Tracking immature reticulocyte proteins for improved detection of recombinant human erythropoietin (rhEPO) abuse
Bibliographic record
Abstract
Athletes abuse recombinant human erythropoietin (rhEPO) and erythropoiesis stimulating agents to increase hemoglobin mass and improve performance. To evade detection, athletes have developed sophisticated blood doping regimens, which often include rhEPO micro-dosing. Detection of these methods requires biomarkers with increased sensitivity and a sample matrix that is more amenable to frequent testing in the field. We have developed a method to measure two immature reticulocyte proteins, CD71 and ferrochelatase (FECH), and one total erythrocyte protein, Band 3, in dried blood spots (DBS). This method was tested in response to rhEPO administration after low doses, 40 IU/kg, micro-doses, 900 IU, or saline injection in 20 healthy subjects. During administration of low-dose rhEPO, the mean CD71/Band 3 and FECH/Band 3 ratio increased by 412 ± 197% and 250 ± 44%, respectively. The mean response for the current biomarker, RET%, increased by 195 ± 35%. During administration of rhEPO micro-doses, CD71/Band 3 increased to 127 ± 25% on day 35 and 139 ± 36% on day 39, while no increase was observed in RET%. After rhEPO administration, during the washout phase, mean values decreased to a minimum of 64 ± 4% and 64 ± 11% for CD71/Band 3 and RET%, respectively. However, CD71/Band 3 remained below 75% of baseline for at least 4 weeks after rhEPO injection, while RET% returned to baseline levels. The results demonstrate that immature reticulocyte proteins have a larger response to rhEPO administration than the current biomarker, RET%, and can be monitored in the DBS matrix.
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.
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 | 0.000 | 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".