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During a public health crisis like the COVID-19 pandemic, a credible and easy-to-access information portal is highly desirable. It helps with disease prevention, public health planning, and misinformation mitigation. However, creating such an information portal is challenging because 1) domain expertise is required to identify and curate credible and intelligible content, 2) the information needs to be updated promptly in response to the fast-changing environment, and 3) the information should be easily accessible by the general public;which is particularly difficult when most people do not have the domain expertise about the crisis. In this paper, we presented an expert-sourcing framework and created Jennifer, an AI chatbot, which serves as a credible and easy-to-access information portal for individuals during the COVID-19 pandemic. Jennifer was created by a team of over 150 scientists and health professionals around the world, deployed in the real world and answered thousands of user questions about COVID-19. We evaluated Jennifer from two key stakeholders' perspectives, expert volunteers and information seekers. We first interviewed experts who contributed to the collaborative creation of Jennifer to learn about the challenges in the process and opportunities for future improvement. We then conducted an online experiment that examined Jennifer's effectiveness in supporting information seekers in locating COVID-19 information and gaining their trust. We share the key lessons learned and discuss design implications for building expert-sourced and AI-powered information portals, along with the risks and opportunities of misinformation mitigation and beyond. © 2023 Owner/Author.
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The fresh e-commerce industry has seen a sudden and substantial rise since the outbreak of COVID-19. The rapid development of this industry calls for a comprehensive and systematic review of its research status, hotspots and future trends, which will have significant implications for researchers in related fields. This paper first conducts a current situation analysis of the core literature on fresh e-commerce retrieved from four databases – CNKI, CSSCI, Wanfang and VIP – to categorize the research status of fresh e-commerce in three dimensions: the year of publication, article sources, and distribution of subjects. CiteSpace is then used to perform a bibliometric analysis of the data and to create visualized knowledge maps. The results show that the research on fresh e-commerce can be divided into three stages: rapid development (2012-2015), exploration and transformation (2016-2019), maturity and upgrade (2020-present). At each stage, the research evolves toward diversity and maturity with policy developments and changes in the external environment. Cold chain logistics, business models, freshness-keeping of products and e-commerce are ongoing research hotspots in fresh produce e-commerce, while later studies focus more on the transformation and upgrade of products, logistics, distribution and platforms to better serve consumers' consumption habits and environmental requirements. This study provides valuable insights for researchers and enterprises who are engaged in the industry and for those who are interested in the development of fresh e-commerce in China. © 2022 International Consortium for Electronic Business. All rights reserved.
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In response to emerging and re-emerging respiratory viral infections with high morbidity and mortality such as Coronavirus Disease 2019 (COVID-19), Middle East respiratory syndrome coronavirus (MERS-CoV), and influenza, early administration of broad-spectrum antivirals can facilitate pandemic control and improve patient outcomes. This provides empiric therapeutic options during the time-lag for developing specific drug/vaccine. AM80 (tamibarotene), an orally active retinoid, was demonstrated with broad-spectrum antiviral efficacy in a recent study. To maximise antiviral efficacy in respiratory tract, an inhalable powder formulation of AM80 was developed by spray freeze drying (SFD) technology with hydroxypropyl-bcyclodextrin (HPbCD) as solubiliser. The formulation showed good aerosol performance, as evaluated by Next Generation Impactor, with a fine particle fraction of 65.1 ± 7.9% and an emitted fraction of 95.1 ± 1.7%. The sublimation of solvent crystal led to the formation of porous particles, which was visualised by scanning electron microscopy. In contrast to the slow-dissolving unformulated AM80, the SFD AM80 powder displayed a burst-release dissolution, which is postulated to be a combined result of enhanced solubility by HPbCD and increased surface area of porous structure. The in vivo pharmacokinetics of the SFD AM80 powder after intratracheal administration was investigated in mice. With the same dose given, inhaled AM80 powder resulted in higher bioavailability in both lungs and plasma than intraperitoneally injected unformulated AM80 in 0.1% DMSO solution. This study demonstrated a strategy to develop an inhaled formulation for a broad-spectrum antiviral agent, which could be a strong candidate in clinical applications for various respiratory viral infections.
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The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This paper proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining excellent performance of capturing the forecasting uncertainties. IEEE
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The original version of this article contained errors in author affiliations and Figures. The correct information author affiliations and Figures should be as follows. The authors would like to apologize for any inconvenience caused.
ABSTRACT
The original version of this article contained errors in author affiliations and Figures. The correct information author affiliations and Figures should be as follows. The authors would like to apologize for any inconvenience caused. © 2021, The Journal of Nutrition, Health & Aging.
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OBJECTIVES: During the 2019 coronavirus disease (COVID-19) outbreak, malnutrition may contribute to COVID-19 adverse outcomes. We conducted a clinical epidemiological analysis to investigate the association of malnutrition with hospitalized duration in patients with COVID-19. DESIGN: Retrospective survey study. SETTING: Taikang Tongji (Wuhan) hospital in Wuhan, China. PARTICIPANTS: 139 patients with COVID-19. METHODS: In total, 139 patients with COVID-19 from patients in the Infection Department of Taikang Tongji (Wuhan) hospital from February 2020 to April 2020 were analyzed retrospectively. We used the "Global leadership Initiative on Malnutrition(GLIM)" assessment standard published in 2019 to assess nutritional status. Prolonged hospitalization was lasting more than the median value of the hospitalized days (17 days) in this population. RESULTS: According to the assessment results of GLIM nutrition assessment, the patients were divided into malnutrition group and normal nutrition group. Compared with the patients in the normal nutrition group, the hospitalization time was longer(15.67±6.26 days versus 27.48±5.04 days, P = 0.001). Kaplan-Meier analysis showed patients with malnutrition were more likely to be hospitalized longer compared with those normal nutrition (mean with 95% confidence interval [CI]: 28.91[27.52-30.30] versus 22.78[21.76-23.79], P = 0.001). COX regression analysis showed that malnutrition (hazard ratio [HR] = 3.773, P for trend = 0.001) was proportional associated with being discharged from hospital delayed. CONCLUSION AND IMPLICATIONS: Present findings suggested that malnutrition contributed to predicting a probability of prolonged hospitalization in patients with COVID-19 infection, to whom extra attentions and precautions should be paid during clinical treatments. Based on the existing results, it is recommended that inpatients with nutritional risk or malnutrition start nutritional support treatment as soon as possible.