SARS-CoV-2 Footprints in the Placenta: What We Know after Three Years of the Pandemic.
Valentina TostoArun MeyyazhaganMalek AlqasemValentina I TsibizovaGian Carlo Di RenzoPublished in: Journal of personalized medicine (2023)
As the COVID-19 pandemic continues into its third year, there is accumulating evidence on the consequences of maternal infection. Emerging data indicate increased obstetrics risks, including maternal complications, preterm births, impaired intrauterine fetal growth, hypertensive disorders, stillbirth, gestational diabetes, and a risk of developmental defects in neonates. Overall, controversial concerns still exist regarding the potential for vertical transmission. Histopathological examination of the placenta can represent a useful instrument for investigation and can contribute significant information regarding the possible immunohistopathological mechanisms involved in developing unfavorable perinatal outcomes. Based on current evidence, SARS-CoV-2 infection can affect placental tissue by inducing several specific changes. The level of placental involvement is considered one of the determining factors for unfavorable outcomes during pregnancy due to inflammation and vascular injuries contributing to complex cascade immunological and biological events; however, available evidence does not indicate a strong and absolute correlation between maternal infection, placental lesions, and obstetric outcomes. As existing studies are still limited, we further explore the placenta at three different levels, using histology, immunohistochemistry, and molecular genetics to understand the epidemiological and virological changes observed in the ongoing pandemic.
Keyphrases
- sars cov
- pregnancy outcomes
- birth weight
- coronavirus disease
- pregnant women
- respiratory syndrome coronavirus
- gestational age
- blood pressure
- healthcare
- type diabetes
- low birth weight
- preterm birth
- machine learning
- electronic health record
- metabolic syndrome
- patient reported outcomes
- social media
- single molecule
- glycemic control
- deep learning
- weight loss
- case control