Geographic variation in environmental Aspergillus and clinical outcomes in COPD
Bibliographic record
Abstract
Background: Aspergillus-associated disease is associated with adverse outcomes in COPD. Environmental variation and individual exposure to Aspergillus in COPD remains unclear. Methods: Patients with COPD were recruited in 2 geographic locations, variable in climate and seasonality: Singapore (Sg) (n=43) and Vancouver (Van), Canada (n=12). Outdoor/Indoor air and surface dust in each participant’s home was prospectively assessed by metagenomic sequencing. Clinical data and environmental parameters including indoor temperature and humidity were concurrently assessed. Results: Indoor temperature (median: 21.7°C: Van vs 29.4: Sg, p<0.001) and relative humidity (median: 41.7%: Van vs 73.8%: Sg, p<0.001) were significantly lower in Vancouver relative to Singapore. Indoor Aspergillus species in the homes of patients with COPD varied between countries. A. terreus, A. mulundensis and A. novofumigatus predominated in Vancouver, while A. nidulans, A. aculeatinus, A. welwitschiae, A. awamori and A. pseudoglaucus predominated in Singapore. A. fumigatus was detected in both locations, and relative abundance positively correlated with exacerbation frequency in COPD, independent of location (r=0.27; p=0.003 in Sg and r=0.49; p=0.019 in Van). Conclusion: While several Aspergillus species vary by geographic region, A. fumigatus in the air are associated with COPD exacerbations, independent of geography or patient origin. Funding: This work is supported by the Singapore Ministry of Health’s National Medical Research Council under the Clinician-Scientist Individual Research Grant MOH-001356 (S.H.C) and a Clinician-Scientist Award MOH-000710 (S.H.C) and Transition Award MOH- 001275-00 (P.Y.T) and the Singapore Ministry of Education under its AcRF Tier 1 Grant (RT1/22) (S.H.C)
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".