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Mitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs. Whether the appearance of cell-free MT-DNA is linked to poor COVID-19 outcomes remains undetermined. Here, we quantified circulating MT-DNA in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at the time of hospital presentation. Circulating MT-DNA were sharply elevated in patients who eventually died, required ICU admission or intubation. Multivariate regression analysis revealed that high circulating MT-DNA levels is an independent risk factor for all of these outcomes after adjusting for age, sex and comorbidities. Additionally, we found that circulating MT-DNA has a similar or superior area-under-the curve when compared to clinically established measures of systemic inflammation, as well as emerging markers currently of interest as investigational targets for COVID-19 therapy. These results show that high circulating MT-DNA levels is a potential indicator for poor COVID-19 outcomes.Rigorous assessment of the cellular and molecular changes during infection typically requires isolation of specific immune cell subsets for downstream application. While there are numerous options for enrichment/isolation of cells from tissues, fluorescent activated cell sorting (FACS) is accepted as a method that results in superior purification of a wide variety of cell types. Flow cytometry requires extensive fluidics and aerosol droplets can be generated during collection of target cells. Pathogens such as Francisella tularensis, Mycobacterium tuberculosis, Yersinia pestis, and SARS-CoV-2 require manipulation at biosafety level-3 (BSL-3). Bafetinib cost Due to the concern of potential aerosolization of these pathogens, use of flow cytometric-based cell sorting in these laboratory settings requires placement of the equipment in dedicated biosafety cabinets within the BSL-3. For many researchers, this is often not possible due to expense, space, or expertise available. Here we describe the safety validation and utility of a completely closed cell sorter that results in gentle, rapid, high purity, and safe sorting of cells on the benchtop at BSL-3. We also provide data demonstrating the need for cell sorting versus bead purification and the applicability of this technology for BSL-3 and potentially BSL-4 related infectious disease projects. Adoption of this technology will significantly expand our ability to uncover important features of the most dangerous infectious diseases leading to faster development of novel vaccines and therapeutics.A successful SARS-CoV-2 vaccine must be not only safe and protective but must also meet the demand on a global scale at low cost. Using the current influenza virus vaccine production capacity to manufacture an egg-based inactivated Newcastle disease virus (NDV)/SARS-CoV-2 vaccine would meet that challenge. Here, we report pre-clinical evaluations of an inactivated NDV chimera stably expressing the membrane-anchored form of the spike (NDV-S) as a potent COVID-19 vaccine in mice and hamsters. The inactivated NDV-S vaccine was immunogenic, inducing strong binding and/or neutralizing antibodies in both animal models. More importantly, the inactivated NDV-S vaccine protected animals from SARS-CoV-2 infections or significantly attenuated SARS-CoV-2 induced disease. In the presence of an adjuvant, antigen-sparing could be achieved, which would further reduce the cost while maintaining the protective efficacy of the vaccine.The benefits of mentored undergraduate research to student success, retention, and persistence in science, technology, engineering, and mathematics (STEM) have long been identified. However, many students miss out on the opportunity to engage in research often due to unfamiliarity of various research opportunities or how to approach potential research mentors. To address this, we developed a scalable online badge, Introduction to Research, that draws on aspects of the Entering Research curriculum (Branchaw, Pfund, & Rediske, 2010) to help students explore and prepare for undergraduate research in the biomedical and behavioral sciences. Students in the BUILD Training Program, part of the larger STEM BUILD at UMBC Initiative, completed the badge in conjunction with a 3-week classroom-based undergraduate research experience (CURE) before the start of their second year of undergraduate study at the University of Maryland, Baltimore County (UMBC). We were interested in investigating how this intervention, online bBadge in combination with a CURE engage in mentored research within a year of completion at higher levels than students who engage in neither.Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learned by self-supervision), and generic (served as source models for generating application-specific target models). Our extensive experiments demonstrate that our Models Genesis significantly outperform learning from scratch in all five target 3D applications covering both segmentation and classification. More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging. This performance is attributed to our unified self-supervised learning framework, built on a simple yet powerful observation the sophisticated yet recurrent anatomy in medical images can serve as strong supervision signals for deep models to learn common anatomical representation automatically via self-supervision. As open science, all pre-trained Models Genesis are available at https//github.com/MrGiovanni/ModelsGenesis.