Porphyrin-based Sensitizers in the Detection and Treatment of Cancer: Recent Progress
Bibliographic record
Abstract
It has been known for some time that porphyrins and related compounds have the ability to selectively accumulate in tumor tissues, and to persist there for long periods of time. This property, along with the well-described photophysical and photosensitizing properties of porphyrin-type molecules, has led to their potential use as adjuvants and sensitizers in a variety of medical applications, such as in photodynamic therapy (PDT), boron neutron capture therapy (BNCT), radiation therapy (RT) and in magnetic resonance imaging (MRI). Both PDT and BNCT are binary cancer therapies that involve activation of tissue-localized sensitizers with either light (in PDT) or low-energy neutrons (in BNCT). In both of these therapeutic methodologies, local tumor control with minimal side effects relative to other forms of cancer treatment (surgery, radiotherapy, chemotherapy) can be achieved. Porphyrins constitute a major class of pharmacological agents currently under investigation. Photofrin, a porphyrin derivative, has been approved in the USA as a PDT drug by the U.S. Food and Drug Administration (FDA), and also in Japan, Canada and in eleven European countries. Recently, the FDA approved Visudyne, another porphyrin derivative for the PDT treatment of the 'wet-form' of age-related macular degeneration. In addition to cancer treatment porphyrins are also under investigation for application in the treatment of a variety of other diseases.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".