A comparison of multichannel singlet oxygen luminescence dosimetry (MSOLD) and singlet oxygen explicit dosimetry (SOED)
Bibliographic record
Abstract
Direct detection of singlet-state oxygen ([1O2]) is a crucial objective in type II photodynamic therapy (PDT), achievable through the implementation of multispectral singlet oxygen dosimetry (MSOLD). To accurately assess the amount of reactive singlet oxygen, the Singlet Oxygen Explicit Dosimetry (SOED) model was developed, incorporating parameters such as light fluence rate, photosensitizer concentration, and ground-state oxygen concentration. This study focuses on comparing the results obtained from MSOLD and SOED by measuring the singlet oxygen signal via a commercial InGaAs spectrometer and subsequently calculating reactive singlet oxygen based on the SOED theory. A continuous-wave laser emitting at 630nm is employed to excite Protoporphyrin IX (PPIX) in methanol, varying the concentration from 10mg/kg to 100mg/kg. Utilizing the Singular Value Decomposition (SVD) algorithm, the measured singlet oxygen spectrum is fitted to extract the singlet oxygen signal. To simulate clinical PDT scenarios, real-time singlet oxygen spectra are collected over 1200 seconds, employing a 1.5mm diameter optic fiber for signal collection. Ground-state oxygen concentration is measured using a commercial oxygen probe while the laser is inactive, and photosensitizer concentration is assessed via a custom-made contact probe. Additionally, the fluence rate of the laser is measured using an isotropic detector. The Reactive Singlet Oxygen is then calculated using the SOED model, incorporating the photosensitizer concentration, oxygen concentration, and photon fluence rate. Detailed comparisons between MSOLD and SOED results are presented, offering valuable insights into the accuracy and reliability of both methods in quantifying singlet-state oxygen during PDT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".