Alleviative Effect of <i>Stropharia rugosoannulata</i> Exopolysaccharide on <scp>d</scp> ‐Galactose‐Induced Gut Inflammation and Oxidative Stress by Modulating Gut Microbiota
Bibliographic record
Abstract
The exopolysaccharide SREP-1, purified from the fermentation broth of Stropharia rugosoannulata, exhibited antiaging potential. As aging significantly alters gut structure and function, protective effect of SREP-1 was investigated using a d-galactose-induced aging mouse model. SREP-1 administration reversed D-galactose-induced body weight loss and colon damage, as evidenced by improved histopathology. SREP-1 mitigated weight loss and colon damage, enhanced the activities of antioxidants (SOD, GSH-Px, and CAT), and reduced the level of MDA. It decreased proinflammatory cytokines (TNF-α, IL-1β, and IL-6) and elevated IL-10 in colon tissue, while boosting serum immunoglobulins (IgG and IgM). Crucially, these effects were abolished by antibiotic pretreatment, highlighting the role of gut microbiota in SREP-1 bioactivity. This role was further confirmed through fecal microbiota transplantation (FMT) and fecal supernatant transplantation (FST) experiments. Based on 16S rRNA sequencing, SREP-1 restored gut microbial diversity, increased beneficial genera (e.g., Faecalibacterium, Akkermansia, Lactobacillus, and Bacteroides), and decreased harmful bacteria (e.g., Escherichia-Shigella and Collinsella). Furthermore, short-chain fatty acids (SCFAs) levels were elevated in the SREP-1 group, which might regulate GPCRs/NF-κB/Nrf2 signaling pathways and exert biological activity. This study revealed the potential of SREP-1 to alleviate aging-related intestinal dysfunction and underscored the crucial role of gut microbiota in mediating these effects.
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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) | 0.000 | 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".