On‐demand treatment with the iron chelator deferasirox is ineffective in preventing blood‐induced joint damage in haemophilic mice
Bibliographic record
Abstract
INTRODUCTION: Early intervention in the devastating process of haemophilic arthropathy (HA) is highly desirable, but no disease-modifying therapy is currently available. Considering the pivotal role of iron in the development of HA, iron chelation is considered a promising therapeutic approach. A previous study in haemophilic mice demonstrated that treatment with the iron chelator deferasirox (DFX) 8 weeks before joint bleed induction, attenuated cartilage damage upon blood exposure. However, in haemophilia patients this approach is not opportune given the unpredictable occurrence of hemarthroses. AIM: To evaluate the effectiveness of on-demand DFX treatment, initiated immediately after joint bleed induction. METHODS: A joint bleed was induced in 66 factor VIII-deficient mice by infra-patellar needle puncture. Mice were randomly assigned to treatment with either placebo (drinking water) or DFX (dissolved in drinking water) throughout the study. Five weeks after joint bleed induction, inflammation and cartilage damage were assessed histologically. Joints of ten bleed naive haemophilic mice served as controls. RESULTS: A joint bleed resulted in significant inflammation and cartilage damage in the blood-exposed joint compared with those of control animals, in both the placebo and DFX group (all p = <.05). No differences in tibiofemoral or patellar inflammation (p = .305 and p = .787, respectively) nor cartilage damage (p = .265 and p = .802, respectively) were found between the blood-exposed joints of both treatment groups. CONCLUSION: On-demand treatment with DFX does not prevent joint damage following blood exposure in haemophilic mice. DFX seems unable to reach the joint in time to exert its effect before the irreversible harmful process is initiated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".