenol acts as a KCNQ2/3 activator, shifting the voltage dependency to negative potentials, most of the activation can be explained by the effect on the KCNQ3, molecular docking simulation and mutagenesis experiments suggest that the binding pocket of eugenol is located at the top of the voltage-sensing domain. We also show that eugenol is a weak activator of the TRPV1 channel yet inhibits the capsaicin and acidic pH-activated current. Diclofenac also inhibits the TRPV1 channel current. In cells co-expressing KCNQ2/3 and TRPV1, eugenol and diclofenac limit the extent of membrane depolarization. Altogether, we report a new target for eugenol that adds to its wide array of biological activities, including its role in modulating acute 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".