<i>In vitro</i> comparison of methods for sampling copper-based antimicrobial surfaces
Bibliographic record
Abstract
ABSTRACT For more than a decade, copper (Cu) and Cu alloy surfaces have been approved and registered as solid antimicrobials to reduce the environmental bioburden of potential pathogens. Bacterial collection and enumeration are important steps in assessing antimicrobial efficacy of these surfaces, yet comparisons of the different collection methods from Cu surfaces are scarce in the literature. This study compared Petrifilm (PF) aerobic count plates applied directly onto Cu surfaces and two indirect (cellulose sponges and Quick Swab) collection methods to evaluate bacterial recovery of Pseudomonas aeruginosa and Staphylococcus aureus from three different formulations of Cu surfaces. ATP bioluminescence (ATPB) and live-dead flow cytometry staining were performed in tandem to corroborate bacterial recovery and antimicrobial findings. While all three collection methods were able to recover bacteria, direct PF contact with Cu surface consistently exhibited 2–3 Log higher colony-forming units (CFU)/20-cm 2 counts. No significant difference in bacterial counts was found when sponges or swabs were applied to Cu surfaces and inoculated onto 5% sheep blood agar plates (BAP). No difference was observed between PF and BAP counts when PF was used for recovery after indirect sample collection using Quick Swab. Comparison of microbial counts with live-dead staining and ATPB results suggests that the PF direct collection method revived stressed and dying bacteria. This phenomenon was not observed with the indirect PF method. Direct sampling of Cu surfaces using PF confers a survival advantage to bacteria stressed by Cu and may induce higher bacterial counts. IMPORTANCE Self-sanitizing surfaces such as copper (Cu) are increasingly used on high-touch surfaces to prevent the spread of harmful viruses and bacteria. Being able to monitor the antimicrobial properties of Cu is fundamental in measuring its antimicrobial efficacy. Thorough investigations into reliable methods to enumerate bacteria from self-sanitizing surfaces are lacking in the literature. This study demonstrates that direct use of Petrifilm on Cu surfaces most likely revives stressed and dying bacteria, which induces increased bacterial counts. This phenomenon was not observed with indirect collection methods. Studies assessing time-kill kinetics or long-term efficacy of Cu should consider the impact of the collection method chosen.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".