Chitosan Nanoparticles at the Biological Interface: Implications for Drug Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The unique properties of chitosan make it a useful choice for various nanoparticulate drug delivery applications. Although chitosan is biocompatible and enables cellular uptake, its interactions at cellular and systemic levels need to be studied in more depth. This review focuses on the various physical and chemical properties of chitosan that affect its performance in biological systems. We aim to analyze recent research studying interactions of chitosan nanoparticles (NPs) upon their cellular uptake and their journey through the various compartments of the cell. The positive charge of chitosan enables it to efficiently attach to cells, increasing the probability of cellular uptake. Chitosan NPs are taken up by cells via different pathways and escape endosomal degradation due to the proton sponge effect. Furthermore, we have reviewed the interaction of chitosan NPs upon in vivo administration. Chitosan NPs are immediately surrounded by a serum protein corona in systemic circulation upon intravenous administration, and their biodistribution is mainly to the liver and spleen indicating RES uptake. However, the evasion of RES system as well as the targeting ability and bioavailability of chitosan NPs can be improved by utilizing specific routes of administration and covalent modifications of surface properties. Ongoing clinical trials of chitosan formulations for therapeutic applications are paving the way for the introduction of chitosan into the pharmaceutical market and for their toxicological evaluation. Chitosan provides specific biophysical properties for effective and tunable cellular uptake and systemic delivery for a wide range of applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it