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1.
European Journal of Pharmacology ; 2020.
Article | WHO COVID | ID: covidwho-1006002

ABSTRACT

The global pandemic COVID-19, caused by novel coronavirus SARS-CoV-2, has emerged as severe public health issue crippling world health care systems Substantial knowledge has been generated about the pathophysiology of the disease and possible treatment modalities in a relatively short span of time As of August 19, 2020, there is no approved drug for the treatment of COVID-19 More than 600 clinical trials for potential therapeutics are underway and the results are expected soon Based on early experience, different treatment such as anti-viral drugs (remdesivir, favipiravir, lopinavir/ritonavir), corticosteroids (methylprednisolone, dexamethasone) or convalescent plasma therapy are recommended in addition to supportive care and symptomatic therapy There are several treatments currently being investigated to address the pathological conditions associated with COVID-19 This review provides currently available information and insight into pathophysiology of the disease, potential targets, and relevant clinical trials for COVID-19

2.
International Journal of Pharma and Bio Sciences ; 11(3):P56-P62, 2020.
Article | WHO COVID | ID: covidwho-845952

ABSTRACT

Currently, the outbreak of the novel human respiratory coronavirus, also popularly known as COVID-19, has sought the attention of the scientific community across the world and stresses on the need for new therapeutic alternatives in order to cease the global health crisis and fight the pandemic The situation, therefore, calls out for new researchcentred on targeting the pathogen A number of studies reveal the potential of different chemical moieties that could possibly act against the virus In our work, we report the semi-empirical based 3D-QSAR 3D-quantitaive structure and activity relationship/QSAR studies of 3 series of compounds viz Ethacrynic Acid Derivatives (E1-E3), Isatin (2,3-oxindole) Inhibitors (I1-I7) and Flavonoid and Biflavonoid Derivatives (F1-F7) on the basis of their reported activities against SARS Co-V The studies are carried out on Hyperchem 8 0 version software using AM1 and PM3 methods Selected QSAR/3D-QSAR equations including different physical parameters of these series are reported

3.
Journal of Indian Association for Child and Adolescent Mental Health ; 16(3):194-198, 2020.
Article | WHO COVID | ID: covidwho-718266
4.
Chaos Solitons Fractals ; 140: 110190, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-696427

ABSTRACT

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

5.
Chaos Solitons Fractals ; 138: 109944, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-401363

ABSTRACT

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

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