Inflammation and oxidant-stress in -thalassemia patients treated with iron chelators deferasirox (ICL670) or deferoxamine: an ancillary study of the Novartis CICL670A0107 trial
Bibliographic record
Abstract
BACKGROUND: We assessed whether oxidant-stress and inflammation in beta-thalassemia could be controlled by the novel oral iron chelator deferasirox as effectively as by deferoxamine. DESIGN AND METHODS: Forty-nine subjects were enrolled from seven sites and studied at baseline, and after 1, 6, and 12 months of therapy. Malondialdehyde, protein carbonyls, vitamins E and C, total non-transferrin bound iron, transferrin saturation, C-reactive protein, cytokines, serum ferritin concentration and liver iron concentration were measured. RESULTS: Liver iron concentration and ferritin declined significantly in both treatment groups during the study. This paralleled a significant decline in the oxidative-stress marker malondialdehyde (deferasirox -22%/year, deferoxamine -28%/year, average decline p=0.006). The rates of decline did not differ between treatment groups. Malondialdehyde was higher in both treatment groups than in a group of 30 non-thalassemic controls (p < 0.001). The inflammatory marker high-sensitivity C-reactive protein decreased significantly only in the group receiving deferasirox (deferasirox -51%/year, deferoxamine +8.5%/year, p = 0.02). This result was confounded by a chance difference in the level of high-sensitivity C-reactive protein between the two groups at baseline, but analyses controlling for this difference suggested an equally large treatment effect. CONCLUSIONS: Iron chelation therapy with deferoxamine or with deferasirox was equally effective in decreasing iron burden and malondialdehyde. The possible differential effect of the two chelators on inflammation warrants further investigation.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".