Protective effect of <i>Trillium tschonoskii</i> saponin on CCl<sub>4</sub>-induced acute liver injury of rats through apoptosis inhibition
Bibliographic record
Abstract
To explore hepatoprotective role and underlying mechanisms of Trillium tschonoskii Maxim (TTM), 36 rats were randomly divided into control, CCl 4 -induced liver injury model, and biphenyl dimethyl dicarboxylate (DDB) and low-, moderate-, and high-dose TTM treatment groups. After CCl 4 -induced model establishment, the rats from DDB and TTM groups were administrated with DDB at 0.2 g/kg per day and TTM at 0.1, 0.5, and 1.0 g/kg per day, while the rats from control and model groups were administrated with saline. After 5 days of treatments, all rats were sacrificed for determining serum ALT and AST levels and liver index, examining histopathological changes in liver through HE and TUNEL staining, and evaluating TNF-α and IL-6 mRNA expression by real-time PCR, and caspase-3, Bcl-2, and Bax expression by Western blot. Results indicated that CCl 4 could induce acute liver injury and abnormal liver function in rats with obvious hepatomegaly, increased liver index, high ALT and AST levels, up-regulated TNF-α and IL-6, and overexpressed Bax and caspase-3. However, DDB and TTM could execute protective role in CCl 4 -induced liver injury in rats through reducing ALT and AST levels, rescuing hepatomegaly, down-regulating inflammatory factors and inhibiting hepatocyte apoptosis in a dose-dependent manner. Therefore, TTM has obvious protective role in CCl 4 -induced liver injury of rats through inhibiting hepatocyte apoptosis.
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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 | 0.001 | 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".