Recent Advances in the Local Drug Delivery Systems for Improvement ofAnticancer Therapy
Bibliographic record
Abstract
The conventional anticancer chemotherapies not only cause serious toxic effects but also produce resistance in tumor cells exposed to long-term therapy. Usually, the selective killing of metastasized cancer cells requires long-term therapy with higher drug doses because the cancer cells develop resistance due to the induction of poly-glycoproteins (P-gps) that act as a transmembrane efflux pump to transport drugs out of the cells. During the last few decades, scientists have been exploring new anticancer drug delivery systems such as microencapsulation, hydrogels, and nanotubes to improve bioavailability, reduce drug-dose requirement, decrease multiple drug resistance, and save normal cells as non-specific targets. Hopefully, the development of novel drug delivery vehicles (nanotubes, liposomes, supramolecules, hydrogels, and micelles) will assist in delivering drug molecules at the specific target site and reduce undesirable side effects of anticancer therapies in humans. Nanoparticles and lipid formulations are also designed to deliver a small drug payload at the desired tumor cell sites for their anticancer actions. This review will focus on the recent advances in drug delivery systems and their application in treating different cancer types in humans.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".