Quercetin modulates NRF2 and NF-κB/TLR-4 pathways to protect against isoniazid- and rifampicin-induced hepatotoxicity in vivo
Why this work is in the frame
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Bibliographic record
Abstract
Isoniazid and rifampicin are crucial for treating tuberculosis (TB); however, they can cause severe hepatotoxicity leading to liver failure. Therapeutic options are limited and ineffective. We hypothesized that prophylaxis with quercetin attenuates isoniazid- and rifampicin-induced liver injury. We randomly divided Wistar rats into seven groups (n = 6). The animals received isoniazid and rifampicin or were co-treated with quercetin or silymarin for 28 days. The protective effect of quercetin was assessed using liver function tests and liver histology. Nuclear factor erythroid 2-related factor 2 (NRF2) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathways were explored to elucidate the mechanism of action. Quercetin co-administration prevented the elevation of alanine transaminase (ALT), aspartate transaminase (AST), alkaline phosphatase (ALP) and bilirubin compared with isoniazid and rifampicin treatment alone. In the histological analysis, we observed that quercetin prophylaxis lessened the severity of hepatic necrosis and inflammation compared with the anti-TB drug–treated group. Quercetin attenuated anti-TB drug–induced oxidative stress by increasing NRF2 activation and expression, boosting endogenous antioxidant levels. Additionally, quercetin blocked inflammatory mediators high mobility group box-1 (HMGB-1) and interferon γ (IFN-γ), inhibiting activation of the NF-κB/ toll like receptor 4 (TLR-4) axis. Quercetin protects against anti-TB liver injury by activating NRF2 and blocking NF-κB/TLR-4.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it