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How mechanistic in silico modelling can improve our understanding of TB disease and treatment.
Pitcher, M J; Dobson, S A; Kelsey, T W; Chaplain, J; Sloan, D J; Gillespie, S H; Bowness, R.
Afiliación
  • Pitcher MJ; School of Computer Science, University of St Andrews, St Andrews, Department of Immunobiology, King´s College London, London.
  • Dobson SA; School of Computer Science, University of St Andrews, St Andrews.
  • Kelsey TW; School of Computer Science, University of St Andrews, St Andrews.
  • Chaplain J; School of Mathematics, University of St Andrews, St Andrews.
  • Sloan DJ; School of Medicine, University of St Andrews, St Andrews.
  • Gillespie SH; School of Medicine, University of St Andrews, St Andrews.
  • Bowness R; School of Medicine, University of St Andrews, St Andrews, Department of Mathematical Sciences, University of Bath, Bath, UK.
Int J Tuberc Lung Dis ; 24(11): 1145-1150, 2020 11 01.
Article en En | MEDLINE | ID: mdl-33172521
ABSTRACT
TB is one of the top 10 causes of death worldwide and the leading cause of death from a single infectious agent. Decreasing the length of time for TB treatment is an important step towards the goal of reducing mortality. Mechanistic in silico modelling can provide us with the tools to explore gaps in our knowledge, with the opportunity to model the complicated within-host dynamics of the infection, and simulate new treatment strategies. Significant insight has been gained using this form of modelling when applied to other diseases - much can be learned in infection research from these advances.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Tuberculosis Límite: Humans Idioma: En Revista: Int J Tuberc Lung Dis Año: 2020 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Tuberculosis Límite: Humans Idioma: En Revista: Int J Tuberc Lung Dis Año: 2020 Tipo del documento: Article