SARS coronavirus 2 (SARS-CoV-2) is a novel viral pathogen that causes a clinical disease called coronavirus disease 2019 (COVID-19). Although most COVID-19 cases are asymptomatic or involve mild upper respiratory tract symptoms, a significant number of patients develop severe or critical disease. Patients with severe COVID-19 commonly present with viral pneumonia that may progress to life-threatening acute respiratory distress syndrome (ARDS). Patients with COVID-19 are also predisposed to venous and arterial thromboses that are associated with a poorer prognosis. The present study identified the emergence of a low-density inflammatory neutrophil (LDN) population expressing intermediate levels of CD16 (CD16 ^Int ) in patients with COVID-19. These cells demonstrated proinflammatory gene signatures, activated platelets, spontaneously formed neutrophil extracellular traps, and enhanced phagocytic capacity and cytokine production. Strikingly, CD16 ^Int neutrophils were also the major immune cells within the bronchoalveolar lavage fluid, exhibiting increased CXCR3 but loss of CD44 and CD38 expression. The percentage of circulating CD16 ^Int LDNs was associated with D-dimer, ferritin, and systemic IL-6 and TNF-α levels and changed over time with altered disease status. Our data suggest that the CD16 ^Int LDN subset contributes to COVID-19–associated coagulopathy, systemic inflammation, and ARDS. The frequency of that LDN subset in the circulation could serve as an adjunct clinical marker to monitor disease status and progression.

Diet has emerged as a key impact factor for gut microbiota function. However, the complexity of dietary components makes it difficult to predict specific outcomes. Here we investigate the impact of plant-derived nanoparticles (PNP) on gut microbiota and metabolites in context of cancer immunotherapy with the humanized gnotobiotic mouse model. Specifically, we show that ginger-derived exosome-like nanoparticle (GELN) preferentially taken up by Lachnospiraceae and Lactobacillaceae mediated by digalactosyldiacylglycerol (DGDG) and glycine, respectively. We further demonstrate that GELN aly-miR159a-3p enhances anti-PD-L1 therapy in melanoma by inhibiting the expression of recipient bacterial phospholipase C (PLC) and increases the accumulation of docosahexaenoic acid (DHA). An increased level of circulating DHA inhibits PD-L1 expression in tumor cells by binding the PD-L1 promoter and subsequently prevents c-myc-initiated transcription of PD-L1. Colonization of germ-free male mice with gut bacteria from anti-PD-L1 non-responding patients supplemented with DHA enhances the efficacy of anti-PD-L1 therapy compared to controls. Our findings reveal a previously unknown mechanistic impact of PNP on human tumor immunotherapy by modulating gut bacterial metabolic pathways.

Studies of interactions among biologically active agents have become increasingly important in many branches of biomedical research. We consider that the Loewe additivity model is one of the best g...

The statistical methods for variable selection and prediction could be challenging when missing covariates exist. Although multiple imputation (MI) is a universally accepted technique for solving missing data problem, how to combine the MI results for variable selection is not quite clear, because different imputations may result in different selections. The widely applied variable selection methods include the sparse partial least-squares (SPLS) method and the penalized least-squares method, e.g. the elastic net (ENet) method. In this paper, we propose an MI-based weighted elastic net (MI-WENet) method that is based on stacked MI data and a weighting scheme for each observation in the stacked data set. In the MI-WENet method, MI accounts for sampling and imputation uncertainty for missing values, and the weight accounts for the observed information. Extensive numerical simulations are carried out to compare the proposed MI-WENet method with the other competing alternatives, such as the SPLS and ENet. In addition, we applied the MI-WENet method to examine the predictor variables for the endothelial function that can be characterized by median effective dose (ED50) and maximum effect (Emax) in an ex-vivo phenylephrine-induced extension and acetylcholine-induced relaxation experiment.