Stabilization of Liposomes by Perfluorinated Compounds
Bibliographic record
Abstract
Abstract Perfluorinated compounds (PFCs) are emerging persistent environmental contaminants that may be toxic to animals and humans. To gain fundamental insights into the mechanism of their toxicity, the interactions of phosphocholine (PC) liposomes as model membranes were studied with three types of PFCs, including perfluorooctanoic acid, perfluorooctane sulfonate, and perfluorohexanesulfonic acid potassium salt, together with three common surfactants: sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB), and sodium 1-heptanesulfonate (SHS). The interactions were systematically characterized by zeta potential measurement, dynamic light scattering, negative-stain transmission electron microscopy, and fluorescence spectroscopy. Unmodified liposomes, calcein-loaded liposomes, and Laurdan dye-embedded liposomes were all tested. By gradually increasing the temperature, the three PFCs and SHS decreased the leakage of calcein-loaded 1,2-dipalmitoyl-sn-glycero-3-phosphocholine liposomes, whereas SDS and CTAB increased the leakage. The PFCs that affected the lipid membranes stronger than SHS were attributable to their perfluoroalkyl carbon chains. Packing of the lipids was further studied using Laurdan dye as a probe. Calcein leakage tests also indicated that PFCs inhibited lipid membrane leakage induced by inorganic nanoparticles such as silica and gold nanoparticles. This study confirmed the similar effect of the PFCs as cholesterol in affecting membrane properties and would be helpful for understanding the interaction mechanism of PFCs and cell membranes.
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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 | 0.000 | 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 | 0.001 |
| 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".