The diminished expression of D1 and D2 dopamine receptors is a well-documented hallmark of Huntington's disease (HD), but relatively little is known about how these changes in receptor populations affect the dopaminergic responses of striatal neurons. Using transgenic mice expressing an N-terminal portion of mutant huntingtin (R6/2 mice), we have examined immediate early gene (IEG) expression as an index of dopaminergic signal transduction. c-fos, jun B, zif268, and N10 mRNA levels and expression patterns were analyzed using quantitative in situ hybridization histochemistry following intraperitoneal administration of selective D1 and D2 family pharmacological agents (SKF-82958 and eticlopride). Basal IEG levels were generally lower in the dorsal subregion of R6/2 striata relative to wild-type control striata at 10-11 weeks of age, a finding in accord with previously reported decreases in D1 and adenosine A2A receptors. D2-antagonist-stimulated IEG expression was significantly reduced in the striata of transgenic animals. In contrast, D1-agonist-induced striatal R6/2 IEG mRNA levels were either equivalent or significantly enhanced relative to control levels, an unexpected result given the reduced level of D1 receptors in R6/2 animals. Understanding the functional bases for these effects may further elucidate the complex pathophysiology of Huntington's disease.
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http://dx.doi.org/10.1016/s0169-328x(02)00216-4 | DOI Listing |
Molecules
January 2025
Chair and Department of Biochemistry and Pharmacogenomics, Medical University of Warsaw, 1 Banacha Str., 02-097 Warsaw, Poland.
Vitamin B (thiamine) plays an important role in human metabolism. It is essential for the proper growth and development of the body and has a positive effect on the functioning of the digestive, cardiovascular, and nervous systems. Additionally, it stimulates the brain and improves the psycho-emotional state.
View Article and Find Full Text PDFInt J Mol Sci
January 2025
Chair and Department of General Biology and Parasitology, Medical University of Warsaw, Chałubińskiego 5, 02-004 Warsaw, Poland.
Diabetes mellitus (DM) and neurodegenerative diseases/disturbances are worldwide health problems. The most common chronic conditions diagnosed in persons 60 years and older are type 2 diabetes mellitus (T2DM) and cognitive impairment. It was found that diabetes mellitus is a major risk for cognitive decline, dementia, Parkinson's disease (PD), Alzheimer's disease (AD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders.
View Article and Find Full Text PDFGenes (Basel)
December 2024
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Background/objectives: Artificial intelligence and large language models like ChatGPT and Google's Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini's potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders.
Methods: By analyzing the model's performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments.
Antioxidants (Basel)
January 2025
Laboratory of Molecular, Cellular and Genomic Biomedicine, Instituto de Investigación Sanitaria La Fe, 46026 Valencia, Spain.
Lipid metabolism plays a critical role in maintaining cellular integrity, especially within the nervous system, where lipids support neuronal structure, function, and synaptic plasticity. However, this essential metabolic pathway is highly susceptible to oxidative stress, which can lead to lipid peroxidation, a damaging process induced by reactive oxygen species. Lipid peroxidation generates by-products that disrupt many cellular functions, with a strong impact on proteostasis.
View Article and Find Full Text PDFSci Rep
January 2025
Cognition and Brain Plasticity Unit, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Barcelona, Spain.
One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics.
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