ACE-2 Expression in the Small Airway Epithelia of Smokers and COPD Patients: Implications for COVID-19
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Bibliographic record
Abstract
Abstract Introduction Coronavirus disease 2019 (COVID-19) is a respiratory infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This virus uses the angiotensin converting enzyme II (ACE-2) as the cellular entry receptor to infect the lower respiratory tract. Because individuals with chronic obstructive pulmonary disease (COPD) are at increased risk of severe COVID-19, we determined whether ACE-2 expression in the lower airways was related to COPD and cigarette smoking. Methods Using RNA-seq, we determined gene expression levels in bronchial epithelia obtained from cytologic brushings of 6 th to 8 th generation airways in individuals with and without COPD. We eternally validated these results from two additional independent cohorts, which used microarray technologies to measure gene expression levels from 6 th to 12 th generation airways. Results In the discovery cohort (n=42 participants), we found that ACE-2 expression levels were increased by 48% in the airways of COPD compared with non-COPD subjects (COPD=2.52±0.66 log2 counts per million reads (CPM) versus non-COPD= 1.70±0.51 CPM, p=7.62×10 −4 ). There was a significant inverse relationship between ACE-2 gene expression and FEV1% of predicted (r=-0.24; p=0.035). Current smoking also significantly increased ACE-2 expression levels compared with never smokers (never current smokers=2.77±0.91 CPM versus smokers=1.78±0.39 CPM, p=0.024). These findings were replicated in the two eternal cohorts. Conclusions ACE-2 expression in lower airways is increased in patients with COPD and with current smoking. These data suggest that these two subgroups are at increased risk of serious COVID-19 infection and highlight the importance of smoking cessation in reducing the risk.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it