<i>Lycium barbarum</i> Polysaccharide Supplementation Improves Alcoholic Liver Injury in Female Mice by Inhibiting Stearoyl‐CoA Desaturase 1
Bibliographic record
Abstract
SCOPE: Lycium barbarum polysaccharide (LBP) is a water fraction of wolfberry, which has been demonstrated to possess a hepatoprotective effect in several liver disease models. However, the anti-alcoholic liver disease (anti-ALD) mechanism of LBP has not been investigated thoroughly. Its protective effects on both male and femal mice are investigated in the current study. METHODS AND RESULTS: A chronic ethanol-fed ALD in vivo model is applied to study the effect of LBP in both male and female mice. It is observed that ethanol causes more severe liver injury in female than male mice, and the ameliorative effects of LBP are also more significant in female mice, which are impaired after complete bilateral oophorectomy. The hepatic SCD1 expression is found to be positively correlated with the severity of the liver damage and the main mediator of LBP inducer of protection. The AMPK-CPT pathway is also activated by LBP to rebalance the dysregulated lipid metabolism during ALD development. By using concurrent sodium palmitate and an ethanol-induced in vitro cell damage model in AML-12 cell line, it is characterized that LBP directly interacts with ERα instead of ERβ to activate the SCD1-AMPK-CPT pathway. CONCLUSIONS: LBP is an effective and safe hepatoprotective agent against ALD primarily through the SCD1-AMPK-CPT pathway after ERα agonist.
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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 | 0.000 | 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 | 0.001 |
| 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".