Differential expression of Angiotensin-Converting Enzyme 2 in Nasal Tissue of Patients with Chronic Rhinosinusitis with Nasal Polyps
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a pandemic and a global health emergency. The SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) is highly expressed in nasal epithelial cells and plays a major role in cellular entry leading to infection. High expression of ACE2 has been suggested to be a potential risk factor for virus infection and disease severity. However the profile of ACE2 gene expression in diseases of the upper airways remains poorly understood. We herein investigated ACE2 gene expression in the nasal tissues of a cohort of Swedish patients with chronic rhinosinusitis with nasal polyps (CRSwNPs) using RT-qPCR. ACE2 mRNA expression was significantly reduced in the nasal mucosa of CRSwNP patients compared to that of controls. Moreover, we observed a sex-dependant difference in nasal ACE2 expression, where significantly lower levels of the ACE2 transcript were detected in the nasal mucosa of only female CRSwNP patients. These findings indicate that CRSwNP patients with a decrease in ACE2 gene expression may thereby be less prone to be infected by SARS-CoV-2. These results enhance our understanding on the profile of ACE2 expression in the nasal mucosa of patients with upper airway diseases, and their susceptibility to infection with SARS-CoV-2.
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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) | 0.001 | 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".