Detection of ESAs in equine urine and blood by SAR‐PAGE
Bibliographic record
Abstract
Abstract Erythropoiesis‐stimulating agents (ESAs) have been used in horses for doping purposes to increase the performance of these animals in endurance sports. Currently, enzyme‐linked immunosorbent assay (ELISA) and mass spectrometry methods are used to detect ESA abuse in equines. However, the sarcosyl polyacrylamide gel‐electrophoresis (SAR‐PAGE) technique could also be used, since its application in human doping control is well established and has proven to be more sensitive. In this work, the SAR‐PAGE method was used to detect recombinant human erythropoietin (rHuEPO), novel erythropoiesis stimulating protein (NESP), continuous erythropoietin receptor activator (CERA), and fusion protein of erythropoietin with human immunoglobulin heavy chain Fc region (EPO‐Fc) in horse blood and urine. The purification technique for human blood using MAIIA kits worked well for horse samples. The major challenge was horse urine immunopurification, which proved difficult due to filter clogging, but heating and cooling of the horse urine followed by filtration in 30‐kDa molecular weight cut‐off filters solved this problem. The limits of detection (LODs) of 1.3, 1.6, 6.6, and 13.3 pg/mL for rHuEPO, NESP, CERA, and EPO‐Fc, respectively, obtained in spiked urine and 40, 100, 80, and 400 pg/mL for rHuEPO, NESP, CERA, and EPO‐Fc, respectively, acquired in spiked blood are lower than the LODs reported in the literature using liquid chromatography–mass spectrometry (LC–MS) methods. In addition, the presence of ESAs was detected up to 9 days after the administration of microdoses of Hemax (rHuEPO), NESP, and CERA in horse blood and urine. SAR‐PAGE may be implemented in the routine analysis of horse doping control laboratories for screening and confirmation of ESA abuse, mainly due to its high sensitivity for both matrices compared to published mass spectrometric methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".