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1.
BMC Pulmonary Medicine ; 22(1), 2022.
Article in English | EMBASE | ID: covidwho-1822183

ABSTRACT

Background: The respiratory rate-oxygenation (ROX) index has been increasingly applied to predict the outcome of high-flow nasal cannula (HFNC) in pneumonia patients with acute hypoxemic respiratory failure (AHRF). However, its diagnostic accuracy for the HFNC outcome has not yet been systematically assessed. This meta-analysis sought to evaluate the predictive performance of the ROC index for the successful weaning from HFNC in pneumonia patients with AHRF. Methods: A literature search was conducted on electronic databases through February 12, 2022, to retrieve studies that investigated the diagnostic accuracy of the ROC index for the outcome of HFNC application in pneumonia patients with AHRF. The area under the hierarchical summary receiver operating characteristic curve (AUHSROC) was estimated as the primary measure of diagnostic accuracy due to the varied cutoff values of the index. We observed the distribution of the cutoff values and estimated the optimal threshold with corresponding 95% confidential interval (CI). Results: Thirteen observational studies comprising 1751 patients were included, of whom 1003 (57.3%) successfully weaned from HFNC. The ROC index exhibits good performance for predicting the successful weaning from HFNC in pneumonia patients with AHRF, with an AUHSROC of 0.81 (95% CI 0.77–0.84), a pooled sensitivity of 0.71 (95% CI 0.64–0.78), and a pooled specificity of 0.78 (95% CI 0.70–0.84). The cutoff values of the ROX index were nearly conically symmetrically distributed;most data were centered between 4.5 and 6.0, and the mean and median values were 4.8 (95% CI 4.2–5.4) and 5.3 (95% CI 4.2–5.5), respectively. Moreover, the AUHSROC in the subgroup of measurement within 6 h after commencing HFNC was comparable to that in the subgroup of measurement during 6–12 h. The stratified analyses also suggested that the ROC index was a reliable predictor of HFNC success in pneumonia patients with coronavirus disease 2019. Conclusions: In pneumonia patients with AHRF, the ROX index measured within 12 h after HFNC initiation is a good predictor of successful weaning from HFNC. The range of 4.2–5.4 may represent the optimal confidence interval for the prediction of HFNC outcome.

2.
Chinese Journal of Evidence-Based Medicine ; 22(4):457-462, 2022.
Article in Chinese | EMBASE | ID: covidwho-1818645

ABSTRACT

Objective To assess the methodological quality of pediatric COVID-19 guidelines using the AGREE Ⅱ. Methods Domestic and foreign pediatric COVID-19 guidelines from inception to 1st Oct 2021 were electronically searched in PubMed, CBM, CNKI, VIP, WanFang Data, Medlive, NGC, GIN, and NICE databases and relevant websites. Two researchers independently assessed the methodological quality of the guidelines by using AGREE Ⅱ. Results A total of 21 guidelines were included. The AGREE Ⅱ results revealed that the average scores of included guidelines in 6 domains (scope and purpose, stakeholder involvement, rigor of development, clarity of presentation, applicability, and editorial independence) were 62.70%, 36.24%, 20.34%, 50.42%, 22.12% and 53.17%, respectively. Conclusion The methodological quality of pediatric COVID-19 guidelines is poor. Guideline developers should follow the requirements of AGREE Ⅱ in guideline development.

3.
Frontiers in Psychology ; 13:789844, 2022.
Article in English | MEDLINE | ID: covidwho-1818009

ABSTRACT

Purpose: College students in the pandemic area are experiencing the problems caused by COVID-19 by themselves or people around them, how to cope with the sudden changes and adjust the psychological stress response, and get experience and grow in the fight against the pandemic is a question worth in-depth discussion. The researchers constructed a mediated regulation model to examine the effects of intrusive rumination on the creativity of college students during the COVID-19 pandemic, as well as the mediating effect of post-traumatic growth and the moderating role of psychological resilience. Methods: A sample of 475 university students from Guangdong Province, China, were surveyed with the Runco Ideational Behavior Scale, the Event Related Rumination Inventory, the Posttraumatic Growth Inventory, and the Psychological Resilience Scale. SPSS (version 23) and PROCESS (version 3.3) were used for correlation analysis, mediation analysis, and mediated moderation analysis. Results: (1) Intrusive rumination was positively correlated with post-traumatic growth and creativity but negatively correlated with psychological resilience. Psychological resilience was positively correlated with post-traumatic growth and creativity. Post-traumatic growth and creativity were positively correlated. (2) Post-traumatic growth played a mediating role in the relationship between intrusive rumination and creativity. (3) Psychological resilience moderated the first half of the pathway "intrusive rumination -> post-traumatic growth -> creativity." Conclusion: Intrusive rumination affected creativity directly and also indirectly through post-traumatic growth. At the same time, psychological resilience played a moderating role between intrusive rumination and creativity. The correlation between intrusive rumination and post-traumatic growth was stronger when levels of psychological resilience levels were higher.

4.
Brief Bioinform ; 2022.
Article in English | PubMed | ID: covidwho-1806277

ABSTRACT

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

5.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333517

ABSTRACT

PURPOSE: Conjunctival signs and symptoms are observed in a subset of patients with COVID-19, and SARS-CoV-2 has been detected in tears, raising concerns regarding the eye both as a portal of entry and carrier of the virus. The purpose of this study was to determine whether ocular surface cells possess the key factors required for cellular susceptibility to SARS-CoV-2 entry/infection. METHODS: We analyzed human post-mortem eyes as well as surgical specimens for the expression of ACE2 (the receptor for SARS-CoV-2) and TMPRSS2, a cell surface-associated protease that facilitates viral entry following binding of the viral spike protein to ACE2. RESULTS: Across all eye specimens, immunohistochemical analysis revealed expression of ACE2 in the conjunctiva, limbus, and cornea, with especially prominent staining in the superficial conjunctival and corneal epithelial surface. Surgical conjunctival specimens also showed expression of ACE2 in the conjunctival epithelium, especially prominent in the superficial epithelium, as well as the substantia propria. All eye and conjunctival specimens also expressed TMPRSS2. Finally, western blot analysis of protein lysates from human corneal epithelium obtained during refractive surgery confirmed expression of ACE2 and TMPRSS2. CONCLUSIONS: Together, these results indicate that ocular surface cells including conjunctiva are susceptible to infection by SARS-CoV-2, and could therefore serve as a portal of entry as well as a reservoir for person-to-person transmission of this virus. This highlights the importance of safety practices including face masks and ocular contact precautions in preventing the spread of COVID-19 disease.

6.
Molecular Immunology ; 141:222-223, 2022.
Article in English | Web of Science | ID: covidwho-1801749
7.
Journal of Image and Graphics ; 27(3):827-837, 2022.
Article in Chinese | Scopus | ID: covidwho-1789675

ABSTRACT

Objective: The corona virus disease 2019 (COVID-19), also known as severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread throughout the world as a result of the increased mobility of populations in a globalized world, wreaking havoc on people's daily lives, the global economy, and the global healthcare system. The novelty and dissemination speed of COVID-19 compelled researchers around the world to move quickly, using all resources and capabilities to analyse and characterize the novel coronavirus in terms of transmission routes and viral latency. Early and effective screening of COVID-19 patients and corresponding medical treatment, care and isolation to cut off the transmission route of the novel coronavirus are the key to prevent the spread of the epidemic. Due to the rapid infection of COVID-19, it is very important to screen COVID-19 threats based on precise segmenting lesions in lung CT images, which can be a low cost and quick response method nowadays. Rapid and accurate segmentation of coronavirus pneumonia CT images is of great significance for auxiliary diagnosis and patient monitoring. Currently, the main method for COVID-19 screening is the reverse transcription polymerase chain reaction like reverse transcription-polymerase chain reaction(RT-PCR) analysis. But, RT-PCR is time consuming to provide the diagnosis results, and the false negative rate is relatively high. Another effective method for COVID-19 screening is computed tomography (CT) technology. The CT scanning technology has high sensitivity and enhanced three-dimensional representation of infection visualization. Computed tomography (CT) has been used as an important method for the diagnosis and treatment of patients with COVID-19, the chest CT images of patients with COVID-19 mostly show multifocal, patchy, peripheral distribution, and ground glass opacity (GGO) which is mostly seen in the lower lobes of both lungs;a high degree of suspicion for novel coronavirus's infection can be obtained if more GGO than consolidation is found on CT images;therefore, detection of GGO in CT slices regions can provide clinicians with important information and help in the fight against COVID-19. The current analysis of COVID-19 pneumonia lesions has low segmentation accuracy and insufficient attention to false negatives. Method: Our accurate segmentation model based on small data set. In view of the complexity and variability of the targeted area of COVID-19 pneumonia, we improved Inf-Net and proposed a multi-scale encoding and decoding network (MED-Net) based on deep learning method. The computational cost may be caused by multi-scale encoding and decoding. The network extends the encoder-decoder structure in FC-Net, in which the decoder part is on the left column;The middle column is atrous spatial pyramid pooling (ASPP) structure;The right column is a multi-scale parallel decoder which is based on the improvement of parallel partial decoder. In this network structure, HarDNet68 is adopted as the backbone in terms of high resource utilization and fast computing speed, which can be as a simplified version of DenseNet, reduces DenseNet based hierarchical connections to get cascade loss deduction. HardNet68 is mainly composed of five harmonious dense blocks (HDB). Based on 5 different scales, We extract multiscale features from the first convolution layer and the 5 HDB sequential steps of HarDNet68 via a five atrous spatial pyramid pooling (ASPP). Meanwhile, as a new decoding component, a multiscale parallel partial decoder (MPPD) is based on the parallel decoder (PPD), which can aggregate the features between different levels in parallel. By decoding the branches of three different receptive fields, we have dealt with information loss issues in the encoder part and the difficulty of small lesions segmentation. Our deep supervision mechanism has melted the multi-scale decoder into the true positive and true negative samples analyses, for improving the sensitivity of the model. Result: Current COVID-19 CT Segmentation provides compl ted segmentation labels as a small data set. This research is improved based on Inf-Net, and the model structure is simple, the edge attention module(EA) is not introduced, and the reverse attention module(RA) is not quoted, only one MPPD is used to optimize the network stricture. The quantitative results show that MED-Net can effectively cope with the problems of fewer samples in the small dataset, the texture, size and position of the segmentation target vary greatly. On the data set with only 50 training images and 50 test images, the Dice coefficient is 73.8%, the sensitivity is 77.7%, and the specificity is 94.3%. Compared with the previous work, it has increased by 8.21%, 12.28% and 7.76% respectively. Among them, Dice coefficient and sensitivity have reached the most advanced level based on the same division mode of this data set. Simultaneously the qualitative results address that the segmentation result of the proposed model is closer to ground-truth in this experiment. We also conducted ablation experiments, that the use of MPPD has obvious effects to deal with small lesions area segmentation and improving segmentation accuracy. Conclusion: Our analysis shows that the proposed method can effectively improve the segmentation accuracy of the lesions in the CT images of the COVID-19 derived lungs disease. Our segmentation accuracy of MED-Net is qualified. The quantitative and qualitative results demonstrate that MED-Net is relatively effective in controlling edges and details, which can capture rich context information, and improve sensitivity. MED-Net can also effectively resolve the small lesions segmentation issue. For COVID-19 CT Segmentation data set, it has several of qualified evaluation indicators based on end-to-end learning. The potential of automatic segmentation of COVID-19 pneumonia is further facilitated. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.

8.
Open Forum Infectious Diseases ; 8(SUPPL 1):S1, 2021.
Article in English | EMBASE | ID: covidwho-1746817

ABSTRACT

Background. The mechanisms associated with COVID-19 in children are not well understood. We sought to define the differences in nasopharyngeal (NP) cytokine profiles according to clinical presentation in children with COVID-19. Methods. Single-center, prospective study in 137 children and adolescents < 21 years of age hospitalized with COVID-19, and 35 age, sex and race matched pre-pandemic (2016-2019) healthy controls. Children with COVID-19 were categorized according to their clinical presentation in: COVID-19-symptomatic;COVID-19-screening, and multisystem inflammatory syndrome (MIS-C). NP swabs were obtained within 24 hours of admission to measure SARS-CoV-2 loads by rt-PCR, and a 92-cytokine panel. Unsupervised and supervised analysis adjusted for multiple comparisons were performed. Results. From 3/2020 to 1/2021, we enrolled 76 COVID-19-symptomatic children (3.5 [0.2-15.75] years);45 COVID-19-screening (11.1 [4.2-16.1] years), and 16 MIS-C (11.2 [5.9-14.6] years). Median NP SARS-CoV-2 loads were higher in COVID-19-symptomatic versus screening and MIS-C (6.8 vs 3.5 vs 2.82 log10 copies/mL;p< 0.001). Statistical group comparisons identified 15 cytokines that consistently differed between groups and were clustered in three functional categories: (1) antiviral/regulatory, (2) pro-inflammatory/chemotactic, and (3) a combination of (1) and (2);(Fig 1). All 15 cytokines were higher in COVID-19-symptomatic versus controls (p< 0.05). Similarly, and except for TNF, CCL3, CCL4 and CCL23, which were comparable in COVID-19-symptomatic and screening patients, the remaining cytokines were higher in symptomatic children (p< 0.05). PDL-1 (p=0.01) and CCL3 (p=0.03) were the only cytokines significantly decreased in children with MIS-C versus symptomatic COVID-19 children. The 15 cytokines identified by multiple comparisons were correlated using Person's in R software. Red reflects a positive correlation and blue a negative correlation with the intensity of the color indicating the strength of the association. Conclusion. Children with symptomatic COVID-19 demonstrated higher viral loads and greater mucosal cytokines concentrations than those identified via screening, whereas in MIS-C concentrations of regulatory cytokines were decreased. Simultaneous evaluation of viral loads and mucosal immune responses using non-invasive sampling could aid with the stratification of children and adolescents with COVID-19 in the clinical setting.

9.
Open Forum Infectious Diseases ; 8(SUPPL 1):S51-S52, 2021.
Article in English | EMBASE | ID: covidwho-1746790

ABSTRACT

Background. Almost 4 million children have tested positive for Coronavirus Disease 2019 (COVID-19) as of June 3 2021, representing 14% of all cases in USA. Children present with diverse clinical findings including the multisystem inflammatory syndrome in children (MIS-C). In this study, we measured serum cytokine concentrations in children with COVID-19 to identify differences in immune profiles according to clinical presentations. Methods. A total of 133 children 0-21 years of age with COVID-19 were enrolled at Nationwide Children's Hospital, in Columbus, Ohio. Nasopharyngeal swab RT-PCR testing was used for SARS-CoV-2 detection and quantification. Clinical and laboratory information were obtained, and blood samples were collected for measurement of cytokines with a 92-plex inflammation assay (Olink). Normalized cytokine expression levels in patients were compared with serum samples from 66 pre-pandemic agematched healthy controls. Results. COVID-19 children included: 1) those identified by universal screening (n=47);2) moderate disease (ward;n=48);3) severe disease (PICU;n=20);4) MIS-C (n=18). Children identified by universal screening were hospitalized for trauma, appendicitis or new onset diabetes among others. Children with symptomatic COVID-19 had significantly higher SARS-CoV-2 viral loads than children with MIS-C or those identified via universal screening. Concentrations of interferon (IFN) related cytokines (IFNg, CXCL9, CXCL10, CXCL11), interleukins (IL6, IL8, IL10, IL17A, IL18, IL24) and other inflammatory cytokines (TGF, TNF, VEGF, MCP, CD40) were significantly increased in children with acute COVID-19 and MIS-C compared with children identified by universal screening and healthy controls. These cytokines were positively correlated with C-reactive protein, D-dimer and disease severity in COVID-19, but negatively correlated with viral loads (Fig 1). MIS-C showed stronger inflammatory response than acute COVID-19 (Fig 2). Correlation of Age-adjusted cytokine expression values with viral load, disease severity, CRP and D-dimer. Pearson correlation coefficient is shown for each pair. Red: positive correlation;blue: negative correlation Cytokines that differentiate MIS-C from acute COVID-19 Heatmap shows the differential expressed cytokines between MIS-C and acute severe COVID-19 (padj<0.05, FC>2). The age-adjusted expression values are normalized the median of healthy controls. Red: up-regulation, blue: down-regulation. Conclusion. We identified three cytokine clusters in children with COVID-19 according to clinical presentations. Correlations of serum cytokines with clinical/laboratory parameters could be used to identify potential biomarkers associated with disease severity in COVID-19.

10.
2021 IEEE International Conference on Engineering, Technology and Education, TALE 2021 ; : 42-47, 2021.
Article in English | Scopus | ID: covidwho-1741274

ABSTRACT

Domain-Specific Architectures (DSAs) and hardware-software co-design are greatly emphasized in the CS community, which demands a significant number of participants with Computer System (CSys) capabilities and skills. Conventional CSys courses in a lecture-lab format are limited in physical resources and inherently difficult to cultivate talents at a large scale. Online teaching is a potential alternative to instantly enlarge the face-to-face class size. Unfortunately, simply putting the lecture contents in CSys courses online lacks 1) personal attention, 2) learner-instructor interactions, and 3) real-hardware experimental environments. To tackle the above challenges, we introduce a four phase online CSys course program and the related teaching methods for a cloud-based teaching platform. The four-phase course program included two basic/required stages and two advanced/optional stages to promote students' knowledge and skill level with appropriate personal attention. We studied if online interaction methods, such as in-class chat and one-on-one online grading interview, can strengthen the connections between teachers and students in both lectures and labs. We created a heterogeneous cloud platform to enable students nationwide to reliably conduct labs or projects on remote programmable hardware. We believe that our proposed course design methodology is beneficial to other CScourses in the post-COVID-19-era. © 2021 IEEE.

11.
21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 ; 2021-December:517-526, 2021.
Article in English | Scopus | ID: covidwho-1730932

ABSTRACT

COVID-19 has been a public health emergency of international concern since early 2020. Reliable forecasting is critical to diminish the impact of this disease. To date, a large number of different forecasting models have been proposed, mainly including statistical models, compartmental models, and deep learning models. However, due to various uncertain factors across different regions such as economics and government policy, no forecasting model appears to be the best for all scenarios. In this paper, we perform quantitative analysis of COVID-19 forecasting of confirmed cases and deaths across different regions in the United States with different forecasting horizons, and evaluate the relative impacts of the following three dimensions on the predictive performance (improvement and variation) through different evaluation metrics: model selection, hyperparameter tuning, and the length of time series required for training. We find that if a dimension brings about higher performance gains, if not well-tuned, it may also lead to harsher performance penalties. Furthermore, model selection is the dominant factor in determining the predictive performance. It is responsible for both the largest improvement and the largest variation in performance in all prediction tasks across different regions. While practitioners may perform more complicated time series analysis in practice, they should be able to achieve reasonable results if they have adequate insight into key decisions like model selection. © 2021 IEEE.

13.
Advanced Materials Technologies ; : 9, 2022.
Article in English | Web of Science | ID: covidwho-1664338

ABSTRACT

A gold nanoparticle (AuNP)-labeled CRISPR-Cas13a nucleic acid assay is developed for sensitive solid-state nanopore sensing. Instead of directly detecting the translocation of RNA through a nanopore, the system utilizes non-covalent conjugates of AuNPs and RNA targets. Upon CRISPR activation, the AuNPs are liberated from the RNA, isolated, and passed through a nanopore sensor. Detection of the AuNPs can be observed as increasing ionic current in the chip. Each AuNP that is detected is enumerated as an event, leading to quantitative of molecular targets. Leveraging the high signal-to-noise ratio enabled by the AuNPs, a detection limit of 50 fM before front-end target amplification is achieved using SARS-CoV-2 RNA segments as a Cas13 target. Furthermore, a dynamic range of six orders of magnitude is demonstrated for quantitative RNA sensing. This simplified AuNP-based CRISPR assay is performed at the physiological temperature without relying on thermal cyclers. In addition, the nanopore reader is similar in size to a smartphone, making the assay system suitable for rapid and portable nucleic acid biomarker detection in either low-resource settings or hospitals.

14.
IEEE Control Systems Letters ; 2021.
Article in English | Scopus | ID: covidwho-1612807

ABSTRACT

Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (w-GSTL) formulas. For learning w-GSTL formulas, we introduce a flexible w-GSTL formula structure in which the user’s preference can be applied in the inferred w-GSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible w-GSTL formula structure. We initially train a neural network to learn the w-GSTL operators, and then train a second neural network to learn the parameters in a flexible w-GSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods. IEEE

15.
Journal of Intelligent & Fuzzy Systems ; 41(6):6739-6754, 2021.
Article in English | Web of Science | ID: covidwho-1581401

ABSTRACT

In practical multiple attribute decision making (MADM) problems, the interest groups or individuals intentionally set attribute weights to achieve their own benefits. In this case, the rankings of different alternatives are changed strategically, which is called the strategic weight manipulation in MADM. Sometimes, the attribute values are given with imprecise forms. Several theories and methods have been developed to deal with uncertainty, such as probability theory, interval values, intuitionistic fuzzy sets, hesitant fuzzy sets, etc. In this paper, we study the strategic weight manipulation based on the belief degree of uncertainty theory, with uncertain attribute values obeying linear uncertain distributions. It allows the attribute values to be considered as a whole in the operation process. A series of mixed 0-1 programming models are constructed to set a strategic weight vector for a desired ranking of a particular alternative. Finally, an example based on the assessment of the performance of COVID-19 vaccines illustrates the validity of the proposed models. Comparison analysis shows that, compared to the deterministic case, it is easier to manipulate attribute weights when the attribute values obey the linear uncertain distribution. And a further comparative analysis highlights the performance of different aggregation operators in defending against the strategic manipulation, and highlights the impacts on ranking range under different belief degrees.

16.
American Journal of Translational Research ; 13(11):12206-12212, 2021.
Article in English | EMBASE | ID: covidwho-1567578

ABSTRACT

Coronavirus disease 2019 (COVID-19) is now a major public health problem worldwide. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity is extremely strong. One major target of the virus is the lung, which can lead to death due to the development of respiratory distress syndrome and even multiple system organ failure. The possible pathophysiology by which SARS-CoV-2 affects the object is by way of the receptor, angiotensin-converting enzyme 2 (ACE2). From the study of the viral structure and infection mechanisms, researchers have discovered that the ACE2 acts as a receptor for SARS-CoV-2. According to previous studies, ACE2 is one of the key enzymes in the RAS system. Physiological functions can be found in angiosarcomas and in the kidney, liver, intestine and so on. Whether SARS-CoV-2 infection leads to male fertility impairment has recently received attention. Nevertheless, the association between SARS-CoV-2 infection and reproductive health is currently poorly understood. Using key words including “SARS-CoV-2”, “reproductive health”, “ACE2” and “2019-nCoV”, we retrieved original articles and reviews from the PubMed and WEB OF SCI databases published before December 16, 2020 and performed a thorough review of them. Compared with females, we discovered that infected person with SARSCoV-2 was higher in males. Men who were infected with SARS-CoV-2 may be easy to suffer from impaired reproductive health. These investigations would help for a comprehensive grasp of the relationship between SARS-CoV-2 infection and reproductive health.

17.
Journal of Nuclear Medicine ; 62:2, 2021.
Article in English | Web of Science | ID: covidwho-1567475
18.
International Symposium on Artificial Intelligence and Robotics 2021 ; 11884, 2021.
Article in English | Scopus | ID: covidwho-1566328

ABSTRACT

Predicting the population density in certain key areas of the city is of great importance. It helps us rationally deploy urban resources, initiate regional emergency plans, reduce the spread risk of infectious diseases such as Covid-19, predict travel needs of individuals, and build intelligent cities. Although current researches focus on using the data of point-of-interest (POI) and clustering belonged to unsupervised learning to predict the population density of certain neighboring cities to define metropolitan areas, there is almost no discussion about using spatial-temporal models to predict the population density in certain key areas of a city without using actual regional images. We 997 key areas in Beijing and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of three parts, which are the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the Data Fountain platform, we evaluate the model and compare it with some typical models. Experimental results show that the Spatial Convolution Layer can merge features of the nodes and edges to reflect the spatial correlation, the Temporal Convolution Layer can extract the temporal dependence, and the Feature Component can enhance the importance of other attributes that affect the population density of the area. In general, the WE-STGCN is better than baselines and can complete the work of predicting population density in key areas. © 2021 SPIE.

19.
Renewable and Sustainable Energy Reviews ; 156, 2022.
Article in English | Scopus | ID: covidwho-1565639

ABSTRACT

Due to rapid urbanization in developing countries, it is necessary for respective governments to seek new approaches to providing sustainable fresh food and clean energy supplies. The implementation of photovoltaic (PV) systems in hydroponic farms offers an innovative solution to shortages of energy and fresh food in urban areas. These shortages have become increasingly serious during the Covid-19 lockdown periods. This paper proposes an approach to analyzing the impacts of the PV system subsidy schemes on solar-assisted hydroponic farm (SAHF) design and planning, in terms of the profit and outputs of the SAHF and the effectiveness of the subsidies. The subsidy schemes considered include the improving electricity tariff (IET), feed-in tariff (FIT) scheme, and investment co-funding (ICF) scheme. A quadratic programming model is developed to optimize the type and capacity of the PV system simultaneously. The proposed model can be solved by most commercial solvers using the linearization approach proposed in this study. A case study in Qatar is analyzed and incentive thresholds that promise PV system adoption profitability for SAHFs under IET, full FIT, surplus FIT, and ICF subsidies are identified. The thresholds are 0.0425 $/kWh, 0.0063 $/kWh, 0.027 $/kWh, and 14.90%, respectively. The subsidy conditions that lead to optimum benefits for hydroponic farmers and the government are identified via sensitivity analysis. Our method helps policymakers to optimize subsidy levels and therefore reduces subsidy inefficiency. For hydroponic farmers, it is profitable to take full advantage of the available space to enlarge their PV systems if the surplus electricity can be sold to the grid or other entities. © 2021 The Authors

20.
Journal of Immunology ; 206:2, 2021.
Article in English | Web of Science | ID: covidwho-1548738
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