An overview of hyperbaric oxygen preconditioning against ischemic stroke

Ischemic stroke (IS) has become the second leading cause of morbidity and mortality worldwide, and the prevention of IS should be given high priority. Recent studies have indicated that hyperbaric oxygen preconditioning (HBO-PC) may be a protective nonpharmacological method, but its underlying mechanisms remain poorly defined. This study comprehensively reviewed the pathophysiology of IS and revealed the underlying mechanism of HBO-PC in protection against IS. The preventive effects of HBO-PC against IS may include inducing antioxidant, anti-inflammation, and anti-apoptosis capacity; activating autophagy and immune responses; upregulating heat shock proteins, hypoxia-inducible factor-1, and erythropoietin; and exerting protective effects upon the blood-brain barrier. In addition, HBO-PC may be considered a safe and effective method to prevent IS in combination with stem cell therapy. Although the benefits of HBO-PC on IS have been widely observed in recent research, the implementation of this technique is still controversial due to regimen differences. Transferring the results to clinical application needs to be taken carefully, and screening for the optimal regimen would be a daunting task. In addition, whether we should prescribe an individualized preconditioning regimen to each stroke patient needs further exploration

Stigmatizing attitudes toward mental illness among caregivers of patients with mental disorders in China

ObjectiveThis study aimed to investigate stigmatizing attitudes toward depression, schizophrenia, and general anxiety disorder (GAD) among caregivers of patients with mental disorders in China.MethodsA cross-sectional study was conducted among 607 caregivers in China, using vignettes that described three mental illnesses. Data on the caregivers’ attitudes and other people’s attitudes toward individuals with mental disorders and their willingness to come in contact with people with mental disorders were collected.ResultsIn the three vignettes, caregivers agreed that positive outcomes outnumbered negative outcomes. The top two statements endorsing the stigma were “the person could snap out of the problem” and “people with this problem are dangerous.” In the section for perceived stigma, caregivers in the GAD vignette agreed that most people believed this problem is not a real medical illness, compared to schizophrenia. The rates of the statement endorsing unpredictability were significantly different in the schizophrenia (57.2%) and depression (45.5%) vignette, in comparison to the GAD (45.6%) vignette. For personal stigma, the caregivers tended to avoid people described in the depression vignette more often than in the GAD vignette. The caregivers were most unwilling to let the person described in the vignettes marry into their family, especially in the schizophrenia vignette.ConclusionDespite the stigma and desire for social distance associated with schizophrenia, depression, and GAD, caregivers often expect positive outcomes. Actions should be taken to improve caregivers’ knowledge about mental health and reduce the stigma

Development and validation of a method for human papillomavirus genotyping based on molecular beacon probes.

We describe a new assaying system for the detection and genotyping of human papillomavirus (HPV) based on linear-after-the-exponential-PCR(LATE-PCR) and melting curve analysis. The 23 most prevalent HPV strains (types 6, 11, 16, 18, 31, 33, 35, 39, 42, 45, 51, 52, 53, 56, 58, 59, 66, 68, 70, 73, 81, 82, and 83) are assayed in two sealed reaction tubes within 2 h. Good sensitivity and specificity was evaluated by testing cloned HPV DNA and clinical samples. The detection limit was 5-500 copies/reaction depending on the genotype. No cross-reactivity was observed with the other HPV types that are not covered by our method or pathogens tested which were commonly found in female genital tract. When compared with the HPV GenoArray Diagnostic kit, the results from 1104 clinical samples suggest good overall agreement between the two methods,(98.37%, 95% CI: 97.44%-98.97%) and the kappa value was 0.954. Overall, this new HPV genotyping assay system presents a simple, rapid, universally applicable, sensitive, and highly specific detection methodology that should be useful for HPV detection and genotyping, therefore, is potentially of great value in clinical application

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy

Due to the complicated engineering operation of the check valve in a high−pressure diaphragm pump, its vibration signal tends to show non−stationary and non−linear characteristics. These leads to difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi−scale weighted permutation entropy (MWPE) is based on extracting multi−scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single−scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non−parallel classification hyperplanes in the equipment state space to improve the model’s applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%

Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis

Construction of cellulose-degrading microbial consortium and evaluation of their ability to degrade spent mushroom substrate

IntroductionSpent mushroom substrate (SMS) is a solid waste in agricultural production that contains abundant lignocellulosic fibers. The indiscriminate disposal of SMS will lead to significant resource waste and pollution of the surrounding environment.The isolation and screening of microorganisms with high cellulase degradation capacity is the key to improving SMS utilization.MethodsThe cellulose-degrading microbial consortiums were constructed through antagonism and enzyme activity test. The effect of microbial consortiums on lignocellulose degradation was systematically evaluated by SMS liquid fermentation experiments.ResultsIn this study, four strains of cellulose-degrading bacteria were screened, and F16, F, and F7 were identified as B. amyloliquefaciens, PX1 identified as B. velezensis. At the same time, two groups of cellulose efficient degrading microbial consortiums (PX1 + F7 and F16 + F) were successfully constructed. When SMS was used as the sole carbon source, their carboxymethyl cellulase (CMCase) activities were 225.16 and 156.63 U/mL, respectively, and the filter paper enzyme (FPase) activities were 1.91 and 1.64 U/mL, respectively. PX1 + F7 had the highest degradation rate of hemicellulose and lignin, reaching 52.96% and 52.13%, respectively, and the degradation rate of F16 + F was as high as 56.30%. Field emission scanning electron microscopy (FESEM) analysis showed that the surface microstructure of SMS changed significantly after microbial consortiums treatment, and the change of absorption peak in Fourier transform infrared spectroscopy (FTIR) and the increase of crystallinity in X-ray diffraction (XRD) confirmed that the microbial consortiums had an actual degradation effect on SMS. The results showed that PX1 + F7 and F16 + F could effectively secrete cellulase and degrade cellulose, which had practical significance for the degradation of SMS.DiscussionIn this study, the constructed PX1 + F7 and F16 + F strains can effectively secrete cellulase and degrade cellulose, which holds practical significance in the degradation of SMS. The results can provide technical support for treating high-cellulose solid waste and for the comprehensive utilization of biomass resources

Hollow carbon cage with nanocapsules of graphitic shell/nickel core as an anode material for high rate lithium ion batteries

Hollow carbon cages (HCCs) with high electrical conductivity were developed by a spray drying-catalytic graphitization-activation process as anode materials for high power lithium ion batteries. The HCC anode has a high reversible capacity of 1135 mA h g at 50 mA g , excellent cyclic stability without capacity degradation over 100 cycles at a current density of 500 mA g , and an ultrafast charge/discharge rate of less than 2 min with a high capacity of 163 mA h g , which are attributed to the unique structure of the hollow cores, the high porosity, and electrically conductive nickel nanoparticles and the graphitic layers produced by the carbothermal reduction of nickel hydroxide and the low-temperature catalytic graphitization

HCV and HIV infection among heroin addicts in methadone maintenance treatment (MMT) and not in MMT in Changsha and Wuhan, China.

OBJECTIVE: To compare HCV and HIV infection among heroin addicts in MMT and not in MMT in two large cities in central China. METHODS: A total of 541 heroin addicts were recruited from MMT clinics and voluntary detoxification centers in Changsha and Wuhan, China. Structured questionnaires collected data on their socio-demographics, clinical status, risk behaviors, and their knowledge of HIV. Their HIV serostatus and Hepatitis C virus (HCV) serostatus were determined by testing antibodies in blood serum. RESULTS: We observed a higher prevalence of HCV infection among MMT heroin addicts (82.3%) than that in the non-MMT group (50.6%). However, our findings indicated that the heroin addicts in MMT had less drug or sexual HIV/HCV risk behaviors and more knowledge about HIV than non-MMT addicts. The heroin addicts in MMT had a significantly higher percentage of individuals who always used condoms (44.9%) compared with patients in the non-MMT group (14.6%, p = 0.039), and they had more knowledge about HIV than non-MMT individuals (p<.001). The percentage of HIV-positive addicts in the MMT group (0.7%) and non-MMT group (0.8%) were almost same. CONCLUSION: Our study indicated that the rate of HCV infection among heroin addicts among MMT or non-MMT settings in central China is very high. The non-MMT heroin addicts have higher risk of becoming infected with HCV in the future, while at present they have lower rates of HCV infection than MMT heroin addicts. Although rates of HIV infection among MMT and non-MMT heroin addicts are low now, they are all at great risk of becoming infected with HIV in the future, especially for non-MMT heroin addicts. We should use the MMT sites as a platform to improve the control of HCV and HIV infection in heroin addicts

Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA&ndash;SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority