Tissue concentrations of Doxorubicin and Doxorubicinol 24 and 96 hours following injection in the rat
Bibliographic record
Abstract
Doxorubicin (DOX) is an anti‐cancer chemotherapeutic and although widely used, its accumulation over time in different tissues has not been thoroughly investigated. The purpose of this study was to examine the concentration of both DOX and its metabolite, doxrubicinol (DOXol) in liver and heart tissue of rats. Two doses (1.5 mg/kg and 4.5 mg/kg) were injected intraperitoneally and tissue samples were collected either 24 or 96 hrs post‐injection. In all tissues DOX and DOXol were elevated (P<0.05) as compared to control at all time points. In both heart and liver the concentration of DOX was higher (P<0.05) following the 4.5 mg/kg as compared to the 1.5 mg/kg dose at 24 and 96 hrs. Heart tissue DOX concentrations decreased (P<0.05) from 0.8±0.1 and 1.8±0.3nmol/g at 24 hrs to 0.4±0.01 and 1.0±0.1 nmol/g at 96 hrs, for the 1.5 and 4.5 mg/kg dose, respectively. Similarly, liver DOX levels decreased (P<0.05) from 3.9±1.1 and 5.3±0.9nmol/g at 24 hrs to 0.6±0.1 and 3.2±0.4 nmol/g at 96 hrs, for the 1.5 and 4.5 mg/kg dose, respectively. DOXol was detectable in all tissues at all time points, however its concentration was lower (P<0.05) than that of DOX. Overall, both heart and liver responded in a dose dependant fashion to the injections of DOX. Furthermore, the accumulation of the drug and its cardiotoxic metabolite decreased over time suggesting that either the drug is rapidly metabolized and/or eliminated or stored in tissues other than the heart and liver. 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) | 0.002 | 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".