Reducing endotoxin contamination in chitosan: An optimized purification method for biomedical applications
Bibliographic record
Abstract
Chitosan, a natural biopolymer, is widely used in biomedical applications due to its biocompatibility. It also possesses immunostimulatory properties and is notably studied and used as a vaccine adjuvant. However, endotoxin contamination, an often-overlooked extrinsic factor, may compromise its safety and confound the interpretation of its biological or immunostimulatory effects. Despite its widespread use, data on endotoxin levels in commercially available chitosan remain limited, potentially contributing to inconsistent immunological outcomes. In this study, we developed a simple four-step purification process that effectively reduces endotoxin levels without altering key chitosan properties, namely the degree of deacetylation (DDA) and molar mass (MW). We characterized the endotoxin content of various commercial chitosan samples using the Limulus Amebocyte Lysate (LAL) assay and evaluated their immunostimulatory properties in vitro, both before and after purification. A 1 N sodium hydroxide treatment for 48 h reduced endotoxin levels by up to 100-fold, lowering the most contaminated sample from 62,000 EU/g to approximately 600 EU/g, while preserving DDA and MW. In vitro immunoassays showed that proinflammatory cytokine responses (IL-1β, TNF-α, IL-6) in J774A.1 macrophages were primarily driven by endotoxin contamination, which was significantly reduced following purification. Structural properties of chitosan showed no measurable impact on cytokine profiles. This study underscores the critical need for endotoxin assessment and removal in chitosan intended for biomedical applications.
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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 | 0.001 |
| 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) | 0.000 | 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".