Inhibitory Effects of Pomegranate Extracts on Recombinant Human Maltase–Glucoamylase
Bibliographic record
Abstract
α-Glucosidase inhibitors are currently used in the treatment of type 2 diabetes. In this study, we investigated the inhibitory activities of aril and pericarp extracts from pomegranates obtained various regions against recombinant human maltase-glucoamylase (MGAM). The inhibitory activities of the aril extracts tended to be stronger than those of the pericarp extracts. The Iranian aril extract was the most effective inhibitor. We investigated the polyphenol content of the pomegranate extracts using the Folin-Ciocalteu method. Among the aril extracts, the Iranian aril extract showed the highest polyphenol content. We further evaluated inhibitory activity against α-glucosidase from the rat small intestine. Pomegranate extract used in this study showed slightly different inhibitory activities according to α-glucosidase origin. Iranian aril extract was the most effective inhibitor of α-glucosidases, especially recombinant human MGAM. Bioassay-guided fractionation of the pomegranate arils led to identification of punicalagin and oenothein B as potent inhibitors of α-glucosidase. Oenothein B showed inhibitory activity with a half-maximal inhibitory concentration (IC(50)) value of 174 μM. Its potency was comparable to that of the α-glucosidase inhibitor acarbose with an IC(50) value of 170 μM. Dixon plot kinetic analysis of oenothein B showed a noncompetitive inhibition with a K(i) value of 102 μM. These results suggest that pomegranate arils would be useful for suppressing postprandial hyperglycemia.
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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 | 0.000 |
| 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".