<i>Veronicastrum axillare</i>Alleviates Ethanol-Induced Injury on Gastric Epithelial Cells via Downregulation of the NF-kB Signaling Pathway
Bibliographic record
Abstract
We used human gastric epithelial cells (GES-1) line in an ethanol-induced cell damage model to study the protective effect ofVeronicastrum axillareand its modulation to NF-κB signal pathway. The goal was to probe the molecular mechanism ofV. axillaredecoction in the prevention of gastric ulcer and therefore provide guidance in the clinical application ofV. axillareon treating injuries from chronic nephritis, pleural effusion, gastric ulcer, and other ailments. The effects ofV. axillare-loaded serums on cell viability were detected by MTT assays. Enzyme-linked immunosorbent assay (ELISA) and Real-Time PCR methods were used to analyze the protein and mRNA expression of TNF-α, NF-κB, IκBα, and IKKβ. The results showed thatV. axillare-loaded serum partially reversed the damaging effects of ethanol and NF-κB activator (phorbol-12-myristate-13-acetate: PMA) and increased cell viability. The protein and mRNA expressions of TNF-α, NF-κB, IκBα, and IKKβwere significantly upregulated by ethanol and PMA while they were downregulated byV. axillare-loaded serum. In summary,V. axillare-loaded serum has significantly protective effect on GES-1 against ethanol-induced injury. The protective effect was likely linked to downregulation of TNF-αbased NF-κB signal pathway.
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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) | 0.002 | 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".