AIMC Topic: Phenols
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An in silico to in vivo approach identifies retinoid-X receptor activating tert-butylphenols used in food contact materials.
Scientific reports
Jul 18, 2025
The potential for food contact chemicals to disrupt genetic programs in development and metabolism raises concerns. Nuclear receptors (NRs) control many of these programs, and the retinoid-X receptor (RXR) is a DNA-binding partner for one-third of th...
Ligand Microenvironment-Regulated Nanozymes Enabled Machine Learning-Assisted Sensor Array for Simultaneous Identification of Phenolic Pollutants.
ACS sensors
Jul 15, 2025
Phenolic pollutants pose a great threat to human health due to high toxicity, whereas existing methods are difficult to achieve the rapid recognition of multiple phenolic pollutants. In this study, we developed a novel machine learning-assisted senso...
Optimization of biological activities of Agaricus species: an artificial intelligence-assisted approach.
Scientific reports
Jul 2, 2025
This study aims to determine the optimum extraction conditions that maximize the biological activities of Agaricus campestris and Agaricus bisporus species. In the study, a total of 64 extraction experiments were carried out at different temperatures...
A Machine Learning-Based Clustering Analysis to Explore Bisphenol A and Phthalate Exposure from Medical Devices in Infants with Congenital Heart Defects.
Environmental health perspectives
Jun 18, 2025
BACKGROUND: Plastic-containing medical devices are commonly used in critical care units and other patient care settings. Patients are often exposed to xenobiotic agents that are leached out from plastic-containing medical devices, including bisphenol...
Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel.
Scientific reports
Jun 3, 2025
The peel of pomegranate (Punica granatum) is rich in bioactive compounds, specifically phenolic compounds and tannin compounds. However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of trad...
Unleashing the power of AI in predicting the technological and phenolic maturity of pomegranates cultivated in Lebanon.
Scientific reports
May 30, 2025
The harvesting time of pomegranates is crucial for maximizing their health benefits and market value. However, traditional methods often fail to consider the intricate interactions between environmental conditions and fruit maturity. This study is th...
Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques.
BMC biotechnology
Mar 28, 2025
In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The extracts were tested for phenolic content, a...
Comparative analysis and investigation of ultrasonication on juice yield and bioactive compounds of kinnow fruit using RSM and ANN models.
Scientific reports
Mar 21, 2025
Ultrasonication (US) is a promising non-thermal technique widely applied in the food sector for improving the extraction process and preserving nutrients. Kinnow fruits have yields of 40-60% juice; the rest of the parts are discarded as waste. The st...
Antioxidant activity of Mentha piperita phenolics on arsenic induced oxidative stress, biochemical alterations, and cyto-genotoxicity in fish, Channa punctatus.
Fish physiology and biochemistry
Mar 20, 2025
The study aims to investigate the synergistic antioxidant effects of the phenolics present in Mentha piperita (MP) against arsenic trioxide-induced oxidative stress, biochemical alteration, and cyto-genotoxicity in the fish, Channa punctatus. The phe...
Bioactivity of Juglans regia kernel extracts optimized using response surface method and artificial neural Network-Genetic algorithm integration.
Scientific reports
Mar 15, 2025
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 A...
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