Cholesterol‐Containing Liposomes Decorated With Au Nanoparticles as Minimal Tunable Fusion Machinery
Bibliographic record
Abstract
Abstract Membrane fusion is essential for the basal functionality of eukaryotic cells. In physiological conditions, fusion events are regulated by a wide range of specialized proteins, operating with finely tuned local lipid composition and ionic environment. Fusogenic proteins, assisted by membrane cholesterol and calcium ions, provide the mechanical energy necessary to achieve vesicle fusion in neuromediator release. Similar cooperative effects must be explored when considering synthetic approaches for controlled membrane fusion. We show that liposomes decorated with amphiphilic Au nanoparticles (AuLips) can act as minimal tunable fusion machinery. AuLips fusion is triggered by divalent ions, while the number of fusion events dramatically changes with, and can be finely tuned by, the liposome cholesterol content. We combine quartz‐crystal‐microbalance with dissipation monitoring (QCM‐D), fluorescence assays, and small‐angle X‐ray scattering (SAXS) with molecular dynamics (MD) at coarse‐grained (CG) resolution, revealing new mechanistic details on the fusogenic activity of amphiphilic Au nanoparticles (AuNPs) and demonstrating the ability of these synthetic nanomaterials to induce fusion regardless of the divalent ion used (Ca 2+ or Mg 2+ ). The results provide a novel contribution to developing new artificial fusogenic agents for next‐generation biomedical applications that require tight control of the rate of fusion events (e.g., targeted drug delivery).
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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".