Examining tissue-level changes in doxorubicin accumulation and nitric oxide formation in skeletal muscle and tumours in a mouse model of breast cancer
Bibliographic record
Abstract
Doxorubicin is a commonly used chemotherapy that rapidly accumulates in skeletal muscle and disrupts nitric oxide (NO) formation. However, studies investigating these effects have largely been performed in tumour-free models, therefore it remains unknown whether intramuscular accumulation and disruptions to NO content persist during tumour growth. Female C57bl/6 mice ( n = 8/group) were randomly assigned to true control, doxorubicin control, tumour only, or tumour plus doxorubicin groups. Tumours were grown for 21, 24, or 28 days using E0771 cells. Doxorubicin was administered as a single 10 mg/kg intraperitoneal dose on day 21. Doxorubicin accumulation was similar in muscle with and without tumours present. Doxorubicinol, a metabolite of doxorubicin, was elevated ( p < 0.05) in 24-day tumour + doxorubicin compared to doxorubicin alone. NO was similar across all groups in muscle; however, tumour NO was 15-fold higher at day 21 compared to 24, or 28 days ( p < 0.05). The results confirm that doxorubicin is sequestered in skeletal muscle when a tumour is present, which may impact bioavailability. Tumour growth transiently increased intramuscular doxorubicinol, potentially exacerbating the toxicity of the drug. Earlier stage tumour growth appeared to profoundly elevate NO, which could suggest temporal angiogenesis and vasodilation to facilitate growth.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".