ACE2 Is Expressed in Immune Cells That Infiltrate the Placenta in Infection-Associated Preterm Birth
Bibliographic record
Abstract
COVID-19 is associated with increased incidence of preterm birth (PTB). We assessed pathways by which SARS-CoV-2 could access the placenta. Placentae, from PTB with or without chorioamnionitis (ChA), or from term pregnancies (n = 12/13/group) were collected. Peripheral blood was collected from healthy pregnant women (n = 6). Second trimester placental explants (16–20 weeks, n = 5/group) were treated with lipopolysaccharide (LPS, to mimic bacterial infection) and ACE2, CCL2, IL-6/8 and TNFα mRNA was assessed. ChA-placentae exhibited increased ACE2 and CCL2 mRNA expression (p < 0.05). LPS increased cytokine and ACE2 mRNA in placental explants. Placental ACE2 protein localized to syncytiotrophoblast, fetal endothelium, extravillous trophoblast and in immune cells-subsets (M1/M2 macrophage and neutrophils) within the villous stroma. Significantly increased numbers of M1 macrophage and neutrophils were present in the ChA-placenta (p < 0.001). Subsets of peripheral immune cells from pregnant women express the ACE2 mRNA and protein. A greater fraction of granulocytes was positive for ACE2 protein expression compared to lymphocytes or monocytes. These data suggest that in pregnancies complicated by ChA, ACE2 positive immune cells in the maternal circulation have the potential to traffic SARS-CoV-2 virus to the placenta and increase the risk of vertical transmission to the placenta/fetus.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".