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Coronavirus Disease 2019 (COVID-19) became a significant challenge for the work and personal experience of physiotherapists (PTs) To investigate how the work activities of PTs in a region in Italy have changed, describe the reasons for change, perceived competence, and effectiveness of professional education, and explore their personal experience We adopted a monocentric convergent mixed-methods light–questionnaire variant study. The questionnaire contains both closed-ended and open-ended questions. Quantitative and qualitative data were combined to interpret the results Among 78 respondents (response rate 24.4%), 87.2% worked during the pandemic, 52.9% treated patients with COVID-19, and 45.6% changed their working activities. Professional competence was perceived as low in intensive and sub-intensive care settings. The major critical aspect of professional education was respiratory rehabilitation. Life-learning education was judged as effective, even if mainly focused on safety. Nine themes emerged from the analysis of the PTs' experiences: 1) Physiotherapy during COVID-19; 2) Fear and negative feelings; 3) Positive aspects; 4) Organization and management; 5) Prevention measures; 6) Patients; 7) Change; 8) Information; and 9) Professional education PTs who have direct experience with patients with COVID-19 showed great resilience. They overcame the first phase of disorientation and fear, despite a specific lack of competence in the respiratory field. [ABSTRACT FROM AUTHOR]

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other. [ABSTRACT FROM AUTHOR]

Motivation: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease., Results: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show., Availability and Implementation: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to computational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods., (© 2019 Wiley Periodicals, Inc.)