Calibration of Flow Cytometers Enables Reproducible Measurements of Extracellular Vesicle Concentrations and Reference Range Establishment
Bibliographic record
Abstract
The concentration of cells is a key component of modern blood tests. Given the biomarker potential of extracellular vesicles (EVs) in blood, we aimed to establish reference ranges for blood cell-derived EVs using flow cytometry. To address the orders-of-magnitude variability in reported EV concentrations between different flow cytometers (FCMs), we first validated a calibration methodology to enable reproducible EV concentration measurements. The methodology was evaluated in an interlaboratory comparison study and shows that calibration reduces the median absolute deviation of EV concentrations measured on 25 different FCMs from 67 % to 25 %-31 %. The calibration methodology was then used to determine reference ranges of erythrocyte-, leukocyte-, and platelet-derived EVs in human blood plasma in a cohort of healthy individuals (n = 224). This study demonstrates that calibration enables comparable concentration measurements of blood cell-derived EVs, thereby bringing EVs one step closer to clinical applications.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".