Chondroitin sulfate mediates liver responses to injury induced by dual endothelin receptor inhibition
Bibliographic record
Abstract
Although dual endothelin receptor antagonists (ERAs) show great promise for treating various conditions, their propensity to induce liver injury limits their clinical usage. Inflammation and fibrosis are important processes in liver responses to injury and it has been suggested that they and dual ERA-induced liver injury are mediated by the proteoglycan component chondroitin sulfate (CS), which is synthesized by CHST3 and CHST13. In this study, we investigated whether dual ER inhibition in the liver could alter CHST3 and CHST13 expression and thus CS production and whether liver CS content could prevent inflammatory and fibrosis responses after liver injury. We observed increased CHST3 and CHST13 expression after liver injury in bile duct ligated mice and histologically confirmed abundant CS deposition in the injured liver. Moreover, treating Hep3B cells with a dual ERA mimic significantly increased CHST3 and CHST13 expression, inflammatory cytokine levels, and glycosaminoglycan deposition. Furthermore, pro-inflammatory and pro-fibrotic markers were observed after dual ERA treatment, while treatment with CS-degrading chondroitinase ABC was able to successfully reverse these phenotypes. These observations suggest that CHST3- and CHST13-induced CS production can mediate liver injury responses caused by dual ER inhibition and thus could be an alternative pathway for treating ERA-induced liver injury.
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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 | 0.001 |
| 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".