Photodynamic molecular beacon as an activatable photosensitizer based on protease-controlled singlet oxygen quenching and activation
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Bibliographic record
Abstract
Molecular beacons are FRET-based target-activatable probes. They offer control of fluorescence emission in response to specific cancer targets, thus are useful tools for in vivo cancer imaging. Photodynamic therapy (PDT) is a cell-killing process by light activation of a photosensitizer (PS) in the presence of oxygen. The key cytotoxic agent is singlet oxygen ((1)O(2)). By combining these two principles (FRET and PDT), we have introduced a concept of photodynamic molecular beacons (PMB) for controlling the PS's ability to generate (1)O(2) and, ultimately, for controlling its PDT activity. The PMB comprises a disease-specific linker, a PS, and a (1)O(2) quencher, so that the PS's photoactivity is silenced until the linker interacts with a target molecule, such as a tumor-associated protease. Here, we report the full implementation of this concept by synthesizing a matrix metalloproteinase-7 (MMP7)-triggered PMB and achieving not only MMP7-triggered production of (1)O(2) in solution but also MMP7-mediated photodynamic cytotoxicity in cancer cells. Preliminary in vivo studies also reveal the MMP7-activated PDT efficacy of this PMB. This study validates the core principle of the PMB concept that selective PDT-induced cell death can be achieved by exerting precise control of the PS's ability to produce (1)O(2) by responding to specific cancer-associated biomarkers. Thus, PDT selectivity will no longer depend solely on how selectively the PS can be delivered to cancer cells. Rather, it will depend on how selective a biomarker is to cancer cells, and how selective the interaction of PMB is to this biomarker.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it