Proteomic landscape of liver tissue in old male mice that are long-term treated with polysaccharides from<i>Sargassum fusiforme</i>
Bibliographic record
Abstract
Sargassum fusiforme is a type of brown algae and well known as a longevity promoting vegetable in Northeastern Asia. The polysaccharides derived from Sargassum fusiforme (SFPs) have been suggested as an antioxidant component for anti-aging function. However, global molecular changes in vivo by SFPs have not been fully elucidated. Here, we present a proteomics study using liver tissues of aged male mice that were fed with SFPs. Of forty-nine protein spots, thirty-eight were up-regulated and eleven were down-regulated, showing significant changes in abundance by two-dimensional gel electrophoresis. These differentially expressed proteins were mainly involved in oxidation-reduction, amino acid metabolism, and energy metabolism. Forty-six proteins were integrated into a unified network, with catalase (Cat) at the center. Intriguingly, most of the proteins were speculated as mitochondrial-located proteins. Our findings suggested that SFPs modulated antioxidant enzymes to scavenge redundant free radicals, thus preventing oxidative damage. In conclusion, our study provides a proteomic view on how SFPs have beneficial effects on the aspects of antioxidant and energy metabolism during the aging process. This study facilitates the understanding of anti-aging molecular mechanisms in polysaccharides derived from Sargassum fusiforme.
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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 | 0.001 | 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 | 0.000 |
| 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".