Imaging of Specific Activation of Photodynamic Molecular Beacons in Breast Cancer Vertebral Metastases
Bibliographic record
Abstract
Breast cancer is the second leading cause of cancer-related death in women. Approximately 85% of patients with advanced cases will develop spinal metastases. The vertebral column is the most common site of breast cancer metastases, where overexpression of matrix metalloproteinases (MMPs) promotes the spread of cancer. Current therapies have significant limitations due to the high associated risk of damaging the spinal cord. An attractive alternative is photodynamic therapy providing noninvasive and site-selective treatment. However, current photosensitizers are limited by their nonspecific accumulation. Photodynamic molecular beacons (PP(MMP)B), activated by MMPs, offer another level of PDT selectivity and image-guidance preserving criticial tissues, specifically the spinal cord. Metastatic human breast carcinoma cells, MT-1, were used to model the metastatic behavior of spinal lesions. In vitro and in vivo evidence demonstrates MMP specific activation of PP(MMP)B in MT-1 cells. Using a clinically relevant metastatic model, fluorescent imaging establishes the specific activation of PP(MMP)B by vertebral metastases versus normal tissue (i.e., spinal cord) demonstrating the specificity of these beacons. Here, we validate that the metastasis-selective mechanism of PP(MMP)Bs can specifically image breast cancer vertebral metastases, thereby differentiating tumor and healthy tissue.
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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".