The benefits of photodynamic therapy on vertebral bone are maintained and enhanced by combination treatment with bisphosphonates and radiation therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) has been shown to ablate tumors within vertebral bone and yield short-term improvements in vertebral architecture and biomechanical strength, in particular when combined with bisphosphonate (BP) treatment. Longer-term outcomes of PDT combined with current treatments for skeletal metastases are essential to understand its therapeutic potential. The objective of this study is to evaluate the response of vertebrae to PDT after a longer (6-week) time period, alone and combined with previous BP or radiation treatment (RT). Sixty-three female rnu/rnu rats were randomized to six treatment groups: untreated control, BP-only, RT-only, PDT-only, combined BP + PDT and combined RT + PDT. L2 vertebrae were structurally analyzed through µCT-based analysis, axial compressive load-to-failure testing and histological analysis of morphology, osteoid formation and osteoclast activity. Combined BP + PDT treatment yielded the largest improvements in bone architecture with combined RT + PDT treatment yielding similar findings, but of a lesser magnitude. Mechanically, ultimate force and stress were correlated to stereological parameters that demonstrated a positive structural effect from combinatory treatment. Increased osteoid formation was observed in both combination therapies without any significant differences in osteoclast activity. Overall, multimodality treatment demonstrated a sustained positive effect on vertebral structural integrity, motivating PDT as a minimally-invasive adjuvant treatment for spinal metastases.
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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".