Characterization of the Retrocyclin Analogue RC-101 as a Preventative of Staphylococcus aureus Nasal Colonization
Bibliographic record
Abstract
Nasal colonization of Staphylococcus aureus is a risk factor for pathogenic autoinfection, particularly in postoperative patients and the immunocompromised. As such, standardized preoperative nasal decolonization of S. aureus has become a major consideration for the prevention of nosocomial infection. However, only a few treatment options for nasal decolonization are currently available, with resistance to these approaches already a concern. Here we have identified the macrocyclic -defensin analogue RC-101 as a promising anti-S. aureus agent for nasal decolonization. RC-101 exhibits bactericidal effects against S. aureus with the use of in vitro epithelium-free systems, while also preventing the pathogen's proliferation and attachment in an ex vivo human nasal epithelial cell adhesion model and an organotypic model of human airway epithelia. Peptide concentrations as low as 2.5 μM elicited significant reductions in S. aureus growth in epithelium-free systems, with 10 μM concentrations being completely bactericidal for all strains tested, including USA300. In ex vivo nasal colonization models, RC-101 significantly reduced adherence, survival, and proliferation of S. aureus on human nasal epithelia. Reductions in S. aureus viability were evident in these assays, with as little as 1 μg of peptide per tissue, while 10 μg of RC-101 completely prevented adhesion of all strains tested. Furthermore, RC-101 did not exhibit cellular toxicity to human nasal epithelia at concentrations up to 200 μM, nor did it induce a proinflammatory response in these cells. Collectively, the findings of this study identify RC-101 as a potential preventative of S. aureus nasal colonization.
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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".