Bacterial and Fungal Gut Community Dynamics Over the First Five Years of Life in Predominantly Rural Communities in Ghana
Bibliographic record
Abstract
Abstract BackgroundBacterial and fungal microbiotas are increasingly recognized as important in health and disease starting early in life. However, microbiota composition has not yet been investigated in most rural, low-resource settings, and in such settings, bacterial and fungal microbiotas have not been compared. Thus, we applied 16S and ITS2 amplicon sequencing, respectively, to investigate bacterial and fungal fecal microbiotas in rural Ghanaian children from birth to 5 years of age. Corresponding maternal fecal and breastmilk microbiotas were additionally investigated. ResultsWhile bacterial communities differed systematically across the age spectrum in composition and diversity, the same was not observed for the fungal microbiota. We also identified a novel and dramatic change in the maternal postpartum microbiota. This change included much higher abundance of E. coli and much lower abundance of Prevotella in the first versus fourth week postpartum. While infants shared more bacterial taxa with their mother’s stool and breastmilk than with those of unrelated mothers, there were far fewer shared fungal taxa. ConclusionGiven the known ability of commensal fungi to influence host health, the distinct pattern of their acquisition likely has important health consequences. Similarly, the dynamics of mothers’ bacterial microbiotas around the time of birth may have important consequences for their children’s health. Both topics require further study.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".