Oglufanide disodium

Metabolomics was first applied to explore the formation pathway and regulatory factors for advanced glycation end products (AGEs) in preserved egg yolk (PEY) during pickling. Most reactions that contributed to formation of AGEs, including oxidation and/or degradation of matrix/precursors and Maillard reaction, occurred primarily in PEY in early stage, and lipid oxidation occurred prior to protein oxidation. Additionally, the formation of N^ε-carboxymethyl-lysine (CML) in PEY was mainly attributed to glyoxal derived from D-glucuronic acid in early stage based on the correlation between metabolites and AGEs. Reactive oxygen radicals from oxidized lipids also promoted the enrichment of CML and N^ε-carboxymethyl-lysine (CEL). Moreover, during later stage, the accumulation of CEL was enhanced through methylglyoxal from Schiff bases, and acidic compounds could inhibit AGEs via hindering Maillard reaction. This manuscript pioneered the application of metabolomics to reveal the formation pathway of AGEs, and will provide new perspective on AGEs formation in foods.

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with experimental validation to identify natural compounds with comprehensive neuroprotective properties. Our computational framework integrated SELFormer, a transformer-based deep learning model, and multiple deep learning algorithms to predict NC bioactivity against seven crucial stroke-related targets (ACE, GLA, MMP9, NPFFR2, PDE4D, and eNOS). The pipeline encompassed IC50 predictions, clustering analysis, quantitative structure-activity relationship (QSAR) modeling, and uniform manifold approximation and projection (UMAP)-based bioactivity profiling followed by molecular docking studies and experimental validation. Analysis revealed six distinct NC clusters with unique molecular signatures. UMAP projection identified 11 medium-activity (6 < pIC50 ≤ 7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking confirming strong correlations between binding energies and predicted pIC50 values. In vitro studies using NGF-differentiated PC12 cells under oxygen-glucose deprivation demonstrated significant neuroprotective effects of four high-activity compounds: feruloyl glucose, l-hydroxy-l-tryptophan, mulberrin, and ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase activity and lipid peroxidation, suppressed TNF-α expression, and upregulated BDNF mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.