Nitric oxide generating copper–chitosan particles for wound healing applications
Bibliographic record
Abstract
Abstract BACKGROUND Nitric oxide (NO) is a signaling molecule that plays many roles during infection, inflammation, and wound healing processes. Due to the role of NO in wound repair, a novel NO generation system was developed based on copper–chitosan complexes that can be used for the topical generation of NO. Chitosan, a biocompatible polymer, chelates copper ions. Copper in the +1 state can reduce nitrite (NO 2 – ) and convert it into NO. With glucose, a reducing sugar, present in the system, Cu +2 can be returned to Cu +1 to complete the catalytic cycle. RESULTS Copper–chitosan milli‐ and micro‐sized particles were produced using microfluidic techniques. Copper–chitosan milli‐particles (Cu‐chito) did produce nitric oxide (NO). The maximum rates of NO production were ∼ 1.40 nmol min ‐1 g ‐1 (Cu‐chito) and 1.08 nmol min ‐1 g ‐1 (Cu‐chito + glucose). The milli‐particles were tested with ARPE‐19 cell lines in cell proliferation assays. Cu‐chito particle treatments with nitrite showed 130% more growth in comparison with chitosan milli‐particles not containing copper. Furthermore, Cu‐chito treatments of nitrite + glucose showed 152% more growth in comparison with control groups, and 118% in comparison with Cu‐chito with nitrite alone. The activity of intracellular NO target, matrix metalloproteinases (MMP‐2 and ‐9), were shown to increase by 60% after 48 h of Cu‐chito ± glucose treatments. CONCLUSION NO‐releasing copper–chitosan derivatives were produced, with proof of concept for nitric oxide release and positive effects on a cell culture model of wound healing. © 2018 Society of Chemical Industry
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".