Comparative characterisation of extracellular vesicles from canine and human plasma: a necessary step in biomarker discovery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it