Hyaluronic acid-based nanoplatforms for Doxorubicin: A review of stimuli-responsive carriers, co-delivery and resistance suppression
Bibliographic record
Abstract
An important motivation for the use of nanomaterials and nanoarchitectures in cancer therapy emanates from the widespread emergence of drug resistance. Although doxorubicin (DOX) induces cell cycle arrest and DNA damage by suppressing topoisomerase activity, resistance to DOX has severely restricted its anti-cancer potential. Hyaluronic acid (HA) has been extensively utilized for synthesizing nanoparticles as it interacts with CD44 expressed on the surface of cancer cells. Cancer cells can take up HA-modified nanoparticles through receptor-mediated endocytosis. Various types of nanostructures such as carbon nanomaterials, lipid nanoparticles and polymeric nanocarriers have been modified with HA to enhance the delivery of DOX to cancer cells. Hyaluronic acid-based advanced materials provide a platform for the co-delivery of genes and drugs along with DOX to enhance the efficacy of anti-cancer therapy and overcome chemoresistance. In the present review, the potential methods and application of HA-modified nanostructures for DOX delivery in anti-cancer therapy are discussed.
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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 | 0.002 |
| 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".