GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- to (...) middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

BackgroundUp-to-date information about the trends of age-related hearing loss and how this varies between countries is essential to plan for an adequate health-system response. Therefore, this study aimed to assess the research hotpots and trends in ARHL and to provide the basis and direction for future research.Materials and methodsThe Web of Science Core Collection database was searched and screened according to the inclusion criteria during 2002–2021. Bibliometric analyses were conducted by CiteSpace software and VOSviewer software.ResultsThe query identified 1,496 publications, which (...) showed a growth trend of this filed. These publications were from 62 countries, the United States of America showed its tremendous impact on this field in publication outputs, total citations, and international collaborations, China following in second. The Journal of Hearing Research was the most productive journal. Weijia Kong published the most papers, and the most productive institution was Washington University. The keyword “presbycusis” ranked first in research frontiers and appeared earlier, and the keywords “age-related hearing loss,” “risk,” “dementia,” “auditory cortex,” “association,” and “decline” began to appear in recent years.ConclusionThe annual number of publications has grown rapidly in the past two decades and will continue to grow. Epidemiological investigation and laboratory research are lasting hot spots, besides future research will focus on the association between ARHL and cognitive decline, dementia, and Alzheimer’s disease. (shrink)

The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of (...) network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis. (shrink)