Evaluation and modeling of the impact of coexposures to VOC mixtures on urinary biomarkers
Bibliographic record
Abstract
CONTEXT: Urinary biomarkers are widely used among biomonitoring studies because of their ease of collection and nonintrusiveness. Chloroform and TEX (i.e., toluene, ethylbenzene, and m-xylene) are chemicals that are often found together because of common use. Although interactions occurring among TEX are well-known, no information exists on possible kinetic interactions between these chemicals and chloroform at the level of parent compound or urinary biomarkers. OBJECTIVE: The objective of this study was therefore to study the possible interactions between these compounds in human volunteers with special emphasis on the potential impact on urinary biomarkers. MATERIALS AND METHODS: Five male volunteers were exposed by inhalation for 6 h to single, binary, and quaternary mixtures that included chloroform. Exhaled air and blood samples were collected and analyzed for parent compound concentrations. Urinary biomarkers (o-cresol, mandelic, and m-methylhippuric acids) were quantified in urine samples. Published PBPK model for chloroform was used, and a Vmax of 3.4 mg/h/kg was optimized to provide a better fit with blood data. Adapted PBPK models from our previous study were used for parent compounds and urinary biomarkers for TEX. RESULTS: Binary exposures with chloroform resulted in no significant interactions. Experimental data for quaternary mixture exposures were well predicted by PBPK models using published description of competitive inhibition among TEX components. However, no significant interactions were observed at levels used in this study. CONCLUSION: PBPK models for urinary biomarkers proved to be a good tool in quantifying exposure to VOC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".