Low molecular weight guluronate prevents TNF-α-induced oxidative damage and mitochondrial dysfunction in C2C12 skeletal muscle cells
Bibliographic record
Abstract
Muscle wasting is associated with a variety of chronic or inflammatory disorders. Evidence suggests that inflammatory cytokines play a vital role in muscle inflammatory pathology and this may result in oxidative damage and mitochondrial dysfunction in skeletal muscle. In our study, we used microwave degradation to prepare a water-soluble low molecular weight guluronate (LMG) of 3000 Da from Fucus vesiculosus obtained from Canada, the Atlantic Ocean. We demonstrated the structural characteristics, using HPLC, FTIR and NMR of LMG and investigated its effects on oxidative damage and mitochondrial dysfunction in C2C12 skeletal muscle cells induced by tumor necrosis factor alpha (TNF-α), a cell inflammatory cytokine. The results indicated that LMG could alleviate mitochondrial reactive oxygen species (ROS) production, increase the activities of antioxidant enzymes (GSH and SOD), promote mitochondrial membrane potential (MMP) and upregulate the expression of mitochondrial respiratory chain protein in TNF-α-induced C2C12 cells. LMG supplement also increased the mitochondrial DNA copy number and mitochondrial biogenesis related genes in TNF-α-induced C2C12 cells. LMG may exert these protective effects through the nuclear factor kappa B (NF-κB) signaling pathway. These suggest that LMG is capable of protecting TNF-α-induced C2C12 cells against oxidative damage and mitochondrial dysfunction.
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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 | 0.001 |
| 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".