Orthotopic animal models for oncologic photodynamic therapy and photodiagnosis
Bibliographic record
Abstract
Photodynamic Therapy is a complex treatment modality where a large array of factors can influence therapeutic outcome. Vascularization, vessel permeability, oxygenation and light distribution in the tissue as well as immune response play a key role in the photodynamic process. Each of these factors can be influenced by the choice of the animal model. It is therefore of the utmost importance to chose an appropriate model for pre clinical oncologic PDT studies. Heterotopic models are easy to reproduce and monitor tumor growth and response to treatment and can be useful to answer specific questions. However, since many factors such as vascularization and microenvironment are different from the native organ, they can not be used as a representative model for PDT in the clinic. Orthotopic tumor models present the closest resemblance to the clinical situation with regard to all the elements involved in PDT. Even then, some existing models have to be adapted in order to exhibit the same features as observed during clinical PDT. We present here a brief organ specific overview of the different orthotopic animal models that can be used for in vivo photodynamic therapy studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".