Background Pro‐protein convertase subtilisin/kexin 9 (PCSK9) is a proenzyme primarily known to regulate low‐density lipoprotein receptor re‐uptake on hepatocytes. Whether PCSK9 can concurrently trigger inflammation or not remains unclear. Here, we investigated the potential association between circulating levels of PCSK9 and mortality in patients with severe sepsis or septic shock. Methods Plasma PCSK9 levels at days 1, 2 and 7 were measured in 958 patients with severe sepsis or septic shock previously enrolled in the Albumin Italian Outcome Sepsis (ALBIOS) trial. Correlations between levels of PCSK9 and pentraxin 3 (PTX3), a biomarker of disease severity, were evaluated with ranked Spearman’s coefficients. Cox proportional hazards models were used to assess the association of PCSK9 levels at day 1 with 28‐ and 90‐day mortality. Results Median plasma PCSK9 levels were 278 [182–452] ng mL−1 on day 1. PCSK9 correlated positively with PTX3 at the three time‐points, and patients with septic shock within the first quartile of PCSK9 showed higher levels of PTX3. Similar mortality rates were observed in patients with severe sepsis across PCSK9 quartiles. Patients with septic shock with lower PCSK9 levels on day 1 (within the first quartile) showed the highest 28‐ and 90‐day mortality rate as compared to other quartiles. Conclusion In our sub‐analysis of the ALBIOS trial, we found that patients with septic shock presenting with lower plasma PCSK9 levels experienced higher mortality rate. Further studies are warranted to better evaluate the pathophysiological role of PCSK9 in sepsis.

This paper provides a quantitative assessment of equity options priced at the Zero Lower Bound, i.e., when interest rates are set essentially to zero. We obtain closed form formulas for American options when the Zero Lower Bound policy holds. We perform numerical implementation of American put options written on the stock Federal National Mortgage Association (FNMA) and of related bounds for the optimal exercise. The results show similarities with the corresponding European options priced at the Zero Lower Bound during the COVID-19 crisis.

No abstract

No abstract

This paper compares the predictive power of different models to forecast the real U.S. GDP. Using quarterly data from 1976 to 2020, we find that the machine learning K-Nearest Neighbour (KNN) model captures the self-predictive ability of the U.S. GDP and performs better than traditional time series analysis. We explore the inclusion of predictors such as the yield curve, its latent factors, and a set of macroeconomic variables in order to increase the level of forecasting accuracy. The predictions result to be improved only when considering long forecast horizons. The use of machine learning algorithm provides additional guidance for data-driven decision making.