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IntroductionThe effect of SARS-CoV-2 infection on pregnant mothers, the placenta, and infants is not fully understood and sufficiently characterized. MethodsWe performed a retrospective, observational cohort study in Chapel Hill, NC of 115 mothers with SARS-CoV-2 and singleton pregnancies from December 1, 2019 to May 31, 2021. We performed a chart review to document the infants weight, length, head circumference, survival, congenital abnormalities, and hearing loss, maternal complications, and placental pathology classified by the Amsterdam criteria. ResultsThe average infant weight, length, and head circumference were all within the normal range for gestational age, the infants had no identifiable congenital abnormalities, and all infants and mothers survived. Only one infant (0.870%) became infected with SARS-CoV-2. Moderate and severe maternal COVID-19 were associated with increased caesarean section, premature delivery, infant NICU admission, and maternal respiratory failure, and were more likely in Type 1 (p=0.0055) and Type 2 (p=0.0285) diabetic mothers. Most placentas (n=63, 54.8%) showed normal or non-specific findings, while a subset had mild maternal vascular malperfusion (n=26, 22.6%) and/or mild microscopic ascending intrauterine infection (n=28, 24.3%). DiscussionMost mothers with SARS-CoV-2 and their infants had a routine clinical course. Maternal SARS-CoV-2 infection was not associated with intrauterine fetal demise, infant death, congenital abnormalities, or hearing loss. Infant infection with SARS-CoV-2 was rare and not via the placenta. Most placentas had non-specific findings and a subset showed mild maternal vascular malperfusion and/or mild microscopic ascending intrauterine infection, which were not associated with maternal COVID-19 severity.
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Despite signs of infection, the involvement of the oral cavity in COVID-19 is poorly understood. To address this, single-cell RNA sequencing data-sets were integrated from human minor salivary glands and gingiva to identify 11 epithelial, 7 mesenchymal, and 15 immune cell clusters. Analysis of SARS-CoV-2 viral entry factor expression showed enrichment in epithelia including the ducts and acini of the salivary glands and the suprabasal cells of the mucosae. COVID-19 autopsy tissues confirmed in vivo SARS-CoV-2 infection in the salivary glands and mucosa. Saliva from SARS-CoV-2-infected individuals harbored epithelial cells exhibiting ACE2 expression and SARS-CoV-2 RNA. Matched nasopharyngeal and saliva samples found distinct viral shedding dynamics and viral burden in saliva correlated with COVID-19 symptoms including taste loss. Upon recovery, this cohort exhibited salivary antibodies against SARS-CoV-2 proteins. Collectively, the oral cavity represents a robust site for COVID-19 infection and implicates saliva in viral transmission.
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Recent studies have developed simple techniques for monitoring and assessing sleep. However, several issues remain to be solved for example high-cost sensor and algorithm as a home-use device. In this study, we aimed to develop an inexpensive and simple sleep monitoring system using a camera and video processing. Polysomnography (PSG) recordings were performed in six subjects for four consecutive nights. Subjects' body movements were simultaneously recorded by the web camera. Body movement was extracted by video processing from the video data and fi ve parameters were calculated for machine learning. Four sleep stages (WAKE, LIGHT, DEEP and REM) were estimated by applying these fi ve parameters to a support vector machine. The overall estimation accuracy was 70.3 ± 11.3% with the highest accuracy for DEEP (82.8 ± 4.7%) and the lowest for LIGHT (53.0 ± 4.0%) compared with correct sleep stages manually scored on PSG data by a sleep technician. Estimation accuracy for REM sleep was 68.0 ± 6.8%. The kappa was 0.19 ± 0.04 for all subjects. The present non-contact sleep monitoring system showed suffi cient accuracy in sleep stage estimation with REM sleep detection being accomplished. Low-cost computing power of this system can be advantageous for mobile application and modularization into home-device.
