Isolation of extracellular vesicles from human plasma samples: The importance of controls
Bibliographic record
Abstract
BACKGROUND: Extracellular vesicles (EV) are enriched with proteins and RNA cargo, promoting cell-to-cell communication. Biofluid derived EV cargo is used for discovering disease specific markers for diagnosis and disease monitoring. RATIONAL: Blood is a complex fluid with an abundance of protiens and thus isolation of EVs is challenging. Therefore, methods for EV isolation, including commercial kits use thromboplastin D (TP-D) for pretreatment of plasma to increase EV purity and yield. This pretreatment can introduce contaminants. METHOD AND RESULTS: We performed a comparative study to evaluate the effect of EV isolation methods focusing on (a) pretreatment of plasma with additives, which include: rabbit TP (rTP) versus human recombinant thromboplastin (huTP), to increase purity and yield (b) an additional centrifugation step prior to freezing plasma and (c) comparison of frozen versus fresh plasma EV isolations. Pretreatment with rTP generated a dynamic range of proteins, however, most of these proteins were contaminants, introduced from the rTP (99.1% purity). As an alternative, huTP was used, which did not introduce any significant contaminants, however, this did not increase yield or purity. Additionally, an extra 10,000 g centrifugation did not improve either EV yield or purity. Finally, comparison of fresh or frozen plasma showed no significant difference, an important factor when sourcing plasma from biobanks. CONCLUSION: Appropriate controlsare required when adding any additives during EV isolation as even a small percentage of contaminants can have a major effect on results. Furthermore, biobanked plasma can be used with no major changes to processing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".