An agent based simulation of COVID-19 history in Catalonia using extensive real datasets.

During the COVID-19 pandemic, effective public policy interventions have been crucial in combating virus transmission, sparking extensive debate on crisis management strategies and emphasizing the necessity for reliable models to inform governmental decisions, particularly at the local level. Leveraging disaggregated socio-demographic microdata, including social determinants, age-specific strata, and mobility patterns, we design a comprehensive network model of Catalonia's population and, through numerical simulation, assess its response to the outbreak of COVID-19 over the two-year period 2020-21. Our findings underscore the critical importance of timely implementation of broad non-pharmaceutical measures and effective vaccination campaigns in curbing virus spread; in addition, the identification of high-risk groups and their corresponding maps of connections within the network paves the way for tailored and more impactful interventions.

Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.

Introduction: Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets.

Measuring and understanding information storage and transfer in a simulated human gut microbiome.

Considering biological systems as information processing entities and analyzing their organizational structure via information-theoretic measures has become an established approach in life sciences. We transfer this framework to a field of broad general interest, the human gut microbiome. We use BacArena, a software combining agent-based modelling and flux-balance analysis, to simulate a simplified human intestinal microbiome (SIHUMI).

A systematic review and meta-analysis of transthoracic echocardiogram vs. cardiac magnetic resonance imaging for the detection of left ventricular thrombus.

Transthoracic echocardiography (TTE) is the most commonly used imaging modality to diagnose left ventricular thrombus (LVT), however, cardiac magnetic resonance (CMR) remains the gold standard investigation. A comparison of the diagnostic performance between two modalities is needed to inform guidelines on a diagnostic approach towards LVT. We performed a systematic review and meta-analysis to investigate the diagnostic performance of three methods of TTE (non-contrast, contrast, and apical wall motion scoring) for the detection of LVT compared to CMR as a reference test.