Morphine-induced osteolysis and hypersensitivity is mediated through toll-like receptor-4 in a murine model of metastatic breast cancer
Bibliographic record
Abstract
ABSTRACT: The propensity for breast cancer to metastasize to bone is coupled to the most common complaint among breast cancer patients: bone pain. Classically, this type of pain is treated using escalating doses of opioids, which lack long-term efficacy due to analgesic tolerance, opioid-induced hypersensitivity, and have recently been linked to enhanced bone loss. To date, the molecular mechanisms underlying these adverse effects have not been fully explored. Using an immunocompetent murine model of metastatic breast cancer, we demonstrated that sustained morphine infusion induced a significant increase in osteolysis and hypersensitivity within the ipsilateral femur through the activation of toll-like receptor-4 (TLR4). Pharmacological blockade with TAK242 (resatorvid) as well as the use of a TLR4 genetic knockout ameliorated the chronic morphine-induced osteolysis and hypersensitivity. Genetic MOR knockout did not mitigate chronic morphine hypersensitivity or bone loss. In vitro studies using RAW264.7 murine macrophages precursor cells demonstrated morphine-enhanced osteoclastogenesis that was inhibited by the TLR4 antagonist. Together, these data indicate that morphine induces osteolysis and hypersensitivity that are mediated, in part, through a TLR4 receptor mechanism.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".