Effect of nicotinamide riboside on lipid metabolism and gut microflora‐bile acid axis in alcohol‐exposed mice
Bibliographic record
Abstract
Alcoholic liver disease (ALD) is the most common complication of alcohol abuse, while we lack safe and effective treatment for ALD. This study aimed to explore the effects of nicotinamide riboside (NR) on lipid metabolism and gut microflora-bile acid axis in alcohol-exposed mice. NR significantly improved liver histopathological damage and abnormal liver function. NR as a provider of nicotinamide adenine dinucleotide (NAD+) increased the NAD+/NADH ratio. Meanwhile, NR inhibited the activation of the protein phosphatase 1 signaling pathway, decreased the liver triglyceride and total bile acid levels, and reduced lipid accumulation. According to the results of gut microflora species analysis, NR intervention changed the microbial community structure at the phylum, family and genus levels, and the species abundances returned to a level similar to these of the normal control group. Besides, the results of high-performance liquid chromatograph-mass spectrometry showed that NR intervention resulted in fecal bile acid levels tending to be normal with decreased chenodeoxycholic acid level and increased deoxycholic acid and hyocholic acid levels. Spearman's correlation analysis showed a correlation between gut microflora and bile acids. Therefore, NR supplementation has the potential to prevent ALD, and its mechanism may be related to regulating lipid metabolism disorders and the gut microflora-bile acid axis.
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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 | 0.001 | 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) | 0.001 | 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".