Synergistic activity of tetrasodium EDTA, ethanol and chlorhexidine hydrochloride against planktonic and biofilm cells of clinically relevant pathogens
Bibliographic record
Abstract
OBJECTIVES: Biofilms associated with implantable medical devices and wounds are clinically relevant, often requiring repeated use of antibiotics without success. A search for non-antibiotic antimicrobial and antibiofilm solutions is warranted, in line with antimicrobial stewardship. Our study aimed to evaluate the broad-spectrum antimicrobial efficacy of tetrasodium EDTA, ethanol and chlorhexidine hydrochloride (HCl) alone and in combination against clinically relevant planktonic and biofilm cells of bacterial and fungal pathogens. METHODS: MICs and MBCs were determined for tetrasodium EDTA, ethanol and chlorhexidine HCl against planktonic cells of test pathogens. The MBEC Assay® biofilm inoculator device was used to evaluate the biofilm eradication ability of test antimicrobials alone and in combination against clinically relevant pathogens. The checkerboard microbroth dilution assay was performed to analyze the synergism between test antimicrobials. RESULTS: Against planktonic cells, the combination of tetrasodium EDTA with ethanol or chlorhexidine HCl resulted in synergistic to indifferent activity, with no antagonism observed. Against mature biofilms, all combinations were synergistic. The MBEC of each test antimicrobial was decreased from 4- to -64-fold when used in combination as compared to when agents were used alone. We optimised the concentration of antimicrobials to achieve rapid eradication of pre-formed biofilms. A triple combination of 3% tetrasodium EDTA, 20% ethanol and 2.5 μg/mL chlorhexidine HCl completely eradicated 48-h-old biofilms of all test strains within 2 h. CONCLUSION: All three antimicrobial agents can be used together for prevention and treatment of biofilms and biofilm-related infections. The observed in vitro efficacy should be tested further through in vivo and clinical studies.
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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 | 0.001 |
| 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".