Ginsenoside Rd Activates Ciliary Beat Frequency <i>via</i> Estrogen Receptor <i>β</i> and P2X7 Receptor
Bibliographic record
Abstract
Basal cells, goblet cells, and ciliated cells compose the human airway epithelium and protect the respiratory tract from foreign substances through their involvement in mucociliary clearance (MCC).In particular, ciliary beat frequency (CBF) is an important indicator of MCC efficiency.Although several herbal medicines have been reported to increase CBF, it is still unclear which compounds in these medicines are responsible for this effect.In the present study, we performed experiments to identify compounds that directly increase CBF and elucidated their mechanisms of action.We screened ginsenosides and their derivatives owned by Kyoto Pharmaceutical University and found that ginsenoside Rd was the most effective in increasing CBF and intracellular cAMP concentration ([cAMP] i ).Furthermore, we identified the estrogen receptor β (ERβ) as a new target of ginsenoside Rd-mediated increases in [cAMP] i and CBF.We also found that ginsenoside Rd increased [Ca 2+ ] i and potentiated the positive effect of ATP on CBF by acting as a positive allosteric modulator (PAM) for the P2X7 receptor in murine airway ciliated cells.In conclusion, the results suggest that ginsenoside Rd increases CBF and that its mechanism of action is as an agonist of ERβ and a PAM of the P2X7 receptor.
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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) | 0.001 | 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".