Simplifying and expanding the screening for peptides <2 kDa by direct urine injection, liquid chromatography, and ion mobility mass spectrometry
Bibliographic record
Abstract
The analysis of low-molecular-mass peptides in doping controls has become a mandatory aspect in sports drug testing and, thus, the number of samples that has to be tested for these analytes has been steadily increasing. Several peptides <2 kDa with performance-enhancing properties are covered by the list of prohibited substances of the World Anti-Doping Agency including Desmopressin, LH-RH, Buserelin, Triptorelin, Leuprolide, GHRP-1, GHRP-2, GHRP-3, GHRP-4, GHRP-5,GHRP-6, Alexamorelin, Ipamorelin, Hexarelin, ARA-290, AOD-9604, TB-500 and Anamorelin. With the presented method employing direct urine injection into a liquid chromatograph followed by ion-mobility time-of-flight mass spectrometry, a facile, specific and sensitive assay for the aforementioned peptidic compounds is provided. The accomplished sensitivity allows for limits of detection between 50 and 500 pg/mL and thus covers the minimum required performance level of 2 ng/mL accordingly. The method is precise (imprecision <20%) and linear in the estimated working range between 0 and 10 ng/mL. The stability of the peptides in urine was tested, and -20°C was found to be the appropriate storage temperature for sports drug testing. Finally, proof-of-concept was shown by analysing elimination study urine samples collected from individuals having administered GHRP-6, GHRP-2, or LHRH.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".