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The structure, UV absorbance, browning intensity, fluorescence changes, antioxidant activity and antimicrobial assessment of Maillard reaction products (MRPs) derived from xylan with chitosan, chitooligomer, glucosamine hydrochloride and taurine model systems were evaluated. The results revealed that all MRPs had similar infrared spectra and molecular structures. MRPs from different model systems on the UV absorbance at 294 nm after heated 90 min and browning intensity at 420 nm showed the similar law: xylan-taurine > xylan-glucosamine hydrochloride > xylan-chitooligomer > xylan-chitosan, and the order of DPPH scavenging activity of MRPs was as follows: xylan-chitosan > xylan-chitooligomer > xylan-glucosamine hydrochloride > xylan-taurine, which revealed that the properties of MRPs were closely related to molecular weight of model systems. Moreover, the highest radical scavenging activity of MRPs from xylan with chitosan/chitooligomer/glucosamine hydrochloride/taurine model systems was 65.9%, 63.7%, 46.4% and 42.5%, respectively.
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http://dx.doi.org/10.1016/j.foodchem.2013.10.044 | DOI Listing |
Crit Rev Ther Drug Carrier Syst
January 2025
Department of Pharmacology, PSG College of Pharmacy, Coimbatore 641004, Tamil Nadu, India.
Treating neurological disorders is challenging due to the blood-brain barrier (BBB), which limits therapeutic agents, including proteins and peptides, from entering the central nervous system. Despite their potential, the BBB's selective permeability is a significant obstacle. This review explores recent advancements in protein therapeutics for BBB-targeted delivery and highlights computational tools.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
JMIR Med Inform
September 2025
Department of Hepatobiliary and Vascular Surgery, First Affiliated Hospital of Chengdu Medical College, Chengdu, China.
Background: Primary liver cancer, particularly hepatocellular carcinoma (HCC), poses significant clinical challenges due to late-stage diagnosis, tumor heterogeneity, and rapidly evolving therapeutic strategies. While systematic reviews and meta-analyses are essential for updating clinical guidelines, their labor-intensive nature limits timely evidence synthesis.
Objective: This study proposes an automated literature screening workflow powered by large language models (LLMs) to accelerate evidence synthesis for HCC treatment guidelines.
JMIR Hum Factors
September 2025
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Background: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs.
View Article and Find Full Text PDF