Gut microbiota composition of lean and obese Lebanese individuals
Bibliographic record
Abstract
An altered gut microbiota has been shown to contribute to the development of metabolic diseases such as obesity. In this study gut microbiota profile of 30 obese and 23 lean Lebanese individuals was performed via DNA isolation and sequencing of the V3-V4 region of the 16S rRNA of faecal samples. The abundance of the phylum Verrucomicrobiota was higher in lean subjects and there was no significant difference in the Bacillota/ Bacteroidota ratio between the obese and lean groups. The evenness and Shannon alpha diversity indices were significantly higher in the lean group than in the obese group ( q = 0.012 and q = 0.030, respectively). Beta diversity was higher in the obese group based for unweighted uniFrac distance variability ( q = 0.047). Lachnoclostridium was the only genus that was higher in obese ( q = 0.013) and it is linked to diet induced obesity, while the abundance of the genera Peptococcus, Ruminococcus_2, Lachnospiraceae UCG-001, Ruminiclostridium 6, the uncharacterised taxon within Coriobacteriaceae, Ruminococcaceae UCG-005, Ruminococcaceae UCG-010 and Oxalobacter, were significantly higher in lean subjects. These bacterial species that were higher in lean people, possess anti-inflammatory properties through the production of short chain fatty acids and are linked with lower body mass index, promote satiety and weight loss and may play a role in the protection against obesity and type 2 diabetes. Further research to generate a clear understanding of the interaction of the gut microbiota and health is needed.
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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".