Designing food delivery systems: challenges related to the in vitro methods employed to determine the fate of bioactives in the gut
Bibliographic record
Abstract
An in depth understanding of the underpinning mechanisms that relate to food disruption and processing in the gastrointestinal tract is necessary to achieve optimal intake of nutrients and their bioefficacy. Although in vivo trials can provide insights on physiological responses of nutrients, in vitro assays are often applied as tools to understand specific mechanisms, or as prescreening methods to determine the factors associated with the uptake of food components in the gastrointestinal tract. In vitro assays are also often utilized to design novel or improved food delivery systems. In this review the available approaches to study delivery and uptake of food bioactives and the associated challenges are discussed. For an in depth understanding of food processing in the gastrointestinal tract, it is necessary to apply multidisciplinary methodologies, at the interface between materials science, chemistry, physics and biology.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".