Intra-tumor delivery of zoledronate mitigates metastasis-induced osteolysis superior to systemic administration
Bibliographic record
Abstract
Bisphosphonates (BPs) have recently been shown to have direct anti-tumor properties. Systemic treatment with BPs can have multiple adverse effects such as osteonecrosis of the jaw and BP induced bone fracturing and spine instability. While benefits of systemic BP treatments may outweigh risks, local treatment with BPs has been explored as an alternate strategy to reduce unwarranted risk. In the present study, we examined whether local delivery of BPs inhibits tumor-induced osteolysis and tumor growth more effectively than systemic treatment in an animal model of tumor-induced bone disease. Following establishment of an intra-tibial model of bone metastases in athymic mice, the experimental group was treated by local administration of zoledronate into the tibial lesion. A comparison of the effect of local versus systemic delivery of zoledronate on the formation of tumor-induced osteolysis was also carried out. A significant increase in mean bone volume/tissue volume % (BV/TV) of the locally treated group (12.30±2.80%) compared to the control group (7.13±1.22%) ( P <0.001). Additionally, there was a significant increase in the BV/TV (10.90±1.25%) in the locally treated group compared to the systemically treated group (7.53±0.75%) ( P =0.005). These preliminary results suggest that local delivery of BPs outperforms both systemic and control treatments to inhibit tumor-induced osteolysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".