Inhibition effect of phytoestrogen calycosin on TGF-β<sub>1</sub>-induced hepatic stellate cell activation, proliferation, and migration via estrogen receptor β
Bibliographic record
Abstract
The present study was designed to investigate the effects of calycosin on hepatic stellate cell (HSC) function and to explore whether the drug exerts its effect through the estrogen receptor. HSC proliferation and migration were measured by MTT assay and transwell chamber assay, respectively. The mRNA and protein expression of α-SMA, COL-I, and ERβ were detected by real-time PCR and Western blotting. The co-localization and expression of α-SMA and ERβ protein were detected by immunofluorescence. All the studies were investigated in the absence or presence of ICI 182,780. The results showed that calycosin inhibited the proliferation of activated HSCs and remarkably inhibited HSC migration. Calycosin significantly reduced the expression of α-SMA and COL-I in activated HSCs. However, with co-treatment with ICI 182,780, the inhibitory effect of calycosin against the above effects was strongly negated. Importantly, calycosin significantly downregulated the expression of ERβ protein, while co-treatment with ICI 182,780 partially reversed the ERβ downregulation. In addition, α-SMA decreased with the decrease of ERβ expression and the subtype of ERβ on HSC is ERβ5. In conclusion, calycosin inhibits proliferation, activation, and migration of TGF-β1-induced HSCs. The effect may be related to binding and downregulation of ERβ5.
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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 | 0.000 |
| 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".