Patients recovering from sepsis have higher rates of CNS morbidities associated with long-lasting impairment of cognitive functions, including neurodegenerative diseases. However, the molecular etiology of these sepsis-induced impairments is unclear. Here, we investigated the role of the receptor for advanced glycation end products (RAGE) in neuroinflammation, neurodegeneration-associated changes, and cognitive dysfunction arising after sepsis recovery. Adult Wistar rats underwent cecal ligation and perforation (CLP), and serum and brain (hippocampus and prefrontal cortex) samples were obtained at days 1, 15, and 30 after the CLP. We examined these samples for systemic and brain inflammation; amyloid-β peptide (Aβ) and Ser-202-phosphorylated Tau (p-Tau) levels; and RAGE, RAGE ligands, and RAGE intracellular signaling. Serum markers associated with the acute proinflammatory phase of sepsis (TNFα, IL-1β, and IL-6) rapidly increased and then progressively decreased during the 30-day period post-CLP, concomitant with a progressive increase in RAGE ligands (S100B, ϵ-[carboxymethyl]lysine, HSP70, and HMGB1). In the brain, levels of RAGE and Toll-like receptor 4, glial fibrillary acidic protein and neuronal nitric-oxide synthase, and Aβ and p-Tau also increased during that time. Of note, intracerebral injection of RAGE antibody into the hippocampus at days 15, 17, and 19 post-CLP reduced Aβ and p-Tau accumulation, Akt/mechanistic target of rapamycin signaling, levels of ionized calcium-binding adapter molecule 1 and glial fibrillary acidic protein, and behavioral deficits associated with cognitive decline. These results indicate that brain RAGE is an essential factor in the pathogenesis of neurological disorders following acute systemic inflammation.
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http://dx.doi.org/10.1074/jbc.M117.786756 | DOI Listing |
Front Biosci (Landmark Ed)
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Department of Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, 400016 Chongqing, China.
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View Article and Find Full Text PDFOncol Res
December 2024
Department of Biology, College of Science, Sultan Qaboos University, Muscat, 123, Oman.
Nanotechnology in cancer therapy has significantly advanced treatment precision, effectiveness, and safety, improving patient outcomes and personalized care. Engineered smart nanoparticles and cell-based therapies are designed to target tumor cells, precisely sensing the tumor microenvironment (TME) and sparing normal cells. These nanoparticles enhance drug accumulation in tumors by solubilizing insoluble compounds or preventing their degradation, and they can also overcome therapy resistance and deliver multiple drugs simultaneously.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
December 2024
One Health Research Group, Universidad de las Americas, Quito, Ecuador.
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from chromaffin cells, with 80-85% originating in the adrenal medulla and 15-20% from extra-adrenal chromaffin tissues (paragangliomas). Approximately 30-40% of PPGLs have a hereditary component, making them one of the most genetically predisposed tumor types. Recent advances in genetic research have classified PPGLs into three molecular clusters: pseudohypoxia-related, kinase-signaling, and -signaling pathway variants.
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December 2024
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background: The aim of this study is to develop deep learning models based on F-fluorodeoxyglucose positron emission tomography/computed tomographic (F-FDG PET/CT) images for predicting individual epidermal growth factor receptor () mutation status in lung adenocarcinoma (LUAD).
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Bioinform Adv
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Laboratory of Experimental Biophysics, Center for Advanced Technologies, Tashkent, 100174, Uzbekistan.
Motivation: Understanding the conformational landscape of protein-ligand interactions is critical for elucidating the binding mechanisms that govern these interactions. Traditional methods like molecular dynamics (MD) simulations are computationally intensive, leading to a demand for more efficient approaches. This study explores how multiple sequence alignment (MSA) clustering enhance AF-Multimer's ability to predict conformational landscapes, particularly for proteins with multiple conformational states.
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