Exploring the therapeutic potential of layered double hydroxides and transition metal dichalcogenides through the convergence of rheumatology and nanotechnology using generative adversarial network

趋同(经济学) 生成语法 过渡金属 对抗制 纳米技术 材料科学 计算机科学 化学 人工智能 催化作用 生物化学 经济 经济增长
作者
Suxian Lin,Weiwei Chen,Mohammed S. Alqahtani,Dalia H. Elkamchouchi,Yisu Ge,Yanjie Lu,Guodao Zhang,Mudan Wang
出处
期刊:Environmental Research [Elsevier]
卷期号:241: 117262-117262 被引量:3
标识
DOI:10.1016/j.envres.2023.117262
摘要

Two-dimensional Layered double hydroxides (LDHs) are highly used in the biomedical domain due to their biocompatibility, biodegradability, controlled drug loading and release capabilities, and improved cellular permeability. The interaction of LDHs with biological systems could facilitate targeted drug delivery and make them an attractive option for various biomedical applications. Rheumatoid Arthritis (RA) requires targeted drug delivery for optimum therapeutic outcomes. In this study, stacked double hydroxide nanocomposites with dextran sulphate modification (LDH-DS) were developed while exhibiting both targeting and pH-sensitivity for rheumatological conditions. This research examines the loading, release kinetics, and efficiency of the therapeutics of interest in the LDH-based drug delivery system. The mean size of LDH-DS particles (300.1 ± 8.12 nm) is -12.11 ± 0.4 mV. The encapsulation efficiency was 48.52%, and the loading efficacy was 16.81%. In vitro release tests indicate that the drug's discharge is modified more rapidly in PBS at pH 5.4 compared to pH 5.6, which later reached 7.3, showing the case sensitivity to pH. A generative adversarial network (GAN) is used to analyze the drug delivery system in rheumatology. The GAN model achieved high accuracy and classification rates of 99.3% and 99.0%, respectively, and a validity of 99.5%. The second and third administrations resulted in a significant change with p-values of 0.001 and 0.05, respectively. This investigation unequivocally demonstrated that LDH functions as a biocompatible drug delivery matrix, significantly improving delivery effectiveness.
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