Natural colorants from plant-based materials have gained increasing popularity due to health consciousness of consumers. Among the many steps involved in the production of natural colorants, pigment extraction is one of the most important. Soxhlet extraction, maceration, and hydrodistillation are conventional methods that have been widely used in industry and laboratory for such a purpose. Recently, various non-conventional methods, such as supercritical fluid extraction, pressurized liquid extraction, microwave-assisted extraction, ultrasound-assisted extraction, pulsed-electric field extraction, and enzyme-assisted extraction have emerged as alternatives to conventional methods due to the advantages of the former in terms of smaller solvent consumption, shorter extraction time, and more environment-friendliness. Prior to the extraction step, pretreatment of plant materials to enhance the stability of natural pigments is another important step that must be carefully taken care of. In this paper, a comprehensive review of appropriate pretreatment and extraction methods for chlorophylls, carotenoids, betalains, and anthocyanins, which are major classes of plant pigments, is provided by using pigment stability and extraction yield as assessment criteria.
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http://dx.doi.org/10.1080/10408398.2015.1109498 | DOI Listing |
Int J Surg
January 2025
Senior researcher and lecturer at the Master Specialized Physical Therapy programs at Avans+, Breda, The Netherlands.
Introduction: Spastic Cerebral Palsy (CP) is a major cause of movement disorders in pediatric rehabilitation. Current treatments are often invasive and may lead to substantial discomfort. Extracorporeal shockwave therapy (ESWT) presents a potential alternative, offering a less invasive approach with a reduced side effect profile.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Background: Adopting appropriate noninvasive radiological method is crucial for periodic surveillance of liver metastases in colorectal cancer (CRC) patients after surgery, which is closely related to clinical management and prognosis. This study aimed to prospectively enroll stage II-III CRC patients for the surveillance of liver metastases, and compare the diagnostic performance of contrast-enhanced CT (CE-CT) and non-enhanced abbreviated MRI (NE-AMRI) during this process.
Methods: 587 CRC patients undergoing radical resection of the primary tumor were evaluated by 1 to 3 rounds of surveillance tests, consisting of abdominal CE-CT and contrast-enhanced MRI (CE-MRI) within 7 days at 6-month intervals.
Mol Omics
January 2025
Departamento de Innovación Biomédica, Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California (CICESE), Carretera Ensenada-Tijuana No. 3918, Zona Playitas, C.P. 22860, Ensenada, Baja California, Mexico.
Metabolic associated steatohepatitis characterized by lipid accumulation, inflammation and fibrosis, is a growing global health issue, contributing to severe liver-related mortality. With limited effective treatments available, there is an urgent need for novel therapeutic strategies. , rich in antioxidants, offers potential for combating steatohepatitis, but its cytotoxicity presents challenges.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
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