Characterization of the gut microbiota of Nicaraguan children in a water insecure context
Bibliographic record
Abstract
OBJECTIVES: The gut microbiota varies across human populations. The first years of life are a critical period in its development. While delivery mode and diet contribute to observed variation, the additional contribution of specific environmental factors remains poorly understood. One factor is waterborne enteric pathogen exposure. In this pilot study, we explore the relationship between household water security and the gut microbiota of children. METHODS: From Nicaraguan households (n = 39), we collected drinking water samples, as well as fecal samples from children aged one month to 5.99 years (n = 53). We tested water samples for total coliforms (CFU/mL) and the presence of common enteric pathogens. Composition and diversity of the gut microbiota were characterized by 16S rRNA sequencing. Households were classified as having drinking water that was "low" (<29 CFU/mL) or "high" (≥29 CFU/mL) in coliforms. We used permutational analyses of variance and Mann-Whitney U-tests to identify differences in the composition and diversity of the gut microbiota of children living in these two home types. RESULTS: Insecure access led households to store drinking water and 85% tested positive for coliforms. High concentrations of Salmonella and Campylobacter were found in water and fecal samples. Controlling for age, the gut microbiota of children from high coliform homes were compositionally different and less diverse than those from low coliform homes. CONCLUSIONS: Results indicate that research exploring the ways water insecurity affects human biology should consider the gut microbiome and that investigations of inter-population variation in the gut microbial community of children should consider pathogen exposure and infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".