ABSTRACT
In this study, a novel magnetic molecularly imprinted polymeric material (Fe3O4@MOF@MIP-160) with a metal-organic backbone (Fe3O4@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 Fe3O4@MOF@MIP160 with the highest adsorption performance. Transmission electron microscopy (TEM), Fourier Transform Infrared Spectra (FT-IR), Vibration Sample Magnetic (VSM), and the Brunauer-Emmett-Teller (BET) method were used to characterize the materials. Compared with Fe3O4@MOF and the magnetic non-imprinted polymeric material (Fe3O4@MOF@NIP), Fe3O4@MOF@MIP-160 possesses the advantages of easy and rapid manipulation of magnetic materials, the advantages of high specific surface area and the stability of metal-organic frameworks, and the advantages of high selectivity of molecularly imprinted polymers. Fe3O4@MOF@MIP-160 has good recognition and adsorption capacity for di-butyl phthalate (DBP) and diethylhexyl phthalate (DEHP): the adsorption capacity for DBP and DEHP is 260 mg·g-1 and 240.2 mg·g-1, and the adsorption rate is fast (reaching equilibrium in about 20 min). Additionally, Fe3O4@MOF@MIP160 could be recycled six times, making it cost-effective, easy to operate, and time-saving as compared to traditional solid-phase extraction materials. The phthalate ester content in drinking water, fruit juice, and white wine was analyzed, with recoveries ranging from 70.3% to 100.7%. This proved that Fe3O4@MOF@MIP160 was suitable for detecting and removing PAEs from food matrices.
ABSTRACT
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. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control. RESULTS: After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients. CONCLUSION: Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.
