The antimicrobial properties of exogenous copper in human synovial fluid against <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
Aims The mechanism by which synovial fluid (SF) kills bacteria has not yet been elucidated, and a better understanding is needed. We sought to analyze the antimicrobial properties of exogenous copper in human SF against Staphylococcus aureus. Methods We performed in vitro growth and viability assays to determine the capability of S. aureus to survive in SF with the addition of 10 µM of copper. We determined the minimum bactericidal concentration of copper (MBC-Cu) and evaluated its sensitivity to killing, comparing wild type (WT) and CopAZB-deficient USA300 strains. Results UAMS-1 demonstrated a greater sensitivity to SF compared to USA300 WT at 12 hours (p = 0.001) and 24 hours (p = 0.027). UAMS-1 died in statistically significant quantities at 24 hours (p = 0.017), and USA300 WT survived at 24 hours. UAMS-1 was more susceptible to the addition of copper at four (p = 0.001), 12 (p = 0.005), and 24 hours (p = 0.006). We confirmed a high sensitivity to killing with the addition of exogenous copper on both strains at four (p = 0.011), 12 (p = 0.011), and 24 hours (p = 0.011). WT and CopAZB-deficient USA300 strains significantly died in SF, demonstrating a MBC-Cu of 50 µM against USA300 WT (p = 0.011). Conclusion SF has antimicrobial properties against S. aureus, and UAMS-1 was more sensitive than USA300 WT. Adding 10 µM of copper was highly toxic, confirming its bactericidal effect. We found CopAZB proteins to be involved in copper effluxion by demonstrating the high sensitivity of mutant strains to lower copper concentrations. Thus, we propose CopAZB proteins as potential targets and use exogenous copper as a treatment alternative against S. aureus.
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) | 0.001 | 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".