Skeletal Muscle an Active Compartment in the Sequestering and Metabolism of Doxorubicin Chemotherapy
Bibliographic record
Abstract
Doxorubicin remains one of the most widely used chemotherapeutic agents however its effect on healthy tissue, such as skeletal muscle, remains poorly understood. The purpose of the current study was to examine the accumulation of doxorubicin (DOX) and its metabolite doxorubicinol (DOXol) in skeletal muscle of the rat up to 8 days after the administration of a 1.5 or 4.5 mg kg-1 i.p. dose. Subsequent to either dose, DOX and DOXol were observed in skeletal muscle throughout the length of the experiment. Interestingly an efflux of DOX was examined after 96 hours, followed by an apparent re-uptake of the drug which coincided with a spike and rapid decrease of plasma DOX concentrations. The interstitial space within the muscle did not appear to play a significant rate limiting compartment for the uptake or release of DOX or DOXol from the tissue to the circulation. Furthermore, there was no evidence that DOX preferentially accumulated in a specific muscle group with either dose. It appears that the sequestering of drug in skeletal muscle plays an acute and important role in the systemic availability and metabolism of DOX which may have a greater impact on the clinical outcome than previously considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".