Easy and effective method to generate endotoxin-free chitosan particles for immunotoxicology and immunopharmacology studies
Bibliographic record
Abstract
OBJECTIVES: The cationic biopolymer chitosan (CH) has emerged as a promising candidate adjuvant due to its safety profile and immunostimulatory properties. The presence of endotoxin contamination in biomaterials is generally underappreciated and can generate misleading results. It is important to establish a convenient methodology to obtain large amounts of high quality chitosan nanoparticles for biomedical applications. METHODS: We developed an easy method to generate endotoxin-free chitosan and assessed its purity using the Limulus amebocyte lysate assay and by measuring dendritic cell activation. KEY FINDINGS: Purified chitosan-based formulations alone failed to induce production of the proinflammatory cytokines tumour necrosis factor alpha (TNF-α) and interleukin (IL)-6 in bone marrow-derived dendritic cells (BMDCs) generated from C57BL/6 mice, while maintaining its ability to promote IL-1β secretion in combination with the Toll-like receptor (TLR)-9 agonist, CpG. Moreover, BMDCs from C3H/HeN and TLR4-deficient mice, C3H/HeJ were stimulated with endotoxin-free chitosan-based formulations and no differences were observed in IL-6 and IL-1β secretion, excluding the involvement of TLR-4 in the immunomodulatory effects of chitosan. CONCLUSIONS: The developed method provides simple guidelines for the production of endotoxin-free chitosan, ideal for biomedical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".