Chitosan-Based Nanogels: Synthesis and Toxicity Profile for Drug Delivery to Articular Joints
Bibliographic record
Abstract
One important challenge in treating avascular-degraded cartilage is the development of new drugs for both pain management and joint preservation. Considerable efforts have been invested in developing nanosystems using biomaterials, such as chitosan, a widely used natural polymer exhibiting numerous advantages, i.e., non-toxic, biocompatible and biodegradable. However, even if chitosan is generally recognized as safe, the safety and biocompatibility of such nanomaterials must be addressed because of potential for greater interactions between nanomaterials and biological systems. Here, we developed chitosan-based nanogels as drug-delivery platforms and established an initial biological risk assessment for osteocartilaginous applications. We investigated the influence of synthesis parameters on the physicochemical characteristics of the resulting nanogels and their potential impact on the biocompatibility on all types of human osteocartilaginous cells. Monodisperse nanogels were synthesized with sizes ranging from 268 to 382 nm according to the acidic solution used (i.e., either citric or acetic acid) with overall positive charge surface. Our results demonstrated that purified chitosan-based nanogels neither affected cell proliferation nor induced nitric oxide production in vitro. However, nanogels were moderately genotoxic in a dose-dependent manner but did not significantly induce acute embryotoxicity in zebrafish embryos, up to 100 µg∙mL−1. These encouraging results hold great promise for the intra-articular delivery of drugs or diagnostic agents for joint pathologies.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".