The Impact of Complement Activation on Tumor Oxygenation During Photodynamic Therapy<sup>†</sup>
Bibliographic record
Abstract
The response to photodynamic therapy (PDT) mediated by photosensitizer Photofrin was examined with Lewis lung carcinomas growing in either complement-proficient C57BL/6 (B6) or complement-deficient complement C3 knockout (C3KO) mice. The results reveal that Photofrin-PDT was more effective in attaining cures of tumors in C3KO than in B6 hosts. Colony-forming ability of cells from tumors excised immediately after Photofrin-PDT confirmed that the direct cell killing effect was more pronounced in C3KO than in B6 hosts. In contrast, PDT mediated by photosensitizer benzoporphyrin derivative (BPD) produced higher cure rates of tumors in B6 hosts than those in C3KO hosts. Determination of tumor C3 levels by ELISA showed that Photofrin-PDT induced markedly more pronounced complement activation than BPD-PDT. Measurements of tumor oxygen tension immediately after PDT by Eppendorf pO2 histograph showed that Photofrin-PDT induced a marked decline in the oxygenation of tumors growing in B6 mice that was much less pronounced in C3KO hosts. With BPD-PDT the oxygen tensions in tumors in B6 and C3KO hosts decreased to a similar extent. This study indicates that complement activation in PDT-treated tumors that varies with different photosensitizers is an important determinant of tumor oxygen limitation effects directly associated with photodynamic action.
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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".