β-Patchoulene represses hypoxia-induced proliferation and epithelial-mesenchymal transition of liver cancer cells
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a malignant tumor originating from liver epithelial cells with a high clinical mortality rate. β-Patchoulene (β-PAE) is a compound extracted from patchouli, which has analgesic, anti-inflammatory and antioxidant effects. This research aims to probe the impacts of β-PAE on hypoxia-induced HCC cell proliferation and epithelial-mesenchymal transition (EMT). Firstly, hypoxic injury models were constructed in HCC Huh-7 and MHCC97 cells, and the hypoxic injury cell models were then treated with different concentrations of β-PAE. The cell viability, proliferation, migration, invasion and apoptosis were checked by the cell counting kit-8 (CCK-8) assay, colony formation assay, Transwell assay, flow cytometry and terminal deoxyribonucleotide transferase (TdT)-mediated dUTP nick end labeling (TUNEL) assay. The expression of Survivin protein, EMT markers and the NF-κB/HIF-1α pathway was gauged by Western blot (WB) or cellular immunofluorescence or reverse transcription-polymerase chain reaction (RT-PCR). The in-vivo experiment was conducted to confirm the anti-tumor role of β-PAE. As a result, β-PAE abated hypoxia-induced HCC cell growth, proliferation, migration, invasion and EMT and facilitated apoptosis in vitro and in vivo dose-dependently. Further mechanism studies displayed that β-PAE inactivated the NF-κB/HIF-1α pathway, and HIF-1α activation significantly reversed the β-PAE-mediated tumor inhibition. β-PAE repressed the proliferation and EMT of hypoxia-induced HCC cells by choking the NF-κB/HIF-1α pathway, suggesting that β-PAE was a potential drug for HCC treatment.
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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".