The Nasopharyngeal Microbiota of Children With Respiratory Infections in Botswana
Bibliographic record
Abstract
BACKGROUND: Nearly half of child pneumonia deaths occur in sub-Saharan Africa. Microbial communities in the nasopharynx are a reservoir for pneumonia pathogens and remain poorly described in African children. METHODS: Nasopharyngeal swabs were collected from children with pneumonia (N = 204), children with upper respiratory infection symptoms (N = 55) and healthy children (N = 60) in Botswana between April 2012 and April 2014. We sequenced the V3 region of the bacterial 16S ribosomal RNA gene and used partitioning around medoids to cluster samples into microbiota biotypes. We then used multivariable logistic regression to examine whether microbiota biotypes were associated with pneumonia and upper respiratory infection symptoms. RESULTS: Mean ages of children with pneumonia, children with upper respiratory infection symptoms and healthy children were 8.2, 11.4 and 8.0 months, respectively. Clustering of nasopharyngeal microbiota identified 5 distinct biotypes: Corynebacterium/Dolosigranulum-dominant (23%), Haemophilus-dominant (11%), Moraxella-dominant (24%), Staphylococcus-dominant (13%) and Streptococcus-dominant (28%). The Haemophilus-dominant [odds ratio (OR): 13.55; 95% confidence interval (CI): 2.10-87.26], the Staphylococcus-dominant (OR: 8.27; 95% CI: 2.13-32.14) and the Streptococcus-dominant (OR: 39.97; 95% CI: 6.63-241.00) biotypes were associated with pneumonia. The Moraxella-dominant (OR: 3.71; 95% CI: 1.09-12.64) and Streptococcus-dominant (OR: 12.26; 95% CI: 1.81-83.06) biotypes were associated with upper respiratory infection symptoms. In children with pneumonia, HIV infection was associated with a lower relative abundance of Dolosigranulum (P = 0.03). CONCLUSIONS: Pneumonia and upper respiratory infection symptoms are associated with distinct nasopharyngeal microbiota biotypes in African children. A lower abundance of the commensal genus Dolosigranulum may contribute to the higher pneumonia risk of HIV-infected children.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".