Desialylation improves the detection of recombinant erythropoietins in urine samples analyzed by SDS‐PAGE
Bibliographic record
Abstract
Recombinant erythropoietin (rhEPO) has been misused for over two decades by athletes, mainly but not only in endurance sports. A direct rhEPO detection method in urine by isoelectric focusing (IEF) was introduced in 2000, but the emergence of third-generation erythropoiesis-stimulating agents and so-called biosimilar rhEPOs, together with the sensitivity of human endogenous EPO (huEPO) pattern to enzymatic activities and its modification following short strenuous exercise, prompted the development of a complementary test based on SDS-PAGE analysis. While Mircera and NESP are easily detected with the existing IEF and SDS-PAGE methods, some samples containing both epoetin-α/β and huEPO present profiles that are still difficult to interpret. As doping practices have moved to micro-dosing, these mixed patterns are more frequently observed. We investigated the impact of enzymatic desialylation on the urinary and serum EPO profiles obtained by SDS-PAGE with the aim of improving the separation of the bands in these mixed EPO populations. We observed that the removal with neuraminidase of the sialic acid moieties from the different EPOs studied reduced their apparent molecular weight (MW) and increased the migration distance between huEPO and rhEPO centroids, therefore eliminating the size overlaps between them and improving the detection of rhEPO.
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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".