Effect of zoledronic acid on the doxycycline-induced decrease in tumour burden in a bone metastasis model of human breast cancer
Bibliographic record
Abstract
Bone is one of the most frequent sites for metastasis in breast cancer patients often resulting in significant clinical morbidity and mortality. Bisphosphonates are currently the standard of care for breast cancer patients with bone metastasis. We have shown previously that doxycycline, a member of the tetracycline family of antibiotics, reduces total tumour burden in an experimental bone metastasis mouse model of human breast cancer. In this study, we combined doxycycline treatment together with zoledronic acid, the most potent bisphosphonate. Drug administration started 3 days before the injection of the MDA-MB-231 cells. When mice were administered zoledronic acid alone, the total tumour burden decreased by 43% compared to placebo treatment. Administration of a combination of zoledronic acid and doxycycline resulted in a 74% decrease in total tumour burden compared to untreated mice. In doxycycline- and zoledronate-treated mice bone formation was significantly enhanced as determined by increased numbers of osteoblasts, osteoid surface and volume, whereas a decrease in bone resorption was also observed. Doxycycline greatly reduced tumour burden and could also compensate for the increased bone resorption. The addition of zoledronate to the regimen further decreased tumour burden, caused an extensive decrease in bone-associated soft tissue tumour burden (93%), and sustained the bone volume, which could result in a smaller fracture risk. Treatment with zoledronic acid in combination with doxycycline may be very beneficial for breast cancer patients at risk for osteolytic bone metastasis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".