New Antibacterial Paper Made of Silver Phosphate Cellulose Fibers: A Preliminary Study on the Elimination of <i>Staphylococcus aureus</i> Involved in Diabetic Foot Ulceration
Bibliographic record
Abstract
Aim. To evaluate in vitro the antibacterial effect of a paper made of silver phosphate cellulose fibers (SPCF) on Staphylococcus aureus, the most common diabetic foot ulceration (DFU) pathogen when compared with other common commercial products. Methods. The antibacterial activity of SPCF samples was evaluated through time with cell counting on agar plates. SPCF samples were then compared with commercial wound care products currently in use in DFU treatments (Silvercel™, Acticoat 7, and Aquacel Ag ExtraTM) through time on agar plates (growth inhibition zones). Results. After 6 hours, there was no viable bacterial cell detected on either plate (p < 0.05). There was a net growth inhibition zone for SPCF samples but no significant difference between the two silver concentrations. Compared with common commercial products, SPCF paper provides results equal to Acticoat 7 (p < 0.05) and superior to Aquacel AG ExtraTM and Silvercel™ at lower silver concentrations (p < 0.001). Conclusions. These results have shown the efficiency of SPCF paper to eliminate Staphylococcus aureus in these conditions. SPCF papers are effective when compared with other common commercial products and could have an industrial potential in wound care. Infected DFU could benefit from the antibacterial effectiveness of SPCF, but more relevant experimentations related to foot ulcers are needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".