A novel polyphenolic prebiotic and probiotic formulation have synergistic effects on the gut microbiota influencing <i>Drosophila melanogaster</i> physiology
Bibliographic record
Abstract
The gut microbiota is a vast community of synergistic bacterial species providing health benefits to the host. Imbalances in the gut microbiota (dysbiosis) due to diet, antibiotic use, age and stress contribute to disease development including diabetes, obesity, colon cancer, inflammatory bowel disease, inflammaging and neurodegeneration. Fortunately, a probiotic regime with a diet rich in prebiotics may reverse dysbiosis promoting health and wellness in age. The current study designs, optimizes and tests a novel probiotic and synbiotic formulation consisting of three metabolically active probiotics Lactobacillus plantarum, Lactobacillus fermentum and Bifidobacteria infantis together with a novel polyphenol-rich prebiotic, Triphala. The prebiotic action of Triphala was characterized using in vitro batch cultures, Drosophila melanogaster and a simulated model of the human gastrointestinal tract (SHIME) where in each model, Triphala supported growth of beneficial bacteria while inhibiting pathogenic species. Neither Triphala at 0.5% w/v nor the individual probiotics at 5.0 × 108 to 7.5 × 109 CFU/ml demonstrated toxicity in Drosophila. Interestingly, motility was combinatorially enhanced by the probiotic and synbiotic formulations reflecting the beneficial variations in the gut microbiota. Altogether, the present study shows that probiotics and synbiotics in combination are more effective at modulating the gut microbiota and eliciting biological effects than their components.
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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) | 0.000 | 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".