Analysis of Kinetics and Efficacy of Anti-Cancer via Oxygen-Enhanced Photodynamic Therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT has been widely used in many medical applications. PDT for anti-cancer is one of the clinically important subjects. This study will analyze the photochemical kinetics and the efficacy of anti-cancer via the critical factors including: the concentrations of photosensitizers and oxygen in the treated target, the exposure time, intensity and does (energy) of the light applied to the target. To achieve high efficacy, one requires the oxygen source term to re-supply the depletion of oxygen and photosensitizers. Higher light intensity has faster rising curve of the efficacy, but it reaches the same steady-state value as that of low intensity. The efficacy follows the Bunsen-Roscoe law (BRL) of reciprocity only when there is no oxygen source term. Higher initial concentration of oxygen and photosensitizers, C0, always provide higher efficacy. To achieve the same efficacy, minimum dose and/or less exposure time for accelerated procedure may be achieved by using a higher intensity (but same dose) for the case of P=0. However, with P>0, higher intensity requires a higher fluence to achieve the same efficacy and it does not follow the BRL reciprocity law.
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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".