Removal of pathogenic bacterial biofilms by combinations of oxidizing compounds
Bibliographic record
Abstract
Bacterial biofilms are commonly formed on medical devices and food processing surfaces. The antimicrobials used have limited efficacy against the biofilms; therefore, new strategies to prevent and remove these structures are needed. Here, the effectiveness of brief oxidative treatments, based on the combination of sodium hypochlorite (NaClO) and hydrogen peroxide (H2O2) in the presence of copper sulfate (CuSO4), were evaluated against bacterial laboratory strains and clinical isolates, both in planktonic and biofilm states. Simultaneous application of oxidants synergistically inactivated planktonic cells and prevented biofilm formation of laboratory Escherichia coli, Salmonella enterica serovar Typhimurium, Klebsiella pneumoniae, and Staphylococcus aureus strains, as well as clinical isolates of Salmonella enterica subsp. enterica, Klebsiella oxytoca, and uropathogenic E. coli. In addition, preformed biofilms of E. coli C, Salmonella Typhimurium, K. pneumoniae, and Salmonella enterica exposed to treatments were removed by applying 12 mg/L NaClO, 0.1 mmol/L CuSO4, and 350 mmol/L H2O2 for 5 min. Klebsiella oxytoca and Staphylococcus aureus required a 5-fold increase in NaClO concentration, and the E. coli clinical isolate remained unremovable unless treatments were applied on biofilms formed within 24 h instead of 48 h. The application of treatments that last a few minutes using oxidizing compounds at low concentrations represents an interesting disinfection strategy against pathogens associated with medical and industrial settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".