Analysis of cobalt for human sports drug testing purposes using ICP‐ and LC‐ICP‐MS
Bibliographic record
Abstract
Abstract Due to the current demands in the fight against manipulation of blood and blood components, commonly referred to as “blood doping” in sports drug testing, specific and sensitive detection methods enabling the detection of prohibited substances and methods of doping are required. Similar to illicit blood transfusions, erythropoiesis stimulating agents have been shown to be misused in sport, aiming at improving an athlete's aerobic capacity and endurance performance. Amongst other strategies, the administration of ionic cobalt (Co 2+ ) can increase the number of erythrocytes by stimulating the endogenous erythropoietin (EPO) biosynthesis. Conversely, several organic Co‐containing compounds such as cyanocobalamin (vitamin B12) are not prohibited in sports, and thus, an analytical differentiation of permitted and banned contributions to urinary Co‐concentrations is desirable. An excretion study with daily applications of either 1 mg of CoCl 2 or 1 mg of cyanocobalamin was conducted with 20 volunteers over a period of 14 consecutive days. Urine, plasma, and concentrated red blood cells were analyzed for their cobalt content. The samples were collected starting 7 days before the administration until 7 days after. Total Co concentrations were analyzed by using inductively coupled plasma mass spectrometry (ICP‐MS), which yielded significantly elevated levels exclusively after inorganic cobalt intake. Furthermore, a liquid chromatography (LC)‐ICP‐MS approach was established and employed for the simultaneous determination of organically bound and inorganic cobalt by chromatographic separation within one single run. The analytical approach offers the option to further develop detection methods of illegal Co 2+ supplementation in sport.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".