Photosensitized singlet oxygen generation and detection: Recent advances and future perspectives in cancer photodynamic therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) uses photosensitizers and visible light in combination with molecular oxygen to produce reactive oxygen species (ROS) that kill malignant cells by apoptosis and/or necrosis, shut down the tumor microvasculature and stimulate the host immune system. The excited singlet state of oxygen ( 1 O 2 ) is recognized to be the main cytotoxic ROS generated during PDT for the majority of photosensitizers used clinically and for many investigational new agents, so that maximizing its production within tumor cells and tissues can improve the therapeutic response, and several emerging and novel approaches for this are summarized. Quantitative techniques for 1 O 2 production measurement during photosensitization are also of immense importance of value for both preclinical research and future clinical practice. In this review, emerging strategies for enhanced photosensitized 1 O 2 generation are introduced, while recent advances in direct detection and imaging of 1 O 2 luminescence are summarized. In addition, the correlation between cumulative 1 O 2 luminescence and PDT efficiency will be highlighted. Meanwhile, the validation of 1 O 2 luminescence dosimetry for PDT application is also considered. This review concludes with a discussion on future demands of 1 O 2 luminescence detection for PDT dosimetry, with particular emphasis on clinical translation. Eye‐catching color image for graphical abstract. magnified image Eye‐catching color image for graphical abstract.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".