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1.
Mar Policy ; : 105285, 2022.
Article in English | ScienceDirect | ID: covidwho-2031549

ABSTRACT

Fighting the COVID-19 pandemic has led to a dramatic increase in plastic waste, which has had a huge impact on the environment, including the marine environment. This work is aimed to evaluate the pattern of national research cooperation, research hotspots, and research evolution before and during the epidemic by systematically reviewing the publications on marine plastic pollution during 2015-2019 (before the pandemic) 2020-2022 (during the pandemic) using the Systematic Literature Review and Latent Semantic Analysis. The results show (i) Compared to pre-pandemic, publications on marine pollution during the COVID-19 pandemic declined briefly and then increased sharply. (ii) Compared with before the pandemic, the national cooperation model has changed during the pandemic, and four major research centers have been formed: Central European countries centered on Italy;Nordic countries centered on United Kingdom;South Korea;Asia and Africa centered on India A developing country and a Pacific Rim country centered on United States and China. (iii) The knowledge map of keyword clustering does not change significantly before and during the COVID-19: ecosystem, spatial distribution, environmental governance and biodegradation. However, there are differences in the sub-category research of the four types of keywords. (iv) The impact of marine plastics on organisms and the governance of marine plastic pollution have become a branch of knowledge that has evolved rapidly during the pandemic. The governance of marine plastic pollution and microplastics are expected to become an important research direction.

2.
Journal of E-Learning and Knowledge Society ; 18(2):1-10, 2022.
Article in English | Scopus | ID: covidwho-2026027

ABSTRACT

The COVID-19 pandemic has forced schools to close and shift to remote education. However, this might create new challenges, as students might have poor self-directed learning skills to keep up with the learning process from home. Although many studies have focused on remote education during said pandemic, there is limited information on the strategies implemented to support and encourage self-directed learning and assessment. Therefore, in this study – focusing on a case in China – focus group interviews were conducted to collect data from different stakeholders on the implemented self-directed learning strategies during the COVID-19 pandemic. The results might help different education stakeholders in future to effectively maintain education in crises, leading to better learning outcomes. © Italian e-Learning Association.

3.
Turkish Online Journal of Distance Education ; 23(3), 2022.
Article in English | Scopus | ID: covidwho-1940292

ABSTRACT

Massive Open Online Courses (MOOCs) have been around for some time, but several studies highlighted different issues associated with them, including quality. The COVID-19 pandemic catalyzed their second blooming, where MOOCs have seen a surge in enrollments since March 2020. This study intended to explore how this enrollment reflected on the research studies included in scientific publications, indexed by Web of Science. Specifically, the bibliometric mapping analyses of 108 studies have revealed an ongoing trend in the countries contributing to the MOOCs research, namely USA, China, UK and Spain. Additionally, MOOCs research coming from US, UK and other western countries was decreasing before the pandemic and showed a continuous dramatic reduction also during the COVID-19 pandemic. Growing attention in MOOCs research among less represented countries was also observed. Besides, most of the topics focused on by MOOCs research during the pandemic were mainly related to education and engineering. © 2022. Turkish Online Journal of Distance Education. All Rights Reserved.

4.
JOURNAL OF HOSPITALITY AND TOURISM MANAGEMENT ; 51, 2022.
Article in English | Web of Science | ID: covidwho-1936785

ABSTRACT

This study aims to explore Chinese residents' outbound travel intentions and preparations in the post-pandemic world that are influenced by media coverage and risk perception. A conceptual model is proposed to test the structural relationships among media coverage, risk perception, outbound travel intentions and preparations. This study administered an online survey to Chinese residents who had outbound travel experiences, and a total of 441 valid responses were collected for data analysis. The results indicated that media coverage exerted significant impact on cognitive and affective risk perceptions, outbound travel intentions and preparations. Furthermore, cognitive risk perception was positively related to affective risk perception, which significantly influenced outbound travel intentions and preparations. Outbound travel intentions were verified as the determinant of outbound travel preparations. Additionally, the mediating roles of affective risk perception and outbound travel intentions were confirmed. This study is amongst the first to introduce the concept of outbound travel preparations as a new research avenue for post-pandemic outbound travel behaviour.

5.
Energy Strategy Reviews ; 41:20, 2022.
Article in English | Web of Science | ID: covidwho-1867134

ABSTRACT

The COVID-19 pandemic has a significant impact on renewable energy. This work investigates the effect of pandemic on the renewable energy research from four aspects: the regional cooperation model of renewable energy research, the research hotspots of renewable energy during the pandemic, the development trend of renewable energy research hotspots in the post-pandemic, policy recommendations for development in the postepidemic era. Systematic literature review (SLR), latent semantic analysis (LSA), and machine learning-based analysis (principle component analysis) are used to analyze the relevant literature on the COVID-19 and renewable energy in the Scopus database. The results of geographic visualization analysis show the COVID-19 pandemic has not hindered but promoted bilateral cooperation in the field of renewable energy among the " the Belt and Road " partner countries, with China at the core. The results of visual analysis of research hotspots show the research in the field of renewable energy during pandemics is divided into two categories: "opportunities" and "crisis", and further obtained five categories: sustainable development, environmental management, carbon emission, solar photovoltaic power, and wind power. The results of the keyword evolution map indicate the two main directions of renewable energy research in the post-pandemic: (1) Clean energy investment has become an important measure to revitalize the economy after the epidemic. (2) Energy efficiency research will effectively promote the sustainable development of renewable energy. Finally, we put forward policy suggestions on how to build a smart energy system in the post-epidemic era.

7.
Nature Machine Intelligence ; 2022.
Article in English | Scopus | ID: covidwho-1805663

ABSTRACT

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.

8.
Discovery Medicine ; 31(164):121-127, 2021.
Article in English | Web of Science | ID: covidwho-1766877

ABSTRACT

Background. Few studies reported the risk factors of fatal outcome of hospitalized patients with coronavirus disease 2019 (COVID-19). We aimed to identify the independent risk factors associated with fatal outcome of hospitalized COVID-19 patients. Methods. The clinical data of 109 consecutive COVID-19 patients including 40 (36.7%) common cases and 69 (63.3%) severe cases were included and analyzed. Results: Multivariate regression analysis indicated that platelets (PLT, OR, 0.988;95% CI, 0.978-0.998;P=0.017) and C-reactive protein (CRP) (OR, 1.047;95% CI, 1.026-1.068;P<0.001) levels were the independent risk factors of fatal outcome in COVID-19 patients. The optimal cut-off value of PLT counts for predicting fatal outcome was 161x109/L with the area under receiver operating characteristic curve (AUROC) of 0.824 (95% CI, 0.739-0.890). The optimal cut-off value of CRP for the prediction of fatal outcome was 46.2 mg/L with the AUROC of 0.954 (95% CI, 0.896-0.985). The CRP levels had higher predictive values for fatal outcome than PLT (P=0.016). The cumulative survival rate was significantly higher in patients with PLT>161x10(9)/L compared with patients with PLT <= 161x10(9)/L (89.4% vs. 12.5%, log-rank test chi(2)=72.17;P<0.001). Survival rate of COVID-19 patients was prominently higher in CRP <= 46.2 mg/L patients compared with patients with CRP>46.2 mg/L (95.9% vs. 22.9%, log-rank test chi(2)=77.85;P<0.001). Conclusions. PLT counts and CRP levels could predict fatal outcome of hospitalized COVID-19 patients with relatively high accuracy.

9.
Discov Med ; 32(165):39-47, 2021.
Article in English | PubMed | ID: covidwho-1711114

ABSTRACT

BACKGROUND: The follow-up data of discharged patients with coronavirus disease 19 (COVID-19) have not yet been fully analyzed and reported. This study aimed to evaluate the clinical features, test results, and outcomes of COVID-19 patients after discharge. METHODS: 149 COVID-19 patients with follow-up data after discharge were included. Post-hospitalization data related to clinical features and outcomes were obtained by following the patients up to 6 weeks. RESULTS: The COVID-19 patients were followed for a median of 28.0 days (range of 22 days to 42 days) after discharge from hospital. At the end of follow-up, four patients (2.7%) still had cough. The proportions of leukopenia and lymphopenia were 7.4% and 4.7%, respectively. The proportions of ALT, AST, and Cr abnormalities were 26.2%, 6.0%, and 0%, respectively. Abnormal chest CT was detected in 94 (63.1%) patients, including 14 (9.4%) unilateral pneumonia and 80 (53.7%) bilateral pneumonia. However, the proportion of chest CT abnormality significantly decreased compared to that at the time of admission. CONCLUSIONS: One month after discharge, few patients with COVID-19 had clinical symptoms;however, a substantial proportion of COVID-19 patients harbored abnormal laboratory and radiological examinations. Moderately long-term medical follow-up would justifiably benefit COVID-19 patients after discharge.

11.
Journal of the Canadian Association of Gastroenterology ; 5(Suppl 1):114-115, 2022.
Article in English | EuropePMC | ID: covidwho-1695333

ABSTRACT

Background The COVID-19 pandemic has brought significant challenges to clinicians caring for liver transplant (LT) recipients. Researchers have sought to better understand the risk and clinical outcomes of LT recipients infected with COVID-19 globally, however, there is a paucity of data from within Canada. Aims Our multi-center study aims to examine the characteristics and clinical outcomes of LT patients with COVID-19 in Canada. Methods We identified a retrospective cohort of adult LT recipients with RT-PCR confirmed COVID-19 from 7 Canadian tertiary care centers between March 2020 and June 2021. Demographic and clinical data were compiled by clinicians within those centers. We identified liver enzyme profile at the time of COVID-19 infection, immunosuppression type and post-infection adjustments, rate of hospitalization, ICU admission, mechanical ventilation, and death. Results A total of 49 patients with a history of LT and COVID-19 infection were identified. Twenty nine patients (59%) were male, the median time from LT was 66 months (1, 128) and the median age at COVID-19 infection was 59 years (52, 65). At COVID-19 diagnosis, the median ALT was 37 U/L (21, 41), AST U/L was 34 (20, 37), ALP U/L was 156 (88, 156), Total Bilirubin was 11 umol/L (7, 14), and INR was 1.1 (1.0, 1.1). The majority of patients (92%) were on tacrolimus monotherapy or a combination of tacrolimus and mycophenolate mofetil (MMF);median tacrolimus level at COVID-19 diagnosis was 5.3 ug/L (4.0, 8.1). Immunosuppression was modified in 8 (16%) patients post-infection;either the tacrolimus dose was reduced or MMF was held. One patient developed acute cellular rejection which recovered after re-initiation of the prior regimen. Eighteen patients (37%) required hospitalization, 6 (12%) were treated with dexamethasone, and 3 (6%) required ICU admission and mechanical ventilation. Four patients (8%) died due to complications of COVID-19. On univariate analysis, neither age, sex, co-morbidities nor duration post-transplant were associated with risk of hospitalization. Conclusions In our national retrospective study, approximately 40% of patients required hospitalization with a mortality rate of < 10%. Previous studies have shown proximity to LT as an independent factor for mortality with COVID-19;the median time from LT for our patients was 5 years, which may explain the lower mortality rate. Of note, the median tacrolimus levels were much lower in comparison to the target of 8–10 ug/L used in the first year post-transplant. As the landscape of COVID-19 changes with vaccination, evolving treatments, and increasing rates of variant transmission, additional studies are required to continue identifying trends in clinical outcomes. Funding Agencies None

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315413

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) has rapidly spread to more than 200 countries. Thus far, reports regarding multi-center data from throughout gestation in women with COVID-19 and newborn outcomes are scarce. Methods: : We retrospectively reviewed data from 92 pregnant women with COVID-19 (PW-COVID-19) and their 78 newborns from 27 hospitals in 12 regions of Hubei, China. The demographic, epidemiological, clinical, laboratory, and therapeutic data and pregnancy, perinatal, and neonatal outcomes were analyzed. Follow-up was censored until April 7, 2020. Findings: Median maternal age was 31.0 years (IQR 28·0-33·0), with nine patients in the first trimester, five in the second trimester, and 78 in the third trimester. None of the patients died, and most (92·4%) recovered and were discharged. Seventy-five deliveries (including three sets of twins) comprised 66 cesarean sections and nine vaginal deliveries, with 21 preterm and 57 full-term infants. Seventeen live births had radiological findings of pulmonary infection. One newborn tested positive for SARS-CoV-2 nucleic acid, and three newborns were viral antibody-positive: two IgG (+) and IgM (-), and one IgG (+) and IgM (+). The median suspected duration of virus exposure was 7 days (IQR 0 to 27). Interpretation: Compared to the pregnant women with other viral infections, such as SARS, MERS, and Zika virus infection, PW-COVID-19 had similar manifestations and relatively better outcomes. The termination time and delivery mode in PW-COVID-19 should be evaluated based on both the maternal and fetal situations. The possibility of maternal-to-fetal transmission of SARS-CoV-2 requires further investigation.Authors Shujie Liao and Renjie Wang contributed equally to this work.

13.
Obesity ; 29(SUPPL 2):168-169, 2021.
Article in English | EMBASE | ID: covidwho-1616052

ABSTRACT

Background: The COVID-19 pandemic interfered with delivery of childhood weight management programs. Get Up & Go is a community group program for children 6-14 years with BMI≥85th%ile and their families that provides fun learning about healthy nutrition, physical activity, and behavior change at no cost to families. The program is effective in improving %BMIp95, reported healthy lifestyle, and physical endurance. This study evaluates the participation and effect of a virtual delivery option offered starting winter 2021. Methods: Groups of 5-12 families met in-person at a YMCA or via synchronous virtual delivery. Parents chose setting. Each of the 10 weekly lessons lasted 60-90 minutes. Data include demographics and pre-and post-measured weights, heights, and parent-completed behavior assessment questionnaires (BAQ), with score range of 0-100, higher indicating healthier behavior. Graduation criterion is attendance at 6 of 9 non-orientation classes. Results: Among the registered families, 46 of 82 (56%) who chose virtual actually attended vs 40 of 56 (71%) who chose in-person (p = 0.07). Baseline characteristics of attenders did not differ (we report virtual, then in-person): mean age (SD) 11.1 (2.5) vs 11.1 (2.2) years, male 29 (63.0%) vs 23 (57.5%), Spanish-speaking 18 (39.1%) vs 12 (31.6%), severe obesity (≥120% BMIp95) in 39 (84.8%) vs 30 (75.0%), and baseline BAQ scores 34.9 (13.2) vs 37.2 (12.1). Virtual participants attended more classes than in-person participants: 7.3 (2.3) vs 6.1 (2.9), p = 0.04. More virtual participants met graduation criterion: 35 (87.5%) vs 24 (63.2%), p = 0.01. Among graduates with post-measures, mean change in %BMIp95 was -3.27 (6.20) for n = 32 virtual vs -1.09 (3.84) for n = 23 in-person, p = 0.11, and mean BAQ increase was 15.4 (13.1) for n = 34 virtual vs 11.4 (10.6) for n = 21 in-person, p = 0.25. Conclusions: The synchronous, group virtual delivery of the Get Up & Go program engaged similar participants, with higher attendance and no difference in %BMIp95 and BAQ outcomes, compared with contemporaneous in-person delivery. Continued virtual delivery option could expand the availability of this program without compromising effect.

14.
Nature Machine Intelligence ; 3(12):1081-1089, 2021.
Article in English | Web of Science | ID: covidwho-1585763

ABSTRACT

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

16.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-293214

ABSTRACT

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

17.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-292843

ABSTRACT

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

18.
American Journal of Gastroenterology ; 116(SUPPL):S513, 2021.
Article in English | EMBASE | ID: covidwho-1534718

ABSTRACT

Introduction: The COVID-19 pandemic has dramatically imperiled the health system worldwide. It may also negatively impact the cascade of care of hepatitis C virus (HCV) infection and the progress on WHO 2030 goal of HCV elimination. In this study, we used a multinational, multicenter cohort to estimate the change in the completion of DAA therapy, HCV RNA testing, and clinical encounter during pandemic. Methods: We collected data patients who underwent DAA therapy at three tertiary medical centers in Los Angeles (US), Xi'an (China), and Nanjing (China) between January 1, 2019 to June 30, 2020 and followed until November 30, 2020. We compared the proportions of HCV patients who completed DAA therapy as well as had HCV RNA testing and follow-up visits during and after the end of the HCV therapy between COVID-19 pandemic and the periods before pandemic. Additionally, we determined the frequency and predictive factors of utilization of telemedicine. Results: A total of 256 patients with HCV infection were included. Despite no significant reduction in the completion of DAA before and during the pandemic, the proportion of patients undergoing HCV RNA testing during DAA treatment decreased from about 80% before pandemic to 67% during the pandemic, with a more prominent decrease in the US. There were less than 10% of patients who had HCV RNA testing 12 weeks post-treatment during COVID-19 era. Compared to pre-pandemic period, post-treatment clinic encounter decreased significant in China but elevated in the US. Further analysis showed that the increase was due to the surge in utilization of telemedicine. However, the increased number of follow-up visits during COVID-19 pandemic period did not result in an increase in HCV RNA testing. Conclusion: COVID-19 pandemic carried profound impact on the cascade of care for HCV patients in both the US and China. Despite the increased use of telemedicine in the US, the adherence to recommendations for HCV RNA testing was still disappointingly low. Stakeholders should identify the modifiable barriers and reinforce the care while withstanding the pandemic.

19.
Thorax ; 76(Suppl 2):A142, 2021.
Article in English | ProQuest Central | ID: covidwho-1506120

ABSTRACT

P138 Table 1Employment status of COVID-19 dischargesN 138 Essential services 41 (30) Office/admin 37 (27) Healthcare 16 (12) Non essential services 15 (11) Public transport 10 (7) Enforcement 4 (3) Heavy Goods Vehicle driver 3 (2) Carers 2 (1) Unknown 10 (7) ConclusionOur preliminary data suggests significant symptom burden within 6 weeks post discharge after a COVID-19 infection admission, which may impact on the ability of patients to return to work. In the present analysis there was no significant interaction between return-to-work status and covid severity.

20.
21st IEEE International Conference on Advanced Learning Technologies, ICALT 2021 ; : 325-329, 2021.
Article in English | Scopus | ID: covidwho-1416214

ABSTRACT

While massive research has been conducted to see how remote learning and teaching is conduct during the COVID-19 pandemic, less focus has been paid on remote special education for students with disabilities. Therefore, it is still not clear how those students learned and what types of challenges they faced. To fill this gap, this study first collected data from the literature via a systematic literature review, and from both 51 teachers and 21 students with disabilities who were involved in this remote teaching and learning experiences via surveys. It then conducted bibliometric, content and thematic analysis to draw conclusions. The obtained findings highlighted that online and offline remote teaching methods from home were applied. Additionally, different learning assessment methods, such as mini-projects and simple quizzes were adopted by teachers to assess the gained knowledge of students remotely, but none of these methods relied on emerging technologies, such as big data and learning analytics. Finally, parents were a core actor to maintain remote learning from home for students with disabilities. © 2021 IEEE.

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