Bibliographic record
Abstract
For well-known toxicants such as perfluorooctanoic acid (PFOA) and its precursor, 8:2 fluorotelomer alcohol (8:2 FTOH), identification of the CYPs driving the metabolism, from the FTOH to the PFOA, could be used to identify the possibility of localized toxicity.This is based on the varying distribution of CYPs in organs and cell membranes.Due to environmental regulations, PFOA and 8:2 FTOH have been phased out in favour of compounds with shorter fluorinated chain lengths, considered less bioaccumulative and toxic.However, there is currently insufficient knowledge of enzyme catalysed metabolism of these replacements.One replacement for 8:2 FTOH is the 6:2 FTOH [4] .This project identifies CYP 3A4 and CYP 2A6 as possible cytochrome P450s responsible for the phase I metabolism of 6:2 FTOH.A Michaelis Menten curve is generated for the metabolism of 6:2 FTOH by CYP 2A6 metabolism.This provided a KM and VMax of 4076.4 ± 581.9 ng/mL and 68.8 ± 2.8 ng/mL/min, respectively.Once inhibited with 35 𝜇M of tranylcypromine, HCl, a selective inhibitor of CYP 2A6, the KM and VMax were determined to be 8796.2± 1366.1 ng/mL and 69.5 ± 4.1 ng/mL/min, representing competitive inhibition.We further demonstrated that CYP 2A6 was responsible for 6:2 FTOH metabolism using human recombinant assays with purified CYP 2A6.These assays yielded a 6:2 FTOH metabolic conversion rate of 0.42 ng/mL/min.This rate significantly decreased with the addition of Tranylcypromine HCl.This confirms CYP 2A6 as an active enzyme for the metabolism of 6:2 FTOH in the human liver.
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 | 0.001 |
| 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 | 0.001 |
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".