The Influence of Oxygen Depletion and Photosensitizer Triplet‐state Dynamics During Photodynamic Therapy on Accurate Singlet Oxygen Luminescence Monitoring and Analysis of Treatment Dose Response
Bibliographic record
Abstract
To date, singlet oxygen ((1)O(2)) luminescence (SOL) detection was predictive of photodynamic therapy (PDT) treatment responses both in vitro and in vivo, but accurate quantification is challenging. In particular, the early and strongest part of the time-resolved signal (500-2000ns) is difficult to separate from confounding sources of luminescence and system noise, and so is normally gated out. However, the signal dynamics change with oxygen depletion during PDT, so that this time gating biases the (1)O(2) measurements. Here, the impact of gating was investigated in detail, determining the rate constants from SOL and direct pO(2) measurements during meso-tetra(hydroxyphenyl)chlorin (mTHPC)-mediated PDT of cells in vitro under well-controlled conditions. With these data as input, numerical simulations were used to examine PDT and SOL dynamics, and the influence of various time gates on cumulative SOL signals. It is shown that gating can underestimate the SOL at early treatment time points by ∼40% and underestimate the cumulative SOL signal by 20-25%, representing significant errors. In vitro studies with both mTHPC and aminolevulinic acid-photosensitizer protoporphyrin IX demonstrate that rigorous analysis of SOL signal kinetics is then crucial in order to use SOL as an accurate and quantitative PDT dose metric.
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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 | 0.001 |
| 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) | 0.000 | 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 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".