Keying Lin

Abstract Background: Type 2 diabetic kidney disease (T2DKD) affects 20–40% of patients with type 2 diabetes mellitus and has become the leading cause of end-stage renal disease globally. Early identification of patients at risk of rapid progression remains challenging, as existing prediction models often rely on complex indicators unsuitable for primary care settings. This study aimed to develop and validate a machine learning model using routine clinical parameters to predict T2DKD progression.

Abstract Knowledge concepts are acquired through both sensory and language-based experiences. Among these, olfactory abstraction is particularly challenging and often neglected in conceptual research. Mandarin Chinese, spoken by hundreds of millions, has a rich olfactory lexicon. This study explored olfactory abstract concepts from a cross-cultural perspective.

1 Introduction Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as nonalcoholic fatty liver disease (NAFLD), is the most prevalent chronic liver disease worldwide, affecting ≈20%–30% of the global population.[1] The new “positive criteria” for MASLD, which were proposed by an international panel of experts from 22 countries, are based on the presence of liver steatosis and one of the following: overweight/obesity, type 2 diabetes, and metabolic...