Milk‐Derived Extracellular Vesicles and <scp>microRNAs</scp> : Potential Modulators of Intestinal Homeostasis
Bibliographic record
Abstract
Milk represents a complex pool of nutrients and bioactive components that are indispensable for the growth and development of the infant. The key well-established roles played by milk's bioactive components in the infant are those at the level of the infant's intestinal and immune development. Through its bioactive components, including proteins, lipids, and oligosaccharides, milk helps the infant develop a mature intestinal identity with fully active digestive, absorptive, and barrier capacity and shapes both innate and adaptive immune responses. Recent evidence points to a new class of milk bioactive components including milk extracellular vesicles (EVs) and microRNAs, which are hypothesized to take part in contributing to infant development. The potential functions of milk microRNAs and EVs in the consumer's systems are not limited to the infant as these components can also be found in bovine milk, both raw and processed. Hence, adult consumers could also be influenced by milk EVs and microRNAs, which could affect their intestinal homeostasis particularly under pathological conditions. Nonetheless, the debate regarding the stability of milk EVs and microRNAs in the digestive tract persists, and their bioavailability and bioactivity in the consumer's tissues are still arguable. In this review, we discuss the potential functions mediated by milk microRNAs and EVs in the epithelial and immune components as well as the microbiome of the intestinal mucosa in health and disease. We also discuss the bioavailability, bioaccessibility, and bioactivity of milk EVs and microRNAs in consumer's tissues.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".