Length-Controlled Nanofiber Micelleplexes as Efficient Nucleic Acid Delivery Vehicles
Bibliographic record
Abstract
Micelleplexes show great promise as effective polymeric delivery systems for nucleic acids. Although studies have shown that spherical micelleplexes can exhibit superior cellular transfection to polyplexes, to date there has been no report on the effects of micelleplex morphology on cellular transfection. In this work, we prepared precision, length-tunable poly(fluorenetrimethylenecarbonate)- b -poly(2-(dimethylamino)ethyl methacrylate) (PFTMC 16 - b -PDMAEMA 131 ) nanofiber micelleplexes and compared their properties and transfection activity to those of the equivalent nanosphere micelleplexes and polyplexes. We studied the DNA complexation process in detail via a range of techniques including cryo-transmission electron microscopy, atomic force microscopy, dynamic light scattering, and ζ-potential measurements, thereby examining how nanofiber micelleplexes form, as well the key differences that exist compared to nanosphere micelleplexes and polyplexes in terms of DNA loading and colloidal stability. The effects of particle morphology and nanofiber length on the transfection and cell viability of U-87 MG glioblastoma cells with a luciferase plasmid were explored, revealing that short nanofiber micelleplexes (length < ca. 100 nm) were the most effective delivery vehicle examined, outperforming nanosphere micelleplexes, polyplexes, and longer nanofiber micelleplexes as well as the Lipofectamine 2000 control. This study highlights the potential importance of 1D micelleplex morphologies for achieving optimal transfection activity and provides a fundamental platform for the future development of more effective polymeric nucleic acid delivery vehicles.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".