Effects of cigarette smoking on <scp>SARS‐CoV</scp>‐2 receptor <scp>ACE2</scp> expression in the respiratory epithelium<sup>†</sup>
Bibliographic record
Abstract
Due to the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, the world is currently facing high morbidity and mortality rates as well as severe disruption to normal societal and social structures. SARS-CoV-2 uses the ACE2 receptor for cellular entry. In a recent publication of The Journal of Pathology, Liu and coworkers highlight the effects of cigarette smoking on ACE2 expression in the respiratory epithelium. The authors studied the effects of acute cigarette smoke exposure in a murine model and confirmed their findings in human lung tissues and gene expression datasets. Their findings demonstrate that cigarette smoking increases ACE2 expression specifically at the apical surface of the airway epithelium. Smoking cessation resulted in lower ACE2 expression, with implications for attenuating the risk of transmission of the virus. The role of ACE2 expression in the development of COVID-19 symptoms is still under investigation, with conflicting results from experimental models on the role of ACE2 expression in SARS-CoV-2-induced lung injury. In this commentary, we highlight the implications and limitations of the study of Liu et al as well as future therapeutic strategies directed towards ACE2. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.012 |
| 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".