Symptomatic asymmetry in the first six months of life: differential diagnosis

Asymmetry in infancy is a clinical condition with a wide variation in appearances (shape, posture, and movement), etiology, localization, and severity. The prevalence of an asymmetric positional preference is 12% of all newborns during the first six months of life. The asymmetry is either idiopathic or symptomatic. Pediatricians and physiotherapists have to distinguish symptomatic asymmetry (SA) from idiopathic asymmetry (IA) when examining young infants with a positional preference to determine the prognosis and the intervention strategy. The majority of cases will be idiopathic, but the initial presentation of a positional preference might be a symptom of a more serious underlying disorder. The purpose of this review is to synthesize the current information on the incidence of SA, as well as the possible causes and the accompanying signs that differentiate SA from IA. This review presents an overview of the nine most prevalent disorders in infants in their first six months of life leading to SA. We have discovered that the literature does not provide a comprehensive analysis of the incidence, characteristics, signs, and symptoms of SA. Knowledge of the presented clues is important in the clinical decision making with regard to young infants with asymmetry. We recommend to design a valid and useful screening instrument

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks