Reactive Oxygen Species Mediated Activation of a Dormant Singlet Oxygen Photosensitizer: From Autocatalytic Singlet Oxygen Amplification to Chemicontrolled Photodynamic Therapy
Bibliographic record
Abstract
Here we show the design, preparation, and characterization of a dormant singlet oxygen ((1)O2) photosensitizer that is activated upon its reaction with reactive oxygen species (ROS), including (1)O2 itself, in what constitutes an autocatalytic process. The compound is based on a two segment photosensitizer-trap molecule where the photosensitizer segment consists of a Br-substituted boron-dipyrromethene (BODIPY) dye. The trap segment consists of the chromanol ring of α-tocopherol, the most potent naturally occurring lipid soluble antioxidant. Time-resolved absorption, fluorescence, and (1)O2 phosphorescence studies together with fluorescence and (1)O2 phosphorescence emission quantum yields collected on Br2B-PMHC and related bromo and iodo-substituted BODIPY dyes show that the trap segment provides a total of three layers of intramolecular suppression of (1)O2 production. Oxidation of the trap segment with ROS restores the sensitizing properties of the photosensitizer segment resulting in ∼40-fold enhancement in (1)O2 production. The juxtaposed antioxidant (chromanol) and prooxidant (Br-BODIPY) antagonistic chemical activities of the two-segment compound enable the autocatalytic, and in general ROS-mediated, activation of (1)O2 sensitization providing a chemical cue for the spatiotemporal control of (1)O2.The usefulness of this approach to selectively photoactivate the production of singlet oxygen in ROS stressed vs regular cells was successfully tested via the photodynamic inactivation of a ROS stressed Gram negative Escherichia coli strain.
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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".