ABSTRACT
Purpose: Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency. Methods: The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM). Results: The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%. Conclusion: A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.
ABSTRACT
Macrophages are divided into two subpopulations: classically activated macrophages (M1) and alternatively activated macrophages (M2). BCG (Bacilli Calmette-GuC)rin) activates disabled naC/ve macrophages to M1 macrophages, which act as inflammatory, microbicidal and tumoricidal cells through cell-cell contact and/or the release of soluble factors. Various transcription factors and signaling pathways are involved in the regulation of macrophage activation and polarization. We discovered that BCG-activated macrophages (BAM) expressed a new molecule, and we named it Novel Macrophage Activated Associated Protein 1 (NMAAP1). The current study found that the overexpression of NMAAP1 in macrophages results in M1 polarization with increased expression levels of M1 genes, such as inducible nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-N1), Interleukin 6 (IL-6), Interleukin 12 (IL-12), Monocyte chemoattractant protein-1 (MCP-1) and Interleukin-1 beta (IL-1N2), and decreased expression of some M2 genes, such as Kruppel-like factor 4 (KLF4) and suppressor of cytokine signaling 1 (SOCS1), but not other M2 genes, including arginase-1 (Arg-1), Interleukin (IL-10), transforming growth factor beta (TGF-N2) and found in inflammatory zone 1 (Fizz1). Moreover, NMAAP1 overexpression in the RAW264.7 cell line increased cytotoxicity against MCA207 tumor cells, which depends on increased inflammatory cytokines rather than cell-cell contact. NMAAP1 also substantially enhanced the phagocytic ability of macrophages, which implies that NMAAP1 promoted macrophage adhesive and clearance activities. Our results indicate that NMAAP1 is an essential molecule that modulates macrophages phenotype and plays an important role in macrophage tumoricidal functions.
