Therapeutic Potential of Bovine Milk-Derived Extracellular Vesicles
Bibliographic record
Abstract
Milk is a fundamental component of the human diet, owing to its substantial nutritional content. In addition, milk contains nanoparticles called extracellular vesicles (EVs), which have indicated their potential beneficial roles such as cell-to-cell communication, disease biomarkers, and therapeutics agents. Amidst other types of EVs, milk EVs (MEVs) have their significance due to their high abundance, easy access, and stability in harsh environmental conditions, such as low pH in the gut. There have been plenty of studies conducted to evaluate the therapeutic potential of bovine MEVs over the past few years, and attention has been given to their engineering for drug delivery and targeted therapy. However, there is a gap between the experimental findings available and clinical trials due to the many challenges related to EV isolation, cargo, and the uniformity of the material. This review aims to provide a comprehensive comparison of various techniques for the isolation of MEVs and offers a summary of the therapeutic potential of bovine MEVs described over the last decade, analyzing potential challenges and further applications. Although a number of aspects still need to be further elucidated, the available data point to the role of MEVs as a potential candidate with therapeutics potential, and the supplementation of MEVs would pave the way to understanding their in-depth effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".