Fermentation of dietary fibers modified by an enzymatic‐ultrasonic treatment and evaluation of their impact on gut microbiota in mice
Bibliographic record
Abstract
Dietary fiber (DF) from sisal waste and Moringa oleifera stem were modified using a successive enzymatic-ultrasonic (E-U) treatment to produce modified DF products (MDFs), with reduced ratio of insoluble DF (IDF) and soluble DF (SDF) and decreased particle size. The water-holding capacity, swelling capacity, and oil-holding capacity of the MDFs were elevated upon E-U treatment. MDF-S (MDF from sisal waste) and MDF-M (MDF from M. oleifera stem) were fermented to produce short-chain fatty acids (SCFAs) by gut bacteria in mice, with total SCFA content of 3.81 μmol/g and 3.34 μmol/g, respectively, when added in basal feed at 10% (wt/wt). Metagenomic analyses demonstrated that MDFs tended to increase the Bacteroidetes/Firmicutes ratio, and significantly increased the relative abundance of Bacteroides, norank_f__Bacteroidales_S24-7_group, norank_f__Erysipelotrichaceae, Ruminococcus_1, and Akkermansia at genus level. These findings suggest that MDF supplementation in diet could favorably modulate gut microbiome in mice. Novelty impact statement We proposed a DF modification strategy to improve its role in intestinal flora modulation. Our results suggest that successive enzymatic and ultrasonic treatment effectively reduced the IDF/SDF ratio of DF. Supplement of the modified DF could alter the intestinal flora of mice to a high Bacteroidetes/Firmicutes ratio and elevate the relative abundance of several beneficial microbiota.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".