Growing a Nucleotide/Lanthanide Coordination Polymer Shell on Liposomes
Bibliographic record
Abstract
Coating liposomes with a shell is a useful strategy to increase membrane stability and prevent leakage or fusion. Nucleotide/lanthanide coordination nanoparticles (NPs) are formed by a simple mixing at ambient conditions. Because some lipid headgroups contain lanthanide binding ligands, they may direct the growth of such coordination NPs. Herein, a gadolinium/adenosine monophosphate (Gd 3+ /AMP) shell was formed on liposomes (liposome@Gd 3+ /AMP) using lipids containing phosphoserine (PS) or cholinephosphate (CP) headgroups, while phosphocholine liposomes did not support the shell. Liposome binding Gd 3+ is confirmed by transmission electron microscopy (TEM). The negatively charged CP and PS liposomes reversed to positive upon Gd 3+ binding, while other metals such as Ca 2+ and Zn 2+ did not reverse the charge. Binding of Gd 3+ did not leak the PS liposomes. Then, AMP was further added to cross-link Gd 3+ on the liposome surface. A shell was formed as indicated by TEM, and the content inside the liposome remained for the PS liposomes. While adding Triton X-100 still induced leakage of the encapsulated liposomes, the shell protected the liposomes from leakage induced by ZnO NPs, suggesting a porous structure of the Gd 3+ /AMP shell which allowed penetration of Triton X-100 but not the larger ZnO NPs. This work provides a simple method to coat liposomes, and also offers a fundamental understanding of liposome adsorption of lanthanide ions.
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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".