Differences in sialic acid O‐acetylation between human urinary and recombinant erythropoietins: a possible mass spectrometric marker for doping control
Bibliographic record
Abstract
Development of a mass spectrometric method for the unambiguous detection of doping with recombinant human erythropoietins (rhEPO) has been attempted for many years. Unfortunately, progress in this field was hampered by the unavailability of highly purified human endogenous EPOs (urinary[uhEPO], serum/plasma EPO)--a prerequisite for generating detailed mass spectrometric glycosylation data necessary for revealing significant differences between uhEPO and rhEPOs. The paper presents the worldwide first analytical data on purified human urinary EPO generated with a high resolution high accuracy mass spectrometer (LTQ-Orbitrap). The focus is on the tryptic O-glycopeptide (E117-R131) and its degree of sialic acid O-acetylation. Data are compared with results obtained from 40 rhEPO pharmaceuticals. It could be demonstrated that the O-glycopeptide of uhEPO (ca 100 IU) contains only trace amounts of mono-acetylated mono-and di-sialylated O-glycans but no other O-acetylated structures and in this respect significantly differs from all rhEPOs. Moreover, Dynepo--a rhEPO previously thought to be not O-acetylated--also contains small amounts of O-acetylations within the O-glycan structure. The results might be useful for anti-doping purposes as well as the development of EPO pharmaceuticals with closer structural similarity to the endogenous hormone.
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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 | 0.000 | 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".