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The rapid and seemingly endless expansion of COVID-19 can be traced back to the inefficiency and shortage of testing kits that offer accurate results in a timely manner. An emerging popular technique, which adopts improvements made in mobile ultrasound technology, allows for healthcare professionals to conduct rapid screenings on a large scale. We present an image-based solution that aims at automating the testing process which allows for rapid mass testing to be conducted with or without a trained medical professional that can be applied to rural environment and third world countries. Our contributions towards rapid large-scale testing includes a novel deep learning architecture capable of analyzing ultrasound data that can run in real time and significantly improve the current state-of-theart detection accuracies using image based COVID-19 detection.

This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system. The adaptive visual diagnostics utilize few-shot learning (FSL) to generate encoded disease state models that are stored and classified using a dictionary of knowns. The novel vocabulary based feature processing of the pipeline adapts the knowledge of a pretrained deep neural network to compress the ultrasound images into discrimative descriptions. The computational efficiency of the FSL approach enables high diagnostic deep learning performance in PoC settings, where training data is limited and the annotation process is not strictly controlled. The algorithm performance is evaluated on the open source COVID-19 POCUS Dataset to validate the system's ability to distinguish COVID-19, pneumonia, and healthy disease states. The results of the empirical analyses demonstrate the appropriate efficiency and accuracy for scalable PoC use. The code for this work will be made publicly available on GitHub upon acceptance.

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The HIV-mediated decline in circulating CD4 T cells correlates with increased risk of active tuberculosis (TB). However, HIV/Mycobacterium tuberculosis (Mtb) co-infected individuals also have an increased incidence of TB prior to loss of CD4 T cells in blood, raising the possibility that HIV co-infection leads to disruption of CD4 T cell responses at the site of lung infection before they are observed systemically. Here we used a rhesus macaque model of SIV/Mtb co-infection to study the early effects of acute SIV infection on CD4 T cells in pulmonary Mtb granulomas. Two weeks after SIV co-infection CD4 T cells were dramatically depleted from granulomas, before significant bacterial outgrowth, disease reactivation as measured by PET-CT imaging, or CD4 T cell loss in blood, airways, and lymph nodes. Mtb-specific CD4 T cells, CCR5-expressing, in granulomas were preferentially depleted by SIV infection. Moreover, CD4 T cells were preferentially depleted from the granuloma core and lymphocyte cuff relative to B cell-rich regions, and live imaging of granuloma explants showed that SIV co-infection reduced T cell motility. Thus, Mtb-specific CD4 T cells in pulmonary granulomas may be decimated before many patients even experience the first symptoms of acute HIV infection.