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人工智能辅助的提取优化增强了Phylloporia ribis的生物活性

Aras Fahrettin Korkmaz¹, Ayşenur Gürgen², Tetiana Krupodorova³

¹Faculty of Health Sciences, Nutrition and Dietetics of Department, İstanbul Kültür University, Şirinevler Campus, 34191, Istanbul, Turkey.

Scientific reports

|November 21, 2025

概括

这项研究使用人工神经网络遗传算法 (ANN-GA) 优化了Phylloporia ribis的提取,产生了强大的抗氧化剂和酶抑制化合物. 在药物应用中,ANN-GA增强了化合物产量和生物疗效.

科学领域:

菌类学菌类学是指菌类学.
自然产品化学自然产品化学
生物技术是生物技术.

背景情况:

菲洛波里亚 (Phylloporia ribis) 是一种具有潜在药用功能的真菌.
有效提取生物活性化合物对于其应用至关重要.
优化提取参数可以提高产量和生物活性.

研究的目的:

为了优化 Phylloporia ribis 的提取.
评估提取物的生物功能.
将响应表面方法 (RSM) 与人工神经网络遗传算法 (ANN-GA) 进行比较,以优化提取.

主要方法:

提取温度,持续时间和溶剂比率的优化.
响应表面方法 (RSM) 的应用.
应用一个集成的人工神经网络-遗传算法 (ANN-GA) 方法.

主要成果:

ANN-GA优化的提取物显示出更高的含量 (酸,奎尔丁,香酸).
ANN-GA 提取物表现出卓越的抗氧化活性 (自由基清除,铁性降解力).
提取物证明了剂量依赖的细胞增殖抑制和酶抑制 (乙胆化酶,丁胆化酶).

结论:

ANN-GA方法有效地最大限度地提高了P. ribis提取物的化合物产量和生物疗效.
P. ribis 提取物具有显著的抗氧化和酶抑制特性.
建议进行进一步的生物技术研究,以探索P. ribis. 的治疗潜力.

关键词:

菲洛波里亚的肋骨 (Phylloporia ribis) 是一个生物活动是生物活动.提取优化的提取优化

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