Ischaemia-reperfusion injury in photodynamic therapy-treated mouse tumours
Bibliographic record
Abstract
Prompted by the observation of ischaemia development during the treatment of tumours by photodynamic therapy (PDT) that is typically followed by a restoration of tumour blood flow and by the indications of secondary superoxide generation after PDT, we aimed in this study to obtain evidence of the induction of ischaemia-reperfusion (I/R) injury in PDT-treated tumours. Using subcutaneous mouse FsaR fibrosarcoma model and Photofrin-based PDT treatment, we have examined the activity of xanthine oxidase (XO, a key enzyme in the I/R injury development) in tumours before and after the therapy. Compared to the levels in nontreated tumours, there was a five-fold increase in the activity of this enzyme in tumours excised immediately after PDT. This burst of elevated XO activity declined rapidly, returning to the pretreatment levels within the next 30 min. Visible reflectance spectroscopy confirmed the occurrence of a PDT-induced strong but temporary reduction in tumour oxygenation. The administration of XO inhibitor oxypurinol prevented this PDT-induced rise in XO activity. The oxypurinol treatment also decreased the extent of neutrophil accumulation in PDT-treated tumours and reduced the level of PDT-mediated cures. These results demonstrate the induction of I/R injury in PDT-treated tumours, and show that it can contribute to the therapy outcome. Since I/R injury is a well-recognised proinflammatory insult, we suggest that its induction in PDT-treated tumours promotes the development of inflammatory response that has become established as a key element of the antitumour effect of 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".