Comparison between upper and lower airway microbiome profiles in chronic rhinosinusitis patients
Bibliographic record
Abstract
BACKGROUND: Dysregulation of the airway microbiota is thought to contribute to airway inflammation in both chronic rhinosinusitis (CRS) and asthma. However, the relationship between the upper and lower airway microbiome remains unclear. METHODS: Sinus and lung brushes were collected from 29 CRS participants undergoing sinus surgery. DNA was extracted and submitted for 16s rRNA microbiome sequencing. Alpha and beta diversity metrics, taxonomic composition, and differences between individual taxa were compared for paired sinus and bronchial samples. RESULTS: Twenty-three out of 29 participants had sufficient samples for analysis. The mean (standard deviation) age was 51.59 (14.57) years, and 10 (44%) patients were female. Twelve (52%) patients had comorbid asthma. Sinus brushes had significantly higher alpha diversity indexes (Shannon and Faith) compared to bronchial brushes (p < 0.001). Beta diversity metrics were significantly different between the sinus and bronchial samples. Principal coordinate analysis showed no clustering of paired nasal and bronchial samples. Sinus brushes had significantly more Lawsonella, Corynebacterium, and Staphylococcus compared to bronchia brushes, while the latter were enriched in Tropheryma and Sphingomonas, among others (false discovery rate [FDR]-adjusted p < 0.01). Finally, CRS patients with comorbid asthma had significantly higher Pseudomonas and Peptoniphilus in sinus brushes and lower Prevotella in bronchial brushes when compared to non-asthmatics (FDR-adjusted p < 0.01). CONCLUSION: The sinus and bronchial bacterial microbiomes differ in important ways. Our study suggests that migration of bacteria from the sinus into the lower airways is unlikely in patients with CRS.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".