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Given that gastrointestinal (GI) symptoms are a prominent extrapulmonary manifestation of coronavirus disease 2019 (COVID-19), we investigated the impact of GI infection on disease pathogenesis in three large cohorts of patients in the United States and Europe. Unexpectedly, we observed that GI involvement was associated with a significant reduction in disease severity and mortality, with an accompanying reduction in key inflammatory proteins including IL-6, CXCL8, IL-17A and CCL28 in circulation. In a fourth cohort of COVID-19 patients in which GI biopsies were obtained, we identified severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) within small intestinal enterocytes for the first time in vivo but failed to obtain culturable virus. selleck chemicals High dimensional analyses of GI tissues confirmed low levels of cellular inflammation in the GI lamina propria and an active downregulation of key inflammatory genes including IFNG, CXCL8, CXCL2 and IL1B among others. These data draw attention to organ-level heterogeneity in disease pathogenesis and highlight the role of the GI tract in attenuating SARS-CoV-2-associated inflammation with related mortality benefit.The COVID-19 global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) continues to place an immense burden on societies and healthcare systems. A key component of COVID-19 control efforts is serologic testing to determine the community prevalence of SARS-CoV-2 exposure and quantify individual immune responses to prior infection or vaccination. Here, we describe a laboratory-developed antibody test that uses readily available research-grade reagents to detect SARS-CoV-2 exposure in patient blood samples with high sensitivity and specificity. We further show that this test affords the estimation of viral spike-specific IgG titers from a single sample measurement, thereby providing a simple and scalable method to measure the strength of an individual's immune response. The accuracy, adaptability, and cost-effectiveness of this test makes it an excellent option for clinical deployment in the ongoing COVID-19 pandemic.We used metagenomic next-generation sequencing (mNGS) to assess the frequencies of alternative viral infections in SARS-CoV-2 RT-PCR negative persons under investigations (PUIs) (n=30) and viral co-infections in SARS-CoV-2 RT-PCR positive PUIs (n=45). mNGS identified both co-infections and alternative viral infections that were not detected by routine clinical workup.With a rising incidence of COVID-19-associated morbidity and mortality worldwide, it is critical to elucidate the innate and adaptive immune responses that drive disease severity. We performed longitudinal immune profiling of peripheral blood mononuclear cells from 45 patients and healthy donors. We observed a dynamic immune landscape of innate and adaptive immune cells in disease progression and absolute changes of lymphocyte and myeloid cells in severe versus mild cases or healthy controls. Intubation and death were coupled with selected natural killer cell KIR receptor usage and IgM+ B cells and associated with profound CD4 and CD8 T cell exhaustion. Pseudo-temporal reconstruction of the hierarchy of disease progression revealed dynamic time changes in the global population recapitulating individual patients and the development of an eight-marker classifier of disease severity. Estimating the effect of clinical progression on the immune response and early assessment of disease progression risks may allow implementation of tailored therapies.Background How aberrant fibrinolysis influences the clinical progression of COVID-19 presents a clinicopathological dilemma challenging intensivists. To investigate whether abnormal fibrinolysis is a culprit or protector or both, we associated elevated plasma D-dimer with clinical variables to identify a panoramic view of the derangements of fibrinolysis that contribute to the pathogenesis of COVID-19 based on studies available in the literature. Methods We performed this systematic review based on both meta-analysis and meta-regression to compute the correlation of D-dimer at admission with clinical features of COVID-19 patients in retrospective studies or case series. We searched the databases until Aug 18, 2020, with no limitations by language. The first hits were screened, data extracted, and analyzed in duplicate. We did the random-effects meta-analyses and meta-regressions (both univariate and multivariate). D-dimer associated clinical variables and potential mechanisms were schematically reasoned and gfficient hyperfibrinolysis (fibrinolysis is accelerated but unable to prevent adverse clinical impact for clinical deterioration COVID-19)" as a peculiar mechanism. Interpretation The findings of this meta-analysis- and meta-regression-based systematic review supports elevated D-dimer as an independent predictor for mortality and severe complications. D-dimer-associated clinical variables draw a landscape integrating the aggregate effects of systemically suppressive and locally (i.e., in the lung) hyperactive derangements of fibrinolysis. D-dimer and associated clinical biomarkers and conceptually parameters could be combined for risk stratification, potentially for tracking thrombolytic therapy or alternative interventions.Although models have been developed for predicting severity of COVID-19 based on the medical history of patients, simplified risk prediction models with good accuracy could be more practical. In this study, we examined utility of simpler models for estimating risk of hospitalization of patients with COVID-19 and mortality of these patients based on demographic characteristics (sex, age, race, median household income based on zip code) and smoking status of 12,347 patients who tested positive at Mass General Brigham centers. The corresponding electronic health records were queried from 02/26/2020 to 07/14/2020 to construct derivation and validation cohorts. The derivation cohort was used to fit a generalized linear model for estimating risk of hospitalization within 30 days of COVID-19 diagnosis and mortality within approximately 3 months for the hospitalized patients. On the validation cohort, the model resulted in c-statistics of 0.77 [95% CI 0.73-0.80] for hospitalization outcome, and 0.72 [95% CI 0.69-0.74] for mortality among hospitalized patients.