Monitoring of bacterial film formation and its breakdown with an angular-based surface plasmon resonance biosensor
Bibliographic record
Abstract
Bacterial biofilms are a leading cause of infection in health-care settings. Surface plasmon resonance (SPR) biosensors stand as valuable tools not only for the detection of biological entities and the characterisation of biomaterials but also as a suitable means to monitor bacterial film formation. This article reports on a proof-of-concept study for the use of an angular-based SPR biosensor for the monitoring of bacterial cell growth and biofilm formation and removal under the effect of different cleaning agents. The benefit of this custom-made SPR instrument is that it records simultaneously both the critical and resonant angles. This provides unique information on the growth of bacterial cells which is otherwise not obtainable with commonly used intensity-based SPR systems. The results clearly showed that a multilayer biofilm can be formed in 48 hours and the steps involved can be monitored in real-time with the SPR instrument through the measurement of the refractive index change and following the evolution in the shape of the SPR curve. The number, the depth and the sharpness of the reflection ripples varied as the film became thicker. Simulation results confirmed that the number of layers of bacteria affected the number of ripples at the critical angle. Real-time monitoring of the film breakdown with three cleaning agents indicated that bleach solution at 4.5% was the most effective in disrupting the biofilm from the gold sensor. Our overall findings suggest that the SPR biosensor with angular modulation presented in this article can perform real-time monitoring of biofilm formation and has the potential to be used as a platform to test the efficiency of disinfectants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".