Environmental perspective of COVID-19.

The discovery of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the outbreak of coronavirus disease 2019 (COVID-19) are causing public health emergencies. A handful pieces of literature have summarized its clinical and radiologic features, whereas therapies for COVID-19 are rather limited. To evaluate the efficacy of convalescent plasma therapy in COVID-19 patients, we did this timely descriptive study. Six laboratory-confirmed COVID-19 patients were enrolled and received the transfusion of ABO-compatible convalescent plasma. The efficacy of this intervention was determined by the alleviation of symptoms, changes in radiologic abnormalities and laboratory tests. No obvious adverse effect observed during the treatment. Transfusion of convalescent plasma led to a resolution of ground-glass opacities and consolidation in patients #1, #2, #3, #4, and #6. In patients #1 and #5 who presented with SARS-CoV-2 in throat swab, convalescent plasma therapy elicited an elimination of the virus. Serologic analysis indicated an immediate increase in anti-SARS-CoV-2 antibody titers in patients #2 and #3, but not in patient #1. This study indicates that convalescent plasma therapy is effective and specific for COVID-19. This intervention has a special significance for eliminating SARS-CoV-2 and is believed to be a promising state-of-the-art therapy during COVID-19 pandemic crisis.

https://onlinelibrary.wiley.com/doi/epdf/10.1002/jmv.25882

Treatment with convalescent plasma for COVID-19 patients in Wuhan, China.

Quantitative real time PCR (RT-PCR) is widely used as the gold standard for clinical detection of SARS-CoV-2. However, due to the low viral load specimens and the limitations of RT-PCR, significant numbers of false negative reports are inevitable, which results in failure to timely diagnose, cut off transmission, and assess discharge criteria. To improve this situation, an optimized droplet digital PCR (ddPCR) was used for detection of SARS-CoV-2, which showed that the limit of detection of ddPCR is significantly lower than that of RT-PCR. We further explored the feasibility of ddPCR to detect SARS-CoV-2 RNA from 77 patients, and compared with RT-PCR in terms of the diagnostic accuracy based on the results of follow-up survey. 26 patients of COVID-19 with negative RT-PCR reports were reported as positive by ddPCR. The sensitivity, specificity, PPV, NPV, negative likelihood ratio (NLR) and accuracy were improved from 40% (95% CI: 27-55%), 100% (95% CI: 54-100%), 100%, 16% (95% CI: 13-19%), 0.6 (95% CI: 0.48-0.75) and 47% (95% CI: 33-60%) for RT-PCR to 94% (95% CI: 83-99%), 100% (95% CI: 48-100%), 100%, 63% (95% CI: 36-83%), 0.06 (95% CI: 0.02-0.18), and 95% (95% CI: 84-99%) for ddPCR, respectively. Moreover, 6/14 (42.9%) convalescents were detected as positive by ddPCR at 5-12 days post discharge. Overall, ddPCR shows superiority for clinical diagnosis of SARS-CoV-2 to reduce the false negative reports, which could be a powerful complement to the RT-PCR.

https://www.tandfonline.com/doi/pdf/10.1080/22221751.2020.1772678

ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

/pdf/the-role-of-imaging-in-the-detection-and-management-of-covid-1qskrjeob5.pdf

The Role of Imaging in the Detection and Management of COVID-19: A Review

The novel coronavirus disease 2019 (COVID-19), detected in Wuhan City, Hubei Province, China in late December 2019, is rapidly spreading and affecting all countries in the world Real-time reverse transcription-polymerase chain reaction (RT-PCR) test has been described by the World Health Organization (WHO) as the standard test method for the diagnosis of the disease However, considering that the results of this test are obtained between a few hours and two days, it is very important to apply another diagnostic method as an alternative to this test The fact that RT-PCR test kits are limited in number, the test results are obtained in a long time, and the high probability of healthcare personnel becoming infected with the disease during the test, necessitates the use of other diagnostic methods as an alternative to these test kits In this study, a hybrid model consisting of two-dimensional (2D) curvelet transformation, chaotic salp swarm algorithm (CSSA) and deep learning technique is developed in order to determine the patient infected with coronavirus pneumonia from X-ray images In the proposed model, 2D Curvelet transformation is applied to the images obtained from the patient's chest X-ray radiographs and a feature matrix is formed using the obtained coefficients The coefficients in the feature matrix are optimized with the help of the CSSA and COVID-19 disease is diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods Experimental results show that the proposed hybrid model can diagnose COVID-19 disease with high accuracy from chest X-ray images

Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique

Hu Shaobo

Papers

Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease.