The international sinonasal microbiome study: A multicentre, multinational characterization of sinonasal bacterial ecology
Bibliographic record
Abstract
The sinonasal microbiome remains poorly defined, with our current knowledge based on a few cohort studies whose findings are inconsistent. Furthermore, the variability of the sinus microbiome across geographical divides remains unexplored. We characterize the sinonasal microbiome and its geographical variations in both health and disease using 16S rRNA gene sequencing of 410 individuals from across the world. Although the sinus microbial ecology is highly variable between individuals, we identify a core microbiome comprised of Corynebacterium, Staphylococcus, Streptococcus, Haemophilus and Moraxella species in both healthy and chronic rhinosinusitis (CRS) cohorts. Corynebacterium (mean relative abundance = 44.02%) and Staphylococcus (mean relative abundance = 27.34%) appear particularly dominant in the majority of patients sampled. Amongst patients suffering from CRS with nasal polyps, a statistically significant reduction in relative abundance of Corynebacterium (40.29% vs 50.43%; P = .02) was identified. Despite some measured differences in microbiome composition and diversity between some of the participating centres in our cohort, these differences would not alter the general pattern of core organisms described. Nevertheless, atypical or unusual organisms reported in short-read amplicon sequencing studies and that are not part of the core microbiome should be interpreted with caution. The delineation of the sinonasal microbiome and standardized methodology described within our study will enable further characterization and translational application of the sinus microbiota.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".