High-Throughput Bioaffinity Mass Spectrometry for Screening and Identification of Designer Anabolic Steroids in Dietary Supplements
Bibliographic record
Abstract
A generic high-throughput bioaffinity liquid chromatography-mass spectrometry (BioMS) approach was developed and applied for the screening and identification of known and unknown recombinant human sex hormone-binding globulin (rhSHBG)-binding designer steroids in dietary supplements. For screening, a semi-automated competitive inhibition binding assay was combined with fast ultrahigh-performance-LC-electrospray ionization-triple-quadrupole-MS (UPLC-QqQ-MS). 17β-Testosterone-D3 was used as the stable isotope label of which the binding to rhSHBG-coated paramagnetic microbeads was inhibited by any other binding (designer) steroid. The assay was performed in a 96-well plate and combined with the fast LC-MS, 96 measurements could be performed within 4 h. The concentration-dependent inhibition of the label by steroids in buffer and dietary supplements was demonstrated. Following an adjusted bioaffinity isolation procedure, suspect extracts were injected into a chip-UPLC(NanoTile)-Q-time-of-flight-MS system for full-scan accurate mass identification. Next to known steroids, 1-testosterone was identified in three of the supplements studied and the designer steroid tetrahydrogestrinone was identified in a spiked supplement. The generic steroid-binding assay can be used for high-throughput screening of androgens, estrogens, and gestagens in dietary supplements to fight doping. When combined with chip-UPLC-MS, it is a powerful tool for early warning of unknown emerging rhSHBG bioactive designer steroids in dietary supplements.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".