Current aging research in China

The mini-review stemmed from a recent meeting on national aging research strategies in China discusses the components and challenges of aging research in China. Highlighted are the major efforts of a number of research teams, funding situations and outstanding examples of recent major research achievements. Finally, authors discuss potential targets and strategies of aging research in China.

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Machine learning methods for biological age estimation

Age stands as a primary risk factor for diseases and disabilities among the elderly.To effectively assess the underlying aging processes,accurate measures of biological age and rates of aging across multiple levels of aging features are essential.Biological age derives from physiological assessments of systems and organs.It has emerged as a superior predictor of age-related diseases and mortality compared to chronological age.Recent advancements in machine learning have catalyzed the development of sophisticated models capable of quantitatively characterizing biological aging with different types of data.This review explores the machine learning models in advancing our understanding of biological aging,highlighting the potential of these innovative approaches to facilitate aging research and personalized healthcare strategies.