Curcumin Relaxes Precontracted Guinea Pig Gallbladder Strips via Multiple Signaling Pathways
Bibliographic record
Abstract
Background: Curcumin (diferuloymethane) is the active ingredient of the dietary spice turmeric. Curcumin modulates various signalling molecules, including inflammatory agents, transcription factors, protein kinases and cell cycle regulatory proteins. The purpose of this study was to determine if curcumin had an effect on gallbladder motility. Methods: A pharmacologic in vitro technique was used. Since curcumin relaxed both cholecystokinin octapeptide- (CCK) and KCl-induced tension of guinea pig gallbladder strips in a concentration dependent manner, an in vitro technique was used to determine which second messenger system(s) mediated the observed relaxation. Paired t -tests, t -tests and analysis of variance were used for statistical analysis. Differences between mean values of P < 0.05 were considered significant. Results: To determine if protein kinase A (PKA) mediated the curcumin-induced relaxation, PKA inhibitor 14-22 amide myristolated (PKA-IM) was used. PKA-IM had no significant effect on the amount of curcumin-induced relaxation. When the protein kinase C (PKC) inhibitors bisindolymaleimide IV and chelerythrine Cl - were used together, a significant (P < 0.01) reduction in the curcumin-induced relaxation was observed. The use of tetraethylammonium chloride (TEA) caused a significant (P < 0.01) decrease in the amount of curcumin-induced relaxation. Adding curcumin prior to the KCl caused a significant (P < 0.001) decrease in the amount of KCl-induced tension. Conclusions: The results suggested that the curcumin-induced relaxation is mediated by multiple signaling pathways including the PKC second messenger system, inhibiting extracellular Ca 2+ entry and K+ channels. Gastroenterol Res. 2015;8(5):253-259 doi: http://dx.doi.org/10.14740/gr689w
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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 | 0.000 | 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".