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3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325180

ABSTRACT

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. Features extracted from the two parallel encoders are concatenated for the subsequent decoder part. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. Our proposed method promotes new insights into annotation-efficient deep learning for COVID-19 infection segmentation and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.

4.
Mathematical Problems in Engineering ; 2022, 2022.
Article in English | ProQuest Central | ID: covidwho-1685749

ABSTRACT

When extracting flight data from airport terminal area, there are matters such as large volume, unclear features, and similar trend in time series. In order to deal with the related issues and to optimize the description, by combining with the TBO (Trajectory-Based Operation), an application proposed by the ICAO (International Civil Aviation Organization) in ASBU (Aviation System Block Upgrade), using multisource dynamic model to establish 4DDW (4D dynamic warping) algorithm, the multisource modeling integrated with evaluation system is proposed to realize the flight path optimization with time series characteristics and accord with the interval concept. The calculation results show that 4DDW can obtain the optimal solution for multiprofile calculation of TBO by comparing the composite trajectory deviation values and time dimension planning using the buffer and threshold values recommended by ICAO in airspace planning and flight procedure design. The results meet the requirements of high accuracy and convergence features of spatial waypoints and can improve the airport operation standards and terminal area capacity.

5.
Resour Policy ; 76: 102584, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1683567

ABSTRACT

The recent Covid-19 pandemic outbreak caused a global economic recession and promoted uncertainty in the natural resources. Also, this uncertainty is linked with the demand and supply of natural resources such as oil and natural gas, which is a substantial factor of industrial and economic activities. Declining natural resource demands substantially drop such activities that adversely affect economic performance. This attracts the attention of policy-makers and governors to efficiently tackle the issue. This study investigates the association of natural resources volatility, global economic performance, and public administration in earlier and Covid-19 pandemic peak periods. The study covers the period from 1990 to 2020 for the global data. The empirical findings of the cointegration test suggested that the variables are cointegrated. This study utilizes three long-run estimators, i.e., fully modified ordinary least square (FMOLS), dynamic OLS (DOLS), and Canonical Cointegrating Regression (CCR). The empirical findings suggest that natural resources volatility (TNR) negatively and significantly affect global economic performance. While natural gas rents, oil rents, and public administration quality (QPA) promote global economic performance. Besides, the results also indicate that the interaction of QPA and TNR enhances economic performance. This study demonstrates that volatility in natural resources is detrimental to global economic performance. However, improved public administrative quality could play a significant role in transforming the negative influence. of natural resources volatility into a positive effect. The findings are robust as validated by Robust regression. This study provides some practical policy insights for the governors and policy-makers to tackle the mentioned issues.

6.
J Med Chem ; 65(1): 876-884, 2022 01 13.
Article in English | MEDLINE | ID: covidwho-1606194

ABSTRACT

Coronavirus disease 2019 (COVID-19) pandemic, a global health threat, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 papain-like cysteine protease (PLpro) was recognized as a promising drug target because of multiple functions in virus maturation and antiviral immune responses. Inhibitor GRL0617 occupied the interferon-stimulated gene 15 (ISG15) C-terminus-binding pocket and showed an effective antiviral inhibition. Here, we described a novel peptide-drug conjugate (PDC), in which GRL0617 was linked to a sulfonium-tethered peptide derived from PLpro-specific substrate LRGG. The EM-C and EC-M PDCs showed a promising in vitro IC50 of 7.40 ± 0.37 and 8.63 ± 0.55 µM, respectively. EC-M could covalently label PLpro active site C111 and display anti-ISGylation activities in cellular assays. The results represent the first attempt to design PDCs composed of stabilized peptide inhibitors and GRL0617 to inhibit PLpro. These novel PDCs provide promising opportunities for antiviral drug design.


Subject(s)
Aniline Compounds/chemistry , Antiviral Agents/metabolism , Benzamides/chemistry , Coronavirus Papain-Like Proteases/metabolism , Drug Design , Naphthalenes/chemistry , Peptides/chemistry , SARS-CoV-2/enzymology , Aniline Compounds/metabolism , Aniline Compounds/pharmacology , Antiviral Agents/chemistry , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Benzamides/metabolism , Benzamides/pharmacology , COVID-19/drug therapy , COVID-19/pathology , COVID-19/virology , Cell Line , Cell Survival/drug effects , Coronavirus Papain-Like Proteases/chemistry , Cytokines/chemistry , Drug Evaluation, Preclinical , Humans , Inhibitory Concentration 50 , Naphthalenes/metabolism , Naphthalenes/pharmacology , SARS-CoV-2/isolation & purification , Ubiquitins/chemistry
7.
Cardiovasc Digit Health J ; 3(1): 31-39, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1520812

ABSTRACT

BACKGROUND: COVID-19 boosted healthcare digitalization and personalization in cardiology. However, understanding patient attitudes and engagement behaviors is essential to achieve successful acceptance and implementation of digital health technologies in personalized care. OBJECTIVE: This study aims to understand current and future trends in wearable device and telemedicine use in the cardiology clinic patient population, recognize patients' attitude towards digital health before and after COVID-19, and identify potential socioeconomic and racial/ethnic differences in adoption of digital health tools in a New Orleans patient population. METHODS: A cross-sectional survey was distributed to Tulane Cardiology Clinic patients between September 2020 and January 2021. Basic demographic information, medical comorbidities, device usage, and opinions on digital health tools were collected. RESULTS: Survey responses from 299 participants (average age = 54 years, 50.8% female, 24.4% African American) showed that digital health use was more prevalent in younger, healthier, and more educated individuals. Wearable use was also higher among White patients compared to African American patients. Patients cited costs and technology knowledge as primary deterrents for using wearables, despite being more inclined to use wearables for disease monitoring (41%). While wearable use did not increase after COVID-19 (36.6% pre-COVID vs 35.4% post-COVID, P = .77), telemedicine use rose significantly (10.8% pre-COVID vs 24.3% during COVID, P < .0001). Patients mostly noted telemedicine's effectiveness in overcoming difficult healthcare access barriers. Additionally, most patients are in support of wearables and telemedicine either complementing or replacing routine tests and traditional clinical visits. CONCLUSION: Demographic and socioeconomic disparities negatively impact wearable health device and telemedicine adoption within cardiovascular clinic patients. Although telemedicine use increased after COVID-19, this effect was not observed for wearables, reflecting significant economic and digital literacy challenges underlying wearable acceptance.

8.
IEEE J Biomed Health Inform ; 25(11): 4152-4162, 2021 11.
Article in English | MEDLINE | ID: covidwho-1507113

ABSTRACT

The novel Coronavirus disease (COVID-19) is a highly contagious virus and has spread all over the world, posing an extremely serious threat to all countries. Automatic lung infection segmentation from computed tomography (CT) plays an important role in the quantitative analysis of COVID-19. However, the major challenge lies in the inadequacy of annotated COVID-19 datasets. Currently, there are several public non-COVID lung lesion segmentation datasets, providing the potential for generalizing useful information to the related COVID-19 segmentation task. In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. The model consists of a general encoder to capture general lung lesion features based on multiple non-COVID lesions, and a target encoder to focus on task-specific features based on COVID-19 infections. We develop a collaborative learning scheme to regularize feature-level relation consistency of given input and encourage the model to learn more general and discriminative representation of COVID-19 infections. Extensive experiments demonstrate that trained with limited COVID-19 data, exploiting shared knowledge from non-COVID lesions can further improve state-of-the-art performance with up to 3.0% in dice similarity coefficient and 4.2% in normalized surface dice. In addition, experimental results on large scale 2D dataset with CT slices show that our method significantly outperforms cutting-edge segmentation methods metrics. Our method promotes new insights into annotation-efficient deep learning and illustrates strong potential for real-world applications in the global fight against COVID-19 in the absence of sufficient high-quality annotations.


Subject(s)
COVID-19 , Lung , Benchmarking , Humans , Lung/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed
10.
Health Science Journal ; 14(2):1-13, 2020.
Article in English | ProQuest Central | ID: covidwho-833462

ABSTRACT

According to folk beliefs, the meats of the wild game have natural healing powers and are thus often used in "dietary therapy" and traditional Chinese medicine. The most common diagnosis tool has been quantitative reverse transcriptase PCR (qRT-PCR) but medical professionals have been pushing to establish diagnostic criteria based on high definition chest CT, in order to circumvent the limited capacity of the PCR kits [69]. [...]Japan, one of the most severely affected countries other than China, is also the host nation for the upcoming 2020 Summer Olympics [21]. [...]Japan is under great pressure to keep the epidemic in check ahead of the world's greatest support event, where heavy international traffic may exacerbate the spread of the virus [51]. [...]there is a speculation that SARSCoV-1 and SARS-CoV-2 have similar transmissions, since they are both coronaviruses.

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