Application of artificial intelligence to the public health education.

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Application of artificial intelligence to the public health education

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Chinese Guideline on Allergen Immunotherapy for Allergic Rhinitis: The 2022 Update.

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

SARS-CoV-2 pseudovirus enters the host cells through spike protein-CD147 in an Arf6-dependent manner.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants is threatening public health around the world. Endocytosis functions as an important way for viral infection, and SARS-CoV-2 bears no exception. However, the specific endocytic mechanism of SARS-CoV-2 remains unknown. In this study, we used endocytic inhibitors to evaluate the role of different endocytic routes in SARS-CoV-2 pseudovirus infection and found that the viral infection was associated with caveolar/lipid raft- and cytoskeleton-mediated endocytosis, but independent of the clathrin-mediated endocytosis and macropinocytosis. Meanwhile, the knockdown of CD147 and Rab5a in Vero E6 and Huh-7 cells inhibited SARS-CoV-2 pseudovirus infection, and the co-localization of spike protein, CD147, and Rab5a was observed in pseudovirus-infected Vero E6 cells, which was weakened by CD147 silencing, illustrating that SARS-CoV-2 pseudovirus entered the host cells via CD147-mediated endocytosis. Additionally, Arf6 silencing markedly inhibited pseudovirus infection in Vero E6 and Huh-7 cells, while little change was observed in CD147 knockout-Vero E6 cells. This finding indicated Arf6-mediated CD147 trafficking plays a vital role in SARS-CoV-2 entry. Taken together, our findings provide new insights into the CD147-Arf6 axis in mediating SARS-CoV-2 pseudovirus entry into the host cells, and further suggest that blockade of this pathway seems to be a feasible approach to prevent the SARS-CoV-2 infection clinically.

The correlation between severity scores in computed tomography lung scans and viral load in the severity of novel coronavirus 2019 progression.

BACKGROUND: This study aimed to find the correlation between severe computed tomography (CT) lung scores and nasopharyngeal viral load (Ct value) in the severity of COVID-19 disease progression. METHOD: In this study, 37 patients diagnosed with COVID-19 were categorized into severely ill and not severely ill samples. Their Ct values, epidemiological data, lung CT, and laboratory test results were collected three times, respectively, on the first day of their hospital admission, 3-5 days thereafter, and prior to hospital discharge. Among the 37 patients, 8 progressed from not severely ill to severely ill; we also paid attention and observed changes in clinical parameters of COVID-19 patients who entered our city from other cities (imported cases) and the infected local residents who contacted these imported patients (non-imported cases). RESULTS: Among the 37 patients, the Ct values and lung severity scores (LSSs) were similar in imported and non-imported cases (F = 0.59 and 2.56; p = 0.45 and 0.12, respectively) but the proportion of severely ill imported patients was significantly higher compared with non-imported patients (F = 7.77; p = 0.01). Additionally, 21.6% of patients' illness worsened; lymphocyte counts and Ct values were significantly lowered, and C-reactive protein and LSS significantly increased during COVID-19 disease progression. Furthermore, LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values (Pearson's rank = -0.763, -0.824, and -0.588; p = 0.028, 0.012, and 0.003, respectively). CONCLUSION: In the severity of COVID-19 disease progression, nasopharyngeal viral load and lung CT severity were closely related, and LSS negatively correlated with lymphocyte and mononuclear cell counts, as well as Ct values.

A Novel Service Provision Mode for Sustainable Development of the Telecom Industry

The deepening integration of telecommunication technology into other industries has giving birth to a variety of new applications, such as self-driving, telemedicine, and intelligent manufacturing. Whether the telecom service is traditional or new, users put forward personalized and multidimensional requirements for performance, which results in a conflict between their requirements for customized service and the failure of telecom enterprises to meet every requirement. This contradiction directly influences the sustainability and stable development of the telecom industry. To address this problem, this paper carried out systematic research into collaboration and adaptation between business-model and technological innovation. From the view of business-model innovation, this paper proposes a novel service provision mode named Multidimensional Customization of Telecom Services for Each User to analyze the value of the business model and the factors that influence it. From the view of technological innovation, it will design an implementation scheme corresponding to the business model, and verify its advantages in network-carrying capacity and significance to enterprises’ business value through a simulation experiment. The research shows that the novel mode is beneficial both to telecom users and telecom enterprises. In addition, it addresses this novel mode’s impact on environmental sustainability and the regulation of the telecom industry, as well as the limitations of this research and future research directions.