Synergistic regulation of crude <scp> <i>Sphallerocarpus gracilis</i> </scp> polysaccharides and probiotics on intestinal flora and its immune‐related metabolites in rats
Bibliographic record
Abstract
Abstract BACKGROUND Sphallerocarpus gracilis (SG) is a medicinal and edible plant in China with high nutritional value. In the present study, the synergistic effect of crude Sphallerocarpus gracilis polysaccharides (CSGP) and specific probiotics on the intestinal flora and its immune‐related metabolites in rats was investigated. Probiotic fermented milk supplemented with 5–15 g L −1 CSGP (YSGP) was prepared. Following a 60‐day feeding experiment conducted on Wistar rats, the levels of inflammatory factors in the serum were measured, and the contents of intestinal metabolites, including short‐chain fatty acids, indole, 3‐methylindole and hydrogen sulfide, were assessed. Additionally, the changes in the fecal microbiota were analyzed using 16S rRNA high‐throughput sequencing. RESULTS The results demonstrated that YSGP containing ≥10 g L −1 CSGP significantly modulated the intestinal flora by increasing the relative abundance of Bacteroidetes , Lactobacillaceae , Erysipelotrichaceae , Prevotella and Turiubacter , at the same time as reducing the relative abundance of Firmicutes , Ruminococcaceae and Dorea ( P < 0.05). The levels of short‐chain fatty acids (acetic acid, propionic acid, butyric acid, isobutyric acid and valeric acid), indole derivatives and serum cytokines [tumor necrosis factor‐α, interleukin (IL)‐6, IL‐8 and IL‐1β] in rats were also enhanced after YSGP consumption ( P < 0.05). Correlation analysis revealed strong positive associations between CSGP, beneficial microbial abundance, intestinal metabolites and cytokine levels. CONCLUSION These findings demonstrate the prebiotic capacity of CSGP to synergize with probiotics in regulating intestinal microecology and immunity, highlighting its potential application in functional fermented dairy products. © 2025 Society of Chemical Industry.
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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) | 0.001 | 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".