Structures of human salivary amylase hydrolysates from starch processed at two water concentrations
Bibliographic record
Abstract
Abstract Digestion of starch in humans starts in the mouth and progresses to the small intestine. A thorough understanding of the progression of digestion, of consequence to glycemic and possibly insulinemic responses, requires a better characterization of the digestion products along the gut – products that are the substrates in the subsequent hydrolysis by sucrase‐isomaltase and maltase‐glucoamylase. This submission focuses on the first step of digestion, i.e., impact of human salivary amylase on the structure of hydrolysis products obtained from cooked starch. Starch was cooked at 1:0.7 (T0.7) or 1:2 (T2) starch:water ratios. To remove the effect of granular structure, starch was also dispersed using DMSO (TD) prior to amylase treatment. Cooked and dispersed starches were subjected to salivary amylase at conditions mimicking oral digestion. All samples gave rise to different and complex mixtures of hydrolysates with broad size‐distributions as measured by gel‐permeation chromatography (GPC). Following hydrolysis, the smallest dextrins (DP <30) constituted 35% in TD and only ∼20% in both T0.7 and T2. Cooking appeared to protect amylose molecules from hydrolysis with less hydrolysis in T0.7. These results show that the amount of water present during processing of starch affects structures of salivary amylase hydrolysates, which potentially impact on glucose homeostasis.
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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".