Influence of lipoplex surface charge on siRNA delivery: application to the<i>in vitro</i>downregulation of CXCR4 HIV-1 co-receptor
Bibliographic record
Abstract
OBJECTIVE: Cationic lipidic formulations have been successfully used to deliver small interfering RNA (siRNA) into cells but they show limitations for in vivo application due to their cytotoxicity and instability in the presence of serum. To overcome these limitations, the authors developed an anionic lipid-based carrier named Neutraplex (Nx). Here, they wanted to investigate the influence of the lipoplex (Lx) surface charge on cytotoxicity, delivery and silencing activity of siRNAs. METHODS: The efficiency of three Nx formulations (cationic, close to neutrality and anionic) to deliver anti-CXCR4 siRNAs in MAGI cells was investigated and compared with the cationic commercial transfection reagent Lipofectamine RNAiMAX. Cellular uptake and intracellular localization of a fluorescent siRNA was monitored in live cells using fluorescence microscopy and silencing activity was measured by flow cytometry and RT-PCR analysis. RESULTS: The authors found that the Lx surface charge influenced cellular uptake and silencing activity of siRNA in cell cultures. Although cationic Lx formulations were the most efficient carriers to deliver active silencing siRNAs, negatively charged lipoplexes were taken up by cells, delivered active siRNAs and presented low cytotoxicity. CONCLUSIONS: Altogether, the findings support further investigation for in vivo delivery of therapeutic siRNAs using Nx. Furthermore, this study indicates that anionic delivery systems may have potential for in vivo RNAi therapeutics.
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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) | 0.001 | 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".