Comparison of methods for pre-processing, exosome isolation, and RNA extraction in unpasteurized bovine and human milk
Bibliographic record
Abstract
Milk is a highly complex, heterogeneous biological fluid that contains non-nutritive, bioactive extracellular vesicles called exosomes. Characterization of milk-derived exosomes (MDEs) is challenging due to the lack of standardized methods that are currently being used for milk pre-processing, storage, and exosome isolation. In this study, we tested: 1) three pre-processing methods to remove cream, fat, cellular debris, and casein proteins from bovine milk to determine whether pre-processing of whole milk prior to long-term storage improves MDE isolations, 2) the suitability of two standard exosome isolation methods for MDE fractionation, and 3) four extraction protocols for obtaining high quality RNA from bovine and human MDEs. MDEs were characterized via Transmission Electron Microscopy (TEM), Nanoparticle Tracking Analysis (NTA), and western immunoblotting for CD9, CD63, and Calnexin protein markers. We also present an optimized method of TEM sample preparation for MDEs. Our results indicate that: 1) Removal of cream and fat globules from unpasteurized bovine milk, prior to long-term storage, improves the MDE yield but not purity, 2) Differential ultracentrifugation (DUC) combined with serial filtration is better suited for bovine MDE isolation compared to ExoQuick (EQ) combined with serial filtration, however both methods were comparable for human milk, and 3) TRIzol LS is better suited for RNA extraction from bovine MDEs isolated by EQ and DUC methods. 4) TRIzol LS, TRIzol+RNA Clean and Concentrator, and TRIzol LS+RNA Clean and Concentrator methods can be used for RNA extractions from human MDEs isolated by EQ, yet the TRIzol LS method is better suited for human MDEs isolated by DUC. The QIAzol + miRNeasy Mini Kit produced the lowest RNA yield for bovine and human MDEs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".