Photodynamic Therapy as an Oxidative Anti-Tumor Modality: Negative Effects of Nitric Oxide on Treatment Efficacy
Bibliographic record
Abstract
Anti-tumor photodynamic therapy (PDT) is a unique oxidative stress-based modality that has proven highly effective on a variety of solid malignancies. PDT is minimally invasive and generates cytotoxic oxidants such as singlet molecular oxygen (1O2). With high tumor site-specificity and limited off-target negative effects, PDT is increasingly seen as an attractive alternative or follow-up to radiotherapy or chemotherapy. Nitric oxide (NO) is a short-lived bioactive free radical molecule that is exploited by many malignant tumors to promote cell survival, proliferation, and metastatic expansion. Typically generated endogenously by inducible nitric oxide synthase (iNOS/NOS2), low level NO can also antagonize many therapeutic interventions, including PDT. In addition to elevating resistance, iNOS-derived NO can stimulate growth and migratory aggressiveness of tumor cells that survive a PDT challenge. Moreover, NO from PDT-targeted cells in any given population is known to promote such aggressiveness in non-targeted counterparts (bystanders). Each of these negative responses to PDT and their possible underlying mechanisms will be discussed in this chapter. Promising pharmacologic approaches for mitigating these NO-mediated responses will also be discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".