The natural product argentatin C attenuates postoperative pain via inhibition of voltage‐gated sodium and T‐type voltage‐gated calcium channels
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Postoperative pain occurs in as many as 70% of surgeries performed worldwide. Postoperative pain management still relies on opioids despite their negative consequences, resulting in a public health crisis. Therefore, it is important to develop alternative therapies to treat chronic pain. Natural products derived from medicinal plants are potential sources of novel biologically active compounds for development of safe analgesics. In this study, we screened a library of natural products to identify small molecules that target the activity of voltage-gated sodium and calcium channels that have important roles in nociceptive sensory processing. EXPERIMENTAL APPROACH: Fractions derived from the Native American medicinal plant, Parthenium incanum, were assessed using depolarization-evoked calcium influx in rat dorsal root ganglion (DRG) neurons. Further separation of these fractions yielded a cycloartane-type triterpene identified as argentatin C, which was additionally evaluated using whole-cell voltage and current-clamp electrophysiology, and behavioural analysis in a mouse model of postsurgical pain. KEY RESULTS: currents as well as excitability in rat and macaque DRG neurons, and reversed mechanical allodynia in a mouse model of postsurgical pain. CONCLUSION AND IMPLICATIONS: These results suggest that the dual effect of argentatin C on voltage-gated sodium and calcium channels supports its potential as a novel treatment for painful conditions.
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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".