Physicochemical characterization of siRNA‐peptide complexes
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| 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