LC-MS/MS measurement of endogenous steroid hormones and phase II metabolites in blood volumetric absorptive microsampling (VAMS) for doping control purposes
Bibliographic record
Abstract
BACKGROUND: Volumetric Absorptive Microsampling (VAMS) is emerging as a valuable technique in the collection of dried biological specimens, offering a potential alternative to traditional sampling methods. The objective of this study was to assess the suitability of 30 μL VAMS for the measurement of endogenous steroid hormones. METHODS: A novel LC-MS/MS method was developed for the quantification of 18 analytes in VAMS samples, including main endogenous free steroids and phase II metabolites of androgens. The method underwent validation in accordance with ISO/IEC 17025:2017 and World Anti-Doping Agency (WADA) requirements. Subsequently, it was applied to authentic VAMS samples obtained from 20 healthy volunteers to assess the stability of target analytes under varying storage conditions. RESULTS: The validation protocol assessed method's selectivity, matrix effect, extraction recovery, quantitative performance, carry-over and robustness. The analysis of authentic samples demonstrated the satisfactory stability of monitored steroids in VAMS stored at room temperature, 4 °C, -20 °C and -80 °C for up to 100 days and subjected to up to 3 freezing-thawing cycles. CONCLUSIONS: The validated LC-MS/MS method demonstrated its suitability for the measurement of steroids in dried blood VAMS. The observed stability of steroidal compounds suggests promising prospects for future applications of VAMS, both in anti-doping contexts and clinical research.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".