Screening for adiponectin receptor agonists and their metabolites in urine and dried blood spots
Bibliographic record
Abstract
AdipoRon and 112254 represent two known synthetic adiponectin receptor (adipoR) agonists. Although AdipoRon is the more potent compound, both have physiological properties that are similar to adiponectin - an adipokine with antidiabetic and antiatherogenic effects. Several transcriptional regulators are activated by adipoR-agonists leading to increased mitochondrial DNA content in vitro , an effect that can be abused by athletes for performance enhancing purposes. In the context of preventive anti-doping research, detection of AdipoRon and 112254 in routine doping control specimens would be valuable. Here, we describe our process for incorporating AdipoRon and 112254 into routine doping control methods involving urine and dried blood spot (DBS) analysis. Method validation including evaluation of specificity, limit of detection, identification capability, carryover, matrix interference, recovery, interday and intraday precision and linearity to standards provided by WADA. For identification in human urine, a liquid chromatography-tandem mass spectrometry-based testing approach was implemented for both adipoR agonists and two respective phase-I metabolites. Recovery of 85–104%, satisfactory limits of detection (i.e., 0.5–1 ng/mL), and imprecision values over three days at three concentration levels of <19% demonstrated the assay’s fitness-for-purpose. For identification from DBS a liquid chromatography-high-resolution/high-accuracy tandem mass spectrometry with online solid-phase extraction was implemented for AdipoRon and 112254. Here also, acceptable recoveries (i.e., 22–33%), limits of detection of 5–10 ng/mL, and imprecision values over three days at three concentration levels of <23%, were demonstrated. Hence, two methods for doping control screening from urine and DBS were established and shown to be fit-for-purpose for routine use.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".