Diagn Pathol
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The world is currently witnessing a major devastating pandemic of Coronavirus disease-2019 (COVID-19). This disease is caused by a novel coronavirus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). It primarily affects the respiratory tract and particularly the lungs. The virus enters the cell by attaching its spike-like surface projections to the angiotensin-converting enzyme-2 (ACE-2) expressed in various tissues. Though the majority of symptomatic patients have mild flu-like symptoms, a significant minority develop severe lung injury with acute respiratory distress syndrome (ARDS), leading to considerable morbidity and mortality. Elderly patients with previous cardiovascular comorbidities are particularly susceptible to severe clinical manifestations. BODY: Currently, our limited knowledge of the pathologic findings is based on post-mortem biopsies, a few limited autopsies, and very few complete autopsies. From these reports, we know that the virus can be found in various organs but the most striking tissue damage involves the lungs resulting almost always in diffuse alveolar damage with interstitial edema, capillary congestion, and occasional interstitial lymphocytosis, causing hypoxia, multiorgan failure, and death. A few pathology studies have also reported intravascular microthrombi and pulmonary thrombembolism. Although the clinical presentation of this disease is fairly well characterized, knowledge of the pathologic aspects remains comparatively limited. ⋯ In this review, we discuss clinical, pathologic, and genomic features of COVID-19, review current hypotheses regarding the pathogenesis, and briefly discuss the clinical characteristics. We also compare the salient features of COVID-19 with other coronavirus-related illnesses that have posed significant public health issues in the past, including SARS and the Middle East Respiratory Syndrome (MERS).
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Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel. ⋯ We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.
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E-cigarette and vaping use-associated acute lung injury (EVALI) has been recently recognized as a complication in individuals who use vaping devices. Another consideration is that EVALI may have an adverse influence on the outcome of intercurrent respiratory infections. We document this deadly combination in the case of a young man who had EVALI and simultaneous 41 Influenza-A infection leading to severe Acute Respiratory Distress Syndrome (ARDS). ⋯ This case documents that EVALI can act as a major factor leading a respiratory infection to progress into severe ARDS with a fatal outcome.
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The outbreak of a novel coronavirus since December 2019, became an emergency of major international concern. As of June 21, 2020, the SARS-CoV-2 pandemic has caused 8,769,844 confirmed infections with 463,745 fatal cases worldwide. The SARS-CoV-2 outbreak is a major challenge for clinicians. In our clinic, we found a rare case that a COVID-19 patient combined with ischemic stroke. ⋯ We report an even more unusual case, a patient who was hospitalized for right limb weakness and was later diagnosed with COVID-19. Here, SARS-CoV-2 infection caused hypoxemia and excessive secretion of inflammatory cytokines, which contribute to the occurrence and development of ischemic stroke. Once COVID-19 patients show acute ischemic stroke, neurologists should cooperate with infectious disease doctors to help patients.