Hibernation, an adaptive mechanism to extreme environmental conditions, is prevalent among mammals. Its main characteristics include reduced body temperature and metabolic rate. However, the mechanisms by which hibernating animals re-enter deep sleep during the euthermic phase to sustain hibernation remain poorly understood. We selected the as a model organism and conducted transcriptomic sequencing of its hypothalamus at multiple time points throughout hibernation. Through the strategies of gene set filtering and intersection analysis, we effectively filtered out redundant data, identifying a subset of genes whose expression was downregulated during the euthermic phase potentially inducing re-enter deep sleep, thereby maintaining the periodic cycles of torpor and arousal. These cycles are crucial for sustaining the overall hibernation process. Notably, genes associated with sodium and potassium ion channels were significantly enriched. Specifically, potassium ion-related genes such as Kcnc1, Kcna2, Kcng4, and Kcna6, along with sodium ion-related genes such as Scn1a and Hcn2, were markedly downregulated. qRT-PCR validation of four of these genes (Kcnc1, Kcna6, Scn1a, and Hcn2) confirmed significant downregulation during the euthermic phase compared to the deep sleep phase, further supporting our transcriptomic findings. This study provides novel insights into the hypothalamic transcriptome dynamics at various hibernation stages. Although the functional roles of these genes require further investigation, our findings lay the groundwork for future studies to elucidate the molecular mechanisms underlying hibernation.
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http://dx.doi.org/10.3389/fnins.2024.1501223 | DOI Listing |
Sensors (Basel)
December 2024
ActiGraph LLC, Pensacola, FL 32502, USA.
Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.
View Article and Find Full Text PDFPharmaceuticals (Basel)
December 2024
Department of Physiology, Faculty of Medicine, Istanbul Medeniyet University, 34700 Istanbul, Türkiye.
With increasing interest in plant-based compounds that can enhance sleep quality without the side effects of caffeine, Alpinia galanga (AG) has emerged as a promising herbal supplement for improving mental alertness. This study assessed the impact of water-soluble AG extract on sleep quality; the activity of GABAergic, glutamatergic, and serotonergic receptors; and concentrations of dopamine and serotonin in the brains of mice. The study employed two experimental models using BALB/c mice to examine the impact of pentobarbital-induced sleep and caffeine-induced insomnia.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Jiangsu Key Laboratory of Intelligent Medical Image Computing, Nanjing 210044, China.
The pivotal role of sleep has led to extensive research endeavors aimed at automatic sleep stage classification. However, existing methods perform poorly when classifying small groups or individuals, and these results are often considered outliers in terms of overall performance. These outliers may introduce bias during model training, adversely affecting feature selection and diminishing model performance.
View Article and Find Full Text PDFEntropy (Basel)
December 2024
Institute of Physics, University of Zielona Góra, 65-069 Zielona Góra, Poland.
This study investigates whether heart rate asymmetry (HRA) parameters offer insights into sleep stages beyond those provided by conventional heart rate variability (HRV) and complexity measures. Utilizing 31 polysomnographic recordings, we focused exclusively on electrocardiogram (ECG) data, specifically the RR interval time series, to explore heart rate dynamics associated with different sleep stages. Employing both statistical techniques and machine learning models, with the Generalized Estimating Equation model as the foundational approach, we assessed the effectiveness of HRA in identifying and differentiating sleep stages and transitions.
View Article and Find Full Text PDFBrain Sci
November 2024
Department of Neurology, Beth Isreal Deaconess Medical Center, Harvard Medical School, Harvard University, Cambridge, MA 02215, USA.
: Manually labeling sleep stages is time-consuming and labor-intensive, making automatic sleep staging methods crucial for practical sleep monitoring. While both single- and multi-channel data are commonly used in automatic sleep staging, limited research has adequately investigated the differences in their effectiveness. In this study, four public data sets-Sleep-SC, APPLES, SHHS1, and MrOS1-are utilized, and an advanced hybrid attention neural network composed of a multi-branch convolutional neural network and the multi-head attention mechanism is employed for automatic sleep staging.
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