Bifunctional Au-templated RNA nanoparticles enable direct cell uptake detection and GRP75 knockdown in prostate cancer
Bibliographic record
Abstract
Nucleic acids templated on gold (Au) surfaces have led to a wide range of functional materials ranging from microarrays, sensors and probes in addition to drug delivery and treatment. In this application, we describe a simple and novel method for templating amino-functionalized RNA onto Au surfaces and their self-assembly into small, discrete nanoparticles. In our method, sample hybridization with a complementary RNA strand with and without a fatty acid (palmitamide) appendage produced functionalized double-stranded RNA on the Au surface. The resulting Au-functionalized RNA particles were found to be stable under reducing conditions according to UV-Vis spectroscopy. Sample characterization by DLS and TEM confirmed self-assembly into primarily small (∼10-40 nm) spherical shaped nanoparticles expected to be amenable to cell biology. However, fluorescence emission (λexc: 350 nm, λem: 650 nm) revealed radiative properties which limited cell uptake detection. Introduction of FITC within the Au-functionalized RNA particles produced a bifunctional probe, in which FITC fluorescence emission (λexc: 494 nm, λem: 522 nm) facilitated cell uptake detection, in a time-dependent manner. The dual encapsulation-release profiles of the FITC-labeled Au-functionalized RNA particles were validated by time-dependent UV-Vis spectroscopy and spectrofluorimetry. These experiments respectively indicated an increase in FITC absorption (λabs: 494 nm) and fluorescence emission (λem: 522 nm) with increased sample incubation times, under physiological conditions. The release of Au-functionalized siRNA particles in prostate cancer (PC-3) cells resulted in concomitant knockdown of GRP75, which led to detectable levels of cell death in the absence of a transfection vector. Thus, the formulation of stable, small and discrete Au-functionalized RNA nanoparticles may prove to be valuable bifunctional probes in the theranostic study of cancer cells.
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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".