Metal-Organic Frameworks as Anchors for Giant Unilamellar Vesicle Immobilization
Bibliographic record
Abstract
Giant Unilamellar Vesicles (GUVs) are ideal for studying cellular mechanisms due to their cell-mimicking morphology and size. The formation, stability, and immobilization of these vesicles are crucial for drug delivery and bioimaging studies. Separately, metal-organic frameworks (MOFs) are actively researched owing to their unique and varied properties, yet little is known about the interaction between MOFs and phospholipids. This study investigates the influence of the metal-phosphate interface on the formation, size distribution, and stability of GUVs with different lipid compositions. GUVs were electroformed in the presence of a series of MOFs. The results show Al, Zn, Cu, Fe, Zr, and Ca metal centers of MOFs can coordinate to phospholipids on the surface of GUVs, leading to the formation of functional GUV@MOF constructs, with stablilities over 12 hours. Macroscopically, society has seen biology (people, plants, microbes) interacting with inorganic materials all the time. We now explore how microscopic biological models behave in the presence of inorganic constructs. This research opens new avenues for advanced biomedical applications interacting tailored frameworks with liposomes.
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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".