Improvement in adiposity with oligofructose is modified by antibiotics in obese rats
Bibliographic record
Abstract
Given the intimate link between gut microbiota and host physiology, there is growing interest in understanding the mechanisms by which diet influences gut microbiota and affects human metabolic health. Using antibiotics and the prebiotic oligofructose, which has been shown to counteract excess fat mass, we explored the gut microbiota-dependent effects of oligofructose on body composition and host metabolism. Diet-induced obese male Sprague Dawley rats, fed a background high-fat/sucrose diet, were randomized to one of the following diets for 6 wk: 1) high-energy control; 2) 10% oligofructose; 3) ampicillin; 4) ampicillin + 10% oligofructose; 5) ampicillin/neomycin; or 6) ampicillin/neomycin + 10% oligofructose. Combining oligofructose with ampicillin treatment blunted the decrease in adiposity seen with oligofructose. Although ampicillin did not affect total bacteria, ampicillin impeded oligofructose-induced increases in Bifidobacterium and Lactobacillus In contrast, the combination of ampicillin and neomycin reduced total bacteria but did not abrogate the oligofructose-induced decrease in adiposity. Oligofructose-mediated effects on host adiposity and metabolic health appear to be in part dependent on the presence of specific microbial species within the gut.-Bomhof, M. R., Paul, H. A., Geuking, M. B., Eller, L. K., Reimer, R. A. Improvement in adiposity with oligofructose is modified by antibiotics in obese rats.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".