Evidence map›Paper›PMID 40089557›Full record

ArticleScientific reports2025

Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration.

Ayşenur Gürgen

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Ayşenur GürgenFaculty of Engineering and Natural Sciences, Department of Industrial Engineering, Osmaniye Korkut Ata University, Osmaniye, 80000, Turkey. aysenurgurgen@osmaniye.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the biological activities of the extracts obtained under optimum extraction conditions of the kernel part of Juglans regia L. were determined. Two different methods, Response Surface Method (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) integration, were used for optimization. The antioxidant capacity of the extracts obtained under the extract conditions suggested by the two methods was evaluated by Rel Assay kits, DPPH and FRAP methods. Anticholinesterase activities of the optimized extracts were measured by the action of acetylcholinesterase and butyrylcholinesterase enzymes. Antiproliferative effects of the extracts were tested on A549 lung cancer cell line. Phenolic compounds were analyzed by LC-MS/MS. It was determined that both extracts exhibited strong activities against A549 lung cancer cell line depending on the concentration increase. In addition, it was determined that both extracts exhibited acetyl and butyrylcholinesterase inhibition activity close to galantamine used as a standard. In both extracts, 13 compounds including gallic acid, catechinhyrate, 4-hydroxybenzoic acid, caffeic acid, vanillic acid, syringic acid, 2-hydoxycinamic acid, resveratrol, myricetin, quercetin, kaempferol, protocatechuic acid and 2-hyroxy1,4 naphthaquinone were identified. It was determined that the extract obtained under the conditions predicted by ANN-GA exhibited higher activities in general.

Indexed as

JuglansNeural Networks, ComputerPlant ExtractsA549 CellsAcetylcholinesteraseAlgorithmsAntioxidantsButyrylcholinesteraseCell ProliferationCholinesterase InhibitorsGenetic AlgorithmsHumansPhenolsTandem Mass SpectrometryAcetylcholinesteraseAntioxidantsButyrylcholinesteraseCholinesterase InhibitorsPhenolsPlant ExtractsBioactivityExtract optimizationJuglans regiaWalnut

Identifiers

PMID40089557
PMCPMC11910572

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.