Two-Pronged Dormant Photosensitizer-Antibiotic Bacterial Inactivation: Mechanism, Dosage, and Cellular Evolution Visualized at the Single-Cell Level
Bibliographic record
Abstract
Innovative therapeutic approaches are required to battle the rise of antibiotic-resistant bacterial strains. Tapping on reactive oxygen species (ROS) generation in bacteria induced by bactericidal antibiotics, here we report a two-pronged strategy for bacterial inactivation relying on the synergistic combination of a bactericidal antibiotic and newly designed dormant photosensitizers (DoPSs) that activate in the presence of ROS. Intramolecular quenching renders DoPS inert in the presence of light. ROS trapping by DoPS aborts the quenching mechanism unmasking, in equal proportions, singlet oxygen ( 1 O 2 ) sensitization and fluorescence emission. Juxtaposed antioxidant-prooxidant activity built within our DoPS enables (i) initial activation of a few molecules by ROS and (ii) subsequent rapid activation of all DoPS in a bacterium via a domino effect mediated by photogenerated 1 O 2 . Bulk colony forming unit studies employing the minimum inhibitory concentration of the antibiotic illustrate rapid and selective inactivation of Escherichia coli and Pseudomonas aeruginosa only in the presence of light, antibiotic, and DoPS. Single-cell, real-time imaging studies on E. coli reveal an autocatalytic progression of DoPS activation from focal points, providing a unique amplification system for sensing. Single-cell analysis further illustrates the impact of DoPS cellular loading on the rate of DoPS activation and cell death times and on the 1 O 2 dosing necessary for cell death to occur. Our two-pronged therapy discriminates based on cell metabolites and has the potential to result in lower toxicity, pave the way to reduced drug resistance, and provide insightful mechanistic information about bacterial membrane response to 1 O 2 .
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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 | 0.001 | 0.001 |
| 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".