Hepatocyte growth control by SOCS1 and SOCS3
Bibliographic record
Abstract
The extraordinary capacity of the liver to regenerate following injury is dependent on coordinated and regulated actions of cytokines and growth factors. Whereas hepatocyte growth factor (HGF) and epidermal growth factor (EGF) are direct mitogens to hepatocytes, inflammatory cytokines such as TNFα and IL-6 also play essential roles in the liver regeneration process. These cytokines and growth factors activate different signaling pathways in a sequential manner to elicit hepatocyte proliferation. The kinetics and magnitude of these hepatocyte-activating stimuli are tightly regulated to ensure restoration of a functional liver mass without causing uncontrolled cell proliferation. Hepatocyte proliferation can become deregulated under conditions of chronic inflammation, leading to accumulation of genetic aberrations and eventual neoplastic transformation. Among the control mechanisms that regulate hepatocyte proliferation, negative feedback inhibition by the 'suppressor of cytokine signaling (SOCS)' family proteins SOCS1 and SOCS3 play crucial roles in attenuating cytokine and growth factor signaling. Loss of SOCS1 or SOCS3 in the mouse liver increases the rate of liver regeneration and renders hepatocytes susceptible to neoplastic transformation. The frequent epigenetic repression of the SOCS1 and SOCS3 genes in hepatocellular carcinoma has stimulated research in understanding the growth regulatory mechanisms of SOCS1 and SOCS3 in hepatocytes. Whereas SOCS3 is implicated in regulating JAK-STAT signaling induced by IL-6 and attenuating EGFR signaling, SOCS1 is crucial for the regulation of HGF signaling. These two proteins also module the functions of certain key proteins that control the cell cycle. In this review, we discuss the current understanding of the functions of SOCS1 and SOCS3 in controlling hepatocyte proliferation, and its implications to liver health and disease.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".