ACE2 as a Therapeutic Target for COVID-19; Its Role in Infectious Processes and Regulation by Modulators of the RAAS System
Bibliographic record
Abstract
Angiotensin converting enzyme 2 (ACE2) is the recognized host cell receptor responsiblefor mediating infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). ACE2bound to tissue facilitates infectivity of SARS-CoV-2; thus, one could argue that decreasing ACE2tissue expression would be beneficial. However, ACE2 catalytic activity towards angiotensin I (AngI) and II (Ang II) mitigates deleterious effects associated with activation of the renin-angiotensinaldosteronesystem (RAAS) on several organs, including a pro-inflammatory status. At the tissuelevel, SARS-CoV-2 (a) binds to ACE2, leading to its internalization, and (b) favors ACE2 cleavage toform soluble ACE2: these actions result in decreased ACE2 tissue levels. Preserving tissue ACE2activity while preventing ACE2 shredding is expected to circumvent unrestrained inflammatoryresponse. Concerns have been raised around RAAS modulators and their effects on ACE2expression or catalytic activity. Various cellular and animal models report conflicting results invarious tissues. However, recent data from observational and meta-analysis studies in SARS-CoV-2-infected patients have concluded that RAAS modulators do not increase plasma ACE2 levels orsusceptibility to infection and are not associated with more severe diseases. This review presentsour current but evolving knowledge of the complex interplay between SARS-CoV-2 infection, ACE2levels, modulators of RAAS activity and the effects of RAAS modulators on ACE2 expression.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".