Application of electromagnetic disturbance technology in predicting ventriculoperitoneal shunt dependency after aneurysm-associated subarachnoid hemorrhage.

BACKGROUND: The aim of this study was to evaluate the predictive power of electromagnetic disturbance technology in patients with hydrocephalus after subarachnoid hemorrhage. METHODS: This prospective, observational cohort study was conducted at The First Affiliated Hospital of Zhengzhou University and Nanfang Hospital. A total of 155 patients with subarachnoid hemorrhage (SAH) were enrolled in this study. Disturbance coefficients were recorded using a continuous sinusoidal signal in real time after SAH. The patients were divided into two groups: hydrocephalus group (patients who underwent shunt insertion within a month after SAH) and non-hydrocephalus group (patients without need for a ventriculoperitoneal shunt). We used SPSS to draw a ROC Curve to assess the ability of disturbance coefficients to predict the probability of hydrocephalus. RESULTS: Hydrocephalus occurred in 37 patients after SAH. The average disturbance coefficient of patients with hydrocephalus decreased by 25.14±9.78, and the disturbance coefficient of patients with no hydrocephalus decreased by 6.58±10.10 (one aspect of the present invention is a system of non-invasively monitoring hydrocephalus, cerebral edema, and intracranial bleeding comprising of a source emitting electromagnetic waves to brain tissue, a detector detecting said wave that propagates through said tissue, a signal conditioning unit amplifying and filtering said wave, a quadrature detector estimating magnitude and phases of said wave, and a parameter estimator calculating the complex wave number, relative attenuation coefficient (RAC), relative phase shift (RPS), wave speed change (WSC), and travel-time difference (TTD) of said brain, and assessing status of hydrocephalus and cerebral edema). The difference was statistically significant (t=9.825, P<0.001). The decrease in disturbance coefficient can be used to predict the occurrence of hydrocephalus, and if the disturbance coefficient decreases by more than 15.5 (sensitivity, 92.37%; specificity, 86.49%), it can be used to indicate the occurrence of hydrocephalus. CONCLUSIONS: The disturbance coefficient can predict the occurrence of hydrocephalus. The greater decline of the disturbance coefficient, the greater probability of occurrence of intracranial hydrocephalus. Hydrocephalus can be early detected. However, the CT scan is necessary to confirm the occurrence of hydrocephalus. Early diagnosis and early treatment may improve the prognosis of patients with hydrocephalus after subarachnoid hemorrhage.

Resting-state functional connectivity of the sensory/somatomotor network associated with sleep quality: evidence from 202 young male samples.

Previous neuroimaging studies have demonstrated that sleep is associated with brain functional changes in some specific brain regions. However, few studies have examined the relationship between all possible functional connectivities (FCs) within the sensory/somatomotor network (SSN) and the sleep quality of young male samples. The SSN consists of two motor cortices and is known to play a critical role in sleep. Poor sleep quality may be associated with increased sensory/somatomotor functional connectivity during rest. Hence, 202 young male participants underwent a resting-state functional magnetic resonance imaging (fMRI) scan and completed the Pittsburgh Sleep Quality Index (PSQI). Results indicated that increased functional connectivity within the SSN was associated with poor sleep quality. Specifically, the total PSQI score was positively correlated with the increased functional connectivity of the left paracentral lobule (PCL), bilateral precentral gyrus (PreCG), supplementary motor area (SMA) and bilateral postcentral gyrus (PoCG). Additionally, our findings also exhibited that (a) the subjective sleep quality factor of PSQI was positively correlated with FC between the bilateral PoCG and the bilateral PCL as well as between the left PreCG and the right SMA; (b) the sleep latency factor of PSQI was positively correlated with FC between the left PoCG and the right precuneus (PCUN); (c) the sleep disturbances factor of PSQI was positively correlated with FC between the left PCL and the right PoCG, and (d) the daytime dysfunction factor of PSQI was positively correlated with FC between the bilateral PoCG and the left PCL as well as between the bilateral PreCG and the SMA. In short, our findings can be comprehensively understood as neural mechanisms of intrinsic SSN connectivity are associated with sleep quality of man. Meanwhile, it may expand our knowledge and provide new insight into a deeper understanding of the neurobiological mechanisms of sleep or sleep problems.

Stress of COVID-19, Anxiety, Economic Insecurity, and Mental Health Literacy: A Structural Equation Modeling Approach.

The COVID-19 pandemic is currently a global health threat attributed to negatively affecting the mental health and well-being of people globally. The purpose of the current study is to examine the mediating roles of economic insecurity and mental health literacy in the relationship between stress about COVID-19 and anxiety. Results from the current study using a large sample of Chinese college students (N = 1,334) showed that stress of COVID-19 was positively associated with economic insecurity and anxiety while negatively associated with mental health literacy, which in turn was negatively associated with anxiety. These results elucidate our understanding of the role of mediators in stress about COVID-19 and anxiety. The findings are useful in terms of providing evidence for tailoring interventions and implementing preventative approaches to mitigate anxiety due to stress of COVID-19. Based on the present findings and within the context of COVID-19, the potential utility of promoting MHL to reduce the psychopathological consequences of COVID-19 is discussed.

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO.

Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.

A Hybrid Fault Diagnosis Approach for Rotating Machinery with the Fusion of Entropy-Based Feature Extraction and SVM Optimized by a Chaos Quantum Sine Cosine Algorithm.

As crucial equipment during industrial manufacture, the health status of rotating machinery affects the production efficiency and device safety. Hence, it is of great significance to diagnose rotating machinery faults, which can contribute to guarantee the running stability and plan for maintenance, thus promoting production efficiency and economic benefits. For this purpose, a hybrid fault diagnosis model with entropy-based feature extraction and SVM optimized by a chaos quantum sine cosine algorithm (CQSCA) is developed in this research. Firstly, the state-of-the-art variational mode decomposition (VMD) is utilized to decompose the vibration signals into sets of components, during which process the preset parameter K is confirmed with the central frequency observation method. Subsequently, the permutation entropy values of all components are computed to constitute the feature vectors corresponding to different kind of signals. Later, the newly developed sine cosine algorithm (SCA) is employed and improved with chaotic initialization by a Duffing system and quantum technique to optimize the support vector machine (SVM) model, with which the fault pattern is recognized. Additionally, the availability of the optimized SVM with CQSCA was revealed in pattern recognition experiments. Finally, the proposed hybrid fault diagnosis approach was employed for engineering applications as well as contrastive analysis. The comparative results show that the proposed method achieved the best training accuracy 99.5% and best testing accuracy 97.89%. Furthermore, it can be concluded from the boxplots of different diagnosis methods that the stability and precision of the proposed method is superior to those of others.

High temperature induces apoptosis and oxidative stress in pufferfish (Takifugu obscurus) blood cells.

Water temperature is an important environmental factor in aquaculture farming that affects the survival and growth of organisms. The change in culture water temperature may not only modify various chemical and biological processes but also affect the status of fish populations. In previous studies, high temperature induced apoptosis and oxidative stress. However, the precise mechanism and the pathways that are activated in fish are still unclear. In the present study, we investigated the effects of high temperature (34°C) on the induction of apoptosis and oxidative stress in pufferfish (Takifugu obscurus) blood cells. The data showed that high temperature exposure increased oxygen species (ROS), cytoplasmic free-Ca(2+) concentration and cell apoptosis. To test the apoptotic pathway, the expression pattern of some key apoptotic related genes including P53, Bax, caspase 9 and caspase 3 were examined. The results showed that acute high temperature stress induced up-regulation of these genes, suggesting that the p53-Bax pathway and the caspase-dependent apoptotic pathway could be involved in apoptosis induced by high temperature stress. Furthermore, the gene expression of antioxidant enzymes (Cu/Zn-SOD, Mn-SOD, CAT, GPx, and GR) and heat shock proteins (HSP90 and HSP70) in the blood cells were induced by high temperature stress. Taken together, our results showed that high temperature-induced oxidative stress may cause pufferfish blood cells apoptosis, and cooperatively activated p53-Bax and caspase-dependent apoptotic pathway.