Boosting antitumor efficacy using docetaxel-loaded nanoplatforms: from cancer therapy to regenerative medicine approaches
Bibliographic record
Abstract
The intersection of nanotechnology and pharmacology has revolutionized the delivery and efficacy of chemotherapeutic agents, notably docetaxel, a key drug in cancer treatment. Traditionally limited by poor solubility and significant side effects, docetaxel's therapeutic potential has been significantly enhanced through its incorporation into nanoplatforms, such as nanofibers and nanoparticles. This advancement offers targeted delivery, controlled release, and improved bioavailability, dramatically reducing systemic toxicity and enhancing patient outcomes. Nanofibers provide a versatile scaffold for the controlled release of docetaxel, utilizing techniques like electrospinning to tailor drug release profiles. Nanoparticles, on the other hand, enable precise drug delivery to tumor cells, minimizing damage to healthy tissues through sophisticated encapsulation methods such as nanoprecipitation and emulsion. These nanotechnologies not only improve the pharmacokinetic properties of docetaxel but also open new avenues in regenerative medicine by facilitating targeted therapy and cellular regeneration. This narrative review highlights the transformative impact of docetaxel-loaded nanoplatforms in oncology and beyond, showcasing the potential of nanotechnology to overcome the limitations of traditional chemotherapy and pave the way for future innovations in drug delivery and regenerative therapies. Through these advancements, nanotechnology promises a new era of precision medicine, enhancing the efficacy of cancer treatments while minimizing adverse effects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".