Fabrication of Magnetic Molecularly Imprinted Polymers for Selective Extraction of Dibutyl Phthalates in Food Matrices.

In this study, a novel magnetic molecularly imprinted polymeric material (FeO@MOF@MIP-160) with a metal-organic backbone (FeO@MOF) carrier was prepared using dibutyl phthalate (DBP) as a template. The material can be used for the efficient, rapid, and selective extraction of trace amounts of phthalic acid esters (PAEs) in food and can detect them via gas chromatography-mass spectrometry (GC-MS). The synthesis conditions of the materials were optimized to prepare the FeO@MOF@MIP160 with the highest adsorption performance.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms.

Background: Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.

Methods: We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models.