Protective effect of glycoprotein isolated from <i>Ulmus davidiana</i> Nakai on carbon tetrachloride-induced mouse liver injury
Bibliographic record
Abstract
This study was carried out to evaluate the hepatoprotective activity of glycoprotein isolated from the stems of Ulmus davidiana Nakai (UDN), which has been used as an anti-inflammatory agent in folk medicine. We evaluated lipid peroxidation in glucose/glucose oxidase (G/GO)-induced BNL CL.2 cells and measured thiobarbituric acid reactive substances (TBARS), lactate dehydrogenase (LDH), nitric oxide (NO), antioxidant enzyme (superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx)), activity of cytotoxic-related signals (hepatic cytochrome c, nuclear factor-kappa B (NF-kappaB) and activator protein-1 (AP-1)) and levels of plasma lipids (triglyceride (TG) and total cholesterol (TC)) in carbon tetrachloride (CCl(4,) 1.0 mL kg(-1))-induced A/J mouse. The results in G/GO-induced BNL CL.2 cells showed that UDN glycoprotein had a dose-dependent inhibitory effect on lipid peroxidation. The results in carbon tetrachloride (CCl(4,) 1.0 mL kg(-1))-induced A/J mouse indicated that treatment with UDN glycoprotein (40 mg kg -1) lowered LDH activity and TBARS formation, and increased NO production and antioxidant enzymes activity, compared with control. Also, our finding from CCl(4)-treated mice after pretreatment with UDN glycoprotein demonstrated that the activity of cytotoxic-related signals decreased but the levels of plasma lipids increased, compared with CCl(4) treatment alone. Here, we speculate that UDN glycoprotein has a protective character to CCl(4)-induced mouse liver injury.
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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) | 0.000 | 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".