Involvement of the ACE2/Ang-(1–7)/MasR Axis in Pulmonary Fibrosis: Implications for COVID-19
Bibliographic record
Abstract
Pulmonary fibrosis is a chronic, fibrotic lung disease affecting 3 million people worldwide. The ACE2/Ang-(1-7)/MasR axis is of interest in pulmonary fibrosis due to evidence of its anti-fibrotic action. Current scientific evidence supports that inhibition of ACE2 causes enhanced fibrosis. ACE2 is also the primary receptor that facilitates the entry of SARS-CoV-2, the virus responsible for the current COVID-19 pandemic. COVID-19 is associated with a myriad of symptoms ranging from asymptomatic to severe pneumonia and acute respiratory distress syndrome (ARDS) leading to respiratory failure, mechanical ventilation, and often death. One of the potential complications in people who recover from COVID-19 is pulmonary fibrosis. Cigarette smoking is a risk factor for fibrotic lung diseases, including the idiopathic form of this disease (idiopathic pulmonary fibrosis), which has a prevalence of 41% to 83%. Cigarette smoke increases the expression of pulmonary ACE2 and is thought to alter susceptibility to COVID-19. Cannabis is another popular combustible product that shares some similarities with cigarette smoke, however, cannabis contains cannabinoids that may reduce inflammation and/or ACE2 levels. The role of cannabis smoke in the pathogenesis of pulmonary fibrosis remains unknown. This review aimed to characterize the ACE2-Ang-(1-7)-MasR Axis in the context of pulmonary fibrosis with an emphasis on risk factors, including the SARS-CoV-2 virus and exposure to environmental toxicants. In the context of the pandemic, there is a dire need for an understanding of pulmonary fibrotic events. More research is needed to understand the interplay between ACE2, pulmonary fibrosis, and susceptibility to coronavirus 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".