The lower airway microbiome in paediatric health and chronic disease
Bibliographic record
Abstract
The advent of next generation sequencing has rapidly challenged the paediatric respiratory physician's understanding of lung microbiology and the role of the lung microbiome in host health and disease. In particular, the role of "microbial key players" in paediatric respiratory disease is yet to be fully explained. Accurate profiling of the lung microbiome in children is challenging since the ability to obtain lower airway samples coupled with processing "low-biomass specimens" are both technically difficult. Many studies provide conflicting results. Early microbiota-host relationships may be predictive of the development of chronic respiratory disease but attempts to correlate lower airway microbiota in premature infants and risk of developing bronchopulmonary dysplasia (BPD) have produced mixed results. There are differences in lung microbiota in asthma and cystic fibrosis (CF). The increased abundance of oral taxa in the lungs may (or may not) promote disease processes in asthma and CF. In CF, correlation between microbiota diversity and respiratory decline is commonly observed. When one considers other pathogens beyond the bacterial kingdom, the contribution and interplay of fungi and viruses within the lung microbiome further increase complexity. Similarly, the interaction between microbial communities in different body sites, such as the gut-lung axis, and the influence of environmental factors, including diet, make the co-existence of host and microbes ever more complicated. Future, multi-omics approaches may help uncover novel microbiome-based biomarkers and therapeutic targets in respiratory disease and explain how we can live in harmony with our microbial companions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".