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1.
China & World Economy ; 29(6):73-94, 2021.
Article in English | EuropePMC | ID: covidwho-1790119

ABSTRACT

This article investigates how the COVID‐19 pandemic and related public health measures affected the consumption of food away from home (FAFH) among Chinese consumers. We obtained access to the complete sales records from a major restaurant chain in China, for 111 sites located in 12 cities, covering over 5.6 million high‐frequency dining transactions made between 1 January 2019 and 31 December 2020. By applying a high‐dimensional fixed‐effects model, we found that, on average, consumers spent more and ordered more calories (as well as carbohydrates, protein, fat, and sodium) after the COVID‐19 outbreak than in the pre‐COVID‐19 period. Our results do not support the hypothesis that COVID‐19 led to healthier eating behaviors during and after the pandemic. Our results underline the importance of nutrition education and awareness programs to mitigate unhealthy eating habits generated by the pandemic and of the continued role of FAFH after the pandemic.

2.
Tertiary Education & Management ; 28(1):1-15, 2022.
Article in English | Academic Search Complete | ID: covidwho-1756859

ABSTRACT

The COVID-19 pandemic has influenced nearly every aspect of people's lives, and has set new conditions for universities to operate their internationalisation practices. Together with the rapidly changing global environment, higher education internationalisation has reached a crossroads. Through a constructivist grounded theory design, this study explores experts' thoughts about the coronavirus crisis's influences on the internationalisation of higher education and its future direction, taking different national and regional contexts into account. Interviews with 20 world-leading scholars in the field suggested that COVID-19 has had complex effects on university internationalisation and it is necessary to consider such effects beyond the simple distinction between challenges and opportunities. New approaches to conceptualise and implement internationalisation are essential, while the logic of capitalism remains powerful. When looking at the future, many factors other than the coronavirus will exert their force. New conditions have raised new requirements for internationalisation, and therefore, new knowledge is needed to maintain its relevance and sustainability. [ FROM AUTHOR] Copyright of Tertiary Education & Management is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

3.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-330137

ABSTRACT

The COVID-19 pandemic has once again brought the significance of biopharmaceutical and medical technology sectors to the spotlight. Seeing that some of the most critical medical breakthroughs such as the speedy mRNA vaccine development were results of cross-border patenting collaboration, we have proposed in a previous work a new method to identify the cross-border collaborative regional centres in the patent networks, using on a clustering comparison approach based on adjusted mutual information (AMI). In this paper, we focus on the UK industrial landscape. We use the UK bioscience and health technology sector statistics from 2015 to 2020 and look into the regional growth of each postcode area. We compare the top growth regions with the cross-border collaborative centers identified using AMI comparison at the postcode area level, and find that areas more central in the long-term cross-regional R&D collaboration tend to have more developed industrial settings with higher business numbers and, potentially more employment and turnover in the field.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-312648

ABSTRACT

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310887

ABSTRACT

We propose an equilibrium-driven deformation algorithm (EDDA) to simulate the inbetweening transformations starting from an initial image to an equilibrium image, which covers images varying from a greyscale type to a colorful type on plane or manifold. The algorithm is based on Fokker-Planck dynamics on manifold, which automatically cooperates positivity, unconditional stability, mass conservation law, exponentially convergence and also the manifold structure suggested by dataset. The thresholding scheme is adapted for the sharp interface dynamics and is used to achieve the finite time convergence. Using EDDA, three challenging examples, (I) facial aging process, (II) coronavirus disease 2019 (COVID-19) invading/treatment process, and (III) continental evolution process are conducted efficiently.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310722

ABSTRACT

Background: In December 2019, Wuhan witnessed the outbreak of an “unexplained pneumonia” caused by a novel coronavirus strain infection and was dubbed the COVID-19 by the WHO. The disease quickly spread to China. This study aimed to investigate the disease’s evolving epidemiological history, as well as analyze the clinical and CT imaging characteristics, treatment regimens, and patients’ prognosis. Methods: : This was a retrospective study whose cases were 64 patients with a confirmed diagnosis of COVID-19. The clinical data were obtained from patients who were admitted to the isolation ward from 21 January 2020 to 19 February 2020. Results: : 60 out of 64 patients had a definitive history of exposure to people who had traveled from Wuhan City. The median time from onset of symptoms to first hospital admission was 3.9±1.9 days. The initial symptoms included fever (46/64), dry cough (38/64), fatigue or myalgia (23/64), sore throat (10/64), diarrhea (3/64) along with late-onset symptoms like chest pains (2/64) and headaches (2/64). The majority of the patients (43/64) had normal white blood cell counts while 29.7 % (19/64) had leukopenia. Only two patients (3.1 %) presented with leukocytosis. 58 of the 64patients had abnormal radiological findings on chest CTs. The first chest CTs (within 2 days) was more sensitive in detecting COVID-19 infection (85.9 %) compared to the initial RT-PCR (56.3 %;p<0.01). The CTs showed lesions in multiple lung lobes in three-quarters of the patients while 15.6 % had lesions localized to one lobe. Most of the lesions were typically dense with ground-glass opacity co-existing with consolidation or cord-like shadows. Most of these patients (50/64) have recovered and got discharged giving a mean length of hospital stay of 13.5±4.8 days. Our hospital unit has not reported any COVID-19 related death so far. Conclusions: : Early intervention in COVID-19 disease improves patients’ prognosis. Our data demonstrate the superiority of early radiological tests ahead of RT-PCR. The initial and dynamic CT changes in COVID-19 patients along with other clinical data shared above can better guide clinical decision making.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308269

ABSTRACT

The COVID-19 pandemic has imposed serious challenges in multiple perspectives of human life. To diagnose COVID-19, oropharyngeal swab (OP SWAB) sampling is generally applied for viral nucleic acid (VNA) specimen collection. However, manual sampling exposes medical staff to a high risk of infection. Robotic sampling is promising to mitigate this risk to the minimum level, but traditional robot suffers from safety, cost, and control complexity issues for wide-scale deployment. In this work, we present soft robotic technology is promising to achieve robotic OP swab sampling with excellent swab manipulability in a confined oral space and works as dexterous as existing manual approach. This is enabled by a novel Tstone soft (TSS) hand, consisting of a soft wrist and a soft gripper, designed from human sampling observation and bio-inspiration. TSS hand is in a compact size, exerts larger workspace, and achieves comparable dexterity compared to human hand. The soft wrist is capable of agile omnidirectional bending with adjustable stiffness. The terminal soft gripper is effective for disposable swab pinch and replacement. The OP sampling force is easy to be maintained in a safe and comfortable range (throat sampling comfortable region) under a hybrid motion and stiffness virtual fixture-based controller. A dedicated 3 DOFs RCM platform is used for TSS hand global positioning. Design, modeling, and control of the TSS hand are discussed in detail with dedicated experimental validations. A sampling test based on human tele-operation is processed on the oral cavity model with excellent success rate. The proposed TOOS robot demonstrates a highly promising solution for tele-operated, safe, cost-effective, and quick deployable COVID-19 OP swab sampling.

9.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326532

ABSTRACT

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

10.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325267

ABSTRACT

Objective: In the battle against COVID-19, most medical resources in China have been directed to infected patients in Wuhan. Thus, patients with hepatobiliary pancreatic tumors who are not suffering from COVID-19 are often not given timely and effective anti-cancer treatments. In this study, we aimed to describe clinical characteristics, treatment, and outcomes of patients with hepatobiliary and pancreatic oncology from our department, which retained normal working during the COVID-19 epidemic. We also sought to formulate a set of standardized hospitalization and treatment processes. Methods: : A retrospective and descriptive study was conducted involving patients hospitalized from February 1, 2020, to February 29, 2020 (Return to work after the Spring Festival), at our Department of Hepatobiliary and Pancreatic Surgical Oncology. Results: : The study included 92 patients from 12 provinces in the north of China who underwent surgical resection at our Department of Hepatobiliary and Pancreatic Surgical Oncology during the COVID-19 epidemic. Robotic surgery was performed on 82% (75/92) of patients, while the rest underwent laparoscopic (2/92) and open surgery (15/92). Eighty-six patients had malignant tumor, and six had emergency benign diseases. Only five patients had severe pancreatic fistula, and three had biliary fistula after operation. Conclusions: : The standardized hospitalization and treatment processes described in this study could prevent cross-infection of patients and still ensure timely treatment of patients with hepatobiliary and pancreatic cancers. These study findings will guide the management of surgical oncology departments and treatment of patients with hepatobiliary and pancreatic oncology during serious epidemics.

11.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324817

ABSTRACT

Background: The novel coronavirus (COVID-19)–infected pneumonia is an international concern as it spreads through human populations and across national and international borders. Methods: In this retrospective study, we consecutively included all cancer cases who had been identified as having a nucleic acid-confirmed COVID-19 infection from two designated hospitals in Wuhan, China. Non-cancer patients were also enrolled for comparison. The clinical data were gathered from the medical recordsfrom Jan 14 to March 12. Results: Among the 117 cancer patients infected with COVID19, the median age was 63 years and 48.7% were male. Male, hematologic cancer, dyspnea on admission, and anti-cancer therapy significantly increased the risk of death. The amounts of cytokines and immune cells were correlated with the outcomeofcancer patients infected with COVIP-19. However, high level of TNF-a, IL-2R, IL-6, IL-8 did not increase the risk of death in non-cancer patients. Moreover, IL-2R and IL-6 markedly decreased in cancer patients recovered from COVID-19. Conclusions: Cancer patients with COVID-19 were associated with high mortality (23.9%).The amounts of cytokines and lymphocytes could be utilized as the reference index in predicting the survival outcome of cancer patients with COVID-19.

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324244

ABSTRACT

The COVID-19 pandemic has spawned a rare opportunity to study some latent social structures using data science. The Chinese government and its people have been blamed for the outbreak of the virus. Face mask wearing can signal an embodied stigma and Chinese people living outside China have been subject to discrimination, assault, and other hate crimes, particularly at the early stages of the crisis. However, as we accumulate more evidence surrounding mask use, the stigma is shifting. As more scientific data become available and people leave even more information on social media during the lockdown, data science can help better understand the trajectories of the stigma. The insights generated have implications for anti-stigma interventions for future undesirable conditions and diseases.

13.
J Affect Disord ; 303: 187-195, 2022 04 15.
Article in English | MEDLINE | ID: covidwho-1676788

ABSTRACT

OBJECTIVE: The microbiota-gut-brain axis is a key pathway perturbed by prolonged stressors to produce brain and behavioral disorders. Frontline healthcare workers (FHWs) fighting against COVID-19 typically experience stressful event sequences and manifest some mental symptoms; however, the role of gut microbiota in such stress-induced mental problems remains unclear. We investigated the association between the psychological stress of FHW and gut microbiota. METHODS: We used full-length 16S rRNA gene sequencing to characterize the longitudinal changes in gut microbiota and investigated the impact of microbial changes on FHWs' mental status. RESULTS: Stressful events induced significant depression, anxiety, and stress in FHWs and disrupted the gut microbiome; gut dysbiosis persisted for at least half a year. Different microbes followed discrete trajectories during the half-year of follow-up. Microbes associated with mental health were mainly Faecalibacterium spp. and [Eubacterium] eligens group spp. with anti-inflammatory effects. Of note, the prediction model indicated that low abundance of [Eubacterium] hallii group uncultured bacterium and high abundance of Bacteroides eggerthii at Day 0 (immediately after the two-month frontline work) were significant determinants of the reappearance of post-traumatic stress symptoms in FHWs. LIMITATIONS: The lack of metabolomic evidence and animal experiments result in the unclear mechanism of gut dysbiosis-related stress symptoms. CONCLUSION: The stressful event sequences of fighting against COVID-19 induce characteristic longitudinal changes in gut microbiota, which underlies dynamic mental state changes.


Subject(s)
COVID-19 , Gastrointestinal Microbiome , Stress Disorders, Post-Traumatic , Animals , Dysbiosis/epidemiology , Dysbiosis/microbiology , Feces/microbiology , Health Personnel , Humans , RNA, Ribosomal, 16S/genetics , SARS-CoV-2
14.
Tertiary Education and Management ; 2022.
Article in English | PMC | ID: covidwho-1628827
15.
Front Med (Lausanne) ; 8: 714387, 2021.
Article in English | MEDLINE | ID: covidwho-1639203

ABSTRACT

This paper reports a complete case of severe acute respiratory distress syndrome (ARDS) caused by coronavirus disease 2019 (COVID-19), who presented with rapid deterioration of oxygenation during hospitalization despite escalating high-flow nasal cannulation to invasive mechanical ventilation. After inefficacy with lung-protective ventilation, positive end-expiratory pressure (PEEP) titration, prone position, we administered extracorporeal membrane oxygenation (ECMO) as a salvage respiratory support with ultra-protective ventilation for 47 days and finally discharged the patient home with a good quality of life with a Barthel Index Score of 100 after 76 days of hospitalization. The purpose of this paper is to provide a clinical reference for the management of ECMO and respiratory strategy of critical patients with COVID-19-related ARDS.

17.
Sustainability ; 14(1):385, 2022.
Article in English | MDPI | ID: covidwho-1580460

ABSTRACT

In order to achieve the goal of carbon neutrality and explore the impact of COVID-19 on urban road carbon emission, this study applied and improved a near real-time road carbon emission estimation method for typical Chinese urban agglomeration to improve the rapid evaluation of sustainable development. As a result, we recorded the daily road carbon emission for 12 cities in the Beijing–Tianjin–Hebei (JJJ) region under the impact of the epidemic, exploring the road carbon reduction effect caused by COVID-19. Singular value decomposition method was used to analyze the temporal and spatial characteristics of road carbon emission changes among cities and to explore the urban resilience oriented to public events. The results show: (1) In the JJJ region, the carbon reduction effect caused by COVID-19 is significant, but it lasted for a short time. In the three periods—before the epidemic, strict lockdown period, and post-lockdown period for prevention and control—the total daily road carbon emissions in the 12 cities were 170,000–190,000 tons, 90,000–110,000 tons, and 160,000–180,000 tons, respectively. (2) Cities in the JJJ region showed different road carbon reduction potential under short-term administrative control. During the “strict lockdown period”(23 January–25 February 2020), the average change rate of road carbon emissions in Beijing was −78.72%, which had great potential for reduction. However, the average change rates of Xingtai and Zhangjiakou were only −7.53% and −8.66%, respectively. (3) There are spatiotemporal differences in carbon emissions of urban roads in the JJJ region under the impact of the epidemic. During the gradual reduction of COVID-19 restrictions, great differences between cities on weekends and holidays arise, showing the road carbon emissions in Beijing on weekends and holidays are far lower than that in other cities. (4) In the face of public emergencies, the larger the city is and the more complex the function of the city is, the more difficult for the city is to maintain a steady state. This study not only provides an idea for the dynamic monitoring of urban carbon emissions to improve the rapid evaluation of urban sustainable development in post- and pre-lockdown but also fills the gap in the research on the differences in the response of cities to sudden security incidents from the perspective of road carbon emissions.

18.
Appl Soft Comput ; 116: 108291, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1568513

ABSTRACT

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods.

20.
Comput Math Methods Med ; 2021: 7259414, 2021.
Article in English | MEDLINE | ID: covidwho-1533111

ABSTRACT

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/statistics & numerical data , COVID-19/diagnosis , Computational Biology , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted/statistics & numerical data , Humans , Imaging, Three-Dimensional/statistics & numerical data , Pandemics , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , SARS-CoV-2
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