Reduced ACE2 and TMPRSS2 immunopositive bronchial cells in asthmatics after inhaled allergen challenge
Bibliographic record
Abstract
Rationale: The incidence of SARS-CoV-2 infection and the impact of corticosteroid treatment in patients with symptomatic airway disease has been a concern. We examined airway expression of SARS-CoV-2 receptors following allergen challenge and steroid intervention in asthmatic patients. Methods: From steroid-naïve mild allergic asthmatic (AA n=23) we collected endobronchial biopsies pre and 24hr post allergen inhalation challenge (AIC). In a subset of AA with allergic rhinitis (AR n=8) we collected inferior nasal turbinate biopsies pre and 24hr post-nasal allergen challenges (NAC) after placebo treatment or after 21 days of 22 mg BID triamcinolone nasal spray. FEV1 and PNIF expressed as % fall from baseline quantified the early (ER, 0-2h) and late (LR, 3-7h) airway responses post challenge. Epithelium and laminae propria were immunostained for ACE2 and TMPRSS2 and expressed as # cells/mm2. Results: AIC reduced FEV1 (31% ER, 19% LR) and the number of bronchial cells immunopositive for ACE2, TMPRSS2 and double positive for ACE2/TMPRSS2 (P=0.0002, P=0.04, P=0.02, respectively). The PNIF reduction by NAC (69% ER, 49% LR) was attenuated by triamcinolone (31% ER, 18% LR), but without changes in ACE2 or TMPRSS2 in nasal tissue after NAC or steroid treatment (all P>0.05). In the nasal tissue, significantly fewer cells expressed ACE2 compared to bronchi (P=0.007). Conclusion: ACE2 and TMPRSS2 expression in bronchial tissue is reduced in the T2 microenvironment post allergen challenge, however it is unknown if this protects lower airways from SARS-CoV-2 infection. Low expression of ACE2 and TMPRSS2 in nasal tissue made it difficult to determine the effects of NAC or steroid.
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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 | 0.001 |
| 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".