Banana flour phenolics inhibit trans-epithelial glucose transport from wheat cakes in a coupled<i>in vitro</i>digestion/Caco-2 cell intestinal model
Bibliographic record
Abstract
Some fruit phenolics are reported to attenuate intestinal glucose transport through inhibitory action at the luminal brush border membrane. This effect may contribute, in part, to the ability of flavonoid-rich food to regulate glucose homeostasis of meals rich in available carbohydrates. For the first time, the potential of green banana flours to inhibit transepithelial glucose transport was investigated in the context of a model starchy meal (wheat cake) using a simulated digestion/Caco-2 human intestinal cell model. A 10% replacement of wheat flour with any of the four banana flours (native and extruded oven-dried and freeze-dried) resulted in cakes with significantly higher total phenolics (68-198 μg per 100 g, p < 0.05), especially using extruded banana flour (197-198 μg per 100 g), as measured by LC/MS. Banana cakes, especially those containing oven-dried and/or extruded banana flours, exhibited from 45.0 to 54.5% higher glucose transport inhibition than the control cake. Interestingly, the digesta of cakes made with freeze-dried and extruded banana flour presented a significantly higher phenolic content (1116 μM, p < 0.05) than the other digesta (745-791 μM), while the phenolic content in control digesta was only 548 μM. These results suggested that the amounts of quercetin and myricetin, even in traces, were critical determinants of glucose transport inhibition.
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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".