Arterial effects of anthracycline: structural and inflammatory assessments in non-human primates and lymphoma patients
Bibliographic record
Abstract
Anthracyclines, such as doxorubicin, are important anti-cancer therapies but are associated with arterial injury. Histopathological insights have been limited to small animal models, and the role of inflammation in the arterial toxic effects of anthracycline is unclear in humans. Our aims were (1) to evaluate aortic media fibrosis and injury in non-human primates treated with anthracyclines; (2) to assess the effect of anthracycline on aortic inflammation in patients treated for lymphoma. African Green monkeys (AGMs) received doxorubicin (30-60 mg/m2/biweekly intravenously, cumulative dose: 240 mg/m2). Blinded histopathologic analyses of the ascending aorta were performed 15 weeks after the last doxorubicin dose and compared to five age- and gender-matched healthy, untreated AGMs. Analysis of the thoracic aorta of patients with diffuse large B-cell lymphoma (DLBCL), at baseline and after doxorubicin exposure, was performed using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in this observational study by maximal tissue-to-background ratio (TBRmax). In AGMs, doxorubicin exposure was associated with greater aortic fibrosis (collagen deposition: doxorubicin 6.23 ± 0.88% vs. controls 4.67 ± 0.54%; P=0.01) and intracellular vacuolization (doxorubicin 66.3 ± 10.1 vs. controls 11.5 ± 4.2 vacuoles/field, P<0.0001) than untreated controls. In 101 patients with DLBCL, there was no change in aortic TBRmax after anthracycline exposure (TBRmax 1.46 ± 0.16 vs. 1.44 ± 0.14, respectively, P=0.14). Univariate analyses yielded similar results. In a large animal model, anthracycline exposure was associated with aortic fibrosis. In patients with lymphoma, anthracycline exposure was not associated with aortic inflammation. Further research is required to elucidate the mechanisms of anthracycline-related vascular harm.
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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 | 0.000 |
| 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".