Red blood cell derived extracellular vesicles during the process of autologous blood doping
Bibliographic record
Abstract
Abstract The purpose of this pilot study was to investigate the effects of the transfusion of one erythrocyte concentrate on the number of circulating red blood cell extracellular vesicles (RBC‐EVs) and their clearance time. Six, healthy volunteers donated their blood and were transfused with their RBC concentrate after 35–36 days of storage. One K 2 EDTA and one serum sample were collected before donation, at four timepoints after donation and at another six timepoints after transfusion. RBC‐EVs were analyzed on a Cytek Aurora flow cytometer. A highly significant increase ( p < 0.001) of RBC‐EVs from an average of 60.1 ± 19.8 (10 3 /μL) at baseline to 179.3 ± 84.7 (10 3 /μL) in the first 1–3 h after transfusion could be observed. Individual differences in the response to transfusion became apparent with one volunteer showing no increase and another an increased concentration at one timepoint after donation due to an influenza infection. We concluded that in an individualized passport approach, increased RBC‐EVs might be considered as additional evidence when interpreting suspicious Athletes Biological Passport (ABPs) but for this additional research related to sample collection and transport processes as well as method development and harmonization would be necessary.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".