Role of ACE2 in pregnancy and potential implications for COVID-19 susceptibility
Bibliographic record
Abstract
In times of coronavirus disease 2019 (COVID-19), the impact of severe acute respiratory syndrome (SARS)-coronavirus (CoV)-2 infection on pregnancy is still unclear. The presence of angiotensin-converting enzyme (ACE) 2 (ACE2), the main receptor for SARS-CoV-2, in human placentas indicates that this organ can be vulnerable for viral infection during pregnancy. However, for this to happen, additional molecular processes are critical to allow viral entry in cells, its replication and disease manifestation, particularly in the placenta and/or feto-maternal circulation. Beyond the risk of vertical transmission, COVID-19 is also proposed to deplete ACE2 protein and its biological actions in the placenta. It is postulated that such effects may impair essential processes during placentation and maternal hemodynamic adaptations in COVID-19 pregnancy, features also observed in several disorders of pregnancy. This review gathers information indicating risks and protective features related to ACE2 changes in COVID-19 pregnancies. First, we describe the mechanisms of SARS-CoV-2 infection having ACE2 as a main entry door and current evidence of viral infection in the placenta. Further, we discuss the central role of ACE2 in physiological systems such as the renin-angiotensin system (RAS) and the kallikrein-kinin system (KKS), both active during placentation and hemodynamic adaptations of pregnancy. Significant knowledge gaps are also identified and should be urgently filled to better understand the fate of ACE2 in COVID-19 pregnancies and the potential associated risks. Emerging knowledge will be able to improve the early stratification of high-risk pregnancies with COVID-19 exposure as well as to guide better management and follow-up of these mothers and their children.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".