Acute coronavirus infection triggers a TNF-dependent osteoporotic phenotype in mice.

AIMS: Millions of people died during the COVID-19 pandemic, but the vast majority of infected individuals survived. Now, some consequences of the disease, known as long COVID, are been revealed. Although the respiratory system is the target of Sars-CoV-2, COVID-19 can influence other parts of the body, including bone. The aim of this work was to investigate the impact of acute coronavirus infection in bone metabolism. MAIN METHODS: We evaluated RANKL/OPG levels in serum samples of patients with and without acute COVID-19. In vitro, the effects of coronavirus in osteoclasts and osteoblasts were investigated. In vivo, we evaluated the bone phenotype in a BSL2 mouse model of SARS-like disease induced by murine coronavirus (MHV-3). KEY FINDINGS: Patients with acute COVID-19 presented decreased OPG and increased RANKL/OPG ratio in the serum versus healthy individuals. In vitro, MHV-3 infected macrophages and osteoclasts, increasing their differentiation and TNF release. Oppositely, osteoblasts were not infected. In vivo, MHV-3 lung infection triggered bone resorption in the femur of mice, increasing the number of osteoclasts at 3dpi and decreasing at 5dpi. Indeed, apoptotic-caspase-3+ cells have been detected in the femur after infection as well as viral RNA. RANKL/OPG ratio and TNF levels also increased in the femur after infection. Accordingly, the bone phenotype of TNFRp55-/- mice infected with MHV-3 showed no signs of bone resorption or increase in the number of osteoclasts. SIGNIFICANCE: Coronavirus induces an osteoporotic phenotype in mice dependent on TNF and on macrophage/osteoclast infection.

A multi-expert system to detect COVID-19 Cases in X-ray images

The year 2020 was marked by the worldwide COVID-19 pandemic, which caused over 2.5 million deaths by the end of February 2021. Different methods have been established since the beginning to identify infected patients and restrict the spread of the virus. In addition to laboratory analysis, used as the gold standard, several applications have been developed to apply deep learning algorithms to chest X-ray (CXR) images to diagnose patients affected by COVID-19. The literature shows that convolutional neural networks (CNNs) perform well on a single image dataset, but fail to generalize to other sources of data. To overcome this limitation, we present a late fusion approach in which multiple CNNs collaborate to diagnose the CXR scan of a patient, improving the generalizability. Experiments on three datasets publicly available show that the ensemble of CNNs outperforms stand-alone networks, achieving promising performance not only in cross-validation, but also when external validation is used, with an average accuracy of 95.18%. © 2021 IEEE.