The effects of doxorubicin administration on intramuscular nitric oxide concentration in rat skeletal muscle (1102.28)
Bibliographic record
Abstract
Doxorubicin (DOX) is a frontline anticancer therapeutic which has proven effective in the treatment of breast cancer. However, very little is known regarding the effects that DOX has on skeletal muscle metabolism, specifically its influence on nitric oxide (NO) production. Therefore, the purpose of the present study was to determine NO concentrations in the plantaris (P, n =6) and gastrocnemius (G, n =6) muscles of Sprague‐Dawley rats after DOX administration at a single dose of 1.5 mg/kg. Muscle samples were collected at 24, 48, 72, 96, 120, 144, 168 and 192 hours post injection. . Following administration, intramuscular NO increased (P<0.05) in the G after 120 (0.99±0.04 mmol/kg dw), 144 (0.91±0.03 mmol/kg dw) and 168 hours (0.79±0.03 mmol/kg dw) compared to control (0.62±0.07 mmol/kg dw). Similarly, NO increased (P<0.05) in the P after 120 (0.89±0.02 mmol/kg dw) and 144 hours (0.89±0.03 mmol/kg dw) compared to control (0.66±0.08 mmol/kg dw). Other than the observation that NO remained higher in the G but not in the P group at 168 hr, there were no other differences between fiber types. These data clearly suggest that DOX administration stimulates intramuscular NO production in a time dependent manner. In addition, these findings may represent the first step in better understanding NO deregulation in skeletal muscle upon DOX administration. Grant Funding Source : Supported by NSERC
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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) | 0.000 | 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".