Gas Chromatography Analysis of Urinary Alkoxyacetic Acids as Biomarkers of Exposure to Aliphatic Alkyl Ethers
Bibliographic record
Abstract
Analysis of alkoxyacetic acids has received considerable research interest in toxicology because these compounds have been reported as metabolites and biomarkers of exposure to widely used industrial chemicals such as alkyl-substituted ethylene glycols and other aliphatic ethers. This paper describes an improved method for the determination of methoxyacetic acid (MAA), ethoxyacetic acid (EAA), and butoxyacetic acid (BAA) in rat urine. Solid-phase extraction with Bakerbond(T) C18 bonded silica cartridges was successfully employed to isolate the acids from rat urine. The acids were then converted to methyl esters with diazomethane derivatization and analyzed using a gas chromatograph (GC) equipped with a mass spectrometry (MS) and a GC with flame ionization detector (FID). Employing GC-MS under selected ion monitoring detection, the lowest detection concentrations for MAA, EAA, and BAA were determined to be from 2 to 4 ng/mL urine in 1 mL of sample size. This method is 5 to 10 times more sensitive than that using GC-FID. The method described here is superior to the existing ones reported in the literature in that it employs an easy sample treatment procedure and gives much higher recoveries, making it suitable for routine assays. The utility of this new method was demonstrated in a toxicology study of aliphatic alkyl ethers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".