Implementation of AICAR analysis by GC‐C‐IRMS for anti‐doping purposes
Bibliographic record
Abstract
AICAR (5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside), is a naturally occurring substance which is part to the World Anti-Doping Agency (WADA) Prohibited List. It is claimed to improve physical performance when administered as a supplement. As for other endogenous compounds such as steroids, the gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) analysis remains an efficient tool to differentiate endogenous substances from exogenous ones. A protocol was described in the literature for the analysis of AICAR by GC-C-IRMS. The aim of the present study was to implement this protocol in our laboratory and to propose solutions to avoid the difficulties encountered. The first point discussed in this study is the derivatization step. Due to the structure of the AICAR molecule, conventional derivatization for GC-C-IRMS such as acetylation could not be applied and silylation was preferred. The improvement of the derivatives stability was achieved thanks to several derivatization conditions tested. This adjustment led to a reproducible derivatization pattern with the 3-TMS form as major derivative product. The second point discussed in this study is the diminution of extracts' background noise. Indeed, the implementation of the published protocol was not easy due to high performance liquid chromatography (HPLC) problems encountered when concentrated urine was injected into our system. Also, too many interferences in the endogenous reference compound fractions were observed. The addition of both a wash step before the HPLC purification and a HPLC purification step for the endogenous reference compound (ERC) fraction allowed us to increase the robustness of the method. This study presents the modified protocol compared to the original protocol as well as the evaluation of the whole method performances. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".