Sample Preparation and Liquid Chromatography-Tandem Mass Spectrometry for Multiple Steroids in Mammalian and Avian Circulation
Bibliographic record
Abstract
Blood samples from wild mammals and birds are often limited in volume, allowing researchers to quantify only one or two steroids from a single sample by immunoassays. In addition, wildlife serum or plasma samples are often lipemic, necessitating stringent sample preparation. Here, we validated sample preparation for simultaneous liquid chromatography--tandem mass spectrometry (LC-MS/MS) quantitation of cortisol, corticosterone, 11-deoxycortisol, dehydroepiandrosterone (DHEA), 17β-estradiol, progesterone, 17α-hydroxyprogesterone and testosterone from diverse mammalian (7 species) and avian (5 species) samples. Using 100 µL of serum or plasma, we quantified (signal-to-noise (S/N) ratio ≥ 10) 4-7 steroids depending on the species and sample, without derivatization. Steroids were extracted from serum or plasma using automated solid-phase extraction where samples were loaded onto C18 columns, washed with water and hexane, and then eluted with ethyl acetate. Quantitation by LC-MS/MS was done in positive ion, multiple reaction-monitoring (MRM) mode with an atmospheric pressure chemical ionization (APCI) source and heated nebulizer (500°C). Deuterated steroids served as internal standards and run time was 15 minutes. Extraction recoveries were 87-101% for the 8 analytes, and all intra- and inter-run CVs were ≤ 8.25%. This quantitation method yields good recoveries with variable lipid-content samples, avoids antibody cross-reactivity issues, and delivers results for multiple steroids. Thus, this method can enrich datasets by providing simultaneous quantitation of multiple steroids, and allow researchers to reimagine the hypotheses that could be tested with their volume-limited, lipemic, wildlife samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".