Formononetin protects against concanavalin-A-induced autoimmune hepatitis in mice through its anti-apoptotic and anti-inflammatory properties
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is a chronic inflammatory liver disease that seriously threatens the health of humans globally. Formononetin (FMN) is a natural herb extract with multiple biological functions. In this study, an experimental model of AIH was established in mice through the use of concanavalin A (ConA). To investigate the effects of FMN on ConA-induced hepatitis, the mice were pretreated with 50 or 100 mg/kg body mass of FMN. The results show that FMN alleviated ConA-induced liver injury of mice in a dose-dependent manner. Moreover, pretreatment with FMN inhibited the apoptosis of hepatocytes in the ConA-treated mice through downregulating the expression of pro-apoptotic proteins (Bax, cleaved caspase 9, and cleaved caspase 3) and upregulating the expression of anti-apoptotic protein (Bcl-2). It was also found that the levels of proinflammatory cytokines were greatly reduced in the serum and liver tissues of mice pretreated with FMN. Further studies showed that FMN reduced the level of phosphorylated nuclear factor kappa B (p-NF-κB) p65 and enhanced the level of IκBα (inhibitor of NF-κB), suggesting that FMN inhibits the activation of the NF-κB signaling pathway. In addition, FMN inhibited activation of the NOD-like receptor protein 3 (NLRP3) inflammasome. Therefore, FMN could be a promising agent for the treatment of AIH.
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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 | 0.001 | 0.001 |
| 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".