To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine.

Acoustic echoes affect the sound quality and may hamper many hands-free communications, making acoustic echo cancellers critical for enhancing the audio quality. Designing them is a challenging issue because of long room impulse responses and nonlinearities present in the power amplifier and/or the loudspeaker. This work proposes a general nonlinear digital filter structure for nonlinear acoustic echo cancellation applications. It is constructed under the assumption that the nonlinearity in typical hands-free speakerphones is of a localized nature then followed by a linear room impulse response. By doing so, it is made upon of a nonlinear discrete dynamic DABNet model cascaded with a FIR filter. This DABNet model is able to approximate the nonlinearity present in the system to any extent, while the FIR deals with the room impulse response. Comparisons of the echo canceller implemented with the DABNet + FIR show a significant performance improvement against either the Neural Network + FIR, and the linear FIR echo canceller, while contrast with an adaptive Volterra scheme, still gives a better, but comparable, performance level. Resumen