Interleukin-6 secreted by bipotential murine oval liver stem cells induces apoptosis of activated hepatic stellate cells by activating NF-κB-inducible nitric oxide synthase signaling
Bibliographic record
Abstract
Liver fibrosis is now well recognized as the causative factor for increased mortality from complications associated with liver pathologies. Activated hepatic stellate cells (HSCs) play a critical role in the progression of liver fibrosis. Therefore, targeting these activated HSCs to prevent and (or) treat liver disease is a worthwhile approach to explore. In the present in vitro study, we investigated the use of bipotential murine oval liver cells (BMOL) in regulating the functions of activated HSCs to prevent progression of liver fibrosis. We used a conditioned medium-based approach to study the effect of BMOL cells on activated HSC survival and function. Our data showed that BMOL cells block the contraction of activated HSCs by inducing apoptosis of these cells. We demonstrated that BMOL cells secrete soluble factors, such as interleukin-6 (IL-6), which induced apoptosis of activated HSCs. Using both pharmacological and molecular inhibitor approaches, we further identified that IL-6-mediated activation of NF-κB-iNOS-NO-ROS signaling in activated HSCs plays a critical role in BMOL-cell-mediated apoptosis of activated HSCs. Thus, the present study provides an alternative cell-based therapeutic approach to treat liver fibrosis.
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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) | 0.000 | 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".