Polyethylene glycol and octa-arginine dual-functionalized nanographene oxide: an optimization for efficient nucleic acid delivery
Bibliographic record
Abstract
The successful application of nucleic acid-based therapy for the treatment of various cancers is largely dependent on a safe and efficient delivery system. A dual-functionalized graphene oxide (GO)-based nanocarrier with the conjugation of aminated-polyethylene glycol (PEG-diamine) and octa-arginine (R8) for the intracellular delivery of nucleic acids is proposed. The functionalized sites are covalently co-conjugated and the PEG : R8 molar ratio is optimized at 10 : 1 to achieve a hydrocolloidally stable size of 252 ± 2.0 nm with an effective charge of +40.97 ± 1.05 and an amine-rich content of 10.87 ± 0.4 μmol g-1. The uptake of the nanocarrier in breast cancer cell lines, MCF-7 and MDA-MB 231, is investigated. The siRNA and pDNA condensation ability in the presence and absence of enzymes and the endosomal buffering capacity, as well as the intracellular localization of the gene/nanocarrier complex are also evaluated. Furthermore, the delivery of functional genes associated with the nanocarrier is assessed using c-Myc protein knockdown and EGFP gene expression. The effective uptake of the nanocarrier by the cells shows superior cytocompatibility, and protects the siRNA and pDNA against enzyme degradation while inhibiting their migration with N : P ratios of 10 and 5, respectively. The co-conjugation of PEG-diamine and the cationic cell-penetrating peptide (CPP) into the GO nanocarrier also provides a superior internalization efficacy of 85% in comparison with a commercially available transfection reagent. The c-Myc protein knockdown and EGFP expression, which are induced by the nanocarrier, confirm that the optimized PEG-diamine/R8-functionalized GO could effectively deliver pDNA and siRNA into the cells and interfere with gene expression.
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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".