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A time-saving, efficient, and environmentally friendly ultrasonic-microwave-assisted natural deep eutectic solvent (UMAE-NADES) extraction method was developed for the extraction of anthocyanins from Aronia melanocarpa. Eight different natural eutectic solvents were screened initially, and choline chloride-glycerol was selected as the extraction solvent. The extraction conditions were optimized using the response surface methodology, and the extraction rate of anthocyanins was higher than those achieved using the traditional ethanol method, natural deep eutectic solvent extraction method, and ultrasonic-microwave-assisted ethanol method. Six anthocyanins, including cyanidin-3-O-galactoside, cyanidin-3-O-glucoside, cyanidin-3-O-arabinoside, cyanidin-3-O-xyloside, cyanidin-3,5-O-dihexoside, and the dimer of cyanidin-hexoside were identified and extracted at a purity of 448.873 mg/g using high performance liquid chromatography-mass spectrometry (HPLC-MS). The compounds extracted using UMAE-NADES had higher antioxidant capacities than those extracted by the other three methods. The UMAE-NADES demonstrated significant efficiency toward the extraction of bioactive substances and has potential utility in the food and pharmaceutical industries.
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http://dx.doi.org/10.1016/j.ultsonch.2022.106102 | DOI Listing |
RSC Med Chem
August 2025
Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Suez Canal University 4.5 Km the Ring Road Ismailia 41522 Egypt.
Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability.
View Article and Find Full Text PDFNEJM AI
September 2025
Department of Biomedical Informatics, Harvard Medical School, Boston.
Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies.
View Article and Find Full Text PDFACS Omega
September 2025
Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Dengue virus remains a significant global health threat, imposing a substantial disease burden on nearly half of the world's population. The urgent need for effective antiviral therapeutics, including therapeutic peptides targeting the Dengue virus, is critical in the current healthcare landscape. However, the availability of anti-Dengue peptides (ADPs) data remains limited in existing data sets, posing a challenge for computational modeling and discovery.
View Article and Find Full Text PDFFront Psychol
August 2025
Universidad Privada Norbert Wiener, Vicerrectorado de Investigación, Lima, Peru.
This article presents a novel perspective on the structure and function of the human cortex, grounded in the Sociobiological Informational Theory (SIT). SIT offers a conceptual framework that integrates biological, psychological, and social dimensions of brain activity, challenging traditional anatomical and physiological models. Under this perspective, the neocortex is interpreted as the system of consciousness, while the paleocortex is associated with unconscious processes.
View Article and Find Full Text PDFCrit Care Res Pract
August 2025
Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sepsis remains one of the leading causes of morbidity and mortality worldwide, particularly among critically ill patients in intensive care units (ICUs). Traditional diagnostic approaches, such as the Sequential Organ Failure Assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria, often detect sepsis after significant organ dysfunction has occurred, limiting the potential for early intervention. In this study, we reviewed how artificial intelligence (AI)-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can aid physicians.
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