Behavioral, Medical Imaging and Histopathological Features of a New Rat Model of Bone Cancer Pain
Bibliographic record
Abstract
Pre-clinical bone cancer pain models mimicking the human condition are required to respond to clinical realities. Breast or prostate cancer patients coping with bone metastases experience intractable pain, which affects their quality of life. Advanced monitoring is thus required to clarify bone cancer pain mechanisms and refine treatments. In our model of rat femoral mammary carcinoma MRMT-1 cell implantation, pain onset and tumor growth were monitored for 21 days. The surgical procedure performed without arthrotomy allowed recording of incidental pain in free-moving rats. Along with the gradual development of mechanical allodynia and hyperalgesia, behavioral signs of ambulatory pain were detected at day 14 by using a dynamic weight-bearing apparatus. Osteopenia was revealed from day 14 concomitantly with disorganization of the trabecular architecture (µCT). Bone metastases were visualized as early as day 8 by MRI (T(1)-Gd-DTPA) before pain detection. PET (Na(18)F) co-registration revealed intra-osseous activity, as determined by anatomical superimposition over MRI in accordance with osteoclastic hyperactivity (TRAP staining). Pain and bone destruction were aggravated with time. Bone remodeling was accompanied by c-Fos (spinal) and ATF3 (DRG) neuronal activation, sustained by astrocyte (GFAP) and microglia (Iba1) reactivity in lumbar spinal cord. Our animal model demonstrates the importance of simultaneously recording pain and tumor progression and will allow us to better characterize therapeutic strategies in the future.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".