Lactobacillus rhamnosus GG Inhibits BID Dependent-Apoptosis in Human Hepatocellular Carcinoma Cells Exposed to Patulin
Bibliographic record
Abstract
Patulin, a mycotoxin, which is a major contaminant in apple juices, has contributed immensely to the occurrence of liver diseases. Consumption of apple juice could over long period of time become harmful to the health of individuals with pre-existing liver disease. Probiotics are known for their role in patulin removal from aqueous media. In this study, we investigated the effects of a probiotic microorganism on patulin toxicity in hepatocellular carcinoma (HepG2) cells and established the protective effect of Lactobacillus rhamnosus (LGG) as mediated by induction of BH3-interacting domain antagonist (BID) in response to patulin toxicity. After 24 hours of patulin exposure followed by 24 hours of treatment with Lactobacillus rhamnosus, cells proliferation decreased with increasing patulin exposure in samples without LGG pre-treatment, whereas with increasing concentration of patulin, cells were relatively rescued in LGG treated samples. It was further observed that pre-treatment of LGG with polysaccharide gums led to a decline in cell proliferation with increasing patulin exposure. Compare to the control, the expression of p53 upregulated moderator of apoptosis (PUMA) increased slightly by 7 % at 10µM patulin exposure in treatment and decreased by 30% in untreated cell. However, the expression of BID decreased by 26% in treatment compared to the control. We further established that the protective effect of Lactobacillus rhamnosus was mediated by the inhibition of BID. Our findings suggest that Lactobacillus rhamnosus GG could potentially function as a therapeutic agent to reverse the damaging effect of patulin on the liver of individuals with pre-existing liver disease.
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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".