Detection of extracellular hemoglobin from <scp><i>Arenicola marina</i></scp> in doping control serum samples by means of liquid chromatography and high‐resolution tandem mass spectrometry
Bibliographic record
Abstract
Abstract The manipulation of blood and blood components in sports is prohibited at all times, and besides blood transfusions, also hemoglobin‐based oxygen carriers (HBOCs) can be employed to artificially improve the oxygen transport capacity of the blood. But while most drug candidates based on stabilized hemoglobin (Hb) were found to be characterized by serious side effects, the natural giant extracellular Hb from the marine invertebrate Arenicola marina (lugworm) could be another candidate for transfusion medicine and cheating athletes, as it was found to be well tolerated in preclinical animal studies. Within this research project, lugworm Hb was implemented into the existing doping control detection method for bovine HBOCs based on ultrafiltration, tryptic digestion, and liquid chromatography coupled with high‐resolution tandem mass spectrometry (LC‐HRMS/MS). For the mass spectrometric identification of lugworm Hb, two precursor–product ion pairs for a total of four tryptic peptides originating from subunits hbA2 (T6), hbB1 (T3 and T6), and the linker chain (T16) were employed. The modified approach was comprehensively characterized and found to allow for the specific and sensitive detection of lugworm Hb down to concentrations of 10 μg/mL from 50 μL of serum/plasma. Therefore, it can serve as confirmation procedure for lugworm Hb following visual or electrophoretic screening. Moreover, a proof‐of‐concept rat administration study was conducted, and the observed detection windows of at least 4 (dose: 200 mg/kg) and 8 h (dose: 600 mg/kg) suggest that the approach can be readily employed to efficiently test in‐competition doping control samples for the presence of the drug candidate.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".