Quercetin ameliorates liver injury induced with <i>Tripterygium</i> glycosides by reducing oxidative stress and inflammation
Bibliographic record
Abstract
Quercetin (Que) is one of main compounds in Lysimachia christinae Hance (Christina loosestrife), and has both medicinal and nutritional value. Glycosides from Tripterygium wilfordii Hook.f. (léi gōng téng [the thunder duke vine]; TG) have diverse and broad bioactivities but with a high incidence of liver injury. Our previous study reported on the hepatoprotective properties of an ethanol extract from L. christinae against TG-induced liver injury in mice. This research is designed to observe, for the first time, the possible protective properties of the compound Que against TG-induced liver injury, and the underlying mechanisms that are involved in oxidative stress and anti-inflammation. The results indicated that TG caused excessive elevation in serum levels of alanine/aspartate transaminase (ALT/AST), alkaline phosphatase (ALP), gamma glutamyl transferase (γ-GT), and pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α), as well as hepatic lipid peroxidation (all P < 0.01). On the other hand, following TG exposure, we observed significantly reduced levels of biomarkers, including hepatic glutathione (GSH), glutathione-S-transferase (GST), glutathione peroxidase (GPx), and the anti-inflammatory cytokine interleukin (IL)-10, as well as the enzyme activity and mRNA expression of copper- and zinc-containing superoxide dismutase (CuZn-SOD) and catalase (CAT) (all P < 0.01). Nevertheless, all of these alterations were reversed by the pre-administration of Que or the drug bifendate (positive control) for 7 consecutive days. Therefore, this study suggests that Que ameliorates TG-induced acute liver injury, probably through its ability to reduce oxidative stress and its anti-inflammatory properties.
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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".