Novel Acylated Naringin Enhances Propionate Release and Stimulates the Growth of Flavanone-Metabolizing Bacteria in an In Vitro Batch Fermentation Model
Bibliographic record
Abstract
The increasing prevalence of non-communicable diseases (NCDs) is strongly associated with gut microbiota (GM) imbalances and reduced short-chain fatty acid (SCFA) production, primarily driven by poor diet and microbial dysbiosis. Since SCFAs are crucial for gut health, immune regulation, and inflammation control, restoring their levels is a key therapeutic target. SCFA-acylated naringin derivatives offer a novel approach by enhancing SCFA delivery and modulating GM composition. In this study, we investigated the effects of naringin acetate and naringin propionate on SCFA production using a 24 h short-term in vitro batch fecal fermentation model with microbiota from two donors. Naringin propionate and naringin plus free propionate significantly increased propionate levels by 0.74 mM and 0.75 mM, respectively (p < 0.0001), while naringin acetate induced a smaller increase of 0.26 mM. Donor-specific reflected differences in microbial communities, yet SCFA enhancement was observed across samples. Additionally, naringin treatments stimulated the growth of beneficial polyphenol-metabolizing bacteria, including Bacteroides, Streptococcus, and Eubacterium siraeum. The strong effect of naringin propionate suggests a sustained SCFA release mediated by microbial enzymes. These preliminary results highlight the potential of SCFA-acylated flavonoids as functional dietary components to increase SCFA bioavailability and support gut health, particularly from citrus-derived co-products.
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) | 0.001 | 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 | 0.001 |
| 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".