Cell death: The underlying mechanisms of photodynamic therapy for skin diseases
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) has significant potential in the treatment of dermatological, oncological, and nonneoplastic conditions through the induction of cell death, immune regulation, antimicrobial effects, etc. However, the response of some patients is unsatisfactory, and there is a lack of an ideal protocol for multiple specific diseases (subtypes) to choose the proper photosensitizer (PS), light source, and dose. A thorough understanding of the underlying mechanism is integral to solving these problems, and cell death has gained much attention. In addition to apoptosis, autophagy, and necrosis, several novel cell death pathways, such as necroptosis, mitotic catastrophe, paraptosis and pyroptosis, have been reported in PDT treatment. The type of induced cell death depends on the dose of PDT, the subcellular location of PSs, and the regulation of signaling pathways. In addition, different types of cell death induced by the same type of PDT, such as apoptosis and autophagy, may interact with each other. Some types of cell death can also trigger immunogenic cell death (ICD), which can ignite an immune response against antigens derived from dying/dead cells and present improved antitumor effects. On the basis of these mechanisms, several strategies, such as targeted PSs, PDT combined with immunotherapy and ICD‐based vaccines, have been proposed to improve therapeutic efficacy. Future studies are needed to elucidate the relationship between cell death and therapeutic effects and to shed new light on the exploration of precise PDT for specific patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".