Expanding PFAS Identification with Transformation Product Libraries: Nontargeted Analysis Reveals Biotransformation Products in Mice
Bibliographic record
Abstract
Per- and polyfluoroalkyl substances (PFAS) are widely used persistent synthetic chemicals that have been linked to adverse health effects. While the behavior of PFAS has been evaluated in the environment, our understanding of reaction products in mammalian systems is limited. This study identified biological PFAS transformation products and generated mass spectral libraries to facilitate an automated search and identification. The biological transformation products of 27 PFAS, spanning 5 chemical subclasses (alcohols, sulfonamides, carboxylic acids, ethers, and esters), were evaluated following enzymatic reaction with mouse liver S9 fractions. Four major pathways were identified by liquid chromatography-high-resolution mass spectrometry: glucuronidation, sulfation, dealkylation, and oxidation. Class-based fragmentation rules and associated PFAS transformation product libraries were generated and integrated into an automated nontargeted PFAS data analysis software (FluoroMatch). Fragmentation was additionally predicted for the potential transformation products of more than 2,500 PFAS in the EPA CompTox Chemicals Dashboard PFASSTRUCTv4. Generated mass spectral libraries were validated by applying FluoroMatch to a data set of urine from aqueous film-forming foam (AFFF)-dosed mice. Toxicity predictions showed identified PFAS transformation products to be potential developmental and mutagenic toxicants. This research enables more comprehensive PFAS characterization in biological systems, which will improve the assessment of exposures and evaluation of the associated health impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".