Cellular crosstalk mediated by platelet-derived growth factor BB and transforming growth factor β during hepatic injury activates hepatic stellate cells
Bibliographic record
Abstract
Apoptotic hepatocytes release factors that activate hepatic stellate cells (HSCs), thereby inducing hepatic fibrosis. In the present study, in vivo and in vitro injury models were established using acetaminophen, ethanol, carbon tetrachloride, or thioacetamide. Histology of hepatotoxicant-induced diseased hepatic tissue correlated with differential expression of fibrosis-related genes. A marked increase in co-staining of transforming growth factor β receptor type II (TGFRIIβ) - desmin or α-smooth muscle actin - platelet-derived growth factor receptor β (PDGFRβ), markers of activated HSCs, in liver sections of these hepatotoxicant-treated mice also depicted an increase in Annexin V - cytokeratin expressing hepatocytes. To understand the molecular mechanisms of disease pathology, in vitro experiments were designed using the conditioned medium (CM) of hepatotoxicant-treated HepG2 cells supplemented to HSCs. A significant increase in HSC proliferation, migration, and expression of fibrosis-related genes and protein was observed, thereby suggesting the characteristics of an activated phenotype. Treating HepG2 cells with hepatotoxicants resulted in a significant increase in mRNA expression of platelet-derived growth factor BB (PDGF-BB) and transforming growth factor β (TGFβ). CM supplemented to HSCs resulted in increased phosphorylation of PDGFRβ and TGFRIIβ along with its downstream effectors, extracellular signal-related kinase 1/2 and focal adhesion kinase. Neutralizing antibodies against PDGF-BB and TGFβ effectively perturbed the hepatotoxicant-treated HepG2 cell CM-induced activation of HSCs. This study suggests PDGF-BB and TGFβ as potential molecular targets for developing anti-fibrotic therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".