Chen Dong

ObjectiveThis study aims to investigate anxiety and its influencing factors in patients with drug-induced liver injury (DILI).Materials and MethodsNinety-four patients with DILI were enrolled and evaluated with a self-rating anxiety scale (SAS). According to the anxiety score, they were divided into four groups: the non-anxiety, mild anxiety, moderate anxiety, or severe anxiety groups, and the scores were analyzed based on demographic and biochemical indicators.ResultsOf the 94 patients with DILI, 63 did not have anxiety and 31 had anxiety (32.9%), of which 27 had mild, 3 had moderate, and 1 had severe anxiety. There were no statistically significant differences in gender, age, occupation, and level of education between the groups (F= 1.42,H= 2.361,H= 6.751,H= 1.796, andP> 0.05); anxiety score and degree of anxiety between the types of drugs that led to the liver injury (H= 0.812,H= 1.712, andP> 0.05); anxiety score between the different degrees of liver injury (H= 2.836,H= 4.957,P> 0.05); or length of hospital stay or prognosis between the degrees of anxiety (F= 1.487,H= 0.761,P> 0.05). However, there were statistically significant differences in the degree of anxiety between different degree and types of liver injury (H= 7.981,H= 8.208,P< 0.05).ConclusionPatients with DILI may have anxiety, especially mild anxiety. The occurrence of anxiety in patients with DILI is not related to gender, age, occupation, or level of education but may be related to the degree and type of liver injury. Anxiety has no impact on the length of stay in hospital or the prognosis of the DILI. These findings may contribute to the development of management strategies for patients with DILI.

As the development of artificial intelligence technology, the deep-learning -based Virtual Reality technology, and DL technology are applied in human-computer interaction, and their impacts on modern film and TV works production and audience psychology are analyzed. In film and TV production, audiences have a higher demand for the verisimilitude and immersion of the works, especially in film production. Based on this, a 2D image recognition system for human body motions and a 3D recognition system for human body motions based on the convolutional neural network algorithm of DL are proposed, and an analysis framework is established. The proposed systems are simulated on practical and professional datasets, respectively. The results show that the algorithm's computing performance in 2D image recognition is 7–9 times higher than that of the Open Pose method. It runs at 44.3 ms in 3D motion recognition, significantly lower than the Open Pose method's 794.5 and 138.7 ms. Although the detection accuracy has dropped by 2.4%, it is more efficient and convenient without limitations of scenarios in practical applications. The AI-based VR and DL enriches and expands the role and application of computer graphics in film and TV production using HCI technology theoretically and practically.

ObjectiveThe psychological condition of healthcare workers since the COVID-19 pandemic has attracted the attention of many studies. However, few have reported on psychosocial problems of primary healthcare workers in the COVID-19 pandemic. This study aimed to examine the mediating roles of social support and resilience in COVID-19-related work stress and symptoms of anxiety and depression.MethodsA total of 840 primary healthcare workers in 17 community health centers in Guangzhou, China, were recruited from May to July 2021. Data on demographic characteristics, COVID-19-related work stress, social support, resilience, anxiety and depression were collected. A structural equation model was used for mediation analysis.ResultsMore than half of participants reported mild or more severe symptoms of anxiety and depression. Social support and resilience mediate the association between COVID-19-related work stress and symptoms of anxiety and depression, respectively. Furthermore, the association between work stress and symptoms of anxiety and depression was also mediated by an accumulation of social support and resilience. The indirect effect of COVID-19-related work stress on anxiety and depression through resilience was much greater than other indirect effects.ConclusionAnxiety and depression were prevalent among primary healthcare workers. This study highlights the psychological impact of the COVID-19-related psychosocial work environment on primary healthcare workers. There is an urgent need to improve working conditions for primary healthcare workers in the COVID-19 and to implement intervention strategies aimed at increasing individual resilience alongside the establishment of external supportive work environments.

Aiming at the problem of low pedestrian target detection accuracy, we propose a detection algorithm based on optimized Mask R-CNN which uses the latest research results of deep learning to improve the accuracy and speed of detection results. Due to the influence of illumination, posture, background, and other factors on the human target in the natural scene image, the complexity of target information is high. SKNet is used to replace the part of the convolution module in the depth residual network model in order to extract features better so that the model can adaptively select the best convolution kernel during training. In addition, according to the statistical law, the length-width ratio of the anchor box is modified to make it more accord with the natural characteristics of the pedestrian target. Finally, a pedestrian target dataset is established by selecting suitable pedestrian images in the COCO dataset and expanded by adding noise and median filtering. The optimized algorithm is compared with the original algorithm and several other mainstream target detection algorithms on the dataset; the experimental results show that the detection accuracy and detection speed of the optimized algorithm are improved, and its detection accuracy is better than other mainstream target detection algorithms.

In the current troubled economic reform in China all kinds of practical problems, a fundamental reform of state-owned property and restrict the overall significance. Especially when the reforms have gradually entered a crucial stage, the need for reform, reform policies are reasonable, the fairness of the reform process, the results of the desirability of reform, and many more issues require in-depth study and appropriate countermeasures. The reform of state-owned property bound to the complexity of this reform has not simply an economic issue, it is an economy as the core, within the framework of the constitutional system of constraints of public selection process, should be a constitutional way of thinking to understand and promote the the reform. Among many realistic problems puzzled China's economic reforming, that of the state-owned property rights is both fundamental and comprehensive conditional. The more this reform moves to its critical stage, the more it needs to deepen its research against the necessity of the reform, the rationality of the reform policy, and impartiality of the reform process, and advisability of the reform result as well. However, the particularity of state-owned property rights reform indicates that it is not only a pure economic problem, hut also an economic centered, and constitutional framed public choice procession. Hence a constitutional thinking is required in any deeper understanding of this reform