Detection of Hemoglobin-Based Oxygen Carriers in Human Serum for Doping Analysis: Confirmation by Size-Exclusion HPLC
Bibliographic record
Abstract
BACKGROUND: Hemoglobin-based oxygen carriers (HBOCs) are being developed as potential substitutes for the oxygen-carrying functions of erythrocytes, but athletes may obtain and experiment with HBOCs as an illicit means of enhancing oxygen transport. An electrophoretic technique has been developed to screen for the presence of HBOCs in blood samples (Lasne et al. Clin Chem 2004;50:410-5). Interest has focused on complementary methods that can provide legally defensible scientific evidence for the presence of HBOCs in blood samples collected for doping control. METHODS: The aim of this research was to develop a size-exclusion SEC-HPLC technique to identify in plasma or serum samples the presence of HBOCs that are currently under development. This method was also used to detect a polymerized bovine hemoglobin (Hemopure) after infusion in 12 healthy males. RESULTS: The chromatograms of all HBOCs tested were clearly separated from the 54-min peak associated with human hemoglobin dimers. It was possible to differentiate between the different HBOC products based solely on their chromatographic profiles, provided they were at high concentrations. Differences were discernible not only based on the presence (or absence) of peaks, but also the separation between respective peaks. The profiles for serum samples collected from the men immediately after infusion of Hemopure showed a distinctive profile. The shape of the chromatographic profile remained consistent for at least 48 h. CONCLUSIONS: Under the analytical conditions reported here, SEC-HPLC was able to separate native hemoglobin from the modified hemoglobin molecules present in each of the HBOC products studied. In tandem with electrophoretic screening, SEC-HPLC provides evidence of the presence of HBOCs and can therefore be regarded as a method that satisfies the criteria for use in an antidoping control setting.
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 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 | 0.000 |
| 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) | 0.000 | 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".