Simultaneous intervention against oxidative stress and inflammation by targeting Nrf2/ARE and NLRP3 inflammasome pathway mitigates thioacetamide-induced liver fibrosis in rat
Bibliographic record
Abstract
Liver fibrosis is a typical pathological state/stage involved in most chronic liver diseases and its persistence results in cirrhosis. Inflammasomes are cytoplasmic sensors that induce inflammation in response to stress. Glibenclamide (GLB) is an USFDA-approved drug for type 2 diabetes and is reported to possess anti-inflammatory activity by inhibiting inflammatory cytokines. Dimethyl fumarate (DMF) is an USFDA-approved drug for multiple sclerosis and has been reported to activate the Nrf2/ARE pathway to maintain the cellular antioxidant balance. A total of 36 rats were randomized into six groups ( n = 6 each). The rats were injected with thioacetamide (TAA) 200 mg/kg, intraperitoneally every third day for eight consecutive weeks to induce liver fibrosis and oral treatment of GLB 0.5 mg/kg/day and DMF 25 mg/kg/day, and their combinations were provided for the last four consecutive weeks. Treatment with GLB, DMF, and GLB+DMF significantly protected against TAA-mediated oxidative stress and inflammatory conditions by improving hepatic function test, triglycerides, hydroxyproline, and histopathological alterations, by inhibiting the NLRP3 inflammasome signaling and fibrogenic markers, and by activating Nrf2/ARE pathway in Wistar rats. The present results suggest that simultaneous Nrf2/ARE activation and NLRP3 inflammasome inhibition could significantly contribute to developing a novel therapy for patients with liver 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) | 0.001 | 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 | 0.000 |
| 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".