Switchable Lipid Provides pH-Sensitive Properties to Lipid and Hybrid Polymer/Lipid Membranes
Bibliographic record
Abstract
Blending amphiphilic copolymers and lipids constitutes a novel approach to combine the advantages of polymersomes and liposomes into a new single hybrid membrane. Efforts have been made to design stimuli-responsive vesicles, in which the membrane’s dynamic is modulated by specific triggers. In this investigation, we proposed the design of pH-responsive hybrid vesicles formulated with poly(dimethylsiloxane)-block-poly(ethylene oxide) backbone (PDMS36-b-PEO23) and cationic switchable lipid (CSL). The latter undergoes a pH-triggered conformational change and induces membrane destabilization. Using confocal imaging and DLS measurements, we interrogated the structural changes in CSL-doped lipid and hybrid polymer/lipid unilamellar vesicles at the micro- and nanometric scale, respectively. Both switchable giant unilamellar lipid vesicles (GUV) and hybrid polymer/lipid unilamellar vesicles (GHUV) presented dynamic morphological changes, including protrusions and fission upon acidification. At the submicron scale, scattered intensity decreased for both switchable large unilamellar vesicles (LUV) and hybrid vesicles (LHUV) under acidic pH. Finally, monitoring the fluorescence leakage of encapsulated calcein, we attested that CSL increased the permeability of GUV and GHUV in a pH-specific fashion. Altogether, these results show that switchable lipids provide a pH-sensitive behavior to hybrid polymer/lipid vesicles that could be exploited for the triggered release of drugs, cell biomimicry studies, or as bioinspired micro/nanoreactors.
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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) | 0.000 | 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 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".