Nanoparticle‐Enabled, Image‐Guided Treatment Planning of Target Specific RNAi Therapeutics in an Orthotopic Prostate Cancer Model
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
The abilities to deliver siRNA to its intended action site and assess the delivery efficiency are challenges for current RNAi therapy, where effective siRNA delivery will join force with patient genetic profiling to achieve optimal treatment outcome. Imaging could become a critical enabler to maximize RNAi efficacy in the context of tracking siRNA delivery, rational dosimetry and treatment planning. Several imaging modalities have been used to visualize nanoparticle-based siRNA delivery but rarely did they guide treatment planning. We report a multimodal theranostic lipid-nanoparticle, HPPS(NIR)-chol-siRNA, which has a near-infrared (NIR) fluorescent core, enveloped by phospholipid monolayer, intercalated with siRNA payloads, and constrained by apoA-I mimetic peptides to give ultra-small particle size (<30 nm). Using fluorescence imaging, we demonstrated its cytosolic delivery capability for both NIR-core and dye-labeled siRNAs and its structural integrity in mice through intravenous administration, validating the usefulness of NIR-core as imaging surrogate for non-labeled therapeutic siRNAs. Next, we validated the targeting specificity of HPPS(NIR)-chol-siRNA to orthotopic tumor using sequential four-steps (in vivo, in situ, ex vivo and frozen-tissue) fluorescence imaging. The image co-registration of computed tomography and fluorescence molecular tomography enabled non-invasive assessment and treatment planning of siRNA delivery into the orthotopic tumor, achieving efficacious RNAi therapy.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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