On the trail of blood doping—<scp>microRNA</scp> fingerprints to monitor autologous blood transfusions in vivo
Bibliographic record
Abstract
Autologous blood doping refers to the illegal re-transfusion of any quantities of blood or blood components with blood donor and recipient being the same person. The re-transfusion of stored erythrocyte concentrates is particularly attractive to high-performance athletes as this practice improves their oxygen capacity excessively. However, there is still no reliable detection method available. Analyzing circulating microRNA profiles of human subjects that underwent monitored autologous blood transfusions seems to be a highly promising approach to develop novel biomarkers for autologous blood doping. In this exploratory study, we randomly divided 30 healthy males into two different treatment groups and one control group and sampled whole blood at several time points at baseline, after whole blood donation and after transfusion of erythrocyte concentrates. Hematological variables were recorded and analyzed following the adaptive model of the Athlete Biological Passport. microRNA profiles were examined by small RNA sequencing and comprehensive multivariate data analyses, revealing microRNA fingerprints that reflect the sampling time point and transfusion volume. Neither individual microRNAs nor a signature of transfusion-dependent microRNAs reached superior sensitivity at 100% specificity compared to the Athlete Biological Passport (≤11% 6 h after transfusion versus ≤44% 2 days after transfusion). However, the window of autologous blood doping detection was different. Due to the heterogenous nature of doping, with athletes frequently combining multiple medications in order to both gain a competitive advantage and interfere with known testing methods, the true applicability of the molecular signature remains to be validated in real anti-doping testings.
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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 | 0.001 |
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".