Influence of Probiotics Administration on Gut Microbiota Core
Bibliographic record
Abstract
An increasing number of studies has shown that dietary probiotics exert beneficial health effects in both humans and animals. It is well established that gut microbiota play a pivotal role in regulating host metabolism, and a growing number of studies has elucidated that probiotics positively interfere with gut microbiota. Accumulating evidence shows that probiotics, through their metabolic activity, produce metabolites that in turn contribute to positively affect host physiology. For these reasons, probiotics have shown significant potential as a therapeutic tool for a diversity of diseases, but the mechanisms through which probiotics act has not been fully elucidated yet. The goal of this review was to provide evidence on the effects of probiotics on gut microbiota changes associated with host metabolic variations, specifically focusing on feed intake and lipid and glucose metabolism. In addition, we review probiotic interaction with the gut microbiota. The information collected here will give further insight into the effects of probiotics on the gut microbiota and their action on metabolite release, energy metabolism, and appetite. This information will help to improve knowledge to find better probiotic therapeutic strategies for obesity and eating disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".