ABSTRACT
SARS-CoV-2 infected cases diagnosis is based on the count of realtime reverse transcription-polymerase chain reaction (RT-PCR). The widely used reverse transcription-polymerase chain reaction (RTPCR) method has some limitations for clinical diagnosis and treatment. However, there are only few reports on the detection of the viral load in the stool and urine samples. While information about other modes of transmission is relatively less, some published literature supporting the possibility of a faecal-oral mode of transmission has been accumulating. Objective(s): The current study's objective was to assess the performance of real-time RT-qPCR assay and a droplet digital RT-PCR (dd RT-PCR) for detecting SARS-CoV-2 in stool and urine specimens. Methodology: One hundred and seven paired samples from 107 COVID-19-confirmed patients were analysed by dd RT-PCR and RTPCR based target gene (N1 and N2). Stool and urine were collected from COVID Care Centers of Pune Region. RNA was isolated using MagMax magnetic beads base procedure for further analysis. Real Time RT-PCR and DD PCR was performed from all the patients. Result(s): In 107 patients, all the stool samples showed 100% positive concordance by both methods, the average of 28.88 cycle threshold (Ct) of RT-PCR was highly correlated with the average copy number of 327.10 copies/mul analyzed in ddPCR. Whereas 27.1% urine samples were tested positive in ddPCR & 1.86% were positive with the average of 36.41 cycle threshold (Ct) in RT-PCR. Using Pangolin COVID-19 Lineage Assigner variants were analyzed and found to be delta prevalent. Conclusion(s): In the context of the COVID-19 pandemic, environmental surveillance for the detection of SARS-CoV-2 has become increasingly important. The findings of this study not only show that SARS-CoV-2 is present in urine and faeces, but they also raise the possibility that low concentrations of the viral target may make it easier to identify positive samples and help resolve situations of inconclusive diagnosis.
ABSTRACT
The digital revolution can help developing countries to overcome the problem of limited healthcare infrastructure in developing nations such as India. The COVID-19 pandemic has shown the urgency of integration of digital technologies into healthcare infrastructure. In order to solve the issue of lack of trained healthcare professionals at public health centres (PHCs), researchers are trying to build tools which can help to tag pulmonary ailment within a fraction of second. Such tagging will help the medical community to utilize their time more efficiently. In this work, we have tried to assess the “lung health” of patients suffering from a variety of pulmonary diseases including COVID-19, tuberculosis and pneumonia by applying Earth Mover’s Distance algorithm to the X-ray images of the patients. The lung X-ray images of patients suffering from pneumonia, TB and COVID-19 and healthy persons are pooled together from various datasets. Our preliminary data based upon 100 random images depicting each type of lung disease such as COVID-19, tuberculosis and pneumonia revealed that patients suffering from tuberculosis have the highest severity as per the values obtained from the EMD scale. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.