Divergent responses to SARS-CoV-2 infection in bronchial epithelium with pre-existing respiratory diseases
Bibliographic record
Abstract
<h2>Summary</h2> Pre-existing respiratory diseases may influence coronavirus disease (COVID-19) susceptibility and severity. However, the molecular mechanisms underlying the airway epithelial response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection severity in patients with chronic respiratory diseases remain unelucidated. Using an <i>in vitro</i> model of differentiated primary bronchial epithelial cells, we aimed to investigate the molecular mechanisms of SARS-CoV-2 infection in pre-existing cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD). Our study revealed reduced susceptibility of CF and COPD airway epithelia to SARS-CoV-2, relative to that in healthy controls. Mechanistically, reduced transmembrane serine protease 2 (TMPRSS2) activity potentially contributed to this resistance of CF epithelium. Upregulated complement and inflammatory pathways in CF and COPD epithelia potentially primed the antiviral state prior to infection. Analysis of a COVID-19 patient cohort validated our findings, correlating specific inflammatory markers (IP-10, SERPINA1, and CFB) with COVID-19 severity. This study elucidates SARS-CoV-2 pathogenesis and identifies potential biomarkers for clinical monitoring.
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How this classification was reachedexpand
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 | 0.039 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".