Programmable Assembly of DNA-Functionalized Liposomes by DNA
Bibliographic record
Abstract
Bionanotechnology involves the use of biomolecules to control both the structure and property of nanomaterials. One of the most studied examples is DNA-directed assembly of inorganic nanoparticles such as gold nanoparticles (AuNPs). However, systematic studies on DNA-linked soft nanoparticles, such as liposomes, are still lacking. We herein report the programmable assembly and systematic characterization of DNA-linked liposomes as a function of liposome size, charge, fluidity, composition, DNA spacer, linker DNA sequence, and salt concentration for direct comparison to DNA-directed assembly of AuNPs. Similar to the assemblies of AuNPs, sharp melting transitions were observed for liposomes where the first derivative of the melting curve full width at half-maximum (fwhm) is equal to or less than 1 °C for all of the tested liposomes, allowing sequence specific DNA detection. We found that parameters such as liposome size, charge, and fluidity have little effect on the DNA melting temperature. Cryo-TEM studies showed that programmable assemblies can be obtained and that the majority of the liposomes maintained a spherical shape in the assembled state. While liposome and AuNP systems are similar in many aspects, there are also important differences that can be explained by their respective physical properties.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".