Switchable Lipids: From Conformational Switch to Macroscopic Changes in Lipid Vesicles
Bibliographic record
Abstract
It has been shown that the use of conformationally pH-switchable lipids can drastically enhance the cytosolic drug delivery of lipid vesicles. Understanding the process by which the pH-switchable lipids disturb the lipid assembly of nanoparticles and trigger the cargo release is crucial to optimize the rational design of pH-switchable lipids. Here, we gather morphological observations (FF-SEM, Cryo-TEM, AFM, confocal microscopy), physicochemical characterization (DLS, ELS), as well as phase behavior studies (DSC, 2 H NMR, Langmuir isotherm, and MAS NMR) to propose a mechanism of pH-triggered membrane destabilization. We demonstrate that the switchable lipids are homogeneously incorporated with other co-lipids (DSPC, cholesterol, and DSPE-PEG 2000 ) and promote a liquid-ordered phase insensitive to temperature variation. Upon acidification, the protonation of the switchable lipids triggers a conformational switch altering the self-assembly properties of lipid nanoparticles. These modifications do not lead to a phase separation of the lipid membrane; however, they cause fluctuations and local defects, which result in morphological changes of the lipid vesicles. These changes are proposed to affect the permeability of vesicle membrane, triggering the release of the cargo encapsulated in the lipid vesicles (LVs). Our results confirm that pH-triggered release does not require major morphological changes, but can result from small defects affecting the lipid membrane permeability.
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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 | 0.000 |
| 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".