Inhibitory effects of capsaicin on hepatic stellate cells and liver fibrosis
Bibliographic record
Abstract
Hepatic stellate cells (HSCs) play an important role in the process of liver fibrosis. In this study, we investigated the inhibitory effects of capsaicin on HSCs and liver fibrosis. Cultured HSCs were incubated with various concentrations of capsaicin. Cell proliferation was examined using a cell counting kit. Production of hydrogen peroxide was determined using a 2',7'-dichlorofluorescin diacetate (DCFH-DA) assay. The mRNA and protein expression of target genes was analyzed by reverse transcription PCR and Western blot analysis, respectively. Cell apoptosis was evaluated by annexin V-FITC and propidium iodide (PI) costaining followed by flow cytometric analysis. A CCl4 rat liver fibrosis model was used to assess in vivo effects of capsaicin by histological examination and measurement of liver fibrosis markers, including hydroxyproline content, serum type III collagen, and hyaluronic acid (HA) levels. Our results show that capsaicin dose-dependently inhibited cell proliferation, suppressed cell activation, and decreased hydrogen peroxide production in cultured HSCs. Capsaicin reduced the mRNA levels of tissue inhibitors of metalloproteinase 1 (TIMP-1) and transforming growth factor-β1 (TGF-β1) in HSCs. Moreover, capsaicin-induced cell apoptosis was associated with increased expression of Bax, cytochrome c (cyt c), and caspase-3, but reduced levels of Bcl-2. The animal studies further revealed that capsaicin efficiently reduced the extent of liver fibrosis, inhibited HSC proliferation, and promoted cell apoptosis. Our findings suggest that capsaicin might inhibit fibrogenesis by inhibiting the activities of HSCs.
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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 | 0.000 |
| 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".