Effect of protein composition of a model dairy matrix containing various levels of beta-casein on the structure and anti-inflammatory activity of<i>in vitro</i>digestates
Bibliographic record
Abstract
An increasing body of evidence demonstrates that differences in protein composition in the food matrix can significantly affect its biological functionality. The present research hypothesized that a matrix containing the same level of dairy protein, but with different composition, even when showing similar properties during digestion, may have a different biological functionality. To test this hypothesis, three matrices, containing 2.8% protein and similar amounts of fat and solid were prepared, either with 100% whey proteins, or with a ratio of caseins to whey protein of 40 : 60, but differing in β-casein ratio. The mixtures were subjected to in vitro digestion, and the digestates were used in uptake experiments using Caco-2 cell monolayers. The basolateral fraction metabolized by the cells was used to stimulate human LPS-stimulated THP-1 macrophages and the concentration of selected cytokines were measured, as an indication of potential differences in biological functionality between the different dairy matrices. All three digestates induced a significant reduction in IL-1β cytokines, with the casein-containing treatments inducing a greater decrease compared to that containing only whey proteins. The matrix containing the highest ratio of β-casein induced the lowest secretion of proinflammatory cytokines TNF-a and IL-6. This study demonstrated that milk protein composition does not only affect the rate of gastric proteolysis and structure of the gastric digestate, but will cause differences in physiological effects. This research stressed the role of milk protein components during digestion, and of β-casein in particular, and their potential to modulate biological functions in the gastrointestinal tract.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".