Cryptolepine derivative-6h inhibits liver fibrosis in TGF-β1-induced HSC-T6 cells by targeting the Shh pathway
Bibliographic record
Abstract
Liver fibrosis is a worldwide problem with a significant morbidity and mortality. Cryptolepis sanguinolenta (family Periplocaceae) is widely used in West African countries for the treatment of malaria, as well as for some other diseases. However, the role of C. sanguinolenta in hepatic fibrosis is still unknown. It has been reported that Methyl-CpG binding protein 2 (MeCP2) had a high expression in liver fibrosis and played a central role in its pathobiology. Interestingly, we found that a cryptolepine derivative (HZ-6h) could inhibit liver fibrosis by reducing MeCP2 expression, as evidenced by the dramatic downregulation of α-smooth muscle actin (α-SMA) and type I collagen alpha-1 (Col1α1) in protein levels in vitro. Meanwhile, we also found that HZ-6h could reduce the cell viability and promote apoptosis of hepatic stellate cells (HSCs) treated with transforming growth factor beta 1(TGF-β1). Then, we investigated the potential molecular mechanisms and found that HZ-6h blocked Shh signaling in HSC-T6 cells, resulting in the decreased protein expression of Patched-1 (PTCH-1), Sonic hedgehog (Shh), and glioma-associated oncogene homolog 1 (GLI1). In short, these results indicate that HZ-6h inhibits liver fibrosis by downregulating MeCP2 through the Shh pathway in TGF-β1-induced HSC-T6 cells.
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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".