Simultaneous quantification of endogenous biomarkers and detection of testosterone esters in human serum using gas chromatography–high‐resolution mass spectrometry
Bibliographic record
Abstract
RATIONALE: High-resolution mass spectrometry (HRMS) has been demonstrated to be an alternative platform for quantitative analyses, identifying unknown compounds and gathering information for the elucidation of chemical structures. This work describes a method to detect 13 esters of testosterone (T) and 5 biomarkers in 0.1 mL of human serum using gas chromatography (GC) coupled to HRMS. METHODS: Analytes were extracted from serum after deproteinization and liquid-liquid extraction. The trimethylsilyl derivatives were analyzed using a gas chromatograph coupled to HRMS at low electron energy to minimize molecule fragmentation. The acquisition in profiling full-scan mode was applied with a resolving power of 30 000 at m/z 400. Linearity, lower limit of quantitation, and measurement uncertainty were assessed. Precision and accuracy were assessed at 0.5 and 2 ng/mL, respectively. Mass accuracy (MA) and mass extraction window (MEW) were also evaluated. RESULTS: T esters showed a linear response between 0.25 and 10 ng/mL (except for undecanoate, enanthate, and propionate that showed lineal responses between 0.5 and 10 ng/mL and isocaproate between 2 and 10 ng/mL); detection limits remained between 0.1 and 0.5 ng/mL and accuracy between 81% and 119%. The MA (MEW = 10 ppm) was maintained between -2.4 and 4.8 ppm. The biomarkers (T, androstenedione, dehydroepiandrosterone [DHEA], estradiol, and 17-OH-progesterone) showed a linear response within the evaluated range; quantification limits remained between 0.1 and 0.5 ng/mL (except for DHEA), the accuracy between 88% and 99%, and precision between 3.5% and 10.8%. Measurement uncertainties were found between 5.6% and 17.2%. MA (MEW = 3 ppm) was maintained between -0.47 and 0.12 ppm. CONCLUSIONS: The method to detect T esters and five endogenous biomarkers in serum using GC coupled to HRMS showed linear responses up to 10 ng/mL with adequate precision, accuracy, and uncertainties. It was possible to distinguish cholesterol from T-isocaproate based on the MEW of 10 ppm, preventing false positives. In addition, this method allows searching for other biomarkers and/or unknown metabolites and other ester forms not included here but at a later stage if necessary.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".