Introduction Antibiotics represent the most common type of medication used during pregnancy and infancy. Antibiotics have been proposed as a possible factor in changes in microbiota composition, which may play a role in the aetiology of autism and attention deficit/hyperactivity disorder (ADHD). Our aim was to investigate the association between maternal and early-life antibiotic use and autism and ADHD in childhood. Methods This Swedish nation-wide population-based cohort study included all first live singleton births (N = 483,459) between January 2006 and December 2016. The association of dispensed antibiotics with autism and ADHD in children aged ≤ 11 years was estimated by applying multivariable logistic regression and generalised estimating equations models. Results Of the mothers, 25.9% (n = 125,106) were dispensed ≥1 antibiotic during the exposure period (from 3 months pre-conception to delivery), and 41.6% (n = 201,040) of the children received ≥ 1 antibiotic in early life (aged ≤ 2 years). Penicillin was the most prescribed antibiotic class (17.9% of mothers, 38.2% of children). Maternal antibiotic use was associated with an increased risk of autism [odds ratio (OR) = 1.16, 95% confidence interval (CI) 1.09-1.23] and ADHD (OR = 1.29, 95% CI 1.21-1.36) in childhood. Early-life exposure to antibiotics showed an even stronger association [autism (OR = 1.46, 95% CI 1.38-1.55); ADHD (OR = 1.90, 95% CI 1.80-2.00)]. Both maternal and childhood-exposure sub-analyses suggested a dose-response relationship. Conclusion Maternal and early-life antibiotic use was associated with an increased risk of autism and ADHD in childhood. However, differences were noted by exposure period and antibiotic classes. Lembris L. Njotto and Johanna Simin shared first authorship. Robin Bruyndonckx and Nele Brusselaers shared the last authorship.
Network is considered naturally as a wide range of different contexts, such as biological systems, social relationships as well as various technological scenarios. Investigation of the dynamic phenomena taking place in the network, determination of the structure of the network and community and description of the interactions between various elements of the network are the key issues in network analysis. One of the huge network structure challenges is the identification of the node(s) with an outstanding structural position within the network. The popular method for doing this is to calculate a measure of centrality. We examine node centrality measures such as degree, closeness, eigenvector, Katz and subgraph centrality for undirected networks. We show how the Katz centrality can be turned into degree and eigenvector centrality by considering limiting cases. Some existing centrality measures are linked to matrix functions. We extend this idea and examine the centrality measures based on general matrix functions and in particular, the logarithmic, cosine, sine, and hyperbolic functions. We also explore the concept of generalised Katz centrality. Various experiments are conducted for different networks generated by using random graph models. The results show that the logarithmic function in particular has potential as a centrality measure. Similar results were obtained for real-world networks. 80Open Journal of Discrete Mathematics cal, social and biological scenarios. Networks are used to model a variety of highly interconnected systems, both in nature and man-made world of technology. These networks include protein-protein interaction networks, social networks, food webs, scientific collaboration networks, metabolic networks, lexical or semantic networks, neural networks, the World Wide Web and others. The use of network analysis is in various situations: from determining network structure and communities, describing the interactions between various elements of the network and investigating the dynamics phenomena taking place in the network [1]. One of the ground laying questions analysis of network is how to determine the "most important" nodes in a given network. Many centrality measures have been proposed, starting with the simplest of all, node degree centrality. This measure has being considered too "local", as it does not take into account the connectivity of the immediate neighbours of the node under consideration. A number of centrality measures have been introduced that take into account the global connectivity properties of the network. These include various types of eigenvector centrality for both directed and undirected networks, Katz centrality, subgraph centrality and PageRank centrality [1]. The use of centrality scoresprovides rankings of the nodes in the networks. The higher the ranking of a node, the more important the node is believed to be within the network. There are many different ranking methods in use, and many algorithms have been developed to compute these rankings.The purpose of this p...
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.