The isolation of a compound from a natural source involves many organic and mostly toxic solvents for extraction and purification. Natural deep eutectic solvents have been shown to be efficient options for the extraction of natural products. They have the advantage of being composed of abundantly available common primary metabolites, being nontoxic and environmentally safe solvents. The aim of this study was to develop a natural deep eutectic solvent-based extraction method for galanthamine, an important therapeutic agent for the treatment of Alzheimer's disease. This alkaloid can be produced by synthesis or by extraction from bulbs. To develop an efficient extraction method, a number of different natural deep eutectic solvents was first tested for their solubilization capacity of galanthamine bromide salt. Promising results were obtained for ionic liquids, as well as some amphoteric and acidic natural deep eutectic solvents. In a two-cycle extraction process, the best solvents were tested for the extraction of galanthamine from bulbs. The ionic liquids produced poor yields, and the best results were obtained with some acid and sugar mixtures, among which malic acid-sucrose-water (1 : 1 : 5) proved to be the best, showing similar yields to that of the exhaustive Soxhlet extraction with methanol. Furthermore, the natural deep eutectic solvent was more selective for galanthamine.

Download full-text PDF

Source
http://dx.doi.org/10.1055/a-1803-3259DOI Listing

Publication Analysis

Top Keywords

natural deep
24
deep eutectic
24
eutectic solvents
12
extraction
9
eutectic solvent
8
extraction galanthamine
8
extraction method
8
solvents tested
8
ionic liquids
8
natural
7

Similar Publications

A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.

NPJ Digit Med

January 2025

Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.

Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.

View Article and Find Full Text PDF

We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.

View Article and Find Full Text PDF

This study aims to explore the mechanism behind the influence of stress on gas adsorption by coal during deep mining and improve the accuracy of gas disaster prevention and control. To achieve this aim, thermodynamic analysis was conducted on the process of gas adsorption by loaded coal, and modified thermodynamic model proposed by previous scholars. It is found that stress plays an important role in gas adsorption by coal.

View Article and Find Full Text PDF

Both over-exploitation and exploitation reduction of groundwater can alter the conditions of groundwater recharge and discharge, thereby impacting the overall quality of groundwater. This study utilizes hydrogeochemical methods and statistical analysis to explore the spatial and temporal evolution characteristics and influencing factors of groundwater chemistry in the saline-freshwater funnel area of Hengshui City under exploitation reduction. The results showed that: With the exception of the deep freshwater funnel area in the western region, which exhibits a trend of water quality deterioration (Cl accounted for more than 25%), groundwater quality in the other funnel areas demonstrates an improving trend (HCO[Formula: see text] accounted for more than 25%).

View Article and Find Full Text PDF

An automatic cervical cell classification model based on improved DenseNet121.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!