Effect of lactic fermentation on soy protein digestive pattern assessed by an <i>in vitro</i> dynamic gastrointestinal digestion model and the influence on human faecal microbiota
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the effect of lactic fermentation on soy protein gastrointestinal digestive pattern and the influence of protein digesta on human faecal microbiota. Soymilk and soy yogurt were prepared in this study and a novel in vitro dynamic gastrointestinal model was employed to simulate gastric and duodenum digestions. Particle size, sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE), and peptide content were monitored at the end of duodenum tract. RESULTS: Ingestion of soy yogurt allowed a rapid drop in pH from 7.0 to 5.0 at simulated duodenal digestion (0-30 min), and resulted in a loss in soluble protein content compared to that of soymilk. The electrophoretic pattern between soymilk and soy yogurt exerted distinctive differences at early stages of duodenal digestion (0-60 min) and resulted in different peptide contents (180 min). Soy yogurt duodenal digesta collected at 180 min (D180), by co-fermentation with human intestinal flora distribution, allowed a higher population in Bifidobacterium spp., Lactobacillus/Enterococcus spp. and Streptococcus/Lactococcus spp., whereas soy yogurt D30 resulted in lower population in Clostridium and Escherichia coli compared to samples co-fermented with soymilk digesta. CONCLUSION: The results demonstrated lactic fermentation of soy protein modulated human intestinal microflora and might relate to the different protein digestive behaviours. © 2020 Society of Chemical Industry.
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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".