Rapid synthesis of lipid nanoparticles containing hydrophobic inorganic nanoparticles
Bibliographic record
Abstract
A straightforward "bottom-up" synthesis is described for efficient entrapment of inorganic hydrophobic nanoparticles (HNPs) consisting of iron oxide, gold, or quantum dots within the hydrophobic core of lipid nanoparticles (LNPs). These LNPs consist of hydrophobic "core" lipids such as triolein surrounded by a monolayer of amphipathic "surface" lipids, such as phosphatidylcholine and polyethylene-glycol-lipid. It is shown that rapid, controlled mixing of HNPs, core lipids and surface lipids in an organic solvent with an aqueous phase resulted in stable, monodisperse LNPs containing HNPs (LNP-HNP). This method allows 40-fold more hydrophobic iron oxide nanoparticles (IONPs) to be entrapped within an LNP than previous methods and can be readily extended to encapsulate other HNPs. The LNP-HNP diameter can be modulated over the range of 35-150 nm by varying the flow rate during particle synthesis or by varying the core-to-surface lipid ratio. LNP-IONPs can be generated using a variety of "core" lipids, including other triglycerides as well as cholesteryl-palmitate and tocopherol. Finally, it is shown that LNP-IONPs are accumulated in the liver, resulting in enhanced contrast for in vivo MRI. It is concluded that the bottom-up approach for encapsulating HNPs within LNPs has advantages of homogeneity, reproducibility and stability required for biomedical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".