Joint diseases like osteoarthritis usually are accompanied with inflammatory processes, in which pro-inflammatory cytokines mediate the generation of intracellular reactive oxygen species (ROS) and compromise survival of subchondral osteoblasts. Melatonin is capable of manipulating bone formation and osteogenic differentiation of mesenchymal stem cells (MSCs). The aim of this work was to investigate the anti-inflammatory effect of melatonin on MSC proliferation and osteogenic differentiation in the absence or presence of interleukin-1 beta (IL-1β), which was used to induce inflammation. Our data showed that melatonin improved cell viability and reduced ROS generation in MSCs in a dose-dependent manner. When exposed to 10 ng/mL IL-1β, various concentrations of melatonin resulted in significant reduction of ROS by 34.9% averagely. Luzindole as a melatonin receptor antagonist reversed the anti-oxidant effect of melatonin in MSCs with co-exposure to IL-1β. Real-time RT-PCR data suggested that melatonin treatment up-regulated the expression of CuZnSOD and MnSOD, while down-regulated the expression of Bax. To investigate the effect of melatonin on osteogenesis, MSCs were cultured in osteogenic differentiation medium supplemented with IL-1β, melatonin, or luzindole. After exposed to IL-1β for 21 days, 1 μm melatonin treatment significantly increased the levels of type I collagen, ALP, and osteocalcin, and 100 μm melatonin treatment yielded the highest level of osteopontin. Our study demonstrated that melatonin maintained MSC survival and promoted osteogenic differentiation in inflammatory environment induced by IL-1β, suggesting melatonin treatment could be a promising method for bone regenerative engineering in future studies.

Because of unparalleled advantages over other cells, stem cells are widely used in genetic diagnosis, drug delivery, and regenerative medicine. However, because the content of stem cells in the organism is far from satisfactory, it is of great significance of stem cells to in vitro proliferation and differentiation. However, many stem cell cultures have low expansion efficiency and stem cells lose their value-adding ability and differentiation ability after many generations of culture. To solve these problems, people hope to more realistically simulate the microenvironment in which stem cells grow in vivo. Cell scaffolds gradually evolve from 2D structures to 3D structures. The addition of growth factors influences cell behavior from internal biochemical conditions and the development of smart bioreactors gradually make progress to more precise regulate the external conditions of stem cell. In this paper, the key factors for constructing the microenvironment of stem cell growth were analyzed, and we reviewed the application of bioreactors and 3D scaffolds in stem cell cultivation. Finally, this paper indicated the development directions of stem cell culture in vitro.

No abstract

Objective: Sleep-stage scoring is important for sleep-quality evaluation and the diagnosis of related diseases. In this study, an automatic sleep-stage scoring method using photoplethysmographic (PPG) signals was proposed. Approach: To construct the classification model, we extracted 14 time-domain features, 17 frequency-domain features, and 20 pulse rate variability (PRV) features along with four SpO2 features from PPG signals. An artificial neural network classifier was used to integrate the results of ten binary support vector machine classifiers and realise sleep-stage classification. Leave-one-subject-out validation was applied to evaluate our proposed model. Main results: Thirty-one subjects were enrolled in the study, in which 21 subjects were with high sleep quality (sleep efficiencies ⩾85%). Our model achieved accuracies of 57% (κ = 0.39), 62% (κ = 0.41), and 78% (κ = 0.54) for the classification of five sleep stages (wake, N1, N2, N3, and rapid eye movement (REM) sleeps), four sleep stages (wake, light, deep, and REM sleeps) and three sleep stages (wake, non-rapid eye movement (NREM), and REM sleeps), respectively. For the remaining ten subjects with poor sleep quality, the results came to 55% (κ = 0.39), 62% (κ = 0.43), and 75% (κ = 0.52). Significance: The satisfactory performance of our proposed model reveals the potential of PPG signals for sleep-stage scoring, which may contribute to automatic sleep monitoring in the home environment.

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods.