Generation of Greater Bacterial Biofilm Biomass using PCR-Plate Deep Well Microplate Devices
Bibliographic record
Abstract
Bacterial biofilms are difficult to eradicate from surfaces using conventional antimicrobial interventions. High-throughput 96-well microplate methods are frequently used to cultivate bacterial biofilms for rapid antimicrobial susceptibility testing to calculate minimal biofilm eradication concentration (MBEC) values. Standard biofilm devices consist of polystyrene pegged-lids fitted to 96-well microplates and are ideal for measuring biofilm biomass and MBEC values, but these devices are limited by available peg surface area for biomass accumulation and cost. Here, we outline a protocol to use self-assembled polypropylene 96-well deep well PCR-plate pegged-lid device to grow Escherichia coli BW25113 and Pseudomonas aeruginosa PAO1 biofilms. A comparison of 24-hour biofilms formed on standard and deep well devices by each species using crystal violet biomass staining and MBEC determination assays are described. The larger surface area of deep well devices expectedly increased overall biofilm formation by both species 2-4-fold. P. aeruginosa formed significantly greater biomass/mm2 on deep well pegs as compared to the standard device. E. coli had greater biomass/mm2 on standard polystyrene devices as compared the deep well device. Biofilm eradication assays with disinfectants such as sodium hypochlorite (bleach) or benzalkonium chloride (BZK) showed that both compounds could eliminate E. coli and P. aeruginosa biofilms from both devices but at different MBEC values. BZK biofilm eradication resulted in variable E. coli MBEC values between devices, however, bleach demonstrated reproducible MBEC values for both species and devices. This study provides a high throughput deep well method for growing larger quantities of biofilms on polypropylene devices for downstream studies requiring higher amounts of static biofilm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".