Steroid profiling by UHPLC-MS/MS in dried blood spots collected from healthy women with and without testosterone gel administration
Bibliographic record
Abstract
The quantification of a large panel of endogenous steroids in serum by LC-MS/MS represents a powerful clinical tool for the screening or diagnosis of diverse endocrine disorders. This approach has also demonstrated excellent sensitivity for the detection of testosterone misuse in the anti-doping field, especially in female athlete population. In both situations, the use of dried blood spots (DBS) could provide a viable alternative to invasive venous blood collection. Here, the evaluation of DBS sampling for the quantification of a panel of endogenous steroids using UHPLC-MS/MS is described. The UHPLC-MS/MS method was validated for quantitative analysis of eleven free and eight conjugated steroids and was then used for the analysis of DBS samples collected in 14 healthy women during a normal menstrual cycle (control phase) followed by a 28-days testosterone gel treatment (treatment phase). Results were compared with those obtained from serum matrix. Satisfactory performance was obtained for all compounds in terms of selectivity, linearity, accuracy, precision, combined uncertainty, stability as well as extraction recovery and matrix effects. In control phase, high correlation was observed between DBS and serum concentrations for most compounds. In treatment phase, higher testosterone concentrations were observed in capillary than in venous DBS, suggesting a possible interference resulting from testosterone contamination on finger(s) used for gel application. Steroid profiling in capillary DBS represents a simple and efficient strategy for monitoring endogenous steroid concentrations and their fluctuation in clinical context of steroid-related disorders, or for the detection of testosterone abuse in anti-doping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".