Impact of Silver-Containing Wound Dressings on Bacterial Biofilm Viability and Susceptibility to Antibiotics during Prolonged Treatment
Bibliographic record
Abstract
The long-term antimicrobial efficacy of silver dressings against bacterial biofilms was investigated in a 7-day treatment in vitro model where the protein-rich medium was refreshed daily in order to mimic the conditions found in a wound bed. The use of plate-to-plate transfer assays demonstrated measurable differences in the effectiveness of several silver dressings on the viability of biofilm bacteria and their susceptibility to antibiotics. Whereas after the first day of treatment, all dressings used resulted in a significant reduction in the number of viable cells in the biofilms and disruption of the biofilm colonies, during prolonged treatment, the efficacy of dressings with hydrophilic base materials diminished with daily transfers, and bacterial populations recovered. For dressings with hydrophobic base materials, the level of efficacy correlated with the silver species loaded. Biofilm bacteria, which survived the initial silver treatment, were susceptible to tobramycin, ciprofloxacin, and trimethoprim-sulfamethoxazole, in contrast to untreated biofilms, which were highly tolerant to the same antibiotics. This acquired susceptibility was unaffected by the longevity of pretreatment with the silver dressings but depended on the dressing used. The antimicrobial efficacy of the dressings correlated with the type of the dressing base material and silver species loaded.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".