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Journal of Complementary Medicine Research ; 12(4):256-264, 2021.
Article in English | Web of Science | ID: covidwho-1667555


Introduction: Non-tested asymptomatic COVID-19 cases poses threat of transmitting the disease silently. The Siddha polyherbal formulation, hnology Kabasurakudineer, (KSK) was found to be effective in preventing viral replication of SARS-CoV-2 by in-silico studies. A pilot study was conducted to test the antiviral activity of KSK in asymptomatic individuals tested positive for COVID-19. Methods: A single centre, open labelled, randomized controlled study was carried out during June-August 2020, in Tertiary Medical College Hospital, after approval from the institutional ethics committee and registered in CTRI. RTPCR confirmed COVID-19 asymptomatic cases, aged 18-65 years, consented to participate were . included and those with co-morbidities like diabetes, hypertension, severe respiratory disease, malignancies, pregnant and lactating mothers were excluded. Hospitals of Semnan Uniiessi y 60 participants were randomly assigned to study and control group. Study group received KSK (60 ml) along with vitamin C in the morning and zinc in the night, while the control group (CZ) received vitamin C and zinc for 10 days. The primary outcome was the reduction in the SARS-CoV-2 load (ct value), prevention of progression to symptomatic state. Results: In the study group, there was faster reduction in the viral load in terms of ct value as all the 30 participants turned negative for SARS-Co-V2, while 4 remain positive in the control group on the 10th day. The inflammatory markers and serum cytokine findings were inconclusive. No one progressed to the symptomatic state and no adverse event was reported in either groups. Conclusion: This study demonstrated the potential of Kabasurakudineer in reducing the viral load. Further clinical studies are warranted with larger sample size.

12th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1605834


One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. The most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, it is not accurate, and hence it is not reliable. Moreover, they are invasive. Another issue with these methods is that the number of labs performing these tests are very limited. It will be beneficial if the already existing clinical infrastructure is used for diagnosing COVID19 accurately in real time. Recently chest CT images are used by researchers to diagnose the COVID19 with impressive accuracy. The state of the art method for detecting COVID19 using CT chest images involves Deep Learning. Deep Learning is expected to provide accurate and reliable results only when the model is trained on a large data set. Due to non-availability of a large data set the existing models have been trained on a smaller size data set. Therefore it would be better to design a model to give good accuracy with reliability. To achieve accuracy along with reliability we proposed a COVID19 detection model with the combination of deep learning model and the traditional machine learning model. The novelty of the proposed model is the fusion of image quality and deep learning. The proposed method outperformed the state of the art method in terms of accuracy, recall and F1 score (more than 99 % in almost all the metrics) on a benchmark data set. The efficacy of the selected features and also explainability of the method are demonstrated through various tests. © 2021 ACM.