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Introduction: The objective of this study is to investigate the relationship between Cycle Threshold (Ct) values and serum biomarkers in COVID-19 patients with Total Severity Score (TSS) on chest computed tomography (CT). Apart from this, this study also aims to explore the role of TSS, serum biomarkers and viral load in predicting the disease severity and clinical outcome of patients with COVID-19. Method(s): In this retrospective cross-sectional study, we included 213 confirmed COVID-19 patients from Hospital Sungai Buloh who conform to the inclusion criteria. A search was performed on the picture achieving and communication system (PACS) and Centricity UV to collect data on the clinical features, laboratory findings (the first one upon admission), epidemiological characteristics as well as the chest CT scans of the targeted group. To quantify the extent of COVID- 19 lung involvement in CT scan, TSS was applied. Data was collected and analysed using SPSS. Result(s): There were significant correlations between TSS of chest CT with four out of the six serum biomarkers studied, namely C-Reactive Protein (CRP), Neutrophil-Lymphocyte Ratio (NLR), creatinine and Lactate Dehydrogenase (LDH). There was an inverse relationship between TSS and Ct values. TSS, serum biomarkers (NLR, CRP, LDH and creatinine) as well as Ct value are good predictors of disease severity. Conclusion(s): TSS is a reliable scoring method to determine the severity of COVID-19 patients. Serum biomarkers which include NLR, CRP, LDH and creatinine are good predictors of disease severity and can be used for stratification of patients according to severity. Ct value is a valuable early indicator of disease severity.
ABSTRACT
COVID-19-like pandemics are a major threat to the global health system that causes a lot of deaths across ages. Large-scale medical images (i.e., X-rays, computed tomography (CT)) dataset is favored to the accuracy of deep learning (DL) in the screening of COVID-19-like pneumonia. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it impossible to obtain large numbers of samples from a single institution. The research attentions have been moved toward sharing medical images from numerous medical institutions. However, owing to the necessity to preserve the privacy of the data of a patient, it is challenging to build a centralized dataset from many institutions, especially during the pandemic. More. The difference in the data acquisition process from one institution to another brings another challenge known as distribution heterogeneity. This paper presents a novel federated learning framework, called Federated Multi-Site COVID-19 (FEDMSCOV), for efficient, generalizable, and privacy-preserved segmentation of COVID-19 infection from multi-site data. In FEDMSCOV, a novel is local drift smoothing (LDS) module encodes the input from feature space to frequency space, aiming to suppress the modules that are not conducive to generalization. Given the smoothed local updated, FEDMSCOV presents a novel Mixture-of-Expert (MoE) scheme to resolve global shift in parameters. An adapted differential privacy method is applied to design and protect the privacy of local updates during the training. Experimental evaluation on a large-scale multi-institutional COVID-19 dataset demonstrated the efficiency of the proposed framework over competing learning approaches with statistical significance. IEEE
ABSTRACT
The major ports along the coast of China that undertake container transportation are all facing problems in collection and dispatching to a certain extent. In particular, due to the recent impact of the COVID-19 epidemic, truck drivers have difficulty moving across regions, and there was once a phenomenon of no containers being transported by vehicles. This paper sorted out the basic situation of container port collection and dispatching methods all over the world. Taking Shenzhen Port as an example, this paper focused on the analysis of the structural characteristics of container transportation and the impact on the rear urban traffic and atmospheric environment. Then it proposed a intermodal transportation network and established the 'Port Shuttle Hub System' model, which would closely link the port with the railway and inland port, and integrate the transportation organization mode, which greatly improves the efficiency of port containers' transportation. © 2021 IEEE.
ABSTRACT
The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.
ABSTRACT
Shelter in place (SIP) orders were instituted by states to alleviate the impact of the COVID-19 pandemic. However, states proceeded to reopen as SIPs were noted to be hurting the economy. We evaluated whether these reopenings affected COVID-19 hospitalizations. We collected public data on US state reopening orders and COVID-19 hospitalizations from March 8 to August 8, 2020. We utilized a doubling time metric to compare increase in hospitalizations in line with reopenings and proceeded to quantify the impact of reopening orders on cumulative hospitalizations. We found that some reopenings increased hospitalizations, and this varied by state. We also discovered that the most negatively impactful reopenings overall tended to be restaurants/bars (-92%) and houses of worship (-63.6%). Without data-backed guidance on reopening states, the healthcare burden from COVID-19 will likely persist. State governments should use data to understand the potential effects of these reopenings to guide future policies.
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We present a differential equation model of the innate immune response to SARS-CoV-2 within the alveolar epithelium. Critical determinants of the viral dynamics and host response, including type I and type II alveolar epithelial cells, interferons, chemokines, toxins and innate immune cells, are included. We estimate model parameters, compute the within-host basic reproductive number, and study the impacts of therapies, prophylactics, and host/pathogen variability on the course of the infection. Model simulations indicate that the innate immune response suppresses the infection and enables the alveolar epithelium to partially recover. While very robust antiviral therapy controls the infection and enables the epithelium to heal, moderate therapy is of limited benefit. Meanwhile interferon therapy is predicted to reduce viral load but exacerbate tissue damage. The deleterious effects of interferon therapy are especially apparent late in the infection. Individual variation in ACE2 expression, epithelial cell interferon production, and SARS-CoV-2 spike protein binding affinity are predicted to significantly impact prognosis.
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Sneezes play a key role in transferring respiratory diseases such as COVID-19 between infectious and susceptible individuals where the timely monitoring and alerting for sneezes can be important in preventing the spread of such diseases. This paper presents Wi-Sneeze, a Wi-Fi based passive radar for sneeze sensing using Wi-Fi signals, as a promising solution to detect the potentially harmful exhaled volume-based sneeze droplets. Using the size distributions of the droplets exhaled by sneezes, an accurate volume RCS for the sneeze droplets is calculated, leading to the computation of the SNR which enable the range prediction for detecting the sneeze droplets. Experimental trials are conducted for real human sneezes and using the associated signal processing schemes, the Doppler frequency of the sneeze droplets can be prominently detected and localized in range. In addition, advanced signal processing techniques are used to improve the performance of the sneeze droplets detections. These promising results clearly validated the concept and feasibility of Wi-Sneeze in a practical environment.
ABSTRACT
The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0;if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods. © 2020 IEEE.
ABSTRACT
In this study, a schedule issue related to the reasonable distribution of medical supplies for impacted cities in the initial stage of an outbreak of public health events was investigated by considering the transport cost and delay loss. Given that medical supplies are limited during the initial stage, a bi-objective optimization model was developed to minimize the total transport cost and delay losses and provide reasonable distribution of medical supplies. The case of coronavirus disease (COVID-19) has been used as an example in this paper. The model enables the addressal of insufficient and unevenly distributed medical supplies across different hospitals. An outer approximation algorithm was applied to solve the proposed model. Case studies were used to test the effectiveness of the developed model and the computational efficiency of the solution algorithm. The results indicate that the developed bi-objective optimization model and the algorithm are able to provide satisfactory medical supply distribution schemes under different conditions. The model could balance the two system objectives-total transport cost and delay loss of medical supplies-well for the allocation of insufficient medical supplies and, thus, generate more reasonable schemes for managing pandemic dynamics and day-to-day medical material demand. © 2020, Editorial Department of China Journal of Highway and Transport. All right reserved.