Chitin and Chitosan: Prospective Biomedical Applications in Drug Delivery, Cancer Treatment, and Wound Healing
Bibliographic record
Abstract
Chitin and its derivative chitosan are highly abundant polymers in nature, appearing in both the shells and exoskeletons of various marine and non-marine species. Since they possess favorable properties, such as biocompatibility, biodegradability, non-toxicity, and non-immunogenicity, they have gained recent attention due to their enormous potential biomedical applications. The polycationic surface of chitosan enables it to form hydrogenic and ionic bonds with drug molecules, which is one of its most useful properties. Because chitosan is biocompatible, it can therefore be used in drug delivery systems. The development of chitosan-based nanoparticles has also contributed to the significance of chitin as a drug delivery system that can deliver drugs topically. Furthermore, chitin can be used in cancer treatment as a vehicle for delivering cancer drugs to a specific site and has an antiproliferative effect by reducing the viability of cells. Finally, chitosan can be used as a wound dressing in order to promote the faster regeneration of skin epithelial cells and collagen production by fibroblasts. As discussed in this review, chitin and chitosan have diverse applications in the medical field. Recognizing the biomedical applications of these two polymers is essential for future research in tissue engineering and nanobiotechnology.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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".