Background In December, 2019, coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China. The number of affected pregnant women is increasing, but scarce information is available about the clinical features of COVID-19 in pregnancy. This study aimed to clarify the clinical features and obstetric and neonatal outcomes of pregnant patients with COVID-19. MethodsIn this retrospective, single-centre study, we included all pregnant women with COVID-19 who were admitted to Tongji Hospital in Wuhan, China. Clinical features, treatments, and maternal and fetal outcomes were assessed.Findings Seven patients, admitted to Tongji Hospital from Jan 1, to Feb 8, 2020, were included in our study. The mean age of the patients was 32 years (range 29-34 years) and the mean gestational age was 39 weeks plus 1 day (range 37 weeks to 41 weeks plus 2 days). Clinical manifestations were fever (six [86%] patients), cough (one [14%] patient), shortness of breath (one [14%] patient), and diarrhoea (one [14%] patient). All the patients had caesarean section within 3 days of clinical presentation with an average gestational age of 39 weeks plus 2 days. The final date of followup wasFeb 12, 2020. The outcomes of the pregnant women and neonates were good. Three neonates were tested for SARS-CoV-2 and one neonate was infected with SARS-CoV-2 36 h after birth.Interpretation The maternal, fetal, and neonatal outcomes of patients who were infected in late pregnancy appeared very good, and these outcomes were achieved with intensive, active management that might be the best practice in the absence of more robust data. The clinical characteristics of these patients with COVID-19 during pregnancy were similar to those of non-pregnant adults with COVID-19 that have been reported in the literature.
Candidate genes involved in both recognition (resistance gene analogs [RGAs]) and general plant defense (putative defense response [DR]) were used as molecular markers to test for association with resistance in rice to blast, bacterial blight (BB), sheath blight, and brown plant-hopper (BPH). The 118 marker loci were either polymerase chain reaction-based RGA markers or restriction fragment length polymorphism (RFLP) markers that included RGAs or putative DR genes from rice, barley, and maize. The markers were placed on an existing RFLP map generated from a mapping population of 116 doubled haploid (DH) lines derived from a cross between an improved indica rice cultivar, IR64, and a traditional japonica cultivar, Azucena. Most of the RGAs and DR genes detected a single locus with variable copy number and mapped on different chromosomes. Clusters of RGAs were observed, most notably on chromosome 11 where many known blast and BB resistance genes and quantitative trait loci (QTL) for blast, BB, sheath blight, and BPH were located. Major resistance genes and QTL for blast and BB resistance located on different chromosomes were associated with several candidate genes. Six putative QTL for BB were located on chromosomes 2, 3, 5, 7, and 8 and nine QTL for BPH resistance were located to chromosomes 3, 4, 6, 11, and 12. The alleles of QTL for BPH resistance were mostly from IR64 and each explained between 11.3 and 20.6% of the phenotypic variance. The alleles for BB resistance were only from the Azucena parent and each explained at least 8.4% of the variation. Several candidate RGA and DR gene markers were associated with QTL from the pathogens and pest. Several RGAs were mapped to BB QTL. Dihydrofolate reductase thymidylate synthase co-localized with two BPH QTL associated with plant response to feeding and also to blast QTL. Blast QTL also were associated with aldose reductase, oxalate oxidase, JAMyb (a jasmonic acid-induced Myb transcription factor), and peroxidase markers. The frame map provides reference points to select candidate genes for cosegregation analysis using other mapping populations, isogenic lines, and mutants.
BACKGROUNDThere is little information about the coronavirus disease 2019 during pregnancy.This study aimed to determine the clinical features and the maternal and neonatal outcomes of pregnant women with Covid-19. METHODSIn this retrospective analysis from five hospitals, we included pregnant women with Covid-19from January 1 to February 20, 2020. The primary composite endpoints were admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Secondary endpoints included the clinical severity of Covid-19, neonatal mortality, admission to neonatal intensive care unit (NICU), and the incidence of acute respiratory distress syndrome (ARDS) of pregnant women and newborns. RESULTSThirty-three pregnant women with Covid-19 and 28 newborns were identified. One (3%) pregnant woman needed the use of mechanical ventilation. No pregnant women admitted to the ICU. There were no moralities among pregnant women or newborns. The percentages of pregnant women with mild, moderate, and severe symptoms were 13 (39.4%),19(57.6%), and 1(3%). One (3.6%) newborn developed ARDS and was admitted to the NICU. The rate of perinatal transmission of SARS-CoV-2 was 3.6%. : medRxiv preprint CONCLUSIONSThis report suggests that pregnant women are not at increased risk for severe illness or mortality with Covid-19 compared with the general population. The SARS-CoV-2 infection during pregnancy might not be associated with as adverse obstetrical and neonatal outcomes that are seen with the severe acute respiratory syndrome coronavirus (SARS-CoV) andMiddle East respiratory syndrome coronavirus (MERS-CoV) infection during pregnancy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.