Transferrin-modified nanostructured lipid carriers as multifunctional nanomedicine for codelivery of DNA and doxorubicin
Bibliographic record
Abstract
BACKGROUND: Nanostructured lipid carriers (NLC), composed of solid and liquid lipids, and surfactants are potentially good colloidal drug carriers. The aim of this study was to develop surface-modified NLC as multifunctional nanomedicine for codelivery of enhanced green fluorescence protein plasmid (pEGFP) and doxorubicin (DOX). METHODS: TWO DIFFERENT NANOCARRIERS: pEGFP- and DOX-loaded NLC, and solid lipid nanoparticles (SLN) were prepared. Transferrin-containing ligands were used for the surface coating of the vectors. Their average size, zeta potential, and drug encapsulation capacity were evaluated. In vitro transfection efficiency of the modified vectors was evaluated in human alveolar adenocarcinoma cell line (A549 cells), and in vivo transfection efficiency of the modified vectors was evaluated in a mouse bearing A549 cells model. RESULTS: Transferrin-modified DOX and pEGFP coencapsulated NLC (T-NLC) has a particle size of 198 nm and a +19 mV surface charge. The in vitro cell viabilities of the T-NLC formulations were over 80% compared with the control. T-NLC displayed remarkably greater gene transfection efficiency and enhanced antitumor activity than DOX- and pEGFP-coencapsulated SLN in vivo. CONCLUSION: The results demonstrate that T-NLC noticeably enhanced antitumor activity through the combination of gene therapy with chemotherapy. Also coating of active transferrin improved the lung cancer cell-targeting of the carriers. In summary, the novel gene and drug delivery system offers a promising strategy for the treatment of lung cancer.
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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".