Neuroblastoma-targeted Nanoparticles Entrapping siRNA Specifically Knockdown ALK
Bibliographic record
Abstract
RNA interference molecules have some advantages as cancer therapeutics, including a proved efficacy on both wild-type (WT) and mutated transcripts and an extremely high sequence-specificity. The most significant hurdle to be overcome if exogenous small interfering RNAs (siRNA) is to be used therapeutically is the specific, effective, nontoxic delivery of siRNA to its intracellular site of action. At present, human applications are confined almost exclusively to targets within the liver, where the delivery systems naturally accumulate, and extra-hepatic targets remain a challenge. Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase that has recently been shown to contribute to the cell growth and progression of human neuroblastoma (NB). We investigated its potential as a therapeutic target in NB by generating anti-GD₂-targeted nanoparticles that carry ALK-directed siRNA, which are specifically and efficiently delivered to GD₂-expressing NB cells. Relative to free ALK-siRNA, anti-GD₂-targeted liposomal formulations of ALK-siRNA had low plasma clearance, increased siRNA stability, and improved binding, uptake, silencing and induction of cell death, and specificity for NB cells. In NB xenografts, intravenous (i.v.) injection of the targeted ALK-siRNA liposomes showed gene-specific antitumor activity with no side effects. ALK-selective siRNA entrapped in anti-GD₂-targeted nanoparticles is a promising new modality for NB treatment.
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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".