Posture, balance and gait in axial spondyloarthritis: a case-control study.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease that primarily involves the axial skeleton but may also present with peripheral joint involvement and extra-articular involvement. The present study aims to quantitatively analyze posture, balance, and gait parameters in patients with axSpA and and assess associated factors. This cross-sectional case-control study included 51 axSpA patients (30 males, 21 females; mean age 40.94 ± 10.48 years) and 51 age- and sex-matched healthy controls. In patients with axSpA, the Ankylosing Spondylitis Disease Activity Score CRP, the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), the Bath Ankylosing Spondylitis Functional Index (BASFI), the Bath Ankylosing Spondylitis Metrology Index (BASMI), the Maastrich Ankylosing Spondylitis Enthesitis Score (MASES), and the Ankylosing Spondylitis Quality of Life (ASQoL) scale were used. For postural analysis, DIERS formetric (Diers GmbH, Schlangenbad, Germany) videoraster- stereography device was utilized. HUR SmartBalance BTG4 (HUR-labs Oy, Kokkola, Finland) balance platform was used for postural balance and limit of stability (LOS) measurement. Participants were evaluated using Berg Balance Scale (BBS), Functional Reach Test (FRT) and Timed Up and Go Test (TUG). The Zebris FDM type 3 (Zebris Medical GmbH, Germany) walking platform was used to measure the spatiotemporal parameters of the participants. Comparison of postural parameters showed that sagittal imbalance and cervical depth distance were increased in the axSpA group than in the healthy participants (p < 0.004). Comparison of functional balance parameters showed that BBS and FRT scores were significantly lower (p < 0.001) in the axSpA group than in the control group, while TUG scores were significantly higher (p < 0.001). The LOS values, which evaluate dynamic balance were significantly lower, indicating impairment, in the axSpA group. In the measurement of postural sway, which indicates static balance, all 23 subparameters were found to be similar. When analyzing the spatiotemporal gait parameters, in the axSpA group compared with those in the control group; Foot angles (p= 0.028) and stride width (p = 0.004) were increased, whereas step lengths (p = 0.004) and stride lengths (P = 0.004) were decreased. In the axSpA group the gait speed was decreased (p = 0.004). When axSpA was analyzed separately as radiographic and nonradiographic axSpA, similar findings were observed in posture, balance, and gait parameters. No significant difference was observed. We found that the clinical assessments most closely associated with posture, balance, and gait analyses were BBS, FRT, TUG, and BASFI.

DEVOUR: Deleterious Variants on Uncovered Regions in Whole-Exome Sequencing.

The discovery of low-coverage (i.e. uncovered) regions containing clinically significant variants, especially when they are related to the patient's clinical phenotype, is critical for whole-exome sequencing (WES) based clinical diagnosis. Therefore, it is essential to develop tools to identify the existence of clinically important variants in low-coverage regions. Here, we introduce a desktop application, namely DEVOUR (DEleterious Variants On Uncovered Regions), that analyzes read alignments for WES experiments, identifies genomic regions with no or low-coverage (read depth < 5) and then annotates known variants in the low-coverage regions using clinical variant annotation databases. As a proof of concept, DEVOUR was used to analyze a total of 28 samples from a publicly available Hirschsprung disease-related WES project (NCBI Bioproject: https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJEB19327), revealing the potential existence of 98 disease-associated variants in low-coverage regions. DEVOUR is available from https://github.com/projectDevour/DEVOUR under the MIT license.

Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.

ML-AdVInfect: A Machine-Learning Based Adenoviral Infection Predictor.

Adenoviruses (AdVs) constitute a diverse family with many pathogenic types that infect a broad range of hosts. Understanding the pathogenesis of adenoviral infections is not only clinically relevant but also important to elucidate the potential use of AdVs as vectors in therapeutic applications. For an adenoviral infection to occur, attachment of the viral ligand to a cellular receptor on the host organism is a prerequisite and, in this sense, it is a criterion to decide whether an adenoviral infection can potentially happen. The interaction between any virus and its corresponding host organism is a specific kind of protein-protein interaction (PPI) and several experimental techniques, including high-throughput methods are being used in exploring such interactions. As a result, there has been accumulating data on virus-host interactions including a significant portion reported at publicly available bioinformatics resources. There is not, however, a computational model to integrate and interpret the existing data to draw out concise decisions, such as whether an infection happens or not. In this study, accepting the cellular entry of AdV as a decisive parameter for infectivity, we have developed a machine learning, more precisely support vector machine (SVM), based methodology to predict whether adenoviral infection can take place in a given host. For this purpose, we used the sequence data of the known receptors of AdVs, we identified sets of adenoviral ligands and their respective host species, and eventually, we have constructed a comprehensive adenovirus-host interaction dataset. Then, we committed interaction predictions through publicly available virus-host PPI tools and constructed an AdV infection predictor model using SVM with RBF kernel, with the overall sensitivity, specificity, and AUC of 0.88 ± 0.011, 0.83 ± 0.064, and 0.86 ± 0.030, respectively. ML-AdVInfect is the first of its kind as an effective predictor to screen the infection capacity along with anticipating any cross-species shifts. We anticipate our approach led to ML-AdVInfect can be adapted in making predictions for other viral infections.