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 (NO2–) 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 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 | 0.000 | 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 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".