Role of the upper airway microbiota in respiratory virus and bacterial pathobiont dynamics in the first year of life
Bibliographic record
Abstract
The mechanisms by which respiratory viruses predispose to secondary bacterial infections remain poorly characterized. Using 2,409 nasopharyngeal swabs from 300 infants enrolled in a prospective cohort study in Botswana, we perform a detailed analysis of factors that influence the dynamics of bacterial pathobiont colonization during infancy. We quantify the extent to which viruses increase the acquisition of Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae. We provide evidence of cooperative interactions between these pathobionts while identifying host characteristics and environmental exposures that influence the odds of pathobiont colonization during early life. Using 16S rRNA gene sequencing, we demonstrate that respiratory viruses result in losses of putatively beneficial Corynebacterium and Streptococcus species that are associated with a lower odds of pathobiont acquisition. These findings provide important insights into viral-bacterial relationships in the upper respiratory tract of direct relevance to respiratory infections and suggest that the bacterial microbiota is a potentially modifiable mechanism by which viruses promote bacterial respiratory infections.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".