Considerations towards a roadmap for collection, handling and storage of blood extracellular vesicles
Bibliographic record
Abstract
There is an increasing interest in exploring clinically relevant information that is present in body fluids, and extracellular vesicles (EVs) are intrinsic components of body fluids ("liquid biopsies"). In this report, we will focus on blood. Blood contains not only EVs but also cells, and non-EV particles including lipoproteins. Due to the high concentration of soluble proteins and lipoproteins, blood, plasma and serum have a high viscosity and density, which hampers the concentration, isolation and detection of EVs. Because most if not all studies on EVs are single-centre studies, their clinical relevance remains limited. Therefore, there is an urgent need to improve standardization and reproducibility of EV research. As a first step, the International Society on Extracellular Vesicles organized a biomarker workshop in Birmingham (UK) in November 2017, and during that workshop several working groups were created to focus on a particular body fluid. This report is the first output of the blood EV work group and is based on responses by work group members to a questionnaire in order to discover the contours of a roadmap. From the answers it is clear that most respondents are in favour of evidence-based research, education, quality control procedures, and physical models to improve our understanding and comparison of concentration, isolation and detection methods. Since blood is such a complex body fluid, we assume that the outcome of the survey may also be valuable for exploring body fluids other than blood.
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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 | 0.115 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".