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Objective:To investigate the effect of smart air cell mattresses on sleep quality.Methods:Twenty healthy young people were enrolled as subjects, and each subject underwent a four-night polysomnographic monitoring experiment, including two nights each on a smart air cell mattress and a general mattress. The differences in sleep quality were compared by self-assessment of sleep quality, objective sleep indicators, and electroencephalogram (EEG) spectral analysis.Results:In the comparison between the smart air cell mattress and the general mattress, the differences in self-assessment of sleep quality and objective sleep indicators were not statistically significant (all P > 0.05), but the smart air cell mattress had a slight overall advantage. The relative power of EEG in the low-frequency band and the relative power of EEG in the high-frequency band were higher in the subjects with the smart air cell mattress. Conclusions:For the healthy young population, the smart air cell mattress can positively influence sleep quality to some extent, and the change in EEG relative power indicates an increase in sleep depth.
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PURPOSE: Breast cancer is the most commonly occurring cancer among women worldwide, and therefore, improved approaches for its early detection are urgently needed. As microRNAs (miRNAs) are increasingly recognized as critical regulators in tumorigenesis and possess excellent stability in plasma, this study focused on using miRNAs to develop a method for identifying noninvasive biomarkers. METHODS: To discover critical candidates, differential expression analysis was performed on tissue-originated miRNA profiles of 409 early breast cancer patients and 87 healthy controls from The Cancer Genome Atlas database. We selected candidates from the differentially expressed miRNAs and then evaluated every possible molecular signature formed by the candidates. The best signature was validated in independent serum samples from 113 early breast cancer patients and 47 healthy controls using reverse transcription quantitative real-time polymerase chain reaction. RESULTS: The miRNA candidates in our method were revealed to be associated with breast cancer according to previous studies and showed potential as useful biomarkers. When validated in independent serum samples, the area under curve of the final miRNA signature (miR-21-3p, miR-21-5p, and miR-99a-5p) was 0.895. Diagnostic sensitivity and specificity were 97.9% and 73.5%, respectively. CONCLUSION: The present study established a novel and effective method to identify biomarkers for early breast cancer. And the method, is also suitable for other cancer types. Furthermore, a combination of three miRNAs was identified as a prospective biomarker for breast cancer early detection.
