Nicotinamide adenine dinucleotide attenuates acetaminophen-induced acute liver injury via activation of PARP1, Sirt1, and Nrf2 in mice
Bibliographic record
Abstract
The aim of this study was to investigate the protective effect of nicotinamide adenine dinucleotide (NAD+) against acute liver injury (ALI) induced by acetaminophen (APAP) overdose in mice. First, serum transaminases were used to assess the protective effect of NAD+, and the data revealed that NAD+ mitigated the APAP-induced ALI in a dose-dependent manner. Then, we performed hematoxylin–eosin staining of liver tissues and found that NAD+ alleviated the abnormalities of histopathology. Meanwhile, increase in the malondialdehyde content and decrease in glutathione, superoxide dismutase (SOD), and glutathione peroxidase were identified in the APAP group, which were partially prevented by the NAD+ pretreatment. Moreover, compared with the mice treated with APAP only, the expression of poly ADP-ribose polymerase 1 (PARP1), Sirtuin1 (Sirt1), SOD2, nuclear factor erythroid 2-related factor 2 (Nrf2), and hemoxygenase-1 was upregulated, while Kelch-like ECH-associated protein 1 and histone H2AX phosphorylated on Ser-139 were downregulated by NAD+ in NAD+ + APAP group. Conversely, NAD+ could not correct the elevated expression of phospho-Jun N-terminal kinase and phospho-extracellular signal-regulated kinase induced by APAP. Taken together, these findings suggest that NAD+ confers an anti-ALI effect to enhance the expression of PARP1 and Sirt1, and to simultaneously stimulate the Nrf2 anti-oxidant signaling pathway.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".