Analysis of the nasal vestibule mycobiome in patients with allergic rhinitis
Bibliographic record
Abstract
Advances in culture-independent sequencing methods have been utilised in recent studies to understand the phylogenetic composition of the human microbiome of healthy and diseased skin. Allergic rhinitis (AR) is an inflammatory condition of the nasal cavity caused by environmental allergens. Although nasal microbial communities have been considered important contributors in human health, no studies to date have comprehensively compared fungal communities (mycobiome) of the nasal vestibule using the culture-independent pyrosequencing method. This study aimed to investigate how fungal communities of the nasal vestibule skin surface are influenced by AR. The phylogenetic composition of the nasal vestibule mycobiome of patients with AR was analysed by culture-independent pyrosequencing methods and compared with healthy individuals. A total of 69 fungal genera were identified from both AR samples and healthy controls, and the genus Malassezia predominated in the nasal vestibule. Species-level analysis classified eight different Malassezia species including M. pachydermatis and M. cuniculi, which were normally isolated from animals, and revealed M. restricta to be the most abundant species in the nasal vestibule. Although high interpersonal variation was observed, some of the AR samples displayed significantly higher diversities than healthy controls at both the genus and species level.
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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 | 0.000 |
| 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".