Isolating Multiple Extracellular Vesicles Subsets, Including Exosomes and Membrane Vesicles, from Bovine Milk Using Sodium Citrate and Differential Ultracentrifugation
Bibliographic record
Abstract
Milk is a complex fluid that contains various types of proteins and extracellular vesicles (EVs). Some proteins can mingle with EVs, and interfere with their isolation. Among these proteins, caseins form micelles of a size comparable to milk EVs, and can thus be co-isolated with EVs. Preliminary steps that affect milk are crucial for EV isolation and impact the purity and abundance of isolated EVs. In the course of our previous works on cow's milk EVs, we found that sodium citrate (1% final), which is a biocompatible reagent capable of breaking down casein micelles into 40-nm monomers, allowed the isolation of high quantities of EVs with low coprecipitation of caseins or other contaminating proteins. Using this protocol, we successfully separated different EV subsets, characterized in depth their morphology, protein content and small RNA enrichment patterns. We were also able to describe their biological function in a mouse model of intestinal inflammation. We, hereby, detail the differential ultracentrifugation procedure that leads to high quantify, medium specificity, isolation of different milk EV subsets from the same sample. More specifically, we highlight the use of sodium citrate as a standardized approach to isolate and study milk EVs and its potential for isolation techniques other than differential ultracentrifugation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".