Recent Advances on Nanotechnology Applications to Cancer Drug Therapy
Bibliographic record
Abstract
One of the greatest challenges in cancer drug therapy is to maximize the effectiveness of the active ingredient while reducing its systemic adverse effects. Conventional (non-targeted) systemic drug therapy is characterized by unspecific distribution of the anticancer drugs: both healthy and affected tissues are thus exposed to the chemotherapeutic agent, giving raise to off-target side-effects. Besides, a number of widely-used chemoterapeutic agents present unfavorable physicochemical properties, such as low solubility or low stability issues, limiting their available routes of administration and therapeutic applications. Nano-delivery systems seem as promising solutions to these issues. They can be used for targeted-drug release, diagnostic imaging and therapy monitoring. Nanosystems allow the formulation of drug delivery systems with tailored properties (e.g. solubility, biodegradability, release kinetics and distribution) that provide means to improve cancer patients' quality of life by lowering the administered dose and, incidentally, the cost of clinical treatments. This article overviews the main features of different nanovehicles (linear and non-linear polymeric nanosystems, lipid-based systems, inorganic nanoparticles) and presents a selection of reports on applications of such systems to cancer therapy published between 2010 and 2013.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".