Objective To investigate the effects of selective serotonin reuptake inhibitors (SSRIs)on the muscle tone during rapid eye movement (REM) sleep in depressive patients. Methods Twenty-one depressive patients in SSRIs treatment were recruited from the polysomnography database. And 21 depressive patients without any pharmacological treatment and 21 normal controls were recruited at the same time. According to Lapierre & Montplaisir criteria, the tonic and phasic electromyography (EMG) in REM sleep was reevaluated. Results Compared to no treatment group and normal control group, SSRIs group experienced more tonic EMG [ ( 10.1 ± 9.4) % vs. (3.3 ± 3.7) % & (2.8 ± 3.4) %, P ＜ 0.001 ] and phasic EMG [submental: (11.5 ± 6.8)% vs. (6.3 ± 4.1)% & (5.0 ± 3.7)%, P＜0.05, anterior tibialis: (18.8 ± 13.2)% vs. (10.3 ± 7.2)% & (9.8 ± 5.5)%, P＜0.05] in REM sleep. In SSRIs group, both tonic and phasic EMG in REM sleep correlated with REM latency positively (γ=4.475, γ=0.397 ,γ=0.402) and correlated with percentage of REM sleep negatively (γ= -0.353,γ=-0. 511 ,γ= -0.463 ). Conclusion SSRIs could increase EMG activity in REM sleep, mainly according to the mechanism of increasing serotonin.
Key words:
Serotonin uptake inhibitors; Depressive disorder; Sleep, REM; Electromyography
Suicidal ideation is a desire, thought, or conception that is closely associated with suicide, which is an important risk factor for suicidal behavior. Negative life events may impact college students’ suicidal ideation. According to the suicide susceptibility-stress model, the interaction between susceptibility factors and stressors may influence college students’ suicidal ideation. The present study investigated the role of entity theory and meaning in life in the influence of negative life events on suicidal ideation among college students. A nationwide questionnaire survey was conducted among 938 college students. The Beck Scale for Suicide Ideation, the Implicit Personality Theory Questionnaire, the Adolescent Life Events Scale, and the Meaning in Life Questionnaire were used. The results showed that negative life events were positively correlated with suicidal ideation, entity theory played a mediating role, and meaning in life moderated the mediation of entity theory. Finally, meaning in life and entity theory may bring some benefits to college students; that is, when faced with negative life events, meaning in life and entity theory may attenuate students’ suicidal ideation.
Ton construct eukaryotic plasmid expressing Hsf4B and to investigate Hsf4b is phosphorylated by MAP kinase P38:The total RNA of human heart tissues were prepared. Hsf4b cDNA were then synthesized with RT-PCR. The PCR products were digested with Kpn I and EcoR I and subcloned into pcDNA3.0, pcDNA-Flag-Hsf4b was transfected into HEK293T cells. The expression of Hsf4b was testified with Western blotting. The interaction between Hsf4b and P38 was assayed by immunoprecipitation. In vivo pull down GST demonstrated that Hsf4B (196-493) could interact with P38, P38 phosphorylation of Hsf4b were testified with Kinase assay.We subcloned the human cDNA of Hsf4b into eukaryotic expression vectors pcDNA3 and PEBG, and Hsf4b was overexpressed in HEK293T cells. Further studies demonstrated that Hsf4b could interact with and phosphorylated by MAP kinase P38.Hsf4b could interact with and phosphorylated by MAP kinase P38. Our results will provide more evidence for understanding the signal regulation of Hsf4b transcription activity during lens development.
Subjective cognitive decline (SCD) may be the first symptomatic manifestation of Alzheimer's disease, but information on its health correlates is still sparse in Chinese older adults. This study aimed to estimate SCD symptoms and its association with socio-demographic characteristics, common chronic diseases among southern Chinese older adults.Participants aged 60 years and older from 7 communities and 2 nursing homes in Guangzhou were recruited and interviewed with standardized assessment tools. Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to measure poor sleep quality, depression symptoms and anxiety symptoms. The SCD symptoms were measured by SCD questionnaire 9 (SCD-Q9) which ranged from 0 to 9 points, with a higher score indicating increased severity of the SCD. Participants were divided into low score group (SCD-Q9 score ≤ 3) and higher score group (SCD-Q9 score > 3). Chi-square tests and multivariate logistic regression analysis were used for exploring the influences of different characteristics of socio-demographic and lifestyle factors on SCD symptoms. Univariate and multivariate logistic regression analysis were applied to explore the association between SCD symptoms with common chronic diseases.A total of 688 participants were included in our analysis with a mean age of 73.79 (SD = 8.28, range: 60-101), while 62.4% of the participants were females. The mean score of the SCD-Q9 was 3.81 ± 2.42 in the whole sample. A total of 286 participants (41.6%) were defined as the low score group (≤3 points), while 402 participants (58.4%) were the high score group (> 3 points). Multivariate logistic regression analysis revealed that female (OR = 1.99, 95%CI: 1.35-2.93), primary or lower education level (OR = 2.58, 95%CI: 1.38-4.83), nursing home (OR = 1.90, 95%CI: 1.18-3.05), napping habits (OR = 1.59, 95%CI: 1.06-2.40), urolithiasis (OR = 2.72, 95%CI: 1.15-6.40), gout (OR = 2.12, 95%CI: 1.14-3.93), poor sleep quality (OR = 1.93, 95%CI: 1.38-2.71), depression symptoms (OR = 3.01, 95%CI: 1.70-5.34) and anxiety symptoms (OR = 3.11, 95%CI: 1.29-7.46) were independent positive related to high SCD-Q9 score. On the other hand, tea-drinking habits (OR = 0.64, 95%CI: 0.45-0.92), current smoking (OR = 0.46, 95%CI: 0.24-0.90) were independent negative related to high SCD-Q9 score.Worse SCD symptoms were closely related to common chronic diseases and socio-demographic characteristics. Disease managers should pay more attention to those factors to early intervention and management for SCD symptoms among southern Chinese older adults.
The automatic identification of epileptic EEG signals is significant in both relieving heavy workload of visual inspection of EEG recordings and treatment of epilepsy. This paper presents a novel method based on the theory of sparse representation to identify epileptic EEGs. At first, the raw EEG epochs are preprocessed via Gaussian low pass filtering and differential operation. Then, in the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l 1 -minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized as the class that yields the minimum represented residual. So unlike the conventional EEG classification methods, the choice and calculation of EEG features are avoided in the proposed framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The satisfactory recognition accuracy of 98.63% for ictal and interictal EEG classification and for ictal and normal EEG classification has been achieved by the kernel SRC. In addition, the fast speed makes the kernel SRC suit for the real-time seizure monitoring application in the near future.
Objective To investigate the influence of selective serotonin reuptake inhibitors(SSRIs) on sleep architecture of depressive patients and the relationship between the sleep architecture and the clinical effect of SSRIs. Methods Totally 26 depressive patients with SSRIs treated in recent 2 weeks were recruited as treatment group from the polysomnography database,and 24 age-and sex-matched depressive patients without medication in recent 3 months as un-treatment group were recruited at the same time.The Hamilton Rating Scale for Depression-24 items(HAMD-24) was used for assess the depression conditions before and after the treatment,and Logistic regression analysis was taken to determine the relationship between the sleep architecture and the clinical effect. Results There were no statistically significant differences between the two groups in sleep duration,sleep latency,percentages of stage II and III sleep(P0.05).After SSRIs treatment,the latency of rapid eye movement(REM) in the treatment group was shortened as compared with the non-treatment group((77±30) min vs.(146±64) min)(P=0.000) and reached into normal range,and percentage of stage I sleep((14±5)% vs.(18±8) %)and arousal index(AI)((14±5)times/h vs.(18±6)times/h) decreased(P0.05),but the AI still was higher than normal(5 times/h).The difference in HAMD-24 score between the two groups was statistically significant((17±6) vs.(27±10),P=0.007).Logistic regression showed that the clinical effect was associated with shorten REM latency(OR=0.627,95%CI(0.517,0.923)) and decreased AI(OR=0.839,95%CI(0.721,0.987)). Conclusion The abnormal REM sleep may be the core of abnormal sleep rhythm in depressive disorder,which supports the hypothesis of advanced sleep phase.
Abstract Background: Sleep is vital for maintaining individual’s physical and mental health. Prior studies have reported close relationships between sleep duration and chronic diseases. However, in China, the prevalence of irregular sleep duration and the associations between sleep duration and chronic conditions still merit studying in Guangdong province. This study aimed at examining the relationship between sleep duration and multiple dimensions of sociodemographic characteristics, mental health and chronic diseases in Guangdong province in China, with a large population-based data of individuals aged from 18 to 85 years old conducted in Guangdong province. Methods: Multistage stratified cluster sampling was applied for this study. 13,768 participants from Guangdong province were interviewed with standardized assessment tools. Basic socio-demographic information, mental health and chronic diseases information were collected. Self-reported sleep duration was classified as three types: short (<7h), normative (7-8h) and long (>8h). Results: The mean sleep duration was 6.75±1.11h. Short sleepers had a higher prevalence of chronic diseases, including anemia (6.2%, P=0.024), gout (2.8%, P=0.010), hyperlipidemia (3.9%, P=0.003) and low back pain (5.6%, P=0.020) than other types of sleeper. Multinomial logistic regression analysis revealed that short sleepers were more likely to have low income level, have depressive symptoms, be ex- or current drinkers and be overweight. Anemia, hyperlipidemia and low back pain were all risk factors for short sleep, while malignant tumor was risky for long sleep. Conclusions: Low income level, drinking status, being overweight, and chronic conditions may be associated with abnormal sleep duration in Guangdong province general population. Short sleepers have a higher risk of suffering from anemia, hyperlipidemia, and low back pain, while long sleepers are more likely to have malignant tumor. Health professionals should value the sleep patterns in general health care and attach importance to conduct further epidemiologic surveys to explore the relationship between sleep duration and health.
Automatic seizure detection plays an important role in long-term epilepsy monitoring, and seizure detection algorithms have been intensively investigated over the years. This paper proposes an algorithm for seizure detection using lacunarity and Bayesian linear discriminant analysis (BLDA) in long-term intracranial EEG. Lacunarity is a measure of heterogeneity for a fractal. The proposed method first conducts wavelet decomposition on EEGs with five scales, and selects the wavelet coefficients at scale 3, 4, and 5 for subsequent processing. Effective features including lacunarity and fluctuation index are extracted from the selected three scales, and then sent into the BLDA for training and classification. Finally, postprocessing which includes smoothing, threshold judgment, multichannels integration, and collar technique is applied to obtain high sensitivity and low false detection rate. The proposed algorithm is evaluated on 289.14 h intracranial EEG data from 21-patient Freiburg dataset and yields a sensitivity of 96.25% and a false detection rate of 0.13/h with a mean delay time of 13.8 s.
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