Synergism inhibition and eradication activity of silver nitrate/potassium tellurite combination against <i>Pseudomonas aeruginosa</i> biofilm
Bibliographic record
Abstract
OBJECTIVES: Antibiotic resistance, biofilm and persistent infection of Pseudomonas aeruginosa is a perilous challenge in the healthcare system. Hence, a vast number of novel antipseudomonas approaches are currently being pursued. Our group focuses on exploring the efficacy of metal(loid)-based antimicrobials (MBAs) towards novel infection control solutions. METHODS: Initially, nine MBAs were tested for biofilm prevention and eradication efficacy. Synergistic potentials were then screened systematically in a total of 1920 combinatorial MBA concentrations, in laboratory media [CAMHB and LB] and infection-related simulated wound fluid (SWF). The antibiofilm efficacy of the silver nitrate (AgNO3; 'Ag') with potassium tellurite (K2TeO3; 'Te') combination was examined against clinical antibiotic-resistant isolates and compared with the most used antibiotics. The in vitro resistance acquisition test, for exploring the chance of getting future resistance, and meta-analysis, for estimating Ag/Te human cell cytotoxicity, were carried out. RESULTS: The Ag/Te combination was identified as the most effective agent against P. aeruginosa biofilm. The application of the Ag/Te combination was quite effective against all clinical isolates. Comparison of clinical isolates with indicator strains showed clinical isolates are gaining resistance against the antibiotics (especially gentamicin) and Ag, while they are susceptible to Te and particularly the Ag/Te combination. The chance of getting future resistance against Ag/Te as a mixture was remarkably lower than the individual application of each metal. Te has significantly lower human cell cytotoxicity in comparison with Ag. CONCLUSIONS: Te could be an appropriate alternative against P. aeruginosa biofilms (existing or prevention thereof), especially in combination with Ag.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".