Measurement of abdominal muscle thickness using ultrasound: A reliability study on patients with chronic nonspecific low back pain

Background and purpose: The purpose of this study was to assess the Within-day and Between-days reliability of abdominal muscles size measurement in patients with chronic non-specific low back pain (LBP) using ultrasound (US). Materials and methods: In this study, 15 patients with chronic non-specific LBP (20-50 years old) were recruited. The reliability of the abdominal muscle size (External and Internal oblique, and Transversus abdominis) was assessed in a relaxed and contraction state by a real time US. Two images were taken on the same day with an hour interval to assess the within day reliability and the third image was taken a week later to determine the between- days reliability. Results: Within-day and between-days reliability of abdominal muscles thickness measurements using US in patients with nonspecific chronic LBP in both rest and contraction state found to be high, ICC = 0.90 for within and ICC=0.85 for between-days of Transversus abdominis muscle in rest state and ICC= 0.82 and 0.86 in contraction state, respectively. For Internal oblique muscle, ICC=0.90 (82) and ICC=0.88 (88) were found for within-day and between-days in rest and contraction state, respectively. Within-day and between-days reliability at rest of ICC=0.86 (79) and in contraction state of ICC=0.79 (75) were demonstrated for External Oblique muscle. Conclusion: Results of the present study suggest US as a reliable method to evaluate the thickness of the abdominal muscles which could be used as a reliable tool in the assessment of patients and also in evaluating the effect of different therapeutic interventions. © 2015, Mazandaran University of Medical Sciences. All rights reserved

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective