Punicalagin Attenuates Palmitate‐Induced Lipid Droplet Content by Simultaneously Improving Autophagy in Hepatocytes
Bibliographic record
Abstract
SCOPE: Several studies show that excessive lipid intake can cause hepatic steatosis. To investigate lipotoxicity on cellular level, palmitate (PA) is often used to highly increase lipid droplets (LDs). One way to remove LDs is autophagy, while it is controversially discussed if autophagy is also affected by PA. It is aimed to investigate whether PA-induced LD accumulation can impair autophagy and punicalagin, a natural autophagy inducer from pomegranate, can improve it. METHODS AND RESULTS: To verify the role of autophagy in LD degradation, HepG2 cells are treated with PA and analyzed for LD and perilipin 2 content in presence of autophagy inducer Torin 1 and inhibitor 3-Methyladenine. PA alone seems to initially induce autophagy-related proteins but impairs autophagic-flux in a time-dependent manner, considering 6 and 24 h PA. To examine whether punicalagin can prevent autophagy impairment, cells are cotreated for 24 h with PA and punicalagin. Results show that punicalagin preserves expression of autophagy-related proteins and autophagic flux, while simultaneously decreasing LDs and perilipin 2. CONCLUSION: Data provide new insights into the role of PA-induced excessive LD content on autophagy and suggest autophagy-inducing properties of punicalagin, indicating that punicalagin can be a health-beneficial compound for future research on lipotoxicity in liver.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".