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https://www.selleckchem.com/products/GDC-0980-RG7422.html yered barrier. The existence of stable symptom clusters with variations or changes in cluster membership and the merging of symptom clusters over time urge us to investigate how symptom relationships change over time. To identify stable symptom clusters and understand networks among symptoms using longitudinal data. Secondary data analysis was conducted using data from a nonblinded randomized clinical trial, which evaluated the effect and feasibility of the developed cancer symptom management system. For the present study, data from all participants of the original trial were analyzed (N=249). The severity of 20 symptoms was measured before the start of chemotherapy (CTx) and during the initial four cycles of CTx. Symptom clusters were identified using principal component and hierarchical cluster analyses, and network analysis was used to explore the relationships among symptoms. Three common symptom clusters were identified. The first cluster consisted of anxiety, depression, sleep disturbance, pain, and dyspnea. F central symptoms. Stable symptom clusters and evolving networks were identified. The most central symptom was fatigue; however, the paucity of studies that investigated symptom networks and central symptoms calls for further investigations on these phenomena. Identification of central symptoms and underlying mechanisms will guide efficient symptom management. Future studies will need to focus on developing comprehensive interventions for managing symptom clusters or targeting central symptoms.Experience-dependent modulation of the visual evoked potential (VEP) is a promising proxy measure of synaptic plasticity in the cerebral cortex. However, existing studies are limited by small to moderate sample sizes as well as by considerable variability in how VEP modulation is quantified. In the present study, we used a large sample (n = 415) of healthy volunteers to compare different quantifications of VE

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