Controlling Methicillin Resistant<i>Staphyloccocus aureus</i>and<i>Pseudomonas aeruginosa</i>Wound Infections with a Novel Biomaterial
Bibliographic record
Abstract
Wound infections, especially those associated with methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa, offer considerable challenges for clinicians. Our laboratory has recently developed novel composite biomaterials (DRDC) for wound dressing applications, and demonstrated their in vitro bactericidal efficacy. In the present study, we assessed the proliferation of planktonic and sessile Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus in porcine full-thickness wounds covered for up to 48 h with either saline- or mafenide acetate-loaded DRDC puffs and meshes. All biomaterials were applied 4 h following bacterial inoculation of the wounds with methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa, to allow colonization of the tissues and initiation of biofilm formation. The drug-loaded biomaterials eradicated both the planktonic and biofilm bacteria in the wounds within 24 h (p <. 05), irrespective of the bacterial strain or architecture of the dressing. While the wound bioburdens increased in the ensuing 24 h, they remained approximately 2 log(10) colony-forming units (CFU) below (p <. 05) their respective baseline values. Similarly, less than 4 log(10) CFU was recovered in the drug-loaded DRDC biomaterials throughout the study. These data show that the DRDC puffs and meshes are effective in delivering certain medications, such as antimicrobial agents, to the wound bed, suggesting considerable value of this material for treating wounds, especially those with irregular shapes, contours, and depths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".