Bacterial Nanocellulose-Enhanced Alginate Double-Network Hydrogels Cross-Linked with Six Metal Cations for Antibacterial Wound Dressing
Bibliographic record
Abstract
Alginate (Alg) and bacterial nanocellulose (BNC) have exhibited great potential in biomedical applications, especially wound dressing. Non-toxicity and a moisture-maintaining nature are common features making them favorable for functional dressing fabrication. BNC is a natural biopolymer that promotes major advances to the current and future biomedical materials, especially in a flat or tubular membrane form with excellent mechanical strength at hydrated state. The main drawback limiting wide applications of both BNC and Alg is the lack of antibacterial activity, furthermore, the inherent poor mechanical property of Alg leads to the requirement of a secondary dressing in clinical treatment. To fabricate composite dressings with antibacterial activity and better mechanical properties, sodium alginate was efficiently incorporated into the BNC matrix using a time-saving vacuum suction method followed by cross-linking through immersion in separate solutions of six cations (manganese, cobalt, copper, zinc, silver, and cerium). The results showed the fabricated composites had not only pH-responsive antibacterial activities but also improved mechanical properties, which are capable of acting as smart dressings. All composites showed non-toxicity toward fibroblast cells. Rat model evaluation showed the skin wounds covered by the dressings healed faster than by BNC.
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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) | 0.000 | 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 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".