Automatically identify COVID-19 and Pneumonia patients using Deep learning in Chest X-rays image

At the end of 2019, a new kind of coronavirus (SARS-CoV-2) suffered worldwide and has become the pandemic coronavirus (COVID-19). The outbreak of this virus let to crisis around the world and kills millions of people globally. On March 2020, WHO (World Health Organization) declared it as pandemic disease. The first symptom of this virus is identical to flue and it destroys the human respiratory system. For the identification of this disease, the first key step is the screening of infected patients. The easiest and most popular approach for screening of the COVID-19 patients is chest X-ray images. In this study, our aim to automatically identify the COVID-19 and Pneumonia patients by the X-ray image of infected patient. To identify COVID19 and Pneumonia disease, the convolution Neural Network was training on publicly available dataset on GitHub and Kaggle. The model showed the 98% and 96% training accuracy for three and four classes respectively. The accuracy scores showed the robustness of both model and efficiently deployment for identification of COVID-19 patients.

Management of arrythmia in the COVID 19 era

There is currently well-documented evidence that SARS CoV2 infection is frequently associated with secondary cardiac damage. Although lung damage is a priority, the evolution over time of COVID 19 infection frequently includes cardiovascular complications, the most common being: myocarditis, heart failure and acute coronary syndrome resulting from coronary artery thrombosis or plaque ruptures associated with SARS-CoV-2. Therefore, for the judicious use of resources, both human and material, and for the protection of medical staff, it is necessary to establish standard procedures to limit the contact of medical staff with the infected patient COVID 19.

Osteonecrosis of the humeral head in a human immunodeficiency virus-infected patient under tenofovir disoproxil fumarate–emtricitabine–lopinavir/ritonavir for 10 years: a case report

Background Human immunodeficiency virus-infected patients are 100 times more likely to develop aseptic osteonecrosis compared with the general population. While 90% of cases concern the femoral head, the involvement of humeral bone remains rare. Case presentation We report a case of aseptic osteonecrosis of the left humeral head complicating antiretroviral therapy in a female, 46-year-old, Bissau-Guinean human immunodeficiency virus-infected patient received in a context of progressive pain in the left shoulder followed by limitation of articular movements. Standard x-ray of the shoulder allowed us to make the diagnosis by showing a typical image of osteonecrosis. The treatment was medical combined with physiotherapy. Conclusions Aseptic osteonecrosis should be systematically looked for in human immunodeficiency virus patients on antiretroviral treatment. In addition to femoral head aseptic necrosis, the involvement of the humeral bone should also be considered.

Dengue NS1 antigen kit shows high sensitivity for detection of recombinant dengue virus-2 NS1 antigen spiked with Aedes aegypti mosquitoes

Dengue, caused by the dengue virus (DENV) is a significant vector-borne disease. In absence of a specific treatment and vaccine, dengue is becoming a rising threat to public health. Currently, control of dengue mainly focuses on the surveillance of the mosquito vectors. Improved surveillance methods for DENV in mosquito populations would be highly beneficial to the public health. However, current methods of DENV detection in mosquitoes requires specialized equipment and expensive reagents and highly trained personnel. As an alternative, commercially available dengue NS1 antigen ELISA kits could be used for detection of DENV infection in Aedes aegypti mosquitoes. In this study, we explored the utility of commercially available Dengue NS1 antigen kit (J. Mitra & Co. Pvt. Ltd) for the detection of recombinant dengue virus-2 (rDENV-2) NS1 protein and serum of dengue infected patient spiked with Ae. aegypti mosquito pools. The kit was found to be highly sensitive and specific towards detection of all serotypes of DENV. Further, it could detect as low as 750 femto gram rDENV-2 NS1 protein. It was also observed that rDENV-2 NS1 antigen spiked with blood-fed and unfed mosquito pools could be detected. In addition, the kit also detected dengue infected patient serum spiked with Ae. aegypti mosquito pools. Overall, the Dengue NS1 antigen kit displayed high sensitivity towards detection of recombinant as well as serum NS1 protein spiked with Ae. aegypti mosquito pools and could be considered for the dengue virus surveillance after a field evaluation in Ae. aegypti mosquitoes.