Microfluidic Mixing: A General Method for Encapsulating Macromolecules in Lipid Nanoparticle Systems
Bibliographic record
Abstract
Previous work has shown that lipid nanoparticles (LNP) composed of an ionizable cationic lipid, a poly(ethylene glycol) (PEG) lipid, distearoylphosphatidylcholine (DSPC), cholesterol, and small interfering RNA (siRNA) can be efficiently manufactured employing microfluidic mixing techniques. Cryo-transmission electron microscopy (cryo-TEM) and molecular simulation studies indicate that these LNP systems exhibit a nanostructured core with periodic aqueous compartments containing siRNA. Here we examine first how the lipid composition influences the structural properties of LNP-siRNA systems produced by microfluidic mixing and, second, whether the microfluidic mixing technique can be extended to macromolecules larger than siRNA. It is shown that LNP-siRNA systems can exhibit progressively more bilayer structure as the proportion of bilayer DSPC lipid is increased, suggesting that the core of LNP-siRNA systems can exhibit a continuum of nanostructures depending on the proportions and structural preferences of component lipids. Second, it is shown that the microfluidic mixing technique can also be extended to encapsulation of much larger negatively charged polymers such mRNA (1.7 kb) or plasmid DNA (6 kb). Finally, as a demonstration of the generality of the microfluidic mixing encapsulation process, it is also demonstrated that negatively charged gold nanoparticles (5 nm diameter) can also be efficiently encapsulated in LNP containing cationic lipids. Interestingly, the nanostructure of these gold-containing LNP reveals a "currant bun" morphology as visualized by cryo-TEM. This structure is fully consistent with LNP-siRNA structure predicted by molecular modeling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".