ObjectiveTo investigate the differences in self-perception level of asthma control and the factors affecting the ability of self-perception in patients with bronchial asthma. MethodsA total of 322 patients who were diagnosed with bronchial asthma at the First Affiliated Hospital of Harbin Medical University from March 2013 to February 2015 were recruited in the study. The clinical data were collected, including the demographic characteristics of the patients, the Asthma Control Test (ACT) and results of routine blood test and pulmonary function test on the same day that they were required to fill out the ACT. Then they were followed up at the 1st, 3rd, 6th, 12th months, and required to fill out the ACT again, and underwent the blood routine test and lung function test. In addition, health education about asthma was offered regularly during these visits. ResultsA total of 226 patients met the inclusion criteria of the study. The patients with asthma had significant differences between self-perception control level and real symptoms control level (P<0.05). The patients who were 65 years old or older perceived their symptoms of bronchial asthma rather poorly (P=0.000). The patients who received senior high school or higher education had a higher ability of self-perceived asthma control (P=0.005). The patients with allergic rhinitis combined were less likely to correctly perceive their illness compared with those who did not suffered from allergic rhinitis, and the difference was statistically significant (P=0.001). In addition, for those diagnosed with allergic rhinitis combined with bronchial asthma, regular treatment also made difference--longer treatment for rhinitis means a higher ability of self-perceived asthma control (P=0.000). The health education did play a constructive role in helping patients correctly perceive their illness (P=0.000). There was no correlation between the absolute value of peripheral blood eosinophils and the accuracy of self-perceived asthma control. Nevertheless,there was a noticeable correlation between the ability of peripheral blood eosinophils of patients with asthma and acute attack of bronchial asthma (P=0.003),which was a meaningful finding in assessing the risk of future acute attack of bronchial asthma (P=0.469). ConclusionsThere is a significant difference between self-perception control level and symptom control level in patients with asthma. The self-perception control level of asthma patients who are elderly, the low degree of educational level, merged allergic rhinitis, and lack of health education are associated with lower accuracy of self-perception control level. The absolute value of peripheral blood eosinophils of the patients with asthma can be used to assess the risk of asthma acute attack in the future, but has no significant correlation with the accuracy of self-perception control level.
Evolutionary psychology holds such an opinion that negative situation may threaten survival, trigger avoidance motive and have poor effects on the human body function and the psychological quality. Both disgusted and sad situations can induce negative emotions. However, differences between the two situations on attention capture and emotion cognition during the emotion induction are still not well known. Typical disgusted and sad situation images were used in the present study to induce two negative emotions, and 15 young students (7 males and 8 females, aged 27±3) were recruited in the experiments. Electroencephalogram of 32 leads was recorded when the subjects were viewing situation images, and event-related potentials (ERP) of all leads were obtained for future analysis. Paired sample t tests were carried out on two ERP signals separately induced by disgusted and sad situation images to get time quantum with significant statistical differences between the two ERP signals. Root-mean-square deviations of two ERP signals during each time quantum were calculated and the brain topographic map based on root-mean-square deviations was drawn to display differences of two ERP signals in spatial. Results showed that differences of ERP signals induced by disgusted and sad situation images were mainly manifested in T1 (120-450 ms) early and T2 (800-1 000 ms) later. During the period of T1, the occipital lobe reflecting attention capture was activated by both disgusted and sad situation images, but the prefrontal cortex reflecting emotion sense was activated only by disgusted situation images. During the period of T2, the prefrontal cortex was activated by both disgusted and sad situation images. However, the parietal lobe was activated only by disgusted situation images, which showed stronger emotional perception. The research results would have enlightenment to deepen understanding of negative emotions and to explore deep cognitive neuroscience mechanisms of negative emotion induction.
Objective To investigate the level and influencing factors of perceived HIV stigma and discrimination among people living with HIV/AIDS (PLWHA). Methods By using convenience sampling method, 123 patients were recruited from the department of infectious diseases in a tertiary hospital in Chengdu from April to May in 2017. Berger HIV stigma scale was used to measure the level of perceived HIV stigma. Results The mean score of Berger HIV stigma scale was 113.72±17.890, which revealed a middle to upper level. Among the four subscales, the score of disclosure concerns (3.07±0.462) was the highest, while the score of negative self-image (2.70±0.494) was the lowest. Multiple regression analysis showed that gender and self-perceived health status were the influencing factors of perceived HIV stigma. Conclusions The level of perceived HIV stigma among PLWHA is from middle to upper level. Female gender and poor self-perceived health status are associated with a higher level of perceived HIV stigma. Individualized interventions are required in order to reduce the level of HIV stigma.
Objective To explore the impact of hospital staff’s risk perception on their emergency responses, and provide reference for future responses to public health emergencies. Methods Based on participatory observation and in-depth interviews, the staff of the First Affiliated Hospital of Guangzhou Medical University who participated in the prevention and control of the coronavirus disease 2019 from April to September 2020 were selected. The information on risk perception and emergency responses of hospital staff was collected. Results A total of 61 hospital staff were included. The positions of hospital staff were involved including hospital leading group, hospital office, medical department, logistics support department and outpatient isolation area. The interview results showed that both individual and organizational factors of hospital staff would affect the risk perception of hospital staff, thus affecting the emergency responses of hospital staff, mainly reflected in the psychological and behavioral aspects. Among them, their psychological reactions were manifested as more confidence, sensitivity, and sense of responsibility and mission; The behavior aspects was mainly reflected in the initiation time, execution ability, and standardization level of emergency responses actions. Conclusion Therefore, relevant departments should pay attention to the risk perception of hospital staff, improve the risk perception and emergency responses of hospital staff by influencing the individual and organizational factors of hospital staff, so as to respond more effectively to future public health emergencies and reduce the adverse impact of public health emergencies on the work of hospital staff.
The construction of brain functional network based on resting-state functional magnetic resonance imaging (fMRI) is an effective method to reveal the mechanism of human brain operation, but the common brain functional network generally contains a lot of noise, which leads to wrong analysis results. In this paper, the least absolute shrinkage and selection operator (LASSO) model in compressed sensing is used to reconstruct the brain functional network. This model uses the sparsity of L1-norm penalty term to avoid over fitting problem. Then, it is solved by the fast iterative shrinkage-thresholding algorithm (FISTA), which updates the variables through a shrinkage threshold operation in each iteration to converge to the global optimal solution. The experimental results show that compared with other methods, this method can improve the accuracy of noise reduction and reconstruction of brain functional network to more than 98%, effectively suppress the noise, and help to better explore the function of human brain in noisy environment.
As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.
In order to address the issues of spatial induction bias and lack of effective representation of global contextual information in colon polyp image segmentation, which lead to the loss of edge details and mis-segmentation of lesion areas, a colon polyp segmentation method that combines Transformer and cross-level phase-awareness is proposed. The method started from the perspective of global feature transformation, and used a hierarchical Transformer encoder to extract semantic information and spatial details of lesion areas layer by layer. Secondly, a phase-aware fusion module (PAFM) was designed to capture cross-level interaction information and effectively aggregate multi-scale contextual information. Thirdly, a position oriented functional module (POF) was designed to effectively integrate global and local feature information, fill in semantic gaps, and suppress background noise. Fourthly, a residual axis reverse attention module (RA-IA) was used to improve the network’s ability to recognize edge pixels. The proposed method was experimentally tested on public datasets CVC-ClinicDB, Kvasir, CVC-ColonDB, and EITS, with Dice similarity coefficients of 94.04%, 92.04%, 80.78%, and 76.80%, respectively, and mean intersection over union of 89.31%, 86.81%, 73.55%, and 69.10%, respectively. The simulation experimental results show that the proposed method can effectively segment colon polyp images, providing a new window for the diagnosis of colon polyps.
Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.
The medical magnetic resonance (MR) image reconstruction is one of the key technologies in the field of magnetic resonance imaging (MRI). The compressed sensing (CS) theory indicates that the image can be reconstructed accurately from highly undersampled measurements by using the sparsity of the MR image. However, how to improve the image reconstruction quality by employing more sparse priors of the image becomes a crucial issue for MRI. In this paper, an adaptive image reconstruction model fusing the double dictionary learning is proposed by exploiting sparse priors of the MR image in the image domain and transform domain. The double sparse model which combines synthesis sparse model with sparse transform model is applied to the CS MR image reconstruction according to the complementarity of synthesis sparse and sparse transform model. Making full use of the two sparse priors of the image under the synthesis dictionary and transform dictionary learning, the proposed model is tackled in stages by the iterative alternating minimization algorithm. The solution procedure needs to utilize the synthesis and transform K-singular value decomposition (K-SVD) algorithms. Compared with the existing MRI models, the experimental results show that the proposed model can more efficiently improve the quality of the image reconstruction, and has faster convergence speed and better robustness to noise.