Natural products from plants were extracted and widely studied for their activities against many disease conditions. The selection of the extracting solvent is crucial to develop selective and effective methods for the extraction and isolation of target compounds in the plant matrices. Pharmacological properties of plant extracts and their bioactive principles are related to their excellent solubility, stability, and bioavailability when administered by different routes. This review aims to critically analyze natural deep eutectic solvents (NADES) as green solvents in their application to improve the extraction performance of plant metabolites in terms of their extractability besides the stability, bioactivity, solubility, and bioavailability. Herein, the opportunities for NADES to be used in pharmaceutical formulations development including plant metabolites-based nutraceuticals are discussed.
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http://dx.doi.org/10.3390/plants10102091 | DOI Listing |
Orphanet J Rare Dis
January 2025
Department of Pediatrics, Guangdong Provincial People's Hospital, The Second School of Clinical Medicine, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
Background: Hepatic glycogen storage diseases (GSD) are inborn errors of metabolism with abnormal storage or utilization of glycogen, a complex disease with significant genetic heterogeneity and similar clinical manifestations. This study aimed to describe the gastrointestinal symptoms and endoscopic features of hepatic GSD, including types Ia, Ib, III, VI, and IX, to provide evidence for etiology and treatment.
Methods: A national cohort survey questionnaire was distributed to patients diagnosed with GSD type Ia, Ib, III, VI, and IX through genetic testing or their parents in mainland China in May 2022.
BMC Med
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, Guoxue Alley, Address: No.37, Chengdu City, Sichuan, 610041, China.
Background: This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images.
Methods: A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images.
J Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, 100044, P. R. China.
This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in osteosarcoma (OS) patients using routine magnetic resonance imaging (MRI). We retrospectively analyzed data from 112 patients with histologically confirmed OS who underwent NACT prior to surgery. Multi-sequence MRI data (including T2-weighted and contrast-enhanced T1-weighted images) and physician annotations were utilized to construct an end-to-end DL model.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematics, School of Advanced Sciences, VIT-AP University, Besides AP Secretariate, Amaravati, Andhra Pradesh, 522237, India.
Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes.
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