Novel properties of cholesterol–dioleoylphosphatidylcholine mixtures
Bibliographic record
Abstract
We have studied the properties of mixtures of cholesterol with dioleoylphosphatidylcholine (DOPC), and with several other phospholipids, including 1-stearoyl-2-oleoylphosphatidylcholine (SOPC) and dioleoleoylphosphatidylserine (DOPS), as a function of cholesterol molar fraction and of temperature. Mixtures of DOPC with a cholesterol molar fraction of 0.4 or greater display polymorphic behavior. This polymorphism includes the formation of structures that give rise to isotropic peaks in 31P NMR at cholesterol molar fractions between 0.4 and 0.6, dependent on the thermal history of the sample. Cryo-electron microscopy studies demonstrate the formation of small globular aggregates that would contribute to a narrowing of the 31P NMR powder pattern. At molar fraction cholesterol 0.6 and higher and at temperatures above 70 degrees C, the mixtures with DOPC convert to the hexagonal phase. Lipid polymorphism is accompanied by the phase separation of cholesterol crystals in the anhydrous form and/or the monohydrate form. The crystals that are formed have substantially altered kinetics of hydration and dehydration, compared with both pure cholesterol monohydrate crystals and with crystals formed in the presence of the other phospholipids that do not form the hexagonal phase in the presence of cholesterol. This fact demonstrates that these cholesterol crystals are in intimate contact with the DOPC phospholipid and are not present as morphologically separate structures.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".