Maternal and neonatal outcomes in pregnant women with multiple sclerosis disease: A systematic review and meta-analysis.

OBJECTIVES: Little is known regarding the impact of multiple sclerosis (MS) on maternal and neonatal outcomes. Consequently, this systematic review and meta-analysis aimed to study the impacts of MS on maternal and neonatal outcomes in pregnant women with a history of MS. METHODS: This review was designed in line with the PRISMA guidelines. Two researchers conducted independent reviews of the literature without time restrictions until January 2023 using international databases, including PubMed, Web of Science, CINAHL Plus, Embase, Scopus, Science Direct, and Google Scholar. A random-effect meta-analysis, using the db metan command in Stata 17.2, was used to calculate the pooled measure of association. RESULTS: The meta-analysis identified 15 studies involving 33,174,541 pregnant women (32,191 with MS and 33,142,350 as controls). The findings indicate that women with a history of MS are at an increased risk of cesarean delivery (OR=1.28, 95% Confidence Intervals [CI]: 1.14-1.45, p-value: 0.042). Also, these women are at higher risk of neonatal outcomes, such as preterm birth (OR= 1.39, 95% CI: 1.08-1.78, p-value: 0.02), congenital malformations (OR=1.32, 95%CI: 1.16-1.50, p-value: 0.031), Apgar score <7 (OR=2.13, 95% CI: 1.19-3.79, p-value: 0.03), and small for gestational age (OR=1.27, 95% CI: 1.08-1.51, p-value: 0.040). CONCLUSION: Pregnant women with MS have a greater chance of adverse pregnancy results than pregnant women without MS. Consequently, pregnant women with MS should create detailed before and after pregnancy plans, in consultation with their doctors, spouses, families, and friends, regarding the necessary care and supplements. Future studies applying a prospective cohort design that control for potential confounders are needed to further validate the findings.

COVID-19 research progress: Bibliometrics and visualization analysis.

Background: Coronavirus primarily targets the human respiratory system, COVID-19 (Coronavirus disease 2019) triggered in China in the late 2019. In March 2020, WHO announced the COVID-19 pandemic. This study aims to analyze and visualize the scientific structure of the COVID-19 publications using co-citation and co-authorship. Methods: This is a scientometric study. Web of Science Core Collection (WoSCC) was searched for all documents regarding COVID-19, MERS-Cov, and SARS-Cov from the beginning to 2020. An Excel spreadsheet was applied to gather and analyze the data and the CiteSpace was used to visualize and analyze the data. Results: A total of 5159 records were retrieved in WoSCC. The structure of the network indicated that the network mean silhouette was low (0.1444), implying that the network clusters' identity is not identifiable with high confidence. The network modularity was 0.7309. The cluster analysis of the co-citation network on documents from 2003 to 2020 provided 188 clusters. The largest cluster entitled, "the Middle East respiratory syndrome coronavirus" had 255 nodes. The coauthorship network illustrated that the most prolific countries, USA, China, and Saudi Arabia, have focused on a specific field and have formed separate clusters. Conclusion: The present study identified the important topics of research in the field of COVID-19 based on co-citation networks as well as the analysis of clusters of countries' collaborations. Despite the similarities in the production behavior in prolific countries, their thematic focus varies so that a country like China plays a role in "Quantitative Detection" cluster, while USA is the leading country in the "Biological Evaluation" cluster.