Microfluidic generation of lipid nanoparticles to facilitate DNA entry into human mast cells
Bibliographic record
Abstract
Abstract Mast cells are highly granulated immune cells that can be targeted for transfection using lipid nanoparticles (LNP). Lipid nanoparticles (LNPs) were composed of cholesterol and 1-Stearoyl-2-oleoyl-sn-glycero-3-phosphocholine, the ionizable lipid 2,2-dilinoleyl-4-(2-dimethylaminoethyl)-[1,3]-dioxolane, and the phospholipid 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene-glycol)-2000] using microfluidic mixing and used to encapsulate a plasmid encoding enhanced green fluorescent protein (EGFP) under the control of a human cytomegalovirus promoter (pEGFP-C1). Encapsulation efficiency was determined using PicoGreen and LNP size was measured using dynamic light scattering. Human mast cells-1 (HMC-1) were transfected with loaded or empty LNPs in the presence or absence of apolipoprotein E (ApoE) for 24, 48 or 72 hours in medium. Cells were analyzed by fluorescence microscopy and flow cytometry. LNPs were approximately 100 nm in diameter (ideal) and efficiently encapsulated plasmids pEGFP-C1 (75.38%). ApoE4 appeared to slightly augment (>10%) LNP uptake after 24 hr. Our study demonstrates that LNPs generated by microfluidic mixing efficiently encapsulate a plasmid payload and can be used to successfully transfect human mast cells. Carrier molecules such as ApoE can be used to augment LNP uptake by mast cells. Further functionalization and optimization of these LNPs can provide powerful new tools for targeting other immune cells.
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".