Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 6 de 6
Filter
1.
COVID-19 Vaccines: Development, Distribution and Mandates ; : 143-146, 2022.
Article in English | Scopus | ID: covidwho-2046689
2.
Advances in Protein Molecular and Structural Biology Methods ; : 405-437, 2022.
Article in English | Scopus | ID: covidwho-1859219

ABSTRACT

Structure-based drug discovery (SBDD) utilizes the three-dimensional (3D) structure of a target protein to identify the lead compounds. This medium is then considered a viable solution based on its availability and correlation with a particular disease. In the case of pandemics like COVID 19, shortening drug development time can save millions of people worldwide;for such a task, classical drug discovery methods will take a long time. Hence, researchers worldwide actively incorporated machine learning (ML) into the drug discovery process, particularly in SBDD, to minimize the lead optimization time. ML uses statistical methods to make a computer perform tasks, take a critical decision, and automate this entire process without being explicitly programmed. With this, the computer can discover new insights about data and unknown patterns crucial to decide the therapeutic use of lead compounds as drugs. The use of ML in the drug discovery field is not new, and it spans an ample research space. By integrating artificial intelligence with ML techniques, viable targets can be found using data clustering, regression, and classification from vast omics databases and sources. In this chapter, we will discuss the methods and applications of ML in SBDD. © 2022 Elsevier Inc. All rights reserved.

3.
2nd International Conference on Computational and Bioengineering, CBE 2020 ; 215:11-15, 2021.
Article in English | Scopus | ID: covidwho-1469668

ABSTRACT

Diagnosing the novel Covid-19 disease is the best way to precluding the loss of human deaths. This research mainly concentrates on visually observable symptoms that can be seen on the lung X-rays of humans. Novel Covid-19 monitoring of health and diagnosing the disease in humans is very critical for sustainability to medical. Nowadays, it is difficult to detect the Covid-19 positive cases because of limited equipments, and also it needs the presence of medical experts in the identification of disease. Moreover, excessive processing time is required. For diagnosing the disease machine learning approaches play a very important role in the normal or abnormal state of Covid-19. For detection various steps are involved, such as acquisition of images, preprocessing, and segmentation of images. For automatic detection of Covid-19 disease, X-ray of lungs plays an important role. Hence we first segment it using various segmentation techniques in artificial intelligence. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

4.
Computers, Materials and Continua ; 70(1):1159-1175, 2021.
Article in English | Scopus | ID: covidwho-1405619

ABSTRACT

The outbreak of COVID-19 affected global nations and is posing serious challenges to healthcare systems across the globe. Radiologists use X-Rays or Computed Tomography (CT) images to confirm the presence of COVID-19. So, image processing techniques play an important role in diagnostic procedures and it helps the healthcare professionals during critical times. The current research work introduces Multi-objective Black Widow Optimization (MBWO)-based Convolutional Neural Network i.e., MBWO-CNN technique for diagnosis and classification of COVID-19. MBWO-CNN model involves four steps such as preprocessing, feature extraction, parameter tuning, and classification. In the beginning, the input images undergo preprocessing followed by CNN-based feature extraction. Then, Multi-objective Black Widow Optimization (MBWO) technique is applied to fine tune the hyperparameters of CNN. Finally, Extreme Learning Machine with autoencoder (ELM-AE) is applied as a classifier to confirm the presence of COVID-19 and classify the disease under different class labels. The proposed MBWO-CNN model was validated experimentally and the results obtained were compared with the results achieved by existing techniques. The experimental results ensured the superior results of the ELM-AE model by attaining maximum classification performance with the accuracy of 96.43%. The effectiveness of the technique is proved through promising results and the model can be applied in diagnosis and classification of COVID-19. © 2021 Tech Science Press. All rights reserved.

5.
Vaccines: Operation Warp Speed, Regulation and Safety ; : 155-219, 2020.
Article in English | Scopus | ID: covidwho-1306218

ABSTRACT

Widespread immunization efforts have been linked to increased life expectancy and reduced illness. U.S. vaccination programs, headed by the Centers for Disease Control and Prevention (CDC) within the Department of Health and Human Services (HHS), have helped eradicate smallpox and nearly eradicate polio globally, and eliminate several infectious diseases domestically. With the Coronavirus Disease 2019 (COVID-19) pandemic now causing major health and economic impacts across the world, efforts are underway to make safe and effective vaccines available quickly to help curb spread of the virus. © 2021 by Nova Science Publishers, Inc.

6.
Vaccines: Operation Warp Speed, Regulation and Safety ; : 33-82, 2020.
Article in English | Scopus | ID: covidwho-1306009

ABSTRACT

In recent months, the Coronavirus Disease 2019 (COVID-19) pandemic has spread globally, with the United States now reporting the highest number of cases of any country in the world. Currently, there are few treatment options available to lessen the health impact of the disease and no vaccines or other prophylactic treatments to curb the spread of the virus. The biomedical community has been working to develop new therapies or vaccines, and to repurpose already approved therapeutics, that could prevent COVID-19 infections or lessen severe outcomes in patients. In addition, efforts have been underway to develop new diagnostic tools (i.e., testing) to help better identify and isolate positive cases, thereby reducing the spread of the disease. To this end, Congress has appropriated funds for research and development into new medical countermeasures (MCMs) in several recent supplemental appropriations acts. MCMs are medical products that may be used to treat, prevent, or diagnose conditions associated with emerging infectious diseases or chemical, biological, radiological, or nuclear (CBRN) agents. MCMs include biologics (e.g., vaccines, monoclonal antibodies), drugs (e.g., antimicrobials, antivirals), and medical devices (e.g., diagnostic tests). This chapter answers frequently asked questions about current efforts related to research and development of medical countermeasures, their regulation, and related policy issues. Although several efforts are underway, medical product research, development, and approval is a difficult and high-risk endeavor that takes years in typical circumstances. In response to COVID-19, this process has been expedited, including through several federal programs and mechanisms covered in this chapter. However, expedited medical product development can carry certain risks, such as a more limited safety profile for new products upon approval. © 2021 by Nova Science Publishers, Inc.

SELECTION OF CITATIONS
SEARCH DETAIL