Iron Availability from Whey Protein Hydrogels: An in Vitro Study
Bibliographic record
Abstract
The influence of whey protein hydrogel microstructure, filamentous versus particulate, on iron delivery was studied under different conditions, including simulated gastrointestinal conditions. Experiments were initially conducted to determine the impact of pH and enzymes on iron release. The results show that different iron release profiles can be obtained from filamentous and particulate gels. Particulate gels released more iron than filamentous gels at acidic pH, but the opposite was observed at alkaline pH. In the presence of pepsin at pH 1.2 or pancreatin at pH 7.5, both gel types showed increased protein hydrolysis, but only filamentous gels showed increased iron release, suggesting that matrix structure plays an important role in iron delivery. A dissolution test was carried out under gastrointestinal conditions to mimic the in vivo dissolution process. Filamentous gel released most of its iron during the intestinal phase of a simulated digestion, hence protecting iron during its transit in the gastric zone. Absorption of iron by the Caco-2 system, used to estimate intestinal absorption, revealed that filamentous gels favored intracellular iron absorption. These results suggest that filamentous gels show promise as matrices for transporting iron and promoting its absorption and therefore should be of major interest in the development of innovative functional foods.
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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) | 0.000 | 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".