Comparative characterisation of extracellular vesicles from canine and human plasma: a necessary step in biomarker discovery
Bibliographic record
Abstract
Extracellular Vesicles (EV) have become an interesting focus as novel biomarkers of disease and are increasingly reported upon in humans and other species. The Minimal Information for Studies of Extracellular Vesicles 2018 (MISEV2018) guidelines were published to improve rigor and standardisation within the EV field and provide a framework for the reliable isolation and characterisation of EV populations. However, this rigor and standardisation has been challenging in the area of comparative medicine. Herein we present the successful isolation of EVs from human and canine plasma using Size Exclusion Chromatography and characterise these EVs according to best international practice. This study provides evidence for the reliable comparison of human and canine EVs isolated by this approach, and a baseline description of the EVs from healthy dogs to inform future biomarker studies. This work also demonstrates that the MISEV2018 guidelines can be successfully applied to EVs isolated from canine plasma.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".