Diagnosis and Possible Use of Traditional Medicinal Plant to combat COVID-19

The novel Coronavirus disease 2019 has turned into a serious public health concern around the globe. Due to its high adaptability in every environment, this novel virus has outspread like fire compared to SARS and MERS, but the fatality rate is lower. This outbreak has caused illness to many people worldwide. Especially, people with lung problems and other chronic diseases are at high risk. Although convincing results have shown the use of chemically synthesized drugs, these drugs have various limitations. Therefore, a medicinal plant might provide a solution for the novel virus along with the recent advancement in computational methods that have paved a new path to operate complex molecules, which will ultimately result in discovering new and advanced drugs. In this review, we have summarized and analyze plant-based natural product which can be used to boost the immune system or act as a remedy for patients suffering from a novel virus. This review also focuses on the structure of COVID-19, various diagnostics tools, preventive measures, and data analysis of the novel Coronavirus of India.Copyright © 2021 Bentham Science Publishers.

Machine Learning Approach for Autonomous Detection and Classification of COVID-19 Virus.

As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify corona virus using machine learning. To spot and identify corona virus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function. While some specialists believe that the RT-PCR test is the best option for diagnosing Covid-19 patients, others believe that CT scans of the lungs can be more accurate in diagnosing corona virus infection, as well as being less expensive than the PCR test. The clinical specimens include serum specimens, respiratory secretions, and whole blood specimens. Overall, 15 factors are measured from these specimens as the result of the previous clinical examinations. The proposed CAD system consists of four phases starting with the CT lungs screening collection, followed by a pre-processing stage to enhance the appearance of the ground glass opacities (GGOs) nodules as they originally lock hazy with fainting contrast. A modified K-means algorithm will be used to detect and segment these regions. Finally, the use of detected, infected areas that obtained in the detection phase with a scale of 50×50 and perform segmentation of the solid false positives that seem to be GGOs as inputs and targets for the machine learning classifiers, here a support vector machine (SVM) and Radial basis function (RBF) has been utilized. Moreover, a GUI application is developed which avoids the confusion of the doctors for getting the exact results by giving the 15 input factors obtained from the clinical specimens.

Viral isolation analysis of SARS-CoV-2 from clinical specimens of COVID-19 patients.

Genetic testing using reverse transcriptase real-time polymerase chain reaction (rRT-PCR) is the mainstay of diagnosis of COVID-19. However, it has not been fully investigated whether infectious viruses are contained in SARS-CoV-2 genome-positive specimens examined using the rRT-PCR test. In this study, we examined the correlation between the threshold Cycle (Ct) value obtained from the rRT-PCR test and virus isolation in cultured cells, using 533 consecutive clinical specimens of COVID-19 patients. The virus was isolated from specimens with a Ct value of less than 30 cycles, and the lower the Ct value, the more efficient the isolation rate. A cytopathic effect due to herpes simplex virus type 1 contamination was observed in one sample with a Ct value of 35 cycles. In a comparison of VeroE6/TMPRSS2 cells and VeroE6 cells used for virus isolation, VeroE6/TMPRSS2 cells isolated the virus 1.7 times more efficiently than VeroE6 cells. There was no significant difference between the two cells in the mean Ct value of the detectable sample. In conclusion, Lower Ct values in the PCR test were associated with higher virus isolation rates, and VeroE6/TMPRSS2 cells were able to isolate viruses more efficiently than VeroE6 cells.

Human Coronavirus NL63 Among Other Respiratory Viruses in Clinical Specimens of Egyptian Children and Raw Sewage Samples.

The objective of this study was to investigate human coronavirus NL63 (HCoV-NL63) prevalence among the other respiratory viruses such as parainfluenza, respiratory syncytial virus, and non-enteric adenoviruses in clinical specimens of Egyptian children and raw sewage samples. One hundred clinical specimens were collected from Egyptian children suffering from upper and lower respiratory viral infections in the years 2005-2006 to detect HCoV-NL63 genome using RT-PCR. All the specimens were negative for the virus. Also, a complete absence of HCoV-NL63 genome was observed in the twenty-four raw sewage samples collected from two wastewater treatment plants within Greater Cairo from February 2006 to January 2007. Using nested RT-PCR, parainfluenza virus type 1, respiratory syncytial virus type A, adenovirus type 4, and adenovirus type 7 were detected in 3%, 2%, 5%, and 2% of the clinical specimens, respectively. Of these viruses, only adenovirus type 4 was detected in 1/24 (4.17%) of the raw sewage samples, while a complete absence of the other investigated respiratory viruses was observed in the raw sewage samples. The low percentage of positivity in the clinical specimens, the concentration method of the raw sewage samples, and the indirect routes of transmission may be the reasons for the absence of respiratory viruses in raw sewage samples. On the other hand, enteric adenoviruses were detected in 21/24 (87.5%) of the raw sewage samples with a higher prevalence of adenovirus type 41 than adenovirus type 40. A direct route of transmission of enteric viruses to raw sewage may be the reason for the high positivity percentage of enteric adenoviruses in raw sewage samples.

Clinical implications of different specimen types for nucleic acid testing in two cases of COVID-19.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid test is currently the gold standard for diagnosing coronavirus disease 2019 (COVID-19). This disease requires high-quality viral nucleic acid tests, and selecting the type of specimen from patients, who are at different disease stages, to use in the nucleic acid test is challenging. This article reports in detail the diagnosis and treatment process for two patients with confirmed COVID-19 and analyzes the results of the SARS-CoV-2 nucleic acid tests that were used for different types of specimens (sputum from deep cough, nasopharyngeal swab, and feces). The nucleic acid testing results of sputum from deep cough showed the best performance for positive detection. Our findings provide a reference for selecting the most suitable specimen for the clinical diagnosis of COVID-19 and improving the positive detection rate.