Publications by authors named "Dries Van Dyck"

Objective: The objective of this cross-sectional study was to analyze the relationship between symptoms of central sensitization (CS) and important cognitive behavioral and psychosocial factors in a sample of patients with chronic nonspecific low back pain.

Methods: Participants with chronic nonspecific low back pain for at least 3 months were included in the study. They completed several questionnaires and a functional test.

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Background: Myofascial trigger points (MTrPs) are common in people with musculoskeletal pain and may play a role in chronic nonspecific low back pain (CLBP). One of the potential treatments of MTrPs is the Nervomatrix Soleve® auto-targeted neurostimulation device, providing targeted transcutaneous electrical nerve stimulation (TENS) to MTrPs in the lower back muscles. To date, no controlled studies have evaluated the effectiveness of this device for the pain management of this population.

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Objective: A systematic review was performed to evaluate the existing evidence related to the prevalence, incidence, localization, and pathophysiology of myofascial trigger points (MTrPs) in patients with spinal (back and neck) pain.

Methods: A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was performed in 2 electronic databases (PubMed and Web of Science) using predefined keywords regarding MTrPs and spinal pain. A "PICOS" questionnaire was used to set up the search strategies and inclusion criteria.

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Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible.

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Correlated motif mining (cmm) is the problem of finding overrepresented pairs of patterns, called motifs, in sequences of interacting proteins. Algorithmic solutions for cmm thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network.

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