Physicochemical characterization of siRNA‐peptide complexes
Bibliographic record
Abstract
Short interfering RNAs (siRNAs) trigger RNA interference (RNAi), where the complementary mRNA is degraded, resulting in silencing of the encoded protein. A delivery carrier is desired to increase the solution stability of siRNA and improve its cellular uptake to overcome its rapid enzymatic degradation and low transfection efficiency. In this study, Arginine-9 (R9), a cell-penetrating peptide derived from the HIV 1 Tat protein, was investigated as a potential carrier for siRNAs. A connective tissue growth factor (CTGF) encoding siRNA was used because of its therapeutic potential of treating breast cancer. The interaction between R9 and siRNA was studied by UV/vis spectroscopy and circular dichroism (CD). The hydrodynamic diameter of the siRNA-R9 complexes was determined by dynamic light scattering (DLS), and the Zeta potential of the complexes was obtained by measuring the electrophoretic mobility. The effect of salt addition is also quantified using UV-vis spectroscopy. The siRNA and R9 readily formed complexes/aggregates through molecular association, accompanying a change in surface charge with increasing peptide concentration, reaching a maximum hydrodynamic diameter of approximately 1 mum at siRNA saturation. The highest binding ratio of R9 to siRNA determined from the UV/vis spectra and CD is 10.3:1 and 39.1:1 from DLS (corresponds to charge ratios of 2.2:1 (+/-) and 8.4:1, respectively). The difference in binding ratio is possibly because of the difference in signal contribution between absorption and light scattering. The physicochemical characterization of CTGF siRNA-R9 complexes presented here have shown that various methods can be used to control the properties of the siRNA-peptide complexes, which provide a basis for the formulation of siRNA therapeutics with peptide carriers.
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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 | 0.001 |
| 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) | 0.002 | 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".