In Vivo and In Silico Study of Photodynamic Necrosis Volume in Rat Liver
Bibliographic record
Abstract
Photodynamic therapy is a treatment modality that can be used to treat various types of lesions. To produce cell death, reaching a certain threshold dose of reactive oxygen species (ROS) is required. The estimation of ROS production is of paramount importance to predict the depth of necrosis and to ensure that the volume to be treated receives doses higher than the threshold. In this study, we compared a theoretical model for PDT based on Monte Carlo simulations of light irradiance and rate equations with a rat liver model. At the end of the simulation, necrosis depths and volumes were estimated, as well as the photosensitizer (PS), oxygen, and ROS concentrations at each position of the treated area. From the in vivo study, we obtained the ROS concentration threshold of about 1 mM for Photogem in rat liver. This proposed method can be used for any PS or tissue, including tissues with multiple layers. The proposed method can be used to estimate parameters for any PS or tissue, including layered tissues, as long as their parameters are known. In addition, other protocols can be tested, or compared with the standard ones, providing the bases for analyzing a diverse range of photodynamic treatment scenarios.
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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 | 0.000 |
| 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".