Development of N,O‐Carboxymethyl Chitosan‐Starch Biomaterial Inks for 3D Printed Wound Dressing Applications
Bibliographic record
Abstract
In this paper, a novel hybrid biomaterial ink consisting of two water-soluble polymers is investigated: starch and N,O-carboxymethyl chitosan (NOCC). The biomaterial ink is used to fabricate controlled release biodegradable wound dressing scaffolds via a novel low-temperature solvent (organic)-free 3D printing technique. NOCC is a variant of chitosan with a high degradation rate that can lead to an immediate release of the drugs, and starch, on the other hand, is used to alter degradation and drug release characteristics of the biomaterial. Mupirocin, a topical anti-infective, is incorporated into the biomaterial inks. Different biomaterial inks in terms of NOCC to starch ratio are prepared and characterized. Printability and rheology of the samples are investigated, and the release of mupirocin over time is quantified. The efficacy of the developed 3D printed wound dressings against Staphylococcus aureus is examined through disk diffusion assays. Increasing NOCC accelerated the release of the drug from the scaffold and led to larger zones of inhibition in the early hours of the in vitro tests; this phenomenon is correlated to the enhanced hydrophilicity of NOCC-dominated scaffolds. The drug release and the zone of inhibition are controlled by altering starch to NOCC ratio in the biomaterial ink.
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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".