Profiling Metal Oxides with Lipids: Magnetic Liposomal Nanoparticles Displaying DNA and Proteins
Bibliographic record
Abstract
Metal oxides include many important materials with various surface properties. For biomedical and analytical applications, it is desirable to engineer their biocompatible interfaces. Herein, a phosphocholine liposome (DOPC) and its headgroup dipole flipped counterpart (DOCP) were mixed with ten common oxides. Using the calcein leakage assay, cryo-TEM, and ζ-potential measurement, these oxides were grouped into three types. The type 1 oxides (Fe3 O4 , TiO2 , ZrO2 , Y2 O3 , ITO, In2 O3 , and Mn2 O3 ) form supported bilayers only with DOCP. Type 2 (SiO2 ) forms supported bilayers only with DOPC; type 3 (ZnO and NiO) are cationic and damage lipid membranes. Magnetic Fe3 O4 nanoparticles were further studied for conjugation of fluorophores, proteins, and DNA to the supported DOCP bilayers via lipid headgroup labeling, covalent linking, or lipid insertion. Delivery of the conjugates to cells and selective DNA hybridization were demonstrated. This work provides a general solution for coating the type 1 oxides with a simple mixing in water, facilitating applications in biosensing, separation, and nanomedicine.
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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".