Reverse–normal immunopurification: An effective approach for purifying recombinant erythropoietin from its analogues in doping analysis
Bibliographic record
Abstract
BACKGROUND: Recombinant erythropoietin (rEPO) is commonly used in therapy but may be abused in sports to enhance endurance. In doping analysis, rEPO can be detected in human urine or blood samples at picogram (pg) levels based on its slightly higher molecular weight (MW) than that of endogenous EPO using western blotting (WB). However, a type of variant erythropoietin (VAR-EPO) encoded by the EPO c.577del variant has a similar MW to rEPO, and these 2 molecules cannot be distinguished using conventional analytical methods. A fit-for-purpose method needs to be developed immediately. METHODS: In this study, we introduced a reverse-normal immunopurification technique for sample pretreatment to remove VAR-EPO from samples to eliminate its interference with rEPO detection. Firstly, a rabbit monoclonal antibody (mAb) that can specifically recognize trace amounts of VAR-EPO with high affinity was generated. Then, using this antibody to enrich VAR-EPO, we developed reverse-normal immunopurification coupled with WB on the purpose of analyzing rEPO in urine and serum samples. Next, the method was fully validated and evaluated using blank samples, spiked samples and rEPO excreted samples. Finally, the identification criteria of rEPO was established. RESULTS: A specific anti-VAR mAb with high affinity was developed. Using it, we developed the doping analytical method for rEPO. Our method effectively detects and removes VAR-EPO, enabling accurate rEPO detection. CONCLUSION: A method has already been applied for rEPO confirmation in routine doping analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".