Author: Cartelle Gestal, Mónica; Dedloff, Margaret R.; Torres-Sangiao, Eva. 2019.
CITE.CC academic search helps you expand the influence of your papers.
Abstract
Infectious diseases are the primary cause of mortality worldwide. The dangers of infectious disease are compounded with antimicrobial resistance, which remains the greatest concern for human health. Although novel approaches are under investigation, the World Health Organization predicts that by 2050, septicaemia caused by antimicrobial resistant bacteria could result in 10 million deaths per year. One of the main challenges in medical microbiology is to develop novel experimental approaches, which enable a better understanding of bacterial infections and antimicrobial resistance. After the introduction of whole genome sequencing, there was a great improvement in bacterial detection and identification, which also enabled the characterization of virulence factors and antimicrobial resistance genes. Today, the use of in silico experiments jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. Herein, the aim of this review is to discuss the latest advances in human health engineering and their applicability in the control of infectious diseases. An in-depth knowledge of host&ndashpathogen&ndashprotein interactions, combined with a better understanding of a host&rsquos immune response and bacterial fitness, are key determinants for halting infectious diseases and antimicrobial resistance dissemination.Cite this article
Cartelle Gestal M, Dedloff MR, Torres-Sangiao E. Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread. Applied Sciences. 2019; 9(12):2486.https://doi.org/10.3390/app9122486 