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Sleep apnea syndrome (SAS) is regarded as one of the most common sleep-related breathing disorders, which can severely affect sleep quality. Since SAS is usually accompanied with the cyclical heart rate variation (HRV), many studies have been conducted on heart rate (HR) to identify it at an earlier stage. While most related work mainly based on clinical devices or signals (e.g., polysomnography (PSG), electrocardiography (ECG)), in this paper we focus on the ballistocardiographic (BCG) signal which is obtained in a non-invasive way. Moreover, as the precision and reliability of BCG signal are not so good as PSG or ECG, we propose a fine-grained feature extraction and analysis approach in SAS recognition. Our analysis takes both the basic HRV features and the breathing effort variation into consideration during different sleep stages rather than the whole night. The breathing effort refers to the mechanical interaction between respiration and BCG signal when SAS events occur, which is independent from autonomous nervous system (ANS) modulations. Specifically, a novel method named STC-Min is presented to extract the breathing effort variation feature. The basic HRV features depict the ANS modulations on HR and Sample Entropy and Detrended Fluctuation Analysis are applied for the evaluations. All the extracted features along with personal factors are fed into the knowledge-based support vector machine (KSVM) classification model, and the prior knowledge is based on dataset distribution and domain knowledge. Experimental results on 42 subjects in 3 nights validate the effectiveness of the methods and features in identifying SAS (90.46% precision rate and 88.89% recall rate).

Radiation is a form of energy derived from a source that is propagated through material in space. It consists of ionizing radiation or nonionizing radiation. Ionizing radiation is a feature of the environment and an important tool in medical treatment, but it can cause serious damage to organisms. A number of protective measures and standards of protection have been proposed to protect against radiation. There is also a need for biomarkers to rapidly assess individual doses of radiation, which can not only estimate the dose of radiation but also determine its effects on health. Proteomics, genomics, metabolomics, and lipidomics have been widely used in the search for such biomarkers. These topics are discussed in depth in this review.

This study aimed to evaluate the biological effects of gamma irradiation on zebrafish embryos. Different doses of gamma rays (0.01, 0.05, 0.1, 0.5 and 1 Gy) were used to irradiate zebrafish embryos at three developmental stages (stage 1, 6 h post-fertilization (hpf); stage 2, 12 hpf; stage three, 24 hpf), respectively. The survival, malformation and hatching rates of the zebrafish embryos were measured at the morphological endpoint of 96 hpf. The activities of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), glutathione peroxidase (GPx) and glutathione S-transferase (GST) were assayed. Morphology analysis showed that gamma irradiation inhibited hatching and induced developmental toxicity in a dose-dependent manner. Interestingly, after irradiation the malformation rate changed not only in a dose-dependent manner but also in a developmental stage-dependent manner, indicating that the zebrafish embryos at stage 1 were more sensitive to gamma rays than those at other stages. Biochemical analysis showed that gamma irradiation modulated the activities of antioxidant enzymes in a dose-dependent manner. A linear relationship was found between GPx activity and irradiation dose in 0.1-1 Gy group, and GPx was a suitable biomarker for gamma irradiation in the dose range from 0.1 to 1 Gy. Furthermore, the activities of SOD, CAT, GR and GPx of the zebrafish embryos at stage 3 were found to be much higher than those at other stages, indicating that the zebrafish embryos at stage 3 had a greater ability to protect against gamma rays than those at other stages, and thus the activities of antioxidant enzymes changed in a developmental stage-dependent manner.

BackgroundSleep Apnea Syndrome (SAS) is a common sleep-related breathing disorder, which affects about 4-7% males and 2-4% females all around the world. Different approaches have been adopted to diagnose SAS and measure its severity, including the gold standard Polysomnography (PSG) in sleep study field as well as several alternative techniques such as single-channel ECG, pulse oximeter and so on. However, many shortcomings still limit their generalization in home environment. In this study, we aim to propose an efficient approach to automatically assess the severity of sleep apnea syndrome based on the ballistocardiogram (BCG) signal, which is non-intrusive and suitable for in home environment.MethodsWe develop an unobtrusive sleep monitoring system to capture the BCG signals, based on which we put forward a three-stage sleep apnea syndrome severity assessment framework, i.e., data preprocessing, sleep-related breathing events (SBEs) detection, and sleep apnea syndrome severity evaluation. First, in the data preprocessing stage, to overcome the limits of BCG signals (e.g., low precision and reliability), we utilize wavelet decomposition to obtain the outline information of heartbeats, and apply a RR correction algorithm to handle missing or spurious RR intervals. Afterwards, in the event detection stage, we propose an automatic sleep-related breathing event detection algorithm named Physio_ICSS based on the iterative cumulative sums of squares (i.e., the ICSS algorithm), which is originally used to detect structural breakpoints in a time series. In particular, to efficiently detect sleep-related breathing events in the obtained time series of RR intervals, the proposed algorithm not only explores the practical factors of sleep-related breathing events (e.g., the limit of lasting duration and possible occurrence sleep stages) but also overcomes the event segmentation issue (e.g., equal-length segmentation method might divide one sleep-related breathing event into different fragments and lead to incorrect results) of existing approaches. Finally, by fusing features extracted from multiple domains, we can identify sleep-related breathing events and assess the severity level of sleep apnea syndrome effectively.ConclusionsExperimental results on 136 individuals of different sleep apnea syndrome severities validate the effectiveness of the proposed framework, with the accuracy of 94.12% (128/136).