Hepcidin and Erythroferrone Complement the Athlete Biological Passport in the Detection of Autologous Blood Transfusion
Bibliographic record
Abstract
ABSTRACT Purpose We investigated whether hepcidin and erythroferrone (ERFE) could complement the athlete biological passport (ABP) in indirectly detecting a 130-mL packed red blood cells (RBC) autologous blood transfusion. Endurance performance was evaluated. Methods Forty-eight healthy men ( n = 24) and women ( n = 24) participated. Baseline samples were collected weekly followed by randomization to a blood transfusion (BT, n = 24) or control group (CON, n = 24). Only the BT group donated 450 mL whole blood from which 130 mL red blood cell was reinfused 4 wk later. Blood samples were collected 3, 7, 14, 21, and 28 d after donation, and 3, 6, and 24 h and 2, 3, and 6 d after reinfusion. In the CON group samples were collected with the same frequency. Endurance performance was evaluated by a 650-kCal time trial ( n = 13) before and 1 and 6 d after reinfusion. Results A time–treatment effect existed ( P < 0.05) for hepcidin and ERFE. Hepcidin was increased ( P < 0.01) ~110 and 89% 6 and 24 h after reinfusion. Using an individual approach (99% specificity, e.g., allowing 1:100 false-positive), sensitivities, i.e., true positives, of 30% and 61% was found for hepcidin and ERFE, respectively. For the ABP, the most sensitive marker was Off-hr score ([Hb] (g·L −1 ) − 60 × √RET%) ( P < 0.05) with a maximal sensitivity of ~58% and ~9% after donation and reinfusion, respectively. Combining the findings for hepcidin, ERFE, and the ABP yielded a sensitivity across all time-points of 83% after reinfusion in BT. Endurance performance increased 24 h (+6.4%, P < 0.01) and 6 d after reinfusion (+5.8%, P < 0.01). Conclusions Hepcidin and ERFE may serve as biomarkers in an antidoping context after an ergogenic, small-volume blood transfusion.
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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 | 0.000 | 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".