Lipid Nanoparticles Containing siRNA Synthesized by Microfluidic Mixing Exhibit an Electron-Dense Nanostructured Core
Bibliographic record
Abstract
Lipid nanoparticles (LNP) containing ionizable cationic lipids are the leading systems for enabling therapeutic applications of siRNA; however, the structure of these systems has not been defined. Here we examine the structure of LNP siRNA systems containing DLinKC2-DMA(an ionizable cationic lipid), phospholipid, cholesterol and a polyethylene glycol (PEG) lipid formed using a rapid microfluidic mixing process. Techniques employed include cryo-transmission electron microscopy, (31)P NMR, membrane fusion assays, density measurements, and molecular modeling. The experimental results indicate that these LNP siRNA systems have an interior lipid core containing siRNA duplexes complexed to cationic lipid and that the interior core also contains phospholipid and cholesterol. Consistent with experimental observations, molecular modeling calculations indicate that the interior of LNP siRNA systems exhibits a periodic structure of aqueous compartments, where some compartments contain siRNA. It is concluded that LNP siRNA systems formulated by rapid mixing of an ethanol solution of lipid with an aqueous medium containing siRNA exhibit a nanostructured core. The results give insight into the mechanism whereby LNP siRNA systems are formed, providing an understanding of the high encapsulation efficiencies that can be achieved and information on methods of constructing more sophisticated LNP systems.
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) | 0.000 | 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".