Crispr biosensing and Ai driven tools for detection and prediction of Covid-19

Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Crispr biosensing and Ai driven tools for detection and prediction of Covid-19

Coronaviridae family consists of many virulent viruses with zoonotic properties that can be transmitted from animals to humans. Different strains of these viruses have caused pandemic in the past such as Severe Respiratory Syndrome Coronavirus (SARS-CoV) in 2002, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 and recently Severe Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also known as COVID-19 in December 2019. Scientists utilised different approaches for the detection and characterisation of CoVs using samples such as serum, throat swabs, nose swabs, nasopharyngeal aspirates and bronchoalveolar lavages. The two common approaches include antigen-based approach and molecular diagnostic approach, which are hindered by limitations such as low sensitivity and requirement for high level of biosafety during isolation of the virus from cell culture. Thus, there is a need for developing a more rapid, sensitive, simple and cheap diagnostic kit for diagnosis of different strains of coronavirus. In this article, we overview 2019 novel coronavirus, pandemic, prior epidemics, diagnosis, treatments, identification of drugs detection based on classification and prediction using artificial intelligence-driven tools. We also overview in-lab molecular testing and on-site testing using CRISPR-based biosensing tools. We also outline limitations of laboratory techniques and open-research issues in the current state of CRISPR-based biosensing applications and artificial intelligence for treatment of Coronaviruses. [ABSTRACT FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach. The dataset used for the study was obtained in the form of optical Coherence Tomography and chest X-ray images made available by Kermany et al. (2018, https://doi.org/10.17632/rscbjbr9sj.3) with a total number of 5853 pneumonia (positive) and normal (negative) images. To evaluate the average efficiency of the model, the dataset was split into on 50:50, 60:40, 70:30, 80:20 and 90:10 for training and testing respectively. To evaluate the performance of the model, 10 K Cross-validation was carried out. The performance of the model using overall dataset was compared with the means of cross-validation and the currents state of arts. The classification model has shown high performance in terms of accuracy, sensitivity and specificity. 70:30 split performed better compare to other splits with accuracy of 98.73%, sensitivity of 98.59% and specificity of 99.84%.