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Abstract Background Osteocytes are critical mechanosensory cells in bone, and mechanically stimulated osteocytes produce exosomes that can induce osteogenesis. MicroRNAs (miRNAs) are important constituents of exosomes, and some miRNAs in osteocytes regulate osteogenic differentiation; previous studies have indicated that some differentially expressed miRNAs in mechanically strained osteocytes likely influence osteoblastic differentiation. Therefore, screening and selection of miRNAs that regulate osteogenic differentiation in exosomes of mechanically stimulated osteocytes are important. Results A mechanical tensile strain of 2500 με at 0.5 Hz 1 h per day for 3 days, elevated prostaglandin E2 (PGE2) and insulin-like growth factor-1 (IGF-1) levels and nitric oxide synthase (NOS) activity of MLO-Y4 osteocytes, and promoted osteogenic differentiation of MC3T3-E1 osteoblasts. Fourteen miRNAs differentially expressed only in MLO-Y4 osteocytes which were stimulated with mechanical tensile strain, were screened, and the miRNAs related to osteogenesis were identified. Four differentially expressed miRNAs (miR-1930-3p, miR-3110-5p, miR-3090-3p, and miR-3058-3p) were found only in mechanically strained osteocytes, and the four miRNAs, eight targeted mRNAs which were differentially expressed only in mechanically strained osteoblasts, were also identified. In addition, the mechanically strained osteocyte-derived exosomes promoted the osteoblastic differentiation of MC3T3-E1 cells in vitro, the exosomes were internalized by osteoblasts, and the up-regulated miR-3110-5p and miR-3058-3p in mechanically strained osteocytes, were both increased in the exosomes, which was verified via reverse transcription quantitative polymerase chain reaction (RT-qPCR). Conclusions In osteocytes, a mechanical tensile strain of 2500 με at 0.5 Hz induced the fourteen differentially expressed miRNAs which probably were in exosomes of osteocytes and involved in osteogenesis. The mechanically strained osteocyte-derived exosomes which contained increased miR-3110-5p and miR-3058-3p (two of the 14 miRNAs), promoted osteoblastic differentiation.

IntroductionIn the evolving field of neurophysiological research, visual light flicker stimulation is recognized as a promising non-invasive intervention for cognitive enhancement, particularly in sleep-deprived conditions.MethodsThis study explored the effects of specific flicker frequencies (40 Hz and 20–30 Hz random flicker) on alertness recovery in sleep-deprived rats. We employed a multidisciplinary approach that included behavioral assessments with the Y-maze, in vivo electrophysiological recordings, and molecular analyses such as c-FOS immunohistochemistry and hormone level measurements.ResultsBoth 40 Hz and 20–30 Hz flicker significantly enhanced behavioral performance in the Y-maze test, suggesting an improvement in alertness. Neurophysiological data indicated activation of neural circuits in key brain areas like the thalamus and hippocampus. Additionally, flicker exposure normalized cortisol and serotonin levels, essential for stress response and mood regulation. Notably, increased c-FOS expression in brain regions related to alertness and cognitive functions suggested heightened neural activity.DiscussionThese findings underscore the potential of light flicker stimulation not only to mitigate the effects of sleep deprivation but also to enhance cognitive functions. The results pave the way for future translational research into light-based therapies in human subjects, with possible implications for occupational health and cognitive ergonomics.

BackgroundEnvironmental noise pollution is serious, and there are few studies on the effects of long-term noise exposure during sleep on cognitive function and possible biological clock mechanism. ObjectiveTo explore the cognitive impairment induced by noise exposure during sleep in mice and possible biological clock mechanism, and to provide a theoretical basis for the protection against noise exposure. MethodsTwenty male C57BL/6J mice were randomly divided into a control group and a noise-exposed group, 10 mice in each group. The noise-exposed group was exposed to sleep-period noise using a noise generator for 12 h (08:00–20:00) per day for a total of 30 d. The calibrated noise intensity was set at 90 dB. No intervention was imposed on the control group. At the end of the noise exposure, cognitive function of mice was examined using the new object recognition experiment and the open field test, and the hippocampal tissue damage of mice were evaluated by Nissl staining, ionized calcium binding adaptor molecule 1 (Iba1) immunofluorescence staining, and real-time fluorescence quantitative PCR for inflammatory factors and biological clock genes. Oxidative stress indicators in the hippocampus of mice were also detected by assay kit. ResultsAfter noise exposure during sleep period, the results of new object recognition experiment showed that the discrimination index of mice in the noise-exposed group was 0.06±0.04, which was significantly lower than that of the control group (0.65±0.13) (P

Clustering data streams has become a hot topic and has been extensively applied to many real-world applications. Compared with traditional clustering, data stream clustering is more challenging. Adaptive Resonance Theory (ART) is a powerful (online) clustering method, it can automatically adjust to learn both abstract and concrete information, and can respond to arbitrarily large non-stationary databases while having fewer parameters, low computational complexity, and less sensitivity to noise, but its limited feature representation hinders its application to complex data streams. In this paper, considering its advantages and disadvantages, we present its flexible extension for stream clustering, called fractional adaptive resonance theory (FRA-ART). FRA-ART enhances data representation by fractionally exponentiating input features using self-interactive basis functions (SIBFs) and incorporating feature interaction through cross-interactive basis functions (CIBFs) at the cost only of introducing an additionally adjustable fractional order. Both SIBFs and CIBFs can be precomputed using existing algorithms, making FRA-ART easily adaptable to any ART variant. Finally, comparative experiments on five data stream datasets, including artificial and real-world datasets, demonstrate FRA-ART’s superior robustness and comparable or improved performance in terms of accuracy, normalized mutual information, rand index, and cluster stability compared to ART and the state-of-the-art G-Stream algorithm.