Increased expression of SARS-CoV-2 (Covid-19) adhesion sites on type II pneumocytes, small airway epithelium, and alveolar macrophages in smokers and patients with COPD
Bibliographic record
Abstract
Aim: To perform detailed tissue analysis of SARS-CoV-2 adhesion sites in surgically resected small airway tissue. Methods: This study included 16 patients with COPD, of which 8 were current smokers with COPD (COPD-CS) and 8 ex-smokers with COPD (COPD-ES), 7 normal lung function smokers (NLFS), 9 patients with small airways disease (SAD), and 10 never-smoking normal controls (NC). Immunostaining was performed for ACE2, TMPRSS2, and FURIN. Type II pneumocytes, small airway epithelium (SAE), and alveolar macrophages (AM) were analysed using Image ProPlus 7.0. software. Results: Total number of type II pneumocytes and AM significantly increased in the pathological groups compared to NC (p<0.01), except SAD (p=0.08). Total AM significantly decreased in COPD-ES (p<0.003). ACE2 expression increased in SAE of all the patients compared to NC (p<0.001). ACE2 expressing type II pneumocytes were significantly higher in pathological groups compared to NC (p < 0.003). Similar significant changes were observed for ACE2 positive AM (p < 0.002), except COPD-ES, which had decrease in ACE2 positive AM (p<0.003). ACE2 positive AM and type II pneumocytes showed significant negative correlation with FEF25-75% (p<0.05, p = 0.05). ACE2 positive AM and total AM negatively correlated to DLCO% predicted (p<0.02; p<0.04). A similar staining pattern was observed for TMPRSS2 and FURIN. Conclusion: The increased expression of ACE2 along with TMPRSS2 and FURIN, in the small airways of smokers and COPD patients, provides further evidence that these patient groups could be more susceptible to severe COVID-19 infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".