Anthocyanin-Rich Butterfly Pea Flower Extract Ameliorating Low-Grade Inflammation in a High-Fat-Diet and Lipopolysaccharide-Induced Mouse Model
Bibliographic record
Abstract
This study aimed to explore the enhancive effects of butterfly pea flower (BF) extracts on metabolic and immune homeostasis in a low-grade inflammation mouse model. The BF extract was found to contain mainly anthocyanins among other flavonoids. BF supplementation alleviated metabolic endotoxemia by lowering the plasma glucose, lipopolysaccharide (LPS), and tumor necrosis factor-α (TNF-α) levels and restored lipid metabolism and the balance between Treg and Th17 cells, thereby inhibiting the dysfunctional liver and abdominal white adipose tissues. BF extract increased the tight junction protein expression and reduced the expression of proinflammatory cytokines, therefore sustaining the colonic mucosa structure. Furthermore, BF extracts reshaped the gut microbiota structure characterized by significantly promoted SCFA-producing gut microbiota such as Akkermansia and Butyricicoccaceae . Additionally, BF extracts enhanced fecal primary bile acid (BA) levels and modulated bile acid signaling in the liver and ileum to facilitate BA synthesis for the restoration of lipid metabolism. In summary, anthocyanin-enriched BF extracts alleviated the profound negative dietary alterations and helped maintain the metabolic health by modulating the various aspects of the gut microenvironment and enhancing hepatic bile acid synthesis.
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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 | 0.001 | 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 | 0.001 |
| 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".