The development of probiotic treatment in obesity: a review
Bibliographic record
Abstract
Recent studies suggested that manipulation of the composition of the microbial ecosystem in the gut might be a novel approach in the treatment of obesity. Such treatment might consist of altering the composition of the microbial communities of an obese individual by administration of beneficial microorganisms, commonly known as probiotics. Here, we intend to contribute to the developmental process of probiotic treatment of human obesity. The aim is to review the evidence regarding the potential effect of probiotic strains on reduction of weight and body fat. A literature study was conducted focusing on clinical trials that examined the effect of specific microorganisms on body weight control. Analysis of the eligible articles pointed out that Lactobacillus gasseri SBT 2055, Lactobacillus rhamnosus ATCC 53103, and the combination of L. rhamnosus ATCC 53102 and Bifidobacterium lactis Bb12 may reduce adiposity, body weight, and weight gain. This suggests that these microbial strains can be applied in the treatment of obesity. Furthermore, short chain fatty acid production and low grade inflammation were found as the underlying mechanisms of action that influence metabolism and affect body weight. These findings might contribute to the development of probiotic treatment of obesity. Further research should be directed to the most effective combination and dosage rate of probiotic microorganisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".