Impact of a Whey Protein Hydrolysate Treated by Electrodialysis with Ultrafiltration Membrane on the Development of Metabolic Syndrome and the Modulation of Gut Microbiota in Mice
Bibliographic record
Abstract
The development of Metabolic Syndrome (MetS) affects a large number of people around the world and represents a major issue in the field of health. Thus, it is important to implement new strategies to reduce its prevalence, and various approaches are currently under development. Recently, an eco-friendly technology named electrodialysis with ultrafiltration membrane (EDUF) was used successfully for the first time at a semi-industrial scale to produce three fractions concentrated in bioactive peptides (BPs) from an enzymatically hydrolyzed whey protein concentrate (WPC): the initial (F1), the final (F2) and the recovery fraction (F3), and it was demonstrated in vitro that F3 exhibited interesting DPP-IV inhibitory effects. Therefore, the present study aimed to evaluate the effect of each fraction on in vivo models of obesity. A daily dose of 312.5 mg/kg was administered to High Fat/High Sucrose diet (HFHS) induced C57BL6/J mice for eight weeks. The physiological parameters of each group and alterations of their gut microbiota by the fractions were assessed. Little effect of the different fractions was demonstrated on the physiological state of the mice, probably due to the digestion process of the BP content. However, there were changes in the gut microbiota composition and functions of mice treated with F3.
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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) | 0.000 | 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".