Formation of Kinetically Trapped Nanoscopic Unilamellar Vesicles from Metastable Nanodiscs
Bibliographic record
Abstract
Zwitterionic long-chain lipids (e.g., dimyristoyl phosphatidylcholine, DMPC) spontaneously form onion-like, thermodynamically stable structures in aqueous solutions (commonly known as multilamellar vesicles, or MLVs). It has also been reported that the addition of zwitterionic short-chain (i.e., dihexanoyl phosphatidylcholine, DHPC) and charged long-chain (i.e., dimyristoyl phosphatidylglycerol, DMPG) lipids to zwitterionic long-chain lipid solutions results in the formation of unilamellar vesicles (ULVs). Here, we report a kinetic study on lipid mixtures composed of DMPC, DHPC, and DMPG. Two membrane charge densities (i.e., [DMPG]/[DMPC] = 0.01 and 0.001) and two solution salinities (i.e., [NaCl] = 0 and 0.2 M) are investigated. Upon dilution of the high-concentration samples at 50 °C, thermodynamically stable MLVs are formed, in the case of both weakly charged and high salinity solution mixtures, implying that the electrostatic interactions between bilayers are insufficient to cause MLVs to unbind. Importantly, in the case of these samples small angle neutron scattering (SANS) data show that, initially, nanodiscs (also known as bicelles) or bilayered ribbons form at low temperatures (i.e., 10 °C), but transform into uniform size, nanoscopic ULVs after incubation at 10 °C for 20 h, indicating that the nanodisc is a metastable structure. The instability of nanodiscs may be attributed to low membrane rigidity due to a reduced charge density and high salinity. Moreover, the uniform-sized ULVs persist even after being heated to 50 °C, where thermodynamically stable MLVs are observed. This result clearly demonstrates that these ULVs are kinetically trapped, and that the mechanical properties (e.g., bending rigidity) of 10 °C nanodiscs favor the formation of nanoscopic ULVs over that of MLVs. From a practical point of view, this method of forming uniform-sized ULVs may lend itself to their mass production, thus making them economically feasible for medical applications that depend on monodisperse lipid-based systems for therapeutic and diagnostic purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".