Porphyrin-based Agents and Their Applications in Cancer Imaging and Therapy
Bibliographic record
Abstract
Porphyrins represent one of the oldest, most widely studied chemical structures, both in nature and in biomedical applications. Due to their tumor avidity and favorable photophysical properties, such as long wavelength absorption and emission, easy derivatization, high singlet oxygen quantum yield and low in vivo toxicity, porphyrins have found particular success for photodynamic therapy and fluorescence imaging of cancer. Additionally, they are excellent metal chelators, forming highly stable metallo-complexes, making porphyrins an efficient delivery vehicle for radioisotopes. Thus, there is great potential in the applications of these multi-modal porphyrin-based agents for cancer imaging and therapy. I have investigated the characteristics of various porphyrin-based probes and their potential application in different clinically relevant models. Here, I will discuss three types of porphyrin-based agents: 1) photodynamic molecular beacons (PPMMPB), 2) targeted peptide porphyrins (PPF) and 3) porphyrin-lipid nanovesicles, porphysomes. I will demonstrate that all of these porphyrin-based agents have potential clinical applications in various fields of cancer imaging and therapy. Although these three agents differ greatly, they all aim to increase the signal-to-background ratio of tumor to healthy tissue uptake of porphyrins, thereby increasing our ability to detect tumor tissue and better preserve healthy tissue during therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".