Comparison of methods for milk pre-processing, exosome isolation, and RNA extraction in bovine and human milk
Bibliographic record
Abstract
Abstract Milk is a highly complex, heterogeneous biological fluid that contains bioactive, membrane-bound 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, exosome isolation, and RNA extraction. In this study, we tested: 1) three pre-processing methods to remove cream, fat, and casein proteins from bovine milk to determine whether pre-processing of whole milk, prior to long-term storage, improves MDE isolations, 2) two commonly-used exosome isolation methods, and 3) four extraction protocols for obtaining high quality MDE RNA from bovine and human milk. MDEs were characterized via Transmission Electron Microscopy (TEM) and Nanoparticle Tracking Analysis (NTA). We also present an optimized method of TEM sample preparation and isolation of total soluble protein from MDEs. Our results indicated that: 1) pre-processing of bovine milk prior to storage does not affect the final exosome yield or the purity, 2) ExoQuick precipitation is better suited for MDE isolation than ultracentrifugation for bovine and human milk, and 3) TRIzol LS produced the highest RNA yield in bovine milk, whereas TRIzol LS, TRIzol+RNA Clean and Concentrator, and TRIzol LS+RNA Clean and Concentrator methods can be used for human milk.
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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 | 0.004 | 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 | 0.000 |
| 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".