Assessment of Photodynamic Destruction of <i>Escherichia coli</i> O157:H7 and <i>Listeria monocytogenes</i> by Using ATP Bioluminescence
Bibliographic record
Abstract
Antimicrobial photodynamic therapy was shown to be effective against a wide range of bacterial cells, as well as for fungi, yeasts, and viruses. It was shown previously that photodestruction of yeast cells treated with photosensitizers resulted in cell destruction and leakage of ATP. Three photosensitizers were used in this study: tetra(N-methyl-4-pyridyl)porphine tetratosylate salt (TMPyP), toluidine blue O (TBO), and methylene blue trihydrate (MB). A microdilution method was used to determine MICs of the photosensitizers against both Escherichia coli O157:H7 and Listeria monocytogenes. To evaluate the effects of photodestruction on E. coli and L. monocytogenes cells, a bioluminescence method for detection of ATP leakage and a colony-forming assay were used. All tested photosensitizers were effective for photodynamic destruction of both bacteria. The effectiveness of photosensitizers (in microgram-per-milliliter equivalents) decreased in the order TBO > MB > TMPyP for both organisms. The MICs were two- to fourfold higher for E. coli O157:H7 than for L. monocytogenes. The primary effects of all of the photosensitizers tested on live bacterial cells were a decrease in intracellular ATP and an increase in extracellular ATP, accompanied by elimination of viable cells from the sample. The time courses of photodestruction and intracellular ATP leakage were different for E. coli and L. monocytogenes. These results show that bioluminescent ATP-metry can be used for investigation of the first stages of bacterial photodestruction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".