Hepatoprotective study of Indonesian water kefir against CCl4-induced liver injury in rats
Bibliographic record
Abstract
Context: Water kefir is a fermented beverage that is typically made in the home by inoculating a sugar-rich solution with a microbial community (water kefir grains). Several studies on the metabolite content and hepatoprotective effects of water kefir have been published, but carbon tetrachloride (CCl4)-induced acute liver injury has not been studied. Aims: To evaluate the efficacy of water kefir in vivo against hepatoprotective CCl4-induced acute liver injury and to in silico investigate metabolites that play an important role in hepatoprotective mechanisms. Methods: The present study aimed to investigate the hepatoprotective activity of water kefir in an animal model caused by CCl4. Furthermore, using molecular docking, the metabolites found in water kefir were evaluated for their role in the NF-κB and Nrf2 signaling pathways. Results: Water kefir significantly and dose-dependently alleviated acute liver injury caused by CCl4. Water kefir administration at all doses produced results comparable to the positive control (Curcuma extract). Molecular docking simulations showed that, compared to Nrf2, the 25 metabolites were more likely to interact with the NF-B receptor. Fumaric acid is the strong metabolite that interacts with the NF-κB receptor with a free energy of binding and an inhibition constant of -6.66 kcal/mol and 13.22 µM, respectively. Conclusions: Water kefir administration improved the condition of liver damage, characterized by a decrease in serum levels of AST, ALT, TNF-, TGF-, and an improvement in the liver tissue profile. In silico evaluation showed that the metabolites in water kefir were able to interact with target proteins in the NF-B and Nrf2 pathways. It was concluded that water kefir improves the condition of the liver by reducing the level of necrosis and fibrosis.
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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) | 0.001 | 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) | 0.002 | 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".