Nitric oxide production by tumour tissue: impact on the response to photodynamic therapy
Bibliographic record
Abstract
The role of nitric oxide (NO) in the response to Photofrin-based photodynamic therapy (PDT) was investigated using mouse tumour models characterized by either relatively high or low endogenous NO production (RIF and SCCVII vs EMT6 and FsaR, respectively). The NO synthase inhibitors Nomega-nitro-L-arginine (L-NNA) or Nomega-nitro-L-arginine methyl ester (L-NAME), administered to mice immediately after PDT light treatment of subcutaneously growing tumours, markedly enhanced the cure rate of RIF and SCCVII models, but produced no obvious benefit with the EMT6 and FsaR models. Laser Doppler flowmetry measurement revealed that both L-NNA and L-NAME strongly inhibit blood flow in RIF and SCCVII tumours, but not in EMT6 and FsaR tumours. When injected intravenously immediately after PDT light treatment, L-NAME dramatically augmented the decrease in blood flow in SCCVII tumours induced by PDT. The pattern of blood flow alterations in tumours following PDT indicates that, even with curative doses, regular circulation may be restored in some vessels after episodes of partial or complete obstruction. Such conditions are conducive to the induction of ischaemia-reperfusion injury, which is instigated by the formation of superoxide radical. The administration of superoxide dismutase immediately after PDT resulted in a decrease in tumour cure rates, thus confirming the involvement of superoxide in the anti-tumour effect. The results of this study demonstrate that NO participates in the events associated with PDT-mediated tumour destruction, particularly in the vascular response that is of critical importance for the curative outcome of this therapy. The level of endogenous production of NO in tumours appears to be one of the determinants of sensitivity to PDT.
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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) | 0.000 | 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) | 0.001 | 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 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".