Hydrophilic Interaction Liquid Chromatography−Tandem Mass Spectrometry Determination of Estrogen Conjugates in Human Urine
Bibliographic record
Abstract
We report a hydrophilic interaction liquid chromatography (HILIC) separation with tandem mass spectrometry (MS) detection method for analysis of seven urinary estrogen conjugates. HILIC separation employing a mobile phase with high organic solvent content resulted in enhanced electrospray ionization efficiency and MS sensitivity compared with reversed-phase (RP) LC-MS methods. Solid-phase extraction (SPE) was used to further improve the limit of detection and to eliminate interferences for the analysis of urine samples. No hydrolysis or derivatization was required in the sample pretreatment. This SPE/HILIC-MS/MS method provided limits of quantification (LOQs at S/N = 10) for the seven conjugates ranging from 2 to 1000 pg/mL with only 1 mL of urine sample, representing an improvement of 1 order of magnitude over the RPLC tandem MS methods previously reported. This method provided a linear dynamic range of 3 orders of magnitude, recovery of 92-109%, intraday accuracy of 84-109%, intraday precision of 1-14%, interday accuracy of 80-111%, and interday precision of 1-22%. We have successfully applied this technique to determine the seven estrogen conjugates in urine samples of a pregnant woman and found unique concentration changes of six estrogen conjugates at different stages of pregnancy while the concentration of estriol-3-glucuronide (E3-3G) remained constant. We further studied the profiles of individual estrogen conjugates in breast cancer patients before and after treatment and found patient-dependent effects of aromatase inhibitor treatment on estrogen phase-II metabolism, which have not been reported previously. This study demonstrates the potential clinical application of the HILIC-MS/MS technique for sensitive monitoring of the changes of urinary estrogen conjugates in a clinical setting.
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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".