Complementary Effect of Non-Persistent Silver Nano-Architectures and Chlorhexidine on Infected Wound Healing
Bibliographic record
Abstract
Surgical site infection (SSI) substantially contributes each year to patients' morbidity and mortality, accounting for about 15% of all nosocomial infections. SSI drastically increases the rehab stint and expenses while jeopardizing health outcomes. Besides prevention, the treatment regime relies on an adequate antibiotic therapy. On the other hand, resistant bacterial strains have currently reached up to 34.3% of the total infections, and this percentage grows annually, reducing the efficacy of the common treatment schemes. Thus, new antibacterial strategies are urgently demanded. Here, we demonstrated in rats the effectiveness of non-persistent silver nano-architectures (AgNAs) in infected wound healing together with their synergistic action in combination with chlorhexidine. Besides the in vivo efficacy evaluation, we performed analysis of the bacteriological profile of purulent wound, histological evaluations, and macrophages polarization quantifications to further validate our findings and elucidate the possible mechanisms of AgNAs action on wound healing. These findings open the way for the composition of robust multifunctional nanoplatforms for the translation of safe and efficient topical treatments of SSI.
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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".