ABSTRACT
Background Maternal infection by SARS-CoV-2 may lead to adverse pregnancy outcomes and causes pathological changes in the placenta. However, consensus regarding characteristic pathological features is lacking. Researchof the placental histopathology in a cohort of women from Mizoram, India, was conducted to relate the SARS-CoV-2 infection's effectswith pregnancy and its outcome. Materials and methods The characteristics of 72 pregnant women diagnosed positive for SARS-CoV-2 who eventually delivered at Zoram Medical College Hospital, Mizoram, neonates' well-being, and histopathological features of placentas were studied. Results Of 72 women in this study, 59 (81.9%) gave birth at full term. Among these births, 5 were normal vaginal deliveries, while the remaining 67 (93.1%) were delivered via cesarean section. The reasons for cesarean delivery were either related to SARS-CoV-2 infection (n = 49), existing obstetric problems (n = 15) or fetal-distress (n = 5). All deliveries resulted in live births of COVID-negative babies, with 80.6% (n = 58) of the newborns having a birth weight of over 2.5 kg. APGAR scores ranged from 4 to 6 in 61 (84.7%) of the babies, and 10 neonates required resuscitation, of which 8 were managed in the neonatal intensive care unit (NICU). The placental histopathology showed increased fibrin thrombi in 8 cases (11.1%), while 20 cases (28%) showed focal infarction, microcalcification levels were elevated in 16 cases (22.2%), and a small percentage of cases (1.4%) exhibited small fibrotic villi and inter-villus agglutination. Placental chorioangiosis was detected in 28 (38.9%) of the cases, while avascular villi were seen in 6 cases. Meconium-stained liquor was observed in a single case. Intervillous hemorrhage was found in 42 cases, whileintervillous inflammation and increased syncytial knots were present in 14 and 5 cases, respectively. The placenta pathology of 10 neonates who required resuscitation/NICU admission was not significantly different from thatofthe 62 neonates who did not require it. However, a higher proportion of placenta from the asymptomatic group showed no abnormality compared to the symptomatic group (p = 0.046). Conclusion SARS-CoV-2 infection causes a range of morphological changes and lesions in the placenta, includingchorangiosis, villositis, chorioamnionitis, fetal vascular malperfusion/thrombosis, fibrin-deposition, increased syncytial-knotting, increased microcalcification, increased villous agglutination, focal infarct, intervilloushemorrhage as well as inflammation. Placental histopathological findings from this study can provide additional information to the existing literature on the subject.
ABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) that causes the COVID-19 pandemic has affected every household of the remotest part of North East India, Mizoram. The pregnant women do not have a higher chance of getting infected, yet the infection seems to be more severe. We are reporting five cases of maternal death and two cases of maternal near miss in ZMC of Mizoram. Case 1 to 5 were maternal death due to COVID-19. All of them were multigravida within the age group of 27–41 years with a period of gestation 24–37 weeks. The presenting complaints were fever and cough more than 3 days with an investigation report revealing raised C-reactive protein (CRP) and severe pneumonia. Case 6 (Near miss) was 20 years, primigravida at term pregnancy in labor with a history of fever for 2 days with an investigation report revealing raised CRP and severe pneumonia, the baby was delivered asphyxiated and died. Case 7 (Near miss) was 17 years old primigravida at term pregnancy admitted as asymptomatic COVID-19 positive, elective caesarean section was done for Obstetrics indication, the patient later developed fever and moderate pneumonia on 5 days of hospitalization, she also developed eclampsia on 7th hospital day, she survived after intensive care in the intensive care unit. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
BACKGROUND AND OBJECTIVE: During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD: A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT: This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.