Determination of p-tert-Octylphenol in Blood and Tissues by Gas Chromatography Coupled with Mass Spectrometry
Bibliographic record
Abstract
A sensitive and reproducible procedure using gas chromatography coupled with mass spectrometry is described for the determination of p-tert-octylphenol (OP), a persistent degradation product of alkylphenol ethoxylates that binds to the estrogen receptor in blood and tissues. The first step involved the extraction of blood (200 microL) or tissue homogenate (400 microL) with methyl tert-butyl ether, including p-tert-butylphenol (BP) as internal standard. After extraction, the sample was evaporated to dryness with a gentle stream of nitrogen at 45 degrees C, and OP and BP were derivatized with an acetylation reaction involving acetic anhydride and catalyzed by pyridine. Samples were then analyzed by a gas chromatograph equipped with a mass spectrometer (single ion monitoring) with a Varian VF-5ms capillary column. The limit of detection and the limit of quantification of the method in blood were 4.6 and 15.5 ng/mL, respectively. The linearity and reproducibility of the method were acceptable, with coefficients of variation of approximately 10% for blood and ranging between 9% and 27% for tissues. This method was applied to the determination of unchanged OP in blood and tissues obtained from Sprague-Dawley rats after oral and IV OP administration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".