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BackgroundThe COVID-19 epidemic may elevate mental distress and depressive symptoms in various populations in China. ObjectiveThis study investigates the levels of depression and mental distress due to COVID-19, and the associations between cognitive, behavioral, and psychosocial factors, and depression and mental distress due to COVID-19 among university students in China. MethodsA large-scale online cross-sectional study (16 cities in 13 provinces) was conducted among university students from February 1 to 10, 2020, in China; 23,863 valid questionnaires were returned. The Patient Health Questionnaire-9 was used to assess depression. Structural equation modeling was performed to test mediation and suppression effects. ResultsOf the 23,863 participants, 47.1% (n=11,235) reported high or very high levels of one or more types of mental distress due to COVID-19; 39.1% (n=9326) showed mild to severe depression. Mental distress due to COVID-19 was positively associated with depression. All but one factor (perceived infection risks, perceived chance of controlling the epidemic, staying at home, contacted people from Wuhan, and perceived discrimination) were significantly associated with mental distress due to COVID-19 and depression. Mental distress due to COVID-19 partially mediated and suppressed the associations between some of the studied factors and depression (effect size of 6.0%-79.5%). ConclusionsBoth mental distress due to COVID-19 and depression were prevalent among university students in China; the former may have increased the prevalence of the latter. The studied cognitive, behavioral, and psychosocial factors related to COVID-19 may directly or indirectly (via mental distress due to COVID-19) affect depression. Interventions to modify such factors may reduce mental distress and depressive symptoms during the COVID-19 epidemic.

BackgroundThe COVID-19 pandemic continues to pose an international public health threat. Prevention is of paramount importance to protect the high-risk group of older adults until specific treatments for COVID-19 become available; however, little work has been done to explore factors that promote preventive behaviors among this population. ObjectiveThis study aims to investigate the knowledge, perceived beliefs, and preventive behaviors towards COVID-19 of older adults in China and determine the factors that influence their practice of preventive behaviors. MethodsFrom February 19 to March 19, 2020, a cross-sectional, web-based survey was administered to Chinese older adults in all 31 provinces of mainland China using a convenience sampling method to assess the respondents’ knowledge, perceived beliefs, and preventive behaviors towards COVID-19. Standard descriptive statistics and hierarchical linear regression analyses were conducted to analyze the data. ResultsA total of 1501 participants responded to the survey, and 1263 valid responses (84.1%) were obtained for further analysis. The overall correct rate on the knowledge questionnaire was 87%, overall positive beliefs regarding COVID-19 were found, and the mean behavior score was 13.73/15 (SD 1.62, range 5-15). The hierarchical linear regression showed that respondents who were married or cohabitating and who lived in areas with community-level control measures were more likely to practice preventive behaviors (P

Background The rapid development of healthcare social work in policy and practice in Beijing, especially since the COVID-19 epidemic, urgently calls for a competence-based evaluation system. Objective This study is to develop a competence-based index system for the training and evaluation of professional healthcare social workers at an institutional and regional level from an interdisciplinary perspective. Methods From August to November 2022, a one-to-one half structured interview was conducted with snowball sampling among 21 members of an interdisciplinary healthcare social work team in T hospital and surrounding communities in Beijing, a focus interview was conducted among the healthcare social workers, and the interview outlines were formulated based on the Spencers' Iceberg Model. Subject analysis method was used to analyze the data, extract the themes of competency for construction of evaluation index system of T hospital. From November to December 2022, a total of 10 experts in healthcare social work practice were invited by using purposive sampling method to imolement the Fuzzy Delphi and form the Beijing competency evaluation system of healthcare social workers. Results (1) A evaluation index system for competency of healthcare social workers in T hospital was formed through qualitive method, including 4 first-level indexes, 16 second-level indexes and 97 third-level indexes. (2) A total of 2 rounds of Fuzzy Delphi were conducted. The enthusiasm coefficient was 100%. According to S1=8.30 and S2=7.00, a revised index system of T hospital and Beijing were respectively formed. Conclusion This study has constructed an evaluation index system for competency of healthcare social workers in Beijing, which covers 4 first-level indexes, 14 second-level indexes and 73 third-level indexes, from the perspective of interdisciplinary including social work, medicine, nursing and community governance.

Isthmin-1 （ISM1） belongs to the Isthmin gene family. It is expressed in various tissues and has multiple biological functions. ISM1 is involved in growth and development， metabolism， immunity， tumorigenesis and other physiological and pathological processes， which is associated with a variety of lung diseases， including acute respiratory distress syndrome， chronic obstructive pulmonary disease and pulmonary fibrosis， etc. In this article， the structure， biological function of ISM1 and its role in pulmonary diseases were reviewed， aiming to provide a theoretical basis for the diagnosis and treatment of ISM1-related diseases.

Target tracking is to predict the state of the current target according to the target context information of video sequence. Deep learning is gradually widely used in the field of target tracking. This paper elaborates on the development background of target tracking algorithms and deep learning, and reviews traditional target tracking. Based on different network task functions, target tracking algorithms based deep learning is divided into: deep learning target tracking algorithm based classification, deep learning target tracking algorithm based regression, and target tracking algorithm based on the combination of regression and classification. This paper selects representative target tracking algorithm for experiments, compares the characteristics of different algorithms. Finally, this paper analyzes the problems of current target tracking algorithms based on deep learning, and prospects the future development direction. The experimental results show that deep Siamese tracking networks are superior in accuracy and speed, which is becoming the mainstream tracking algorithm framework at present.

TP53, functioning as the keeper of the genome, assumes a pivotal function in the inhibition of tumorigenesis. Recent studies have revealed that p53 regulates ferroptosis pathways within tumor cells and is closely related to tumorigenesis. Therefore, we summarize the pathways and mechanisms by which p53 regulates ferroptosis and identify a series of upstream and downstream molecules involved in this process. Furthermore, we construct a p53-ferroptosis network centered on p53. Finally, we present the progress of drugs to prevent wild-type p53 (wtp53) degeneration and restore wtp53, highlighting the deficiencies of drug development and the prospects for p53 in cancer treatment. These findings provide novel strategies and directions for future cancer therapy.

Under the background of new engineering construction and the requirements of new curriculum reform, the problem of using big data technology to combine with the teaching of the “Artificial Intelligence Programming” course has caused many educational researchers to focus on the problem. In this paper, we propose data mining based on a clustering algorithm to build a teaching management system to optimize the teaching path of the Artificial Intelligence Programming course and build a web-based teaching management system based on clustering analysis and clustering validity index. Then the system structure and database are designed through the teaching system requirements, and the course teaching evaluation index system is built based on the teaching objectives of Artificial Intelligence Programming. Finally, the course’s teaching efficiency is analyzed using the teaching system with 20 weeks of teaching Artificial Intelligence Programming in one semester. The results show the clustering algorithm: the average course teaching efficiency is 39.95%, and the BP neural algorithm: the average course teaching efficiency is 34.71%. Genetic algorithm: the average course teaching efficiency is 33.96%. Based on the clustering algorithm teaching system improves the course teaching efficiency by 39.95% compared with the other two better performances, which is conducive to improving the quality of course teaching and teaching reform. This study provides better information technology support for new engineering construction and engineering teaching optimization and has reference guiding value for course teaching research.

In order to reveal the correspondence between the microstructure and mechanical properties of in-situ TiC/Ti composite coatings by induction fusion coating, the nano-mechanical property variation patterns of different phase structures within the coatings were investigated by using the isolated nanoindentation test method, and the mechanical properties of the coating micro-zone structure were studied by using grid indentation test method. The isolated indentation results show that the nanoindentation hardness and elastic modulus of the in-situ TiC-reinforced phases are 21.3 GPa and 275 GPa, respectively, while the average nano hardness of the matrix phase in the α-Ti-rich and β-Ti-rich regions is 4 GPa and 6 GPa, respectively, and the average elastic modulus is 130 GPa and 155 GPa, respectively. The correspondence between the grid indentation and isolated indentation test results is good. The minimum peak obtained from the three-peak Gaussian fitting of the fractional nanoindentation test results represents the mechanical properties of the matrix phase of the coating, the middle peak reflects the comprehensive mechanical properties of the coating, and the maximum peak is lower than the real mechanical properties of the in-situ TiC reinforcement due to the influence of the reinforcement size and indentation position. Under the consideration of coating microstructure and reinforcement size, by reasonably setting the fractional indentation test conditions and selecting the appropriate test area, it can reveal comprehensive properties of the coating while obtaining real mechanical properties of different phase structures of in-situ TiC composite coatings.