ULTRASOUND-ASSISTED MAGNETIC NANOPARTICLE-BASED GENE DELIVERY
Bibliographic record
Abstract
Abstract Low-intensity pulsed ultrasound (LIPUS), a special type of ultrasonic stimulation, is attracting a lot of attention for both clinical and scientific research. In this paper, we report a concept of a new method using magnetic nanoparticles (MNPs) for LIPUS-assisted gene delivery. The MNPs are iron oxide superparamagnetic nanoparticles, coated with polyethyleneimine (PEI), which introduces a high positive surface charge, favorable for the binding of genetic material. Due to the paramagnetic properties of the MNPs, the application of an external magnetic field increases transfection efficiency; meanwhile, LIPUS stimulation enhances cell permeability. We found out that stimulation at the intensity of 30 mW/cm 2 for 10 minutes yields optimal results with a minimal adverse effect on the cells. Combining the effect of the external magnetic field and LIPUS, the genetic material (GFP or Cherry Red plasmid in our case) can enter the cells. The flow cytometry results showed that by using just a magnetic field to direct the genetic material, the transfection efficiency of HEK 293 cells that were treated with our MNPs was 56.1%. Coupled with LIPUS stimulation, it increased to 61.5% or 19% higher than the positive control (Lipofectamine 2000). In addition, compared with the positive control, our method showed less toxicity. Cell viability after transfection was 63.61%, 19% higher than with the standard transfection technique. In conclusion, we designed a new gene-delivery technique that is affordable, targeted, shows low-toxicity, yet high transfection efficiency, compared to other conventional approaches. The Graphical Abstract
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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 | 0.000 | 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".