Influence of bacterial growth modes on the susceptibility to light‐activated disinfection
Bibliographic record
Abstract
AIM: To evaluate the efficacy of light-activated disinfection (LAD) using methylene blue (MB) and a non-coherent light source on gram-positive and gram-negative bacteria in different growth modes. The influence of different photosensitizer (PS) formulations in the MB-mediated LAD of biofilms was also evaluated. METHODOLOGY: Light-activated disinfection using MB was tested on Enterococcus faecalis in a planktonic suspension, coaggregated suspension and mono-species biofilms and on Pseudomonas aeruginosa in a planktonic suspension and mono-species biofilms. Further, the difference in susceptibility of E. faecalis and P. aeruginosa biofilms to LAD with modified PS formulations was assessed by conventional culturing methods and confocal laser scanning microscopy (CLSM). RESULTS: Higher energy dose was required for LAD of bacteria in a coaggregated suspension and in biofilm compared to their planktonic counterparts. Biofilm mode of growth offered the greatest resistance to LAD in both the tested strains of pathogens (P<0.001). Gram-positive E. faecalis was more susceptible to LAD than the gram-negative P. aeruginosa, and the use of modified PS formulations was found to enhance the efficacy of LAD to destroy the biofilm (P<0.001). CONCLUSIONS: Bacterial growth modes play a vital role in influencing the susceptibility to LAD in a dose-dependent manner. The nature of the PS formulation influences the susceptibility of biofilms to LAD.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".