The effects of secondary iron overload and iron chelation on a radiation-induced acute myeloid leukemia mouse model
Bibliographic record
Abstract
BACKGROUND: Patients with myelodysplastic syndrome (MDS) require chronic red blood cell (RBC) transfusion due to anemia. Multiple RBC transfusions cause secondary iron overload and subsequent excessive generation of reactive oxygen species (ROS), which leads to mutations, cell death, organ failure, and inferior disease outcomes. We hypothesize that iron loading promotes AML development by increasing oxidative stress and disrupting important signaling pathways in the bone marrow cells (BMCs). Conversely, iron chelation therapy (ICT) may reduce AML risk by lowering iron burden in the iron-loaded animals. METHODS: We utilized a radiation-induced acute myeloid leukemia (RI-AML) animal model. Iron overload was introduced via intraperitoneal injection of iron dextran, and iron chelation via oral gavage of deferasirox. A total of 86 irradiated B6D2F1 mice with various levels of iron burden were monitored for leukemia development over a period of 70 weeks. The Kaplan-Meier estimator was utilized to assess AML free survival. In addition, a second cohort of 30 mice was assigned for early analysis at 5 and 7 months post-irradiation. The BMCs of the early cohort were assessed for alterations of signaling pathways, DNA damage response and gene expression. Statistical significance was established using Student's t-test or ANOVA. RESULTS: Iron loading in irradiated B6D2F1 mice accelerated RI-AML development. However, there was a progressive decrease in AML risk for irradiated mice with increase in iron burden from 7.5 to 15 to 30 mg. In addition, ICT decreased AML incidence in the 7.5 mg iron-loaded irradiated mice, while AML onset was earlier for the 30 mg iron-loaded irradiated mice that received ICT. Furthermore, analysis of BMCs from irradiated mice at earlier intervals revealed accelerated dysregulation of signaling pathways upon iron loading, while ICT partially mitigated the effects. CONCLUSIONS: We concluded that iron is a promoter of leukemogenesis in vivo up to a peak iron dose, but further iron loading decreases AML risk by increasing cell death. ICT can partially mitigate the adverse effects of iron overload, and to maximize its benefit this intervention should be undertaken prior to the development of extreme iron overload.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".