Analysis of cobalt for human sports drug testing purposes using ICP‐ and LC‐ICP‐MS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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 it