An LC-MS/MS Methodological Framework for Steroid Hormone Measurement from Human Serum
Bibliographic record
Abstract
Abstract Estrogens and androgens are important regulators of sexual development and physiological processes in men and women, acting on numerous organs throughout the body. Moreover, they can contribute to a variety of pathologies, including osteoporosis, cancer, and cardiovascular and neurologic diseases. Analysis of estrogens and androgens in biological samples has been commonly performed using immunoassays for many years. However, these assays are suboptimal, as there is cross-reactivity with similar analytes, and they have moderate specificity and sensitivity. Thus, there is a clinical need to develop highly sensitive and specific methods for the accurate measurement of estrogen and androgen concentrations. Herein, we describe the development of three liquid chromatography coupled tandem mass spectrometry-based methods that incorporate the use of a Triple Quadrupole Mass Spectrometer for quantitative measurement of endogenous concentrations of various steroid hormones in human serum samples: (1) the simultaneous measurement of testosterone, androstenedione, and cortisol, (2) dehydroepiandrosterone (DHEA), and (3) 17β-estradiol (E2). The use of derivatizing reagents, Girard’s reagent P and dansyl chloride, allowed for significant gains in sensitivity in the analysis of DHEA and E2, respectively, relative to the underivatized analyte. These procedures proved efficient and adequately sensitive for steroid hormone analysis in extracted patient sera samples from older men and postmenopausal women, providing reliable data down to low nanogram/ml and sub-nanogram/ml levels. Moreover, utilizing the combination of highly specific mass transitions associated with these analytes and their respective internal deuterated standards provided a high degree of specificity to the identity of these hormones.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".