Imatinib suppresses activation of hepatic stellate cells by targeting STAT3/IL‐6 pathway through miR‐124
Bibliographic record
Abstract
Abstract The activation of hepatic stellate cells is the primary function of facilitating liver fibrosis. Interfering with the coordinators of different signaling pathways in activated hepatic stellate cells (aHSCs) could be a potential approach in ameliorating liver fibrosis. Regarding the illustrated anti‐fibrotic effect of imatinib in liver fibrosis, we investigated the imatinib′s potential role in inhibiting HSC activation through miR‐124 and its interference with the STAT3/hepatic leukemia factor (HLF)/IL‐6 circuit. The anti‐fibrotic effect of imatinib was investigated in the LX‐2 cell line and carbon tetrachloride (CCl4)‐induced Sprague‐Dawley rat. The expression of IL‐6, STAT3, HLF, miR‐124, and α‐smooth muscle actin (α‐SMA) were quantified by quantitative real‐time PCR (qRT‐PCR) and the protein level of α‐SMA and STAT3 was measured by western blot analysis both in vitro and in vivo. The LX‐2 cells were subjected to immunocytochemistry (ICC) for α‐SMA expression. After administering imatinib in the liver fibrosis model, histopathological examinations were done, and hepatic function serum markers were checked. Imatinib administration alleviated mentioned liver fibrosis markers. The expression of miR‐124 was downregulated, while IL‐6/HLF/STAT3 circuit agents were upregulated in vitro and in vivo. Notably, imatinib intervention decreased the expression of IL‐6, STAT3, and HLF. Elevated expression of miR‐124 suppressed the expression of STAT3 and further inhibited HSCs activation. Our results demonstrated that imatinib not only ameliorated hepatic fibrosis through tyrosine kinase inhibitor (TKI) activity but also interfered with the miR‐124 and STAT3/HLF/IL‐6 pathway. Considering the important role of miR‐124 in regulating liver fibrosis and HSCs activation, imatinib may exert its anti‐fibrotic activity through miR‐124.
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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".