Isomer Distribution of Perfluorocarboxylates in Human Blood: Potential Correlation to Source
Bibliographic record
Abstract
Detection of perfluorocarboxylate anions (PFCAs), such as perfluorooctanoate (C7F15COO-, PFOA), at ng/g levels in human tissues has engendered public scrutiny of industrial fluorochemicals. Routes of PFCA exposure for the general human population are likely diverse given direct (industrially produced) and indirect (production from precursor organofluorines) sources. Major industrial production of organofluorines, including PFCAs, stems from either electrochemical fluorination (ECF) or telomerization. ECF products are a mixture of structural isomers (linear and branched perfluoroalkyls) and telomerization products are assumed to have one perfluorocarbon arrangement, typically linear. The objective of this research was to investigate structural isomer patterns of PFCAs in human blood. Volatile derivatives of PFCAs in human blood were analyzed by GC-(NCI)-MS for quantitation and isomers. PFOA was the dominant PFCA (mean 4.4 ng/g). Blood serum isomer profiles consisted of predominantly (mean approximately 98%) the linear isomer for each PFCA (C8-C11). There were similarities in branched isomer patterns of an ECF PFOA standard with both PFOA and PFNA in blood. Direct exposure to ECF PFOA, which has a legacy of production for uses in fluoropolymer industries, is postulated to be a source of the observed branched isomer pattern. Predominance of linear PFCA isomers and the [even PFCA] > [odd PFCA] concentration trend in blood is suggestive of additional input from a strictly linear perfluoroalkyl source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".