First characterization of the intestinal microbiota in healthy Tunisian adults using 16S rRNA gene sequencing
Bibliographic record
Abstract
The gut microbiota is currently recognized as an important factor influencing the host's metabolism, immune, and central nervous systems. Determination of the composition of the gut microbiota of healthy subjects is therefore necessary to establish a baseline for the detection of alterations in the microbiota under pathological conditions. So far, most studies describing the gut microbiota have been performed in populations from Asia, North America, and Europe, whereas populations from Africa have been overlooked. Here, we present the first characterization of the intestinal microbiota in healthy Tunisian adults using 16S rRNA gene sequencing. We further compare the gut microbiota composition based on gender and BMI. Our results showed that the Tunisian gut microbiota is dominated by the phyla Firmicutes and Bacteroidota in accordance with studies from western countries. However, some specificities have been identified, including a higher proportion of Firmicutes in males and higher proportions of Atopobiaceae and Peptostreptococcaceae in Tunisian overweight individuals. Moreover, we were able to identify bacterial species differently represented between males and females and between normal weight and overweight individuals. These results constitute an important baseline that can be used to identify the dysbiosis associated with the main diseases affecting the Tunisian population.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".