Considerations towards a roadmap for collection, handling and storage of blood extracellular vesicles
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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