Machine Learning‐Assisted High‐Throughput Screening of Nanozymes for Ulcerative Colitis
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Abstract
Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature of UC symptoms, current pharmacological treatments fail to meet patients' expectations. This study presents a machine learning-assisted high-throughput screening strategy to expedite the discovery of efficient nanozymes for UC treatment. Therapeutic requirements, including antioxidant property, acid stability, and zeta potential, are quantified and predicted by using a machine learning model. Non-quantifiable attributes, including intestinal barrier repair efficacy and biosafety, are assessed via high-throughput screening. Feature significance analysis, sure independence screening, and sparsifying operator symbolic regression reveal the high-dimensional structure-activity relationships between material features and therapeutic needs. SrDy2O4 with high stability, low toxicity, targeting ability, and reactive oxygen species (ROS) scavenging capability is identified, which reduces ROS production, lowers cytochrome C levels in cytoplasm, and inhibits apoptosis in intestinal epithelial cells by stabilizing the mitochondrial membrane potential. Mice treated with SrDy2O4 show improvements in colon length and body weight compared with dextran sodium sulfate salt-treated model group. Transcriptomic and 16S rRNA sequencing analyses show that SrDy2O4 boosts beneficial gut bacteria, and decreases pathogenic bacteria, thereby effectively restoring gut microbiota balance. Moreover, SrDy2O4 offers the advantage of X-ray imaging without side effects.
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