ABSTRACT
Improving the supply chain resilience of the mineral resources industry is crucial for ensuring national economic security in China. Based on the supply and demand data of China's mineral resources industry from 2002 to 2018, this study adopts system dynamics model to simulate the supply chain resilience of the mineral resources industry, the mining industry, and the smelting and processing industry under the scenario of steady economic development and the scenario of supply chain crisis. From the simulation results, the reserves of the mineral resources industry and the smelting and processing industry under the two scenarios are nearly the same, indicating that they are weakly affected by the foreign market, and both have strong resilience. The mining industry has a high dependence on imports and a lack of supply chain resilience. Under the condition of steady economic development, the output of the mining industry needs to develop at a low speed to reduce production capacity. More attention should be paid to the high level of import dependence and insufficient supply chain resilience of the mining industry. In the stable international trade situation, reserves of important minerals should be increased to alleviate the resource shortage during the supply chain crisis.
ABSTRACT
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.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Artificial Intelligence , Curriculum , Health EducationABSTRACT
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.
ABSTRACT
Background: The coronavirus disease 2019 (COVID-19) has swept the world and led to delays in the treatment of Crohn's disease patients treated with biologics. This study aims to investigate the risk factors for delayed treatment during the epidemic and to observe the short- and long-term influences of such delays among them to provide some reference on treatments. Methods: This study retrospectively enrolled patients diagnosed with Crohn's disease who received infliximab treatment between January 23, 2020 and April 30, 2020. Univariate and multivariate logistic regression were used to analyze the risk factors for delayed treatment. Propensity score matching was utilized to compare the effects of delayed treatment on the short- and long-term outcomes. Result: Our cohort identified a total of 53 patients with a delay rate of 71.7%. Of these patients, 38 were in the delayed group, and 15 were in the non-delayed group. Logistic regression analysis showed that the baseline levels of C-reactive protein were an influence factor for delaying treatment (OR = 0.967, 95% CI = 0.935–1.000, p = 0.047). Regarding short-term effects, the delayed group had a lower decrease in the Crohn's Disease Activity Index than the non-delayed group [−43.3 (−92.7, −9.7) vs. −17.3 (−29.0, 79.9), p = 0.038] and significantly higher long-term readmission rates (33.3% vs. 0%, p = 0.014). Conclusion: Delayed infliximab treatment could affect the short- and long-term outcomes in patients with Crohn's disease. Our study suggested that the regulated course of treatment with biological agents should be performed effectively and that education should be enhanced to minimize delays in treatment.
ABSTRACT
The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.
ABSTRACT
OBJECTIVES: With the ongoing outbreak of COVID-19 around the world, it has become a worldwide health concern. One previous study reported a family cluster with an asymptomatic transmission of COVID-19. Here, we report another series of cases and further demonstrate the repeatability of the transmission of COVID-19 by pre-symptomatic carriers. METHODS: A familial cluster of five patients associated with COVID-19 was enrolled in the hospital. We collected epidemiological and clinical characteristics, laboratory outcomes from electronic medical records, and also verified them with the patients and their families. RESULTS: Among them, three family members (Case 3/4/5) had returned from Wuhan. Additionally, two family members, those who had not traveled to Wuhan, also contracted COVID-19 after contacting with the other three family members. Case 1 developed severe pneumonia and was admitted to the ICU. Case 3 and Case 5 presented fever and cough on days two through three of hospitalization and had ground-glass opacity changes in their lungs. Case 4 presented with diarrhea and pharyngalgia after admission without radiographic abnormalities. Case 2 presented no clinical nor radiographic abnormalities. All five cases had an increasing level of C-reactive protein. CONCLUSIONS: Our findings indicate that COVID-19 can be transmitted by asymptomatic carriers during the incubation period.