Concentrates of two subsets of extracellular vesicles from cow’s milk modulate symptoms and inflammation in experimental colitis
Bibliographic record
Abstract
Extracellular vesicles (EVs) are involved in cell-to-cell communication and modulation of numerous physiological and pathological processes. EVs are found in large quantities in milk and contain several inflammation- and immunity-modulating proteins and microRNAs, through which they exert beneficial effects in several inflammatory disease models. Here, we investigated the effects of two EV subsets, concentrated from commercial cow's milk, on a murine model of colitis induced with dextran sodium sulfate (DSS). P35K EVs, isolated by ultracentrifugation at 35,000 g, and P100K EVs, isolated at 100,000 g, were previously characterized and administered by gavage to healthy and DSS-treated mice. P35K EVs and, to a lesser extent, P100K EVs improved several outcomes associated to DSS-induced colitis, modulated the gut microbiota, restored intestinal impermeability and replenished mucin secretion. Also, P35K EVs modulated innate immunity, while P100K EVs decreased inflammation through the downregulation of colitis-associated microRNAs, especially miR-125b, associated with a higher expression of the NFκB inhibitor TNFAIP3 (A20). These results suggest that different milk EV subsets may improve colitis outcomes through different, and possibly complementary, mechanisms. Further unveiling of these mechanisms might offer new opportunities for improving the life of patients with colitis and be of importance for milk processing, infant milk formulation and general public health.
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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) | 0.001 | 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".