Egg white hydrolysate and peptide reverse insulin resistance associated with tumor necrosis factor-α (TNF-α) stimulated mitogen-activated protein kinase (MAPK) pathway in skeletal muscle cells
Bibliographic record
Abstract
PURPOSE: Excessive formation of tumor necrosis factor-α (TNF-α), a pro-inflammatory cytokine, has been implicated in the development of insulin resistance in obesity and type-2 diabetes. In skeletal muscle, chronic exposure to TNF-α impairs insulin-stimulated glucose uptake and insulin signaling. The aim of this study is to investigate the effects of enzymatic egg white hydrolysate (EWH) and its responsible peptide, IRW, on TNF-α-induced insulin resistance and the underlying molecular mechanisms using rat skeletal muscle cells (L6 cells). METHODS: Insulin resistance was induced by treating L6 cells with 5 ng/ml TNF-α for 24 h. Effects of EWH and IRW on glucose uptake were detected by glucose uptake assay, glucose transporter 4 (GLUT4) translocation by immunofluorescence, and western blot, while insulin-signaling pathway and mitogen-activated protein kinase (MAPK) pathway were investigated using western blot. RESULTS: Adding both EWH and IRW significantly improved glucose uptake in TNF-α-treated cells, increased activation of insulin receptor substrate (IRS-1) tyrosine residue and protein kinase B (Akt), whereas decreased activation of IRS-1 serine residue. In addition, TNF-α-induced activation of p38-mitogen-activated protein kinase (p38) and c-Jun N-terminal kinases (JNK) 1/2 were decreased by either EWH or IRW treatment. CONCLUSION: EWH and IRW improve impaired insulin sensitivity by down-regulating the activation of p38 and JNK1/2 in TNF-α-treated skeletal muscle cells.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".