Ethylene glycol: Evidence of glucuronidation <i>in vivo</i> shown by analysis of clinical toxicology samples
Bibliographic record
Abstract
Abstract In the search for improved laboratory methods for the diagnosis of ethylene glycol poisoning, the in vivo formation of a glucuronide metabolite of ethylene glycol was hypothesized. Chemically pure standards of the β‐O‐glucuronide of ethylene glycol (EG‐GLUC) and a deuterated analog (d4‐EG‐GLUC) were synthesized. A high‐performance liquid chromatography and tandem mass spectrometry method for determination of EG‐GLUC in serum after ultrafiltration was validated. Inter‐assay precision (%RSD) was 3.9% to 15.1% and inter‐assay %bias was −2.8% to 12.2%. The measuring range was 2–100 μmol/L (0.48–24 mg/L). Specificity testing showed no endogenous amounts in routine clinical samples (n = 40). The method was used to analyze authentic, clinical serum samples (n = 31) from patients intoxicated with ethylene glycol. EG‐GLUC was quantified in 15 of these samples, with a mean concentration of 6.5 μmol/L (1.6 mg/L), ranging from 2.3 to 15.6 μmol/L (0.55 to 3.7 mg/L). In five samples, EG‐GLUC was detected below the limit of quantification (2 μmol/L) and it was below the limit of detection in 11 samples (1 μmol/L). Compared to the millimolar concentrations of ethylene glycol present in blood after intoxications and potentially available for conjugation, the concentrations of EG‐GLUC found in clinical serum samples are very low, but comparable to concentrations of ethyl glucuronide after medium dose ethanol intake. In theory, EG‐GLUC has a potential value as a biomarker for ethylene glycol intake, but the pharmacokinetic properties, in vivo/vitro stability and the biosynthetic pathways of EG‐GLUC must be further studied in a larger number of patients and other biological matrices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".