Tissue optical properties correction of multispectral singlet oxygen luminescent dosimetry (MSOLD) for Photofrin-mediated photodynamic therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a promising cancer treatment modality that involves the administration of a photosensitizing agent followed by light activation at a specific wavelength. Upon activation, the photosensitizer generates reactive oxygen species, including singlet-state oxygen ([1O2]), which causes cellular damage leading to cancer cell death. Direct detection of singlet-state oxygen constitutes the holy grail dosimetric method for type II PDT, a goal that can be quantified using multispectral singlet oxygen dosimetry (MSOLD). The optical properties of tissues, specifically their scattering and absorption coefficients, play a crucial role in determining how light interacts within a medium. Variations in these optical properties can significantly impact various aspects, including the distribution of treatment laser, the generation of singlet oxygen, and the detection of singlet oxygen signals using the MSOLD device. In this study, we have investigated the influence of optical properties variation on the spatial distribution of treatment laser energy in tissue simulated phantom and the distribution of generated singlet oxygen signals using Monte Carlo simulations (MC). Additionally, we conducted a comparative analysis by examining singlet oxygen signals generated by Photofrin in MeOH, as detected by an InGaAs spectrometer in vitro, and compared these results to our Monte Carlo simulations. The experimental findings validate the accuracy of our Monte Carlo simulations, further affirming the robustness of our research. Our research advanced the comprehension of singlet oxygen generation and enhanced the accuracy of singlet oxygen detection using the MSOLD device, especially when optical properties undergo changes.
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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) | 0.001 | 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".