Leflunomide-induced cardiac injury in adult male mice and bioinformatic approach identifying Nrf2/NF-κb signaling interplay
Bibliographic record
Abstract
Leflunomide (LFND) is an immunosuppressive and immunomodulatory disease-modifying antirheumatic drug (DMARD) that was approved for treating rheumatoid arthritis. LFND-induced cardiotoxicity was not fully investigated since its approval. We investigated the cardiac injury in male mice and identified the role of nuclear factor erythroid 2-related factor 2/nuclear factor-κ B (Nrf2/NF-κB) signaling. Male albino mice were assigned into five groups as control, vehicle, and LFND (2.5, 5, and 10 mg/kg). We investigated cardiac enzymes, histopathology, and the mRNA expression of Nrf2, NF-κB, BAX, and tumor necrosis factor-α (TNF-α). The bioinformatic study identified the interaction between LFND and Nrf2/NF-κB signaling; this was confirmed by amelioration in mRNA expression (0.5- to 0.34-fold decrease in Nrf2 and 2.6- to 4.61-fold increases in NF-κB genes) and increased (1.76- and 2.625-fold) serum creatine kinase (CK) and 1.38- and 2.33-fold increases in creatine kinase-MB (CK-MB). Histopathological results confirmed the dose-dependent effects of LFND on cardiac muscle structure in the form of cytoplasmic, nuclear, and vascular changes in addition to increased collagen deposits and apoptosis which were increased compared to controls especially with LFND 10 mg/kg. The current study elicits the dose-dependent cardiac injury induced by LFND administration and highlights, for the first time, dysregulation in Nrf2/NF-κB signaling.
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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) | 0.000 | 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) | 0.001 | 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".