Time-Resolved Fluorescence in Photodynamic Therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) has been used clinically for treating various diseases including malignant tumors. The main advantages of PDT over traditional cancer treatments are attributed to the localized effects of the photochemical reactions by selective illumination, which then generate reactive oxygen species and singlet oxygen molecules that lead to cell death. To date, over- or under-treatment still remains one of the major challenges in PDT due to the lack of robust real-time dose monitoring techniques. Time-resolved fluorescence (TRF) provides fluorescence lifetime profiles of the targeted fluorophores. It has been demonstrated that TRF offers supplementary information in drug-molecular interactions and cell responses compared to steady-state intensity acquisition. Moreover, fluorescence lifetime itself is independent of the light path; thus it overcomes the artifacts given by diffused light propagation and detection geometries. TRF in PDT is an emerging approach, and relevant studies to date are scattered. Therefore, this review mainly focuses on summarizing up-to-date TRF studies in PDT, and the effects of PDT dosimetric factors on the measured TRF parameters. From there, potential gaps for clinical translation are also discussed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".