Nanomedicine and drug delivery systems in cancer and regenerative medicine
Bibliographic record
Abstract
Nanomedicine and drug delivery technologies play a prominent role in modern medicine, facilitating better treatments than conventional drugs. Nanomedicine is being increasingly used to develop new methods of cancer diagnosis and treatment, since this technology can modulate the biodistribution and the target site accumulation of chemotherapeutic drugs, thereby reducing their toxicity. Regenerative medicine provides another area where innovative drug delivery technology is being studied for improved tissue regeneration. Drug delivery systems can protect therapeutic proteins and peptides against degradation in biological environments and deliver them in a controlled manner. Similarly, the combination of drug delivery systems and stem cells can improve their survival, differentiation, and engraftment. The present review summarizes the most important steps carried-out by the group of Prof Blanco-Prieto in nanomedicine and drug delivery technologies. Throughout her scientific career, she has contributed to the area of nanomedicine to improve anticancer therapy. In particular, nanoparticles loaded with edelfosine, doxorubicin, or methotrexate have demonstrated great anticancer activity in preclinical settings of lymphoma, glioma, and pediatric osteosarcoma. In regenerative medicine, a major focus has been the development of drug delivery systems for brain and cardiac repair. In this context, several microparticle-based technologies loaded with biologics have demonstrated efficacy in clinically relevant animal models such as monkeys and pigs. The latest research by this group has shown that drug delivery systems combined with cell therapy can achieve a more complete and potent regenerative response. Cutting-edge areas such as noninvasive intravenous delivery of cardioprotective nanomedicines or extracellular vesicle-based therapies are also being explored. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".