Bibliographic record
Abstract
Synthetic polymers, biopolymers, and their nanocomposites are being studied, and some of them are already used in different medical areas. Among the synthetic ones that can be mentioned are polyolefins, fluorinated polymers, polyesters, silicones, and others. Biopolymers such as polysaccharides (chitosan, hyaluronic acid, starch, cellulose, alginates) and proteins (silk, fibroin) have also become widely used and investigated for applications in medicine. Besides synthetic polymers and biopolymers, their nanocomposites, which are hybrids formed by a macromolecular matrix and a nanofiller (mineral or organic), have attracted great attention in the last decades in medicine and in other fields due to their outstanding properties. This review covers studies done recently using the polymers, biopolymers, nanocomposites, polymer micelles, nanomicelles, polymer hydrogels, nanogels, polymersomes, and liposomes used in medicine as drugs or drug carriers for cancer therapy and underlines their responses to internal and external stimuli able to make them more active and efficient. They are able to replace conventional cancer drug carriers, with better results.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".