The human fecal microbiome contributes to the biotransformation of the PFAS surfactant 8:2 monosubstituted polyfluoroalkyl phosphate ester
Bibliographic record
Abstract
testing was completed by incubating the fecal samples with 8:2 monoPAP (400-10,000 nM) up to 120 minutes in an anaerobic chamber. Reactions were then terminated and the samples prepared for GC- and LC-MS/MS analysis. Metabolites of interest were the immediate hydrolysis product, the 8:2 fluorotelomer alcohol (FTOH), and 11 additional metabolites previously shown to form from 8:2 FTOH in both oxic and anoxic environments. The kinetics of 8:2 monoPAP transformation by gut microbiota were compared to those in human S9 liver and intestine fractions, both of which have active levels of hydrolyzing and oxidative enzymes that transform 8:2 monoPAP. Transformation rates from 8:2 monoPAP to 8:2 FTOH were highest in liver S9 > intestine S9 > fecal suspensions. The gut microbiome also produced a unique composition of oxidative metabolites, where the following intermediate metabolites were more abundant than terminal PFCAs: 8:2 fluorotelomer unsaturated carboxylic acid (FTUCA) > 8:2 fluorotelomer carboxylic acid (FTCA) > 7:2 Ketone ≈ perfluorohexanoic acid (PFHxA). Hydrolytic and oxidative metabolites contributed up to 30% of the molar balance after microbial 8:2 monoPAP transformation. Together, the results suggest that the gut microbiome can play a notable role in PAP biotransformation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".