Photodynamic Therapy of Vertebral Metastases: Evaluating Tumor‐to‐Neural Tissue Uptake of BPD‐MA and ALA‐PpIX in a Murine Model of Metastatic Human Breast Carcinoma<sup>†</sup>
Bibliographic record
Abstract
Photodynamic therapy has been successfully applied to numerous cancers. Its potential to treat cancer metastases in the spine has been demonstrated previously in a preclinical animal model. The aim of this study was to test two photosensitizers, benzoporphyrin-derivative monoacid ring A (BPD-MA) and by 5-aminolevulinic acid (5-ALA)-induced protoporphyrin IX (PpIX), for their potential use to treat bony metastases. The difference in photosensitizer concentration in the spinal cord and the surrounding tumor-bearing vertebrae was of particular interest to assess the risk of potential collateral damage to the spinal cord. Vertebral metastases in a rat model were generated by intracardiac injection of human breast cancer cells. When tumor growth was confirmed, photosensitizers were injected systemically and the animals were euthanized at different time points. The following tissues were harvested: liver, kidney, ovaries, appendicular bone, spinal cord and lumbar vertebrae. Photosensitizer tissue concentration of BPD-MA or PpIX was determined by fluorescence spectrophotometry. In contrast to BPD-MA, ALA-PpIX did not demonstrate an appreciable difference in the uptake ratio in tumor-bearing vertebrae compared to spinal cord. The highest ratio for BPD-MA concentration was found 15 min after injection, which can be recommended for therapy in this model.
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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".