Evidence map›Paper›PMID 41318632›Full record

ArticleScientific reports2025

Maximization of bioactive potential of Fomes fomentarius via artificial Intelligence-Assisted optimization.

Emre Cem Eraslan

Abstract read
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Artificial Intelligence-Assisted Optimization ofFoods (Basel, Switzerland) · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Emre Cem EraslanDepartment of Biology, Faculty of Engineering and Natural Sciences, University of Osmaniye Korkut Ata, Osmaniye, 80000, Turkey. emrecemeraslan@osmaniye.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, in order to maximize the biological activity of the medicinal mushroom species Fomes fomentarius, extraction parameters were evaluated by Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid optimization. The effects of extraction temperature, time and ethanol/water ratio on total antioxidant capacity (TAC) were modeled. As a result of the study, the optimum conditions were determined as 41.080 °C, 41.751 min and 49.149% ethanol/water ratio for RSM. For ANN-GA, the optimum conditions were determined as 39.848 °C, 39.689 min and 88.490% ethanol/water ratio. Extracts obtained under optimal conditions were compared in terms of antioxidant (FRAP, DPPH, TAS, TOS, OSI), anticholinesterase (AChE, BChE), antiproliferative (A549, MCF-7, DU-145) activities, and LC-MS/MS phenolic compound profiles. RSM extracts exhibited higher activities than ANN-GA in most parameters. Particularly high values were obtained for FRAP, DPPH, phenolic compound concentrations, and antiproliferative activity. The results demonstrated that the optimization approach was decisive in the bioactive compound profile and pharmacological potential of the extract. These results support the position of F. fomentarius as an important natural resource that can be used in pharmaceutical and functional food fields.

Indexed as

AgaricalesAntioxidantsArtificial IntelligenceCell Line, TumorCell ProliferationCholinesterase InhibitorsHumansNeural Networks, ComputerPhenolsAntioxidantsCholinesterase InhibitorsPhenolsANN-GAAntioxidant activityAntiproliferative activityEnzyme inhibition assaysExtraction modelingLC–MS/MSOptimizationPhenolic compoundsRSM

Identifiers

PMID41318632
PMCPMC12780258

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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.