Exploitation of De Novo Helper-Lipids for Effective Gene Delivery.
Bibliographic record
Abstract
PURPOSE: In gene delivery, a fusogenic lipid such as dioleyl phosphatidylethanolamine (DOPE) which is a component of cationic liposomal vector is important factor for effective transfection efficiency. We investigated the effect of penetration enhancers as alternative helper-lipids to DOPE. METHODS: Transdermal penetraion enhancers such as N-lauroylsarcosine (LS), (R)-(+)-limonene (LM), vitamin E (VE), and phosphatidyl choline from eggs (EggPC) were used in this experiments as helper-lipids with N-[1-(2, 3-dioleyloxy) propyl]-N, N, N-trimethlylammonium chloride (DOTMA) and cholesterol (CHOL). We examined in vitro transfection efficiency, cytotoxicity, hematotoxicity, and in vivo transfection efficiency of plasmid DNA/cationic liposomes complexes. RESULTS: In transfection experiments in vitro, the cationic lipoplexes containing LS had highest transfection efficiency among the other lipoplexes independently of FBS. Furthermore, the lipoplexes containing LS had lowest cell toxicity among the other lipoplexes in the presence of FBS. As the results of erythrocytes interaction experiment, DOTMA/LS/CHOL, DOTMA/VE/CHOL, and DOTMA/EggPC/CHOL lipoplexes showed extremely lower hematotoxicity. On the basis of these results, the in vivo transfection efficiencies of the lipoplexes were examined. The lipoplexes containing LS had the highest transfection activity among the other lipoplexes. CONCLUSION: In conclusion, several transdermal penetration enhancers are available for alternative helper-lipids to DOPE in cationic liposomal vectors. Among them, DOTMA/LS/CHOL lipoplexes showed superior characteristics in in vitro transfection efficiency, cell toxicity, hematotoxicity, and in vivo transfection efficiency.
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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) | 0.001 | 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) | 0.001 | 0.001 |
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".