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Quantitative approaches in clinical reproductive endocrinology.
Voliotis, Margaritis; Hanassab, Simon; Abbara, Ali; Heinis, Thomas; Dhillo, Waljit S; Tsaneva-Atanasova, Krasimira.
Afiliação
  • Voliotis M; Department of Mathematics and Living Systems Institute, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom.
  • Hanassab S; Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
  • Abbara A; Department of Computing, Imperial College London, London, United Kingdom.
  • Heinis T; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom.
  • Dhillo WS; Section of Endocrinology and Investigative Medicine, Imperial College London, London, United Kingdom.
  • Tsaneva-Atanasova K; Department of Computing, Imperial College London, London, United Kingdom.
Curr Opin Endocr Metab Res ; 27: 100421, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36643692
ABSTRACT
Understanding the human hypothalamic-pituitary-gonadal (HPG) axis presents a major challenge for medical science. Dysregulation of the HPG axis is linked to infertility and a thorough understanding of its dynamic behaviour is necessary to both aid diagnosis and to identify the most appropriate hormonal interventions. Here, we review how quantitative models are being used in the context of clinical reproductive endocrinology to 1. analyse the secretory patterns of reproductive hormones; 2. evaluate the effect of drugs in fertility treatment; 3. aid in the personalization of assisted reproductive technology (ART). In this review, we demonstrate that quantitative models are indispensable tools enabling us to describe the complex dynamic behaviour of the reproductive axis, refine the treatment of fertility disorders, and predict clinical intervention outcomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article