Progress in Delivery of siRNA-Based Therapeutics Employing Nano-Vehicles for Treatment of Prostate Cancer
Bibliographic record
Abstract
Prostate cancer (PCa) accounts for a high number of deaths in males with no available curative treatments. Patients with PCa are commonly diagnosed in advanced stages due to the lack of symptoms in the early stages. Recently, the research focus was directed toward gene editing in cancer therapy. Small interfering RNA (siRNA) intervention is considered as a powerful tool for gene silencing (knockdown), enabling the suppression of oncogene factors in cancer. This strategy is applied to the treatment of various cancers including PCa. The siRNA can inhibit proliferation and invasion of PCa cells and is able to promote the anti-tumor activity of chemotherapeutic agents. However, the off-target effects of siRNA therapy remarkably reduce its efficacy in PCa therapy. To date, various carriers were designed to improve the delivery of siRNA and, among them, nanoparticles are of importance. Nanoparticles enable the targeted delivery of siRNAs and enhance their potential in the downregulation of target genes of interest. Additionally, nanoparticles can provide a platform for the co-delivery of siRNAs and anti-tumor drugs, resulting in decreased growth and migration of PCa cells. The efficacy, specificity, and delivery of siRNAs are comprehensively discussed in this review to direct further studies toward using siRNAs and their nanoscale-delivery systems in PCa therapy and perhaps other cancer types.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".