Nanodomain Formation in Lipid Membranes Probed by Time-Resolved Fluorescence
Bibliographic record
Abstract
Time-resolved fluorescence measurements on liposomes prepared with 1 mol % pyrene-labeled lipids (PLLs) with a headgroup bearing either an alcohol (PSOH) or an imido diacetic acid (PSIDA) and 99 mol % 1-palmitoyl-2-oleyl-3-sn-phosphatidylcholines (POPC) or 99 mol % distearylphosphatidylcholine (DSPC) were performed to investigate how lipids phase separate within the membrane bilayer. Global analysis of the fluorescence decays with the fluorescence blob model (FBM) led to the conclusion that the PLLs were homogeneously distributed on the surface of POPC vesicles while the PLLs phase-separated in the DSPC vesicles. The analysis yielded the fraction of aggregated pyrenes, f(agg). The large f(agg) values found for PSIDA suggest that the imido diacetic acid headgroup of PSIDA induces self-aggregation and phase separation in both membranes. The addition of external cations such as Cu(2+) and La(3+) was shown to hinder diffusional encounters between PSIDAs. The cations seem to target preferentially unassociated PSIDAs rather than aggregated PSIDA clusters. Accounting for the quenching of pyrene by Cu(2+) enables one to use PSIDA to probe the microviscosity of the lipid membrane. Using this effect, the environment of PSIDA in the DSPC membrane was found to be about 6 times more viscous than that in the POPC membrane. This difference is attributed to the difference in viscosity of the fluid POPC membrane and the gel-like DSPC 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".