Highly Potent Photoinactivation of Bacteria Using a Water-Soluble, Cell-Permeable, DNA-Binding Photosensitizer
Bibliographic record
Abstract
Antimicrobial photodynamic therapy (APDT) employs a photosensitizer, light, and molecular oxygen to treat infectious diseases via oxidative damage, with a low likelihood for the development of resistance. For optimal APDT efficacy, photosensitizers with cationic charges that can permeate bacteria cells and bind intracellular targets are desired to not limit oxidative damage to the outer bacterial structure. Here we report the application of brominated DAPI (Br-DAPI), a water-soluble, DNA-binding photosensitizer for the eradication of both Gram-negative and Gram-positive bacteria (as demonstrated on N99 Escherichia coli and Bacillus subtilis, respectively). We observe intracellular uptake of Br-DAPI, ROS-mediated bacterial cell death via one- and two-photon excitation, and selective photocytotoxicity of bacteria over mammalian cells. Photocytotoxicity of both N99 E. coli and B. subtilis occurred at submicromolar concentrations (IC 50 = 0.2–0.4 μM) and low light doses (5 min irradiation times, 4.5 J cm –2 dose), making it superior to commonly employed APDT phenothiazinium photosensitizers such as methylene blue. Given its high potency and two-photon excitability, Br-DAPI is a promising novel photosensitizer for in vivo APDT applications.
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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 | 0.000 | 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) | 0.001 | 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".