Douchi Peptides VY and SFLLR Improve Glucose Homeostasis and Gut Dysbacteriosis in High‐Fat Diet‐Induced Insulin Resistant Mice
Bibliographic record
Abstract
SCOPE: Two peptides VY (Val-Tyr) and SFLLR (Ser-Phe-Leu-Leu-Arg) are recently identified from soy-fermented douchi with hypoglycemic activity in cells. The study aims to understand their potential effects on glucose metabolism and insulin sensitivity as well as their mechanisms of action in a high-fat diet (HFD) induced insulin resistant model. METHODS AND RESULTS: body weight) for 8 weeks. Peptides supplementation, especially SFLLR, reduces body weight gain, insulin resistance, hyperglycemia, inflammation, liver injury, and lipid accumulation. In both muscle and liver, both peptides activate glycogen synthase (GS), the key enzyme for glycogen synthesis, and also inhibit phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6PC), two rate-limiting enzymes for gluconeogenesis, via insulin and AMPK (5'-adenosine monophosphate-activated protein kinase) signaling pathways. Furthermore, VY and SFLLR supplementation reverse HFD-induced gut dysbacteriosis by decreasing the abundance of Enterococcus, Oscillibacter, and Deferribacter, and also increase the abundances of Alistipes, Lactobacillus, Faecalibaculum, Akkermansia, and Bifidobacterium (usually beneficial in the intestine). CONCLUSION: The study reveals the potential applications of peptides VY and SFLLR as a diet-based strategy for the prevention of type 2 diabetes.
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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 | 0.001 | 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".