A systematic review of the literature on the relationship between ace2 and sars-cov infection in animal models
Bibliographic record
Abstract
Background: similar to the severe acute respiratory syndrome coronavirus (SARS-CoV), research suggests severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interacts with angiotensin-converting enzyme 2 (ACE2) to gain entry into cells. The primary objective is to synthesize existing animal model research on the association between ACE2 and severe acute respiratory syndrome (SARS) infection. The secondary objective is to describe the consequences of infection on ACE2 expression. Methods: we performed a systematic literature search of Medline, Embase, and Global Health databases. We included animal studies on the connection between SARS-CoV infection and variations in ACE2 receptor or expression thereof. Included studies were assessed for quality using the CAMARADES checklist. Results: we included nine studies, all determining the role of ACE2 in SARS-CoV infections. Five low to moderate quality studies showed that increased ACE2 expression was correlated with increased SARS-CoV infection. Five low to moderate quality studies showed post-infection downregulation of ACE2 to be associated with increased clinical symptoms, morbidity, and mortality. Conclusion: this review shows that pre-infection, greater ACE2 expression correlates with increased infection leading to worse clinical outcomes. Assuming similar mechanisms for SARS-CoV-2 as in SARS-CoV, it is plausible that ACE2 has some role and impact in COVID-19 infections. Further high-quality animal model research is needed to determine the role of ACE2, specifically in COVID-19 infections. © 2021, University of Toronto. All rights reserved.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".