Recent Advancements in Cancer RNA-interference Therapy with Nanotechnology Strategies
Bibliographic record
Abstract
Cancer is a global challenge, and genetics play a major role in onsets and in designing the next generation of cancer therapies. One of the key approaches is the downregulation of genes through RNA interference (RNAi) which has been proposed as a promising tool for controlling cancer-causing genes by employing a complementary base-pairing mechanism. Crucial tools in RNAi; small interfering RNA (siRNA), and microRNA (miRNA) have been extensively utilized in cancer therapy. Despite their discovery nearly two decades ago, only a handful of RNAi-based products have recently gained approval from regulatory authorities like the FDA, principally due to inherent limitations. The efficacy of RNAi therapies is significantly hindered by barriers related to absorption, distribution, metabolism, and excretion, leading to rapid elimination from the systemic circulation before reaching the cytosol of targeted cells. Nevertheless, RNAi therapeutics hold immense promise in various diseases, especially in various types of cancer. Overcoming these challenges demands the design of suitable drug delivery systems and the implementation of strategies aimed at enhancing pharmacokinetic parameters associated with RNAi therapies. Nanotechnology-based vehicles such as polymer-based nanoparticles, lipid nanoparticles, liposomes, dendrimers, and inorganic nanoparticles may serve as efficient carriers for targeting RNAi therapies to their desired sites. This review discusses the recent advances in nanotechnology strategies for RNAi delivery, with the overarching objective of facilitating effective targeting and gene silencing for the advancement of RNAi in cancer therapy.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".