Antifibrotic Function of Itaconate-Based Degradable Polyester Materials
Bibliographic record
Abstract
Pathological fibrosis is a chronic disease, characterized by excessive extracellular matrix deposition, that remains a significant global health challenge. Despite its prevalence, current antifibrotic therapies are limited due to the complex interplay and signaling of profibrotic macrophages and fibroblast cells that underlies fibrotic tissue microenvironments. This study investigates a novel approach to combat fibrosis, harnessing the antifibrotic properties of the endogenous metabolite itaconate (IA) to target the pathological activation of the macrophage-fibroblast axis in fibrotic disease. To achieve therapeutic delivery relevant to the chronic nature of fibrotic conditions, we incorporated IA into the backbone of biodegradable polyester polymers, poly(dodecyl itaconate) (poly[IA-DoD]), capable of long-term localized release of IA. Degradation characterization of poly(IA-DoD) revealed that IA, as well as water-soluble IA-containing oligomeric groups, is released in a sustained manner. Treatment of murine bone marrow-derived macrophages and human dermal fibroblasts demonstrated that the degradation products of poly(IA-DoD) effectively modulated profibrotic behavior. Macrophages exposed to the degradation products exhibited reduced profibrotic responses, while fibroblasts showed decreased proliferation and myofibroblast α-smooth muscle actin expression. These findings suggest that poly(IA-DoD) has the potential to disrupt the fibrotic cycle by targeting key cellular players. This polymer-based delivery system offers a promising strategy for the treatment of fibrotic diseases.
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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".