Modulation of Starch Digestion for Slow Glucose Release through “Toggling” of Activities of Mucosal α-Glucosidases
Bibliographic record
Abstract
Starch digestion involves the breakdown by α-amylase to small linear and branched malto-oligosaccharides, which are in turn hydrolyzed to glucose by the mucosal α-glucosidases, maltase-glucoamylase (MGAM) and sucrase-isomaltase (SI). MGAM and SI are anchored to the small intestinal brush-border epithelial cells, and each contains a catalytic N- and C-terminal subunit. All four subunits have α-1,4-exohydrolytic glucosidase activity, and the SI N-terminal subunit has an additional exo-debranching activity on the α-1,6-linkage. Inhibition of α-amylase and/or α-glucosidases is a strategy for treatment of type 2 diabetes. We illustrate here the concept of "toggling": differential inhibition of subunits to examine more refined control of glucogenesis of the α-amylolyzed starch malto-oligosaccharides with the aim of slow glucose delivery. Recombinant MGAM and SI subunits were individually assayed with α-amylolyzed waxy corn starch, consisting mainly of maltose, maltotriose, and branched α-limit dextrins, as substrate in the presence of four different inhibitors: acarbose and three sulfonium ion compounds. The IC(50) values show that the four α-glucosidase subunits could be differentially inhibited. The results support the prospect of controlling starch digestion rates to induce slow glucose release through the toggling of activities of the mucosal α-glucosidases by selective enzyme inhibition. This approach could also be used to probe associated metabolic diseases.
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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".