ABSTRACT
Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV -2) is a highly pathogenic and rapidly spreading pathogen. It can invade and infect cells by recognizing receptors on the surface of host cells with spike (5) glycoprotein. Systematic bioinformatics analysis and prokaryotic expression of the S protein can aid understanding of its function and clarify the molecular mechanism of viral infection mediated by this protein. The physicochemical properties, subcellular localization, post - translational modifications and protein - interaction network of the S protein were analyzed systematically using Protparam. Pfam, TMHMM ExPASy - ProtScale, PSORT II, SignalP, UniProt, NetPhos 3.1. NetNGlyc 1.0, NetOGlyc 4.0. BLAST and other bioinformatics software and databases. In addition. Clustal X2 and MEGA7.0 were used to analyze the homology and phylogeny of S glycoproteins based on amino-acid sequences. Finally, recombinant expression vector of the S glycoprotein was constructed by molecular cloning technology and expressed in Escherichia call. Results showed that the S glycoprotein is composed of 1,273 amino acids, with a molecular weight of 141.2 kD and an isoelectric point of 6.24. It had two coiled helical structures and one transmembrane helix region. It was a hydrophobic protein, contained a spike receptor-binding domain and 52 glycoprotein domain. This protein was distributed mainly in the endoplasmic-reticulum membrane (39.1%) and cell membrane (21.7%) of host cells. and contained 136 potential phosphorylation sites and 20 possible glycosylation sites. SARS-CoV, SARS-Coy WI-120 and bat coronavirus HKU3 showed the highest sequence identity with the spike-glycoprotein sequence of SARS-CoV-2 (76%). SARS-CoV-2, SARS-CoV and bat coronavirus clustered into a large branch. suggesting that they may have a common ancestor. The S protein was expressed mainly in the precipitate after centrifugation of bacterial lysates, which lays a foundation for future structural analysis and vaccine development. The S glycoprotein was highly conserved between SARS-CoV and bat coronavirus, suggesting that this glycoprotein has a vital role in viral invasion into host cells. SARS-CoV-2 may have a common ancestor with SARS-CoV and bat coronavirus. Our study provides an important data basis for expression. purification. structural and functional analysis of the S glycoprotein of SARS-CoV-2. Our data may help to reveal the biological functions of the S glycoprotein, and provide a scientific basis for the design and screening of new antiviral drugs targeting this protein.
ABSTRACT
SARS-Cov 2 is the etiological agent responsible for Covid 19 disease, that has become a pandemic, whose natural history of infection, severity and mortality depends largely on the presence or non-identifiable risk factors in the incidence populations that allow the estimation of the risk status, being the fundamental objective of assessing the risk factors for infection by said coronavirus in the Canton Ambato, Tungurahua Province, Ecuador;through an integrated platform based on the use of concurrent technologies. It is applied research, based on an experimental, longitudinal and prospective explanatory study, descriptive and inferential statistical methods were used. 626 patients evaluated through the Risk Factors module enabled in the computer application were included. The predominant population was young, around the age of 18 to 26, with 463 women (73,96%). 8% had coronavirus infection, 98% showed mild symptoms, 8% of identified cases had a pathological history considered clinical risk, the presence of social risk factors was estimated in 6% of patients. The assessment of risk factors through the platform showed a high specificity of 87% which shows the existence of few false positives in the risk estimate and increases the validity of the result with a Positive Likelihood Rate of 1,5. The development of a risk factor assessment module for Covid 19 as part of an integrated technology platform showed effectiveness in identifying and analyzing it, en allow risk stratification according to its potential to association with the severity of covid infection with this virus that undere bottoms the development of community prevention strategies.
ABSTRACT
This review discusses some of the possible technological concepts and their role to fight against COVID-19, which could possibly help to control the outbreak. The use of quantum computers, machine learning, facial recognition, satellite technology, robots, health sensory and mobile apps for monitoring and gathering sufficient quality data are covered.
ABSTRACT
This article helps in presenting a clear view of detecting social distance in order to evaluate the distance covered by the people from each other. It helps in providing alarming notification to the people in making safety during this pandemic period by the use of video feed. The frame of the video clipping is used as an input and is implemented based on the hybrid computer vision. The Deep neural network dependent algorithm named YOLOv3 has been used in alarming the detection of distance between the people. It is used along with the mapping technique known as Inverse Perspective Mapping (IPM) which is one of the tracking mechanisms that helps in monitoring the social distance. It is tested against MS COCO and image data sets that has been obtained from Google. The precision was found to be 97% that helps to design the outlook of the place where the public involvement is too high. It could help in controlling the violations of trespassers who does not obey the rules of social distancing and also servers to be precaution to control the disease prone zones.
ABSTRACT
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.
ABSTRACT
Introduction: Bioinformatics is a multi-disciplinary field that usually uses approaches in Computer Science such as algorithms and machine learning to solve problems in the domains of Biology, Biochemistry, and other domains involving molecular biology data. This approach can also be used to screen natural products that have certain properties. Jamu or Indonesian herbal medicine works with the principle of multi-component multi-target. This principle focuses on the complex interactions of system components that describe how multi-components (compounds) can work together to affect multi-targets (protein targets). This mechanism is also popularly called Network Pharmacology. In this study, we introduce a workflow to screen herbal compounds based on Network Pharmacology and machine learning approach.
ABSTRACT
The health service system in Indonesia is still a homework that needs to be addressed by the government. Both in terms of equity, regulation, and integration between offline and online systems. As for what is happening now, there are at least some obstacles that need to be addressed and addressed as soon as possible. On the other hand, health development should be seen as an investment to improve the quality of human resources and support economic development, and have an essential role in poverty reduction efforts. Today's primary health development problems include the high disparity in health status between socioeconomic levels, regions, and urban and rural areas. In general, the health status of people with high socioeconomic levels in western Indonesia and urban areas tends to be better. On the other hand, the health status of people with low socioeconomic status in eastern Indonesia and rural areas is still lagging. The quality, equity, and affordability of health services are also still low. The quality of service is an obstacle because medical personnel is minimal and equipment is inadequate. In terms of numbers, the ratio of health workers to the population to be served is still low. The affordability of services is closely related to the number and distribution of health facilities. In terms of the limitations of medical equipment, this is due to the high price of the medical equipment. Besides, special training is also needed in medical equipment, which training takes quite a long time, and the material is quite complex. With these conditions, it is necessary to optimize technology to develop medical equipment, especially diagnostic support equipment that is cheap, easy to use, and has high accuracy. Our research aims to address optimizing image processing technology and artificial intelligence in developing technology to support health services in rural areas. We tried to implement several cases of health services that often occur in rural areas, namely early detection of cataracts, optimizing the use of ultrasound machines, optimizing the use of low- quality MRI machines in the diagnosis of Alzheimer's, using microscopic images to identify leukocytes and using X-ray images to support the diagnosis. COVID19. Of the several modules that we have developed for health care support technology, the results show promising prospects with a good level of accuracy, ease to use, and use modalities that already exist in the lowest health service units to reduce prices. It is hoped that this image processing and artificial intelligence-based health service support module can be used in rural areas to overcome disparities in health services in Indonesia.
ABSTRACT
Objective: To explore the mechanism of Hanshi Zufei Formula (HSZFF) treating coronavirus disease 2019 (COVID-19) by the data mining analysis of network pharmacology and the molecular docking.