The COE/EBF gene family marks a subset of prospective neurons in the vertebrate central and peripheral nervous system, including neurons deriving from some ectodermal placodes. Since placodes are often considered unique to vertebrates, we have characterised an amphioxus COE/EBF gene with the aim of using it as a marker to examine the timing and location of peripheral neuron differentiation. A single COE/EBF family member, AmphiCoe, was isolated from the amphioxus Branchiostoma floridae. AmphiCoe lies basal to the vertebrate COE/EBF genes in molecular phylogenetic analysis, suggesting that the duplications that formed the vertebrate COE/EBF family were specific to the vertebrate lineage. AmphiCoe is expressed in the central nervous system and in a small number of scattered ectodermal cells on the flanks of neurulae stage embryos. These cells become at least largely recessed beneath the ectoderm. Scanning electron microscopy was used to examine embryos in which the ectoderm had been partially peeled away. This revealed that these cells have neuronal morphology, and we infer that they are the precursors of epidermal primary sensory neurons. These characters lead us to suggest that differentiation of some ectodermal cells into sensory neurons with a tendency to sink beneath the embryonic surface represents a primitive feature that has become incorporated into placodes during vertebrate evolution.

Objectives: Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement. Material and Methods: An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset was comprised of 1324 COVID-19, 1108 Normal, and 1344 Pneumonia CXR images. Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. The model was retrospectively used to test CXRs from reverse transcriptase-polymerase chain reaction (RT-PCR) proven COVID-19 patients to test positive predictive value and accuracy. Results: A total of 460 RT-PCR positive hospitalized patients CXRs in various stages of disease involvement were retrospectively analyzed. There were 248 males (53.92%) and 212 females (46.08%) in the cohort, with a mean age of 50.1 years (range 12–89 years). The commonly observed alterations included lung consolidations, ground-glass opacities, and reticular–nodular opacities. Bilateral involvement was more common compared to unilateral involvement. Of the 460 CXRs analyzed, the model reported 445 CXRs as COVID -19 with an accuracy of 96.73%. Conclusion: Our model, based on a two-level classification decision fusion and output information computation, makes it a robust, accurate and reproducible tool. Based on the initial promising results, our application can be used for mass screening.

Background:The most frequent manifestations of COVID-19 occur due to involvement of lung, the SARS-CoV-2 virus may also lead to multi-system involvement including the liver damage. Case Report: We present a patient who presented with fever, multiple episodes of vomiting followed by altered mentation with deranged liver biochemistries attributable to COVID-19 infection. During hospital stay, patient was treated for COVID-19 and subsequently showed clinical improvement as well as improvement in lab parameters. Conclusion: This case report paves the way for further studies to determine the various mechanisms of liver involvement as well as the frequency of acute liver failure in COVID-19 and to help in better understanding of this disease.