Stimuli-responsive (nano)architectures for phytochemical delivery in cancer therapy
Bibliographic record
Abstract
The use of phytochemicals for purpose of cancer therapy has been accelerated due to resistance of tumor cells to conventional chemotherapy drugs and therefore, monotherapy does not cause significant improvement in the prognosis and survival of patients. Therefore, administration of natural products alone or in combination with chemotherapy drugs due to various mechanisms of action has been suggested. However, cancer therapy using phytochemicals requires more attention because of poor bioavailability of compounds and lack of specific accumulation at tumor site. Hence, nanocarriers for specific delivery of phytochemicals in tumor therapy has been suggested. The pharmacokinetic profile of natural products and their therapeutic indices can be improved. The nanocarriers can improve potential of natural products in crossing over BBB and also, promote internalization in cancer cells through endocytosis. Moreover, (nano)platforms can deliver both natural and synthetic anti-cancer drugs in combination cancer therapy. The surface functionalization of nanostructures with ligands improves ability in internalization in tumor cells and improving cytotoxicity of natural compounds. Interestingly, stimuli-responsive nanostructures that respond to endogenous and exogenous stimuli have been employed for delivery of natural compounds in cancer therapy. The decrease in pH in tumor microenvironment causes degradation of bonds in nanostructures to release cargo and when changes in GSH levels occur, it also mediates drug release from nanocarriers. Moreover, enzymes in the tumor microenvironment such as MMP-2 can mediate drug release from nanocarriers and more progresses in targeted drug delivery obtained by application of nanoparticles that are responsive to exogenous stimulus including light.
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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 | 0.001 |
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".