Researchers from the National University of Singapore (NUS) have revealed that diet and exercise can change gene regulation in the skeletal muscle of East Asians, highlighting the critical role of gene-lifestyle (G x L) interactions in metabolic health.

Singapore’s first Doctor of Nursing Practice and three new Master’s programmes are set to develop nursing leadership locally and globally, while deepening expertise in community health, critical care and infection control.  

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD) and aging are locked in a vicious circle: senescence of liver cells accelerates fat accumulation, inflammation and fibrosis, while chronic steatosis in turn hastens hepatic decline. Up to 38 % of adults worldwide have MASLD, and prevalence, severity and mortality all rise with age. Ageing livers shrink by ~30 %, clear lipids and glucose less efficiently, and regenerate more slowly after injury. Lipid deposition is driven by falling β-oxidation, leptin resistance and the GPCPD1–glycerophosphocholine pathway; glucose intolerance emerges from insulin resistance linked to visceral obesity and telomere-p53 signalling. Senescent hepatocytes, endothelial cells, stellate cells and Kupffer macrophages each contribute distinct pathologies, but their shared secretion of the senescence-associated secretory phenotype (SASP) propagates damage throughout the organ.

Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology.