Designing tailored nanocarriers for delivering ribonucleic acid interference-based therapeutics: Advancing targeted cancer gene therapy
Bibliographic record
Abstract
Early detection of cancer, along with the administration of effective antineoplastic therapies, remain imperative, as cancer continues to represent one of the leading causes of mortality worldwide.RNA interference (RNAi) is a revolutionary technique of gene therapy that utilizes short RNA molecules, including microRNAs (miRNAs) and small interfering RNAs (siRNAs), to target and degrade specific messenger RNAs (mRNAs), effectively silencing oncogenic genes post-transcriptionally.The primary challenge in the effective application of these therapeutics for treating various diseases lies in overcoming the substantial intracellular and extracellular barriers associated with their delivery.As compared to traditional drug delivery systems, poly (lactic-coglycolic acid) (PLGA) nanoplatforms offer numerous advantages, including low toxicity, high bioavailability, superior drug entrapment, sustained release profiles, and enhanced protection of the encapsulated cargo.Furthermore, the enhanced permeability and retention effect exhibited by cancer cells facilitates the preferential accumulation of PLGA nanoparticles in cancer tissues, enabling targeted delivery of these therapeutics.Inorganic nanoparticles ......
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".