Disclosing Environmental Ligands of L-FABP and PPARγ: Should We Re-evaluate the Chemical Safety of Hydrocarbon Surfactants?
Bibliographic record
Abstract
Chemical contaminants can cause adverse effects by binding to the liver-fatty acid binding protein (L-FABP) and peroxisome proliferator-activated nuclear receptor γ (PPARγ), which are vital in lipid metabolism. However, the presence of numerous compounds in the environment has hindered the identification of their ligands, and thus only a small portion have been discovered to date. In this study, protein A ffinity P urification with N ontargeted A nalysis (APNA) was employed to identify the ligands of L-FABP and PPARγ in indoor dust and sewage sludge. A total of 83 nonredundant features were pulled-out by His-tagged L-FABP as putative ligands, among which 13 were assigned as fatty acids and hydrocarbon surfactants. In contrast, only six features were isolated when His-tagged PPARγ LBD was used as the protein bait. The binding of hydrocarbon surfactants to L-FABP and PPARγ was confirmed using both recombinant proteins and reporter cells. These hydrocarbon surfactants, along with >50 homologues and isomers, were detected in dust and sludge at high concentrations. Fatty acids and hydrocarbon surfactants explained the majority of L-FABP (57.7 ± 32.9%) and PPARγ (66.0 ± 27.1%) activities in the sludge. This study revealed hydrocarbon surfactants as the predominant synthetic ligands of L-FABP and PPARγ, highlighting the importance of re-evaluating their chemical safety.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".