MULTI-CLASSIFICATION MODEL FOR COVID-19 PREDICTION USING IMBALANCED X-RAY DATASET BASED ON TRANSFER LEARNING AND CLASS WEIGHTING-SMOTE METHOD

This paper proposes a deep neural networks model to predict COVID-19 patients automatically based on chest X-ray images. The model is trained using imbalance dataset with a new hybrid balancing technique proposed to solve this problem. The Deep Convolutional Neural VGG-16 is trained and utilized to extract features from a given chest X-ray image after some preprocessing steps. To overcome the data imbalance issue, a new hybrid Class Weights-SMOTE is applied to the extracted feature vector and compared with traditional balancing techniques. The feature vector is then classified utilizing a Fine-tuning VGG-16. The model provides a multi-classification for the input x-ray images into COVID-19, Normal, and Pneumonia. Comparison with existing methods shows that the proposed model achieves a superior classification accuracy and outperforms all other models, providing 98% accurate prediction and improving the model's performance on minority-class samples to achieve high accuracy 100%. The findings of this study could be useful for diagnosing COVID-19 from chest X-ray images. © 2022 Little Lion Scientific. All rights reserved.

In-silico analysis of protein receptors contributing to SARS-CoV-2 high infectivity

SARS-CoV-2 attacked more than 120 million people and causing the death of more than two million worldwide. Because of the crucial role of ACE2 protein as an entry for SRS-COV2, we investigated the protein's sequence in seventy-three living species. Data analysis of protein sequences, ACE2 mRNA, expression analysis, and protein interaction for humans and other living species were obtained from databases. The phylogenetic tree was constructed using MEGA6. We found 95% or more similarity between the conserved protein domains between Homo sapiens and Felis catus, Pan troglodytes, Pan paniscus, and Equus caballus. These species could be expressed the protein in their cell surface with the same properties as Homo sapiens. This leads to the idea of being an actual transmitter of the virus SARS-COV2, and maybe a possible reason for the spread of the virus when work or play with it, eating, cooking it, or transfer from one place to another. Expression analyses provide more explanations about organs in the body that expressed more genes like lung, heart, small intestine, and colon, which are affected more than other organs or tissues during infection or are supposed to be an infection transmitter when dealing with it in the animal after sacrifices or die. We concluded that the possibility of high SARS-CoV-2 infectivity via both zoonosis and reverse zoonosis is interesting and needs more research to develop a new strategy for dealing with this virus. © 2021 NSP Natural Sciences Publishing Cor.

Positive Retrospective SARS-CoV-2 Testing in a Case of Acute Respiratory Distress Syndrome of Unknown Etiology.

In order to elucidate the cause of acute respiratory distress syndrome of unknown etiology in a pre-pandemic patient, molecular techniques were used for detection of SARS-CoV-2. We used a SARS-CoV-2 nucleocapsid protein immunofluorescence stain to retrospectively identify an individual with diffuse alveolar damage on autopsy histology who had negative respiratory virus panel results in February, 2020, in Birmingham, Alabama. In situ hybridization for SARS-CoV-2 RNA revealed evidence of widespread multiorgan SARS-CoV-2 infection. This death antecedes the first reported death of a State of Alabama resident diagnosed with SARS-CoV-2 by 26 days.

One-health approach to MERS-COV, 2012-2017: Experience from Qatar

Objectives: The emergence of Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 was accompanied by uncertainty about its epidemiological and clinical characteristics. Once camelus dromedarius was found to be the natural reservoir of the virus public health systems across the Arabian Peninsula came under unprecedented pressure to control its transmission. This study describes how a One Health approach was used in Qatar to manage the MERS-CoV outbreak between 2012 and 2017. Method(s): The One Health approach adopted brought together professionals working in the health, animal welfare, and environmental sectors. To manage the MERS outbreak the Qatar National Outbreak Control Taskforce (OCT) was reactivated in November 2012 and experts from the animal health sector were invited to join. Later, technical expertise was requested from the WHO, FAO, CDC, Erasmus University (EMC), and Public Health England (PHE). A One Health roadmap was subsequently delivered addressing surveillance and investigation, epidemiological studies and increased local diagnostic capacity. Result(s): The joint OCT, once trained, was allocated resources and had access to high risk areas to gather evidence on the potential source of the virus and investigate all cases within 24-48 hours of reporting. Lack of sufficient technical guidance on veterinary surveillance and poor risk perception among vulnerable populations constituted major obstacles to maintaining systematic One Health performance. Conclusion(s): A One Health approach is essential for generating evidence and implementing control measures to restrain MERS-CoV and other zoonotic diseases.