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  1. Ultrasonographic measurement indicated patients with chronic neck pain had reduced diaphragm thickness and mobility along with declined respiratory functions
    Hii EY, Kuo YL, Cheng KC, Hung CH, Tsai YJ
    Musculoskelet Sci Pract, 2024 Aug;72:102951.
    PMID: 38615408 DOI: 10.1016/j.msksp.2024.102951
    BACKGROUND: Chronic neck pain (CNP) is a prevalent musculoskeletal condition including notable impairments in respiratory function. The diaphragm, serving dual roles in respiration and spinal stability, is intricately linked to the cervical spine through fascial, neurophysiological, and biomechanical connections. However, to date, none has investigated the diaphragm function in patients with CNP.

    OBJECTIVES: To investigate the diaphragm function, respiratory muscle strength, and pulmonary function in patients with CNP. In addition, their associations were also examined.

    DESIGN: A case-control study.

    METHODS: A total of 54 participants were recruited including 25 patients with CNP (CNP group) and 29 healthy adults (CON group). Pulmonary function including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1), and respiratory muscle strength represented by maximal inspiratory (MIP) and maximal expiratory pressure (MEP), as well as diaphragm function including ultrasonographic measures of mobility and thickness changes during maximal inspiration and expiration were assessed in all participants. Additionally, the intensity of pain and disability were evaluated using a Visual Analog Scale and Neck Disability Index only in patients with CNP.

    RESULTS: Significant reductions of the FVC, FEV1, MIP, and MEP were found in the CNP group compared to the CON group (p 

  2. Fulltext A deep learning-based pipeline for analyzing the influences of interfacial mechanochemical microenvironments on spheroid invasion using differential interference contrast microscopic images
    Ngo TKN, Yang SJ, Mao BH, Nguyen TKM, Ng QD, Kuo YL, et al.
    Mater Today Bio, 2023 Dec;23:100820.
    PMID: 37810748 DOI: 10.1016/j.mtbio.2023.100820
    Metastasis is the leading cause of cancer-related deaths. During this process, cancer cells are likely to navigate discrete tissue-tissue interfaces, enabling them to infiltrate and spread throughout the body. Three-dimensional (3D) spheroid modeling is receiving more attention due to its strengths in studying the invasive behavior of metastatic cancer cells. While microscopy is a conventional approach for investigating 3D invasion, post-invasion image analysis, which is a time-consuming process, remains a significant challenge for researchers. In this study, we presented an image processing pipeline that utilized a deep learning (DL) solution, with an encoder-decoder architecture, to assess and characterize the invasion dynamics of tumor spheroids. The developed models, equipped with feature extraction and measurement capabilities, could be successfully utilized for the automated segmentation of the invasive protrusions as well as the core region of spheroids situated within interfacial microenvironments with distinct mechanochemical factors. Our findings suggest that a combination of the spheroid culture and DL-based image analysis enable identification of time-lapse migratory patterns for tumor spheroids above matrix-substrate interfaces, thus paving the foundation for delineating the mechanism of local invasion during cancer metastasis.
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