While most previous studies regarding patients with chronic low back pain (CLBP) mainly focused on pain, disability, psychological damage, and intervention measures, the effect of CLBP on personal space remains unclear. The study aimed to assess the personal space of patients with CLBP and healthy controls, explored the differences between the two groups, and examined whether pain, dysfunction, anxiety, and depression affected the personal space regulation. The cross-sectional study recruited 24 patients with CLBP and 24 healthy controls at Shanghai Shangti Orthopedic Hospital and Shanghai University of Sport, Shanghai, China, from December 2018 to January 2019. A stop-distance paradigm was applied to measure the comfortable and uncomfortable distance under four conditions. A self-rating anxiety scale (SAS) and a self-rating depression scale (SDS) were used to examine the anxiety and depression levels of all participants. The pain intensity and dysfunction in the CLBP group were evaluated by the numeric rating scale and Roland-Morris questionnaire (RMDQ), respectively. When approaching another individual or when being approached, the interpersonal distance under all the conditions in the CLBP group significantly differed from that in the healthy control group with larger space distances ( < 0.01). Gender had a significant main effect on the regulation of personal space in patients with CLBP ( < 0.05). The average pain intensity, scores on RMDQ, SAS, and SDS had a significant positive correlation with the interpersonal distance under the Same or Opposite Gender condition ( < 0.05). People with CLBP show an atypical personal space behavior and indeed have a greater interpersonal distance to strangers. The higher the pain intensity, dysfunction, anxiety, and depression, the greater the interpersonal distance in patients with CLBP. In the future, the effect and underlying neural mechanisms of pain and negative emotions on social withdrawal in patients should be examined.
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http://dx.doi.org/10.3389/fpsyt.2021.719271 | DOI Listing |
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Manchester Institute of Biotechnology (MIB), Department of Chemistry, University of Manchester, Manchester, M1 7DN, UK.
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College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China.
Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD).
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Department of Computer Science, University of Sheffield, UK.
This paper presents the Cadenza Woodwind Dataset. This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles.
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State Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, 430079, China.
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December 2024
Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China. Electronic address:
Investigating the formation mechanism and effective manipulation of multi-component crystal polymorphs is crucial for facilitating industrial drug development. Herein, five novel Osimertinib-caffeic acid forms were first strategically tailored by varying solvent selection. Theoretical analysis demonstrated this polymorphism is correlated with multiple hydrogen bond donors-acceptors within multi-component system, which provides manipulation space for reconfiguration of intermolecular interactions and structural competition, while solvent further induced or involved in hydrogen-bonded rearrangements.
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