Silibinin protects GLUTag cells from PA-induced injury via suppressing endoplasmic reticulum stress
Bibliographic record
Abstract
Abstract Silibinin is a natural extract exhibiting anti-diabetic effects. Lipotoxicty induced by excessive accumulation of free fatty acids (FFAs) leads to both insulin resistance and β cell insufficiency, which can trigger the pathogenesis of type 2 diabetes mellitus (T2DM). Glucagon-like peptide-1 (GLP-1), an intestinal hormone mainly secreted from L cells, regulates insulin production and sensitivity, and protection of the functional GLP-1 producing L cells appears to be a potential therapeutic strategy for T2DM patients. The current study aims to determine the protective effect of silibinin against palmitic acid (PA)-induced damage in L cell line GLUTag cells. In PA-treated GLUTag cells, silibinin was shown to decrease endoplasmic reticulum (ER) stress-mediated apoptosis. Furthermore, the autophagy inhibitor 3-methyladenine (3-MA) reversed PA-induced apoptosis, indicating that protective autophagic response was accompanied by apoptosis in GLUTag cells. Based on the estrogen-like effects of silibinin and the role of estrogen receptors in regulating glycolipid metabolism, the involvement of estrogen receptors in protective effects of silibinin in GLUTag cells were further determined. The results showed estrogen receptor α and β-specific inhibitors reversed the inhibitory impact of silibinin on ER stress. Our study demonstrated that silibinin protects GLUTag cells from PA-induced injury by decreasing ER stress under the regulation of estrogen receptor α and β.
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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".