Apoptotic and survival signals in hepatic stellate cells.
Bibliographic record
Abstract
Hepatic stellate cells (HSCs) play an important role in hepatic fibrogenesis. In response to liver injury, HSCs undergo a process called activation, which involves 2 steps jonit nation from quiescent phenotype to myofibroblast-like phenotype, and perpetuation that maintains the activated phenotype of HSCs. The fate of the activated HSCs depends on the apoptotic and survival signals that they receive. The apoptosis of HSCs results from a series of complex and interrelated signaling events. Apoptotic signals for the activated HSCs include proteins from membrane receptors, such as death receptors, nerve growth factor receptor and peripheral-type benzodiazepine receptor, as well as proteins from cytoplasm such as Bcl-2 family members. The survival signals for the activated HSCs are induced by some kinases and cytokines including tissue inhibitors of metalloproteinase-1, Rho/Rho kinase, platelet-derived growth factor, transforming growth factor beta-1, and insulin-like growth factor-1. Approaches that specifically initiate HSC apoptosis are promising to be direct and effective strategies to treat liver fibrosis. Although it remains unclear whether the activated HSCs could be reversed back to the quiescent phenotype, the different expression and sensitivity of pro-apoptotic and survival molecules between quiescent and activated HSCs provide a prospect to develop therapeutic approaches that specifically targets apoptosis of the activated HSCs. These therapeutic strategies to induce HSC apoptosis are current research hotspot and the future for the patients with liver fibrosis and cirrhosis.
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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) | 0.003 | 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".