HA of H1N1 enhanced the expression of ICAM-1 and IL-6 in HUVECs and pathological injury in the lungs in mice.

OBJECTIVE: Both COVID-19 and influenza are viral respiratory tract infections and the epidemics of viral respiratory tract infections remain highly prevalent with lethal consequences in susceptible individuals. Expression of ICAM-1 on vascular endothelium recruits leukocytes which initiates inflammation. IL-6 induces ICAM-1. Both ICAM-1 and IL-6 can be enhanced in influenza virus infection and COVID-19 patients. Besides initiation of virus entry host cells, whether HA alone, instead of whole virus, of influenza has the effects on expression of ICAM-1 and IL-6 in vascular endothelium with injury in the lungs, remains to be demonstrated. METHODS: RT-qPCR and Western blot as well as histopathologic examination were used to examine mRNA and protein of ICAM-1 and IL-6 as well as pathological injury in the lung tissues, respectively. RESULTS: After incubation of the Human Umbilical Vein Endothelial Cells (HUVECs) with HA of H1N1 for 24 h, the mRNA and protein of ICAM-1 and IL-6 in HUVECs were increased in group of 5 µg/ml concentration with statistical significance (p < 0.05). Pathological injury in lung tissues of the mice was shown 12 h after tail intravenous injection with 100 µl of HA (50 µg/ml and 100 µg/ml in normal saline), including widened alveolar spaces with angiotelectasis in alveolar wall, alveolar luminal and interstitial inflammatory infiltrates, alveolar luminal erythrocyte effusion. CONCLUSIONS: HA alone, instead of whole H1N1 virus, induced more expression of ICAM-1 and IL-6, two molecules involving in pathological and inflammatory responses, in HUVECs and pathological injury in lung tissues of the mice. This knowledge provides a new HA-targeted potential direction for prevention and treatment of disease related to H1N1 infection.

The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE).

Hospitals commonly project demand for their services by combining their historical share of regional demand with forecasts of total regional demand. Hospital-specific forecasts of demand that provide prediction intervals, rather than point estimates, may facilitate better managerial decisions, especially when demand overage and underage are associated with high, asymmetric costs. Regional point forecasts of patient demand are commonly available, e.g., for the number of people requiring hospitalization due to an epidemic such as COVID-19. However, even in this common setting, no probabilistic, consistent, computationally tractable forecast is available for the fraction of patients in a region that a particular institution should expect. We introduce such a forecast, DICE (Demand Intervals from Consistent Estimators). We describe its development and deployment at an academic medical center in California during the 'second wave' of COVID-19 in the Unite States. We show that DICE is consistent under mild assumptions and suitable for use with perfect, biased and unbiased regional forecasts. We evaluate its performance on empirical data from a large academic medical center as well as on synthetic data.