Modulation of gut microbiota and markers of metabolic syndrome in mice on cholesterol and fat enriched diet by butterfly pea flower kombucha
Bibliographic record
Abstract
Clitoria ternatea, with an alternative name, Butterfly pea, is increasingly being explored for medical purposes and the development of a wide range of processed products. This study aimed to incorporate Butterfly pea into an innovative probiotic drink through a symbiotic culture of bacteria and yeast (SCOBY) fermentation and to evaluate the biological activity. The benefits of the drink, referred to as butterfly pea flower kombucha (KBPF) was determined in vitro and in metabolically disorder mice that receive a diet rich in cholesterol and fat (CFED). Forty white male were categorized into four groups, i.e., A = Control/Normal Diet; B = CFED alone; C = CFED + KBPF 65 mg/kg BW (Body Weight); D = CFED + KBPF 130 mg/kg BW, and then sacrificed after 6 weeks of intervention. Seventy-nine secondary metabolite compounds were successfully identified in KBPF using LC-HRMS. In vitro studies showed the potential activity of KBPF in inhibiting not only ABTS, but also lipid (lipase) and carhodyrate (α-amylase, α-glucosidase) hydrolyzing enzymes to levels similar to acarbose control at 50 – 250 μg/mL. In the in vivo study, the administration of KBPF (130 mg/kg BW) significantly alleviated metabolic disorders caused by high-fat diet. Specifically, lipid profile (HDL, LDL, TC, TG), blood glucose, markers of oxidative stress (SOD liver), metabolic enzymes (lipase, amylase), and markers of inflammation (PGC-1α, TNF-α, and IL-10) were in most cases restored to normal values. Additionally, the gut microbiota community analysis showed that KBPF has a positive effect (p=0.01) on both the Bacteroidetes phylum and the Firmicutes phylum. The new KBPF drink is a promising therapeutic functional food for preventing metabolic diseases.
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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) | 0.002 | 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".