The functions of sleep remain largely unclear, and even less is known about its role in development. A general strategy to tackle these questions is to disrupt sleep and measure the outcomes. However, some existing sleep deprivation methods may not be suitable for studying the effects of chronic sleep disruption, due to their lack of effectiveness and/or robustness, substantial stress caused by the deprivation method, or consuming a large quantity of time and manpower. More problems may be encountered when applying these existing protocols to young, developing animals, because of their likely heightened vulnerability to stressors, and difficulties in precisely monitoring sleep at young ages. Here, we report a protocol of automated sleep disruption in mice using a commercially available, shaking platform-based deprivation system. We show that this protocol effectively and robustly deprives both non-rapid-eye-movement (NREM) sleep and rapid-eye-movement (REM) sleep without causing a significant stress response, and does not require human supervision. This protocol uses adolescent mice, but the method also works with adult mice. Graphical abstract The platform of the deprivation chamber was programmed to shake in a given frequency and intensity to keep the animal awake while its brain and muscle activities were continuously monitored by electroencephalography and electromyography.
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http://dx.doi.org/10.21769/BioProtoc.4620 | DOI Listing |
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
View Article and Find Full Text PDFRespir Res
January 2025
National Heart and Lung Institute, Imperial College London, London, UK.
Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.
Methods: We evaluated the potential of an automated ILD quantification system (icolung) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD.
Sci Rep
January 2025
International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki, 305- 8575, Japan.
We explore an innovative approach to sleep stage analysis by incorporating complexity features into sleep scoring methods for mice. Traditional sleep scoring relies on the power spectral features of electroencephalogram (EEG) and the electromyogram (EMG) amplitude. We introduced a novel methodology for sleep stage classification based on two types of complexity analysis, namely multiscale entropy and detrended fluctuation analysis.
View Article and Find Full Text PDFATS Sch
January 2025
Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Emory University Department of Medicine, Emory School of Medicine, Atlanta, Georgia.
Training programs around the country receive many applications every year with a limited time window to send out invitations for interviews. This poses major barriers to conducting holistic application reviews. To create and implement a data-driven selection process that promotes holistic reviews within a tight timeline to select applicants for invitation to interview.
View Article and Find Full Text PDFSci Rep
January 2025
Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
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