Green banana flour supplementation improves obesity-associated systemic inflammation and regulates gut microbiota profile in mice fed high-fat diets
Bibliographic record
Abstract
This study evaluated the effect of green banana flour (GBF) consumption on obesity-related conditions in mice fed high-fat diets. GBF was prepared using stage 1 green banana pulp, which was dehydrated and milled. Mice were fed a control diet (n = 20; 10% of energy from lipids) or a high-fat diet (n = 20; 50% of energy from lipids). After 10 weeks, mice were divided into 4 groups based on feed: standard chow (SC; n = 10), standard with 15% GBF (SB; n = 10), high-fat diet (HF; n = 10) and high-fat diet with 15% GBF (HFB; n = 10) for 4 weeks. HFB exhibited lower gains in body weight (–21%; p < 0.01) and in all fat pads (p < 0.01) compared with the HF group. SC, SB, and HFB showed smaller retroperitoneal white adipose tissue diameters (p < 0.001). SB and HFB-treated mice showed lower levels of leptin, IL-6, and TNF-α compared with the SC and HF groups (p < 0.01). In the GBF-fed groups, there was a reduction in the abundance of Firmicutes (SB: –22%; HFB: –23%) and an increase in Bacteroidetes (SB: +25%; HFB: +29%) compared with their counterparts. We demonstrated that GBF consumption attenuated inflammation and improved metabolic status, adipose tissue remodeling, and the gut microbiota profile of obese mice. Novelty: Green banana flour (GBF) consumption, rich in resistant starch, regulates body weight in mice fed high-fat diets. GBF consumption improves fat pad distribution in mice fed high-fat diets. GBF improves obesity-associated systemic inflammation and regulates gut microbiota profile in mice fed high-fat diets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".