Scholarlink

<p>In this study, extraction parameters to increase the biological activities of <i>Lentinula edodes</i> extracts were optimized using both Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid models. Antioxidant, anticholinesterase, antiproliferative activities, and phenolic contents of the extracts obtained under optimum conditions were determined. According to the findings of the study, it was determined that the extracts obtained with ANN-GA optimization had higher TAS (6.612 mmol/L), FRAP (176.25&#xa0;mg TE/g), and DPPH (133.00&#xa0;mg TE/g) values ​​compared to RSM. In addition, TOS (4.167 µmol/L) and OSI (0.063) levels were lower. In anticholinesterase assays, ANN-GA extracts were found to be more effective than RSM in inhibiting AChE (72.47&#xa0;µg/mL) and BChE (132.13&#xa0;µg/mL). Furthermore, in antiproliferative assays on A549, MCF-7, and DU-145 cancer cell lines, ANN-GA-optimized extracts were observed to have stronger cytotoxic effects. LC-MS/MS analyses revealed that ANN-GA optimization enriched biologically important phenolic compounds such as gallic acid, protocatechuic acid, and caffeic acid at higher levels. Consequently, AI-assisted optimization offers a powerful strategy for enhancing the biological value of mushroom extracts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence-assisted optimization of Lentinula edodes extracts for enhanced bioactive profile and therapeutic potential

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月26日

查看全文
  • Mustafa Sevindik,
  • Vadim Tagirovich Khassanov,
  • Ayşenur Gürgen,
  • Ilgaz Akata

摘要

In this study, extraction parameters to increase the biological activities of Lentinula edodes extracts were optimized using both Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid models. Antioxidant, anticholinesterase, antiproliferative activities, and phenolic contents of the extracts obtained under optimum conditions were determined. According to the findings of the study, it was determined that the extracts obtained with ANN-GA optimization had higher TAS (6.612 mmol/L), FRAP (176.25 mg TE/g), and DPPH (133.00 mg TE/g) values ​​compared to RSM. In addition, TOS (4.167 µmol/L) and OSI (0.063) levels were lower. In anticholinesterase assays, ANN-GA extracts were found to be more effective than RSM in inhibiting AChE (72.47 µg/mL) and BChE (132.13 µg/mL). Furthermore, in antiproliferative assays on A549, MCF-7, and DU-145 cancer cell lines, ANN-GA-optimized extracts were observed to have stronger cytotoxic effects. LC-MS/MS analyses revealed that ANN-GA optimization enriched biologically important phenolic compounds such as gallic acid, protocatechuic acid, and caffeic acid at higher levels. Consequently, AI-assisted optimization offers a powerful strategy for enhancing the biological value of mushroom extracts.