Targeting of Lipid-Protamine-DNA (LPD) Lipopolyplexes Using RGD Motifs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The incorporation of pegylated lipid into Lipid-Protamine-DNA (LPD-PEG) lipopolyplexes causes a decrease of their in vitro transfection activity. This can be partially attributed to a reduction in particle binding to cells. To restore particle binding and specifically target LPD formulations to tumor cells, the lipid-peptide conjugate DSPE-PEG5K-succinyl-ACDCRGDCFCG-COOH (DSPE-PEG5K-RGD-4C) was generated and incorporated into LPD formulations (LPD-PEG-RGD). LPD-PEG-RGD was characterized with respect to its biophysical and biological properties. The Incorporation of DSPE-PEG5K-RGD-4C ligands into LPD formulations results in a 5 and a 15 fold increase in the LPD-PEG-RGD binding and uptake, respectively, over an LPD-PEG formulation. Enhancement of binding and uptake resulted in a 100 fold enhancement of transfection activity. Moreover, this transfection enhancement was specific to cells expressing appropriate integrin receptors (MDA-MB-231). Huh7 cells, known for their low level of alphavbeta3 and alphavbeta5 integrin expression, failed to show RGD mediated transfection enhancement. This transfection enhancement can be abolished in a competitive manner using free RGD peptide, but not an RGE control peptide. Results demonstrated RGD mediated enhanced LPD-PEG cell binding and transfection in cells expressing the integrin receptor. These formulations provide the basis for effective, targeted, systemic gene delivery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 it