GSK126 an inhibitor of epigenetic regulator EZH2 suppresses cardiac fibrosis by regulating the <i>EZH2-PAX6-CXCL10</i> pathway
Bibliographic record
Abstract
Myocardial fibrosis is a common pathological companion of various cardiovascular diseases. To date, the role of enhancer of zeste homolog 2 (EZH2) in cancer has been well demonstrated including in renal carcinoma and its inhibitors have entered the stage of phase I/II clinical trials. However, the precise mechanism of EZH2 in cardiac diseases is largely unclear. In the current study, we first found that EZH2 expression was increased in Ang-II-treated cardiac fibroblasts (CFs) and mouse heart homogenates following isoproterenol (ISO) administration for 21 days, respectively. Ang-II induces CFs activation and increased collagen-I, collagen-III, α-SMA, EZH2, and trimethylates lysine 27 on histone 3 (H3K27me3) expressions can be reversed by EZH2 inhibitor (GSK126) and EZH2 siRNA. The ISO-induced cardiac hypertrophy, and fibrosis in vivo which were also related to the upregulation of EZH2 and its downstream target, H3K27me3, could be recovered by GSK126. Furthermore, the upregulation of EZH2 induces the decrease of paired box 6 (PAX6) and C-X-C motif ligand 10 (CXCL10) "which" were also reversed by GSK126 treatment. In summary, the present evidence strongly suggests that GSK126 could be a therapeutic intervention, blunting the development and progression of myocardial fibrosis in an EZH2-PAX6-CXCL10-dependent manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".