Anthocyanins-rich purple potato extract prevents low-grade chronic inflammation-associated metabolic disorders
Bibliographic record
Abstract
Dietary polyphenols including anthocyanins possess strong antioxidant and anti-inflammatory properties, and are known to help reduce risks of oxidative stress-induced chronic diseases. However, their effects on various aspects of the gut microenvironment towards preventing the unhealthy diet-induced metabolic disorders are still not well understood. The present study aims to verify the in vitro antioxidant and anti-inflammatory effects of the anthocyanin-rich extracts of purple potato (PPE), using a lipopolysaccharide (LPS) and high-fat diet (HFD)-induced obesity C57/BL6J mouse model, and to examine the effects of PPE on LPS+HFD-impaired metabolic homeostasis and the underlying mechanisms. We found that PPE, especially at higher dose significantly improved the glucose and lipid metabolism, and reduced inflammation in the plasma and various tissues. It significantly improved intestinal barrier integrity, altered fecal metabolite profile and gut microbiota composition. Our findings provide new insights into the roles of highly-pigmented vegetable-derived anthocyanins in maintaining gut health and ameliorating metabolic syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".