Objective: To investigate the effect of glucagon-like peptide 1 (GLP-1) on cognitive dysfunction in diabetic rats.
Methods: Male SD rats were randomly divided into normal control group, diabetes mellitus (DM) group, and GLP-1 treatment group. Rat models of type 2 diabetes were established by high-sugar and high-fat feeding and streptozotocin (STZ) injection, and 25 days after the onset of diabetes, GLP-1 was infused in GLP-1 treatment group at the rate of 30 pmol·kg·min via a subcutaneous osmotic pump for 7 days. The learning and cognitive ability of the rats was assessed with Morris water maze test, and the expression of cognition-related genes in the hippocampus tissue was detected with real-time PCR, Western blotting and immunohistochemical staining.
Results: Compared with the normal control group, the diabetic rats showed significantly decreased learning and memory abilities (P<0.05) with increased hippocampal expressions of APP, BACE1, Arc, ERK1/2, PKA, and PKC mRNAs (P<0.05) and Arc protein. Compared with diabetic rats, GLP-1-treated rats showed significantly improvements in the learning and memory function (P<0.05) with decreased expressions of APP, BACE1, Arc, ERK1/2, and PKA mRNAs (P<0.05) and Arc protein.
Conclusion: GLP-1 can improve cognitive dysfunctions in diabetic rats possibly by regulating the PKC, PKA, and ERK1/2 pathways and inhibiting Arc expression in the hippocampus.
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Psychopharmacology (Berl)
January 2025
Department of Pharmacology and Toxicology, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
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School of Mathematics and Computer Science, Tongling University, Tongling, 244061, China.
The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs.
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Single Molecule Analysis Group, Department of Chemistry, The University of Michigan, Ann Arbor, Michigan 48109, United States.
Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding factors such as low signal-to-noise ratios, photophysical effects such as bleaching and blinking, and the complexity of modeling the underlying biomolecular states and kinetics. The dynamic distance information shaping the smFRET trace powerfully uncovers even transient conformational changes in single biomolecules both at or far from equilibrium, relying on trace idealization to identify specific interconverting states.
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Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
The human hippocampus, essential for learning and memory, is implicated in numerous neurological and psychiatric disorders, each linked to specific neuronal subpopulations. Advancing our understanding of hippocampal function requires computational models grounded in precise quantitative neuronal data. While extensive data exist on the neuronal composition and synaptic architecture of the rodent hippocampus, analogous quantitative data for the human hippocampus remain very limited.
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Department of Communicative Sciences and Disorders, New York University, New York, NY, USA.
Studies have shown that exposure to multiple talkers during learning is beneficial in a variety of spoken language tasks, such as learning speech sounds in a second language and learning novel words in a lab context. However, not all studies find the multiple talker benefit. Some studies have found that processing benefits from exposure to multiple talkers depend on factors related to the linguistic profile of the listeners and to the cognitive demands during learning (blocked versus randomized talkers).
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