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Ruodi Yang, Shichao Zhang and Zishan Fu contribute equally to this manuscript. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement No new primary data were generated in this study. All data and information supporting the findings of this review are available in the cited publications and the reference list.
Abstract The integration of digital twins (DT) and artificial intelligence (AI) is driving a paradigm shift in diabetes mellitus (DM) care from traditional population-based, symptomatic intervention to a lifecycle-oriented precision management approach. This review systematically elaborates on the cross-disciplinary integration mechanisms of the two technologies.
Abstract Type 2 diabetes mellitus (T2DM) is a global metabolic pandemic affecting hundreds of millions of people, with current pharmacological therapies limited by adverse effects, long-term tolerability issues, and cost barriers. Natural polysaccharides—high-molecular-weight carbohydrate polymers derived from plants, fungi, marine organisms, and animal sources—have emerged as a promising class of multi-target bioactive agents for T2DM management.
Get full text access Log in, subscribe or purchase for full access. References 1. Xia, M. ∙ Lu, J. ∙ Lan, J. ... Elevated IL-22 as a result of stress-induced gut leakage suppresses septal neuron activation to ameliorate anxiety-like behavior Immunity. 2024; 58:218-231.e12 2. McEwen, B.S. Allostasis and allostatic load: implications for neuropsychopharmacology Neuropsychopharmacology. 2000; 22:108-124 3. Menard, C. ∙ Pfau, M.L. ∙ Hodes, G.E. ...
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