Publications by authors named "G M Dempsey"
Article Synopsis
- Fragile X syndrome (FXS) is caused by hypermethylation of CGG repeats in the FMR1 gene, resulting in loss of FMRP, which is crucial for normal neuronal function.
- Research has shown that FMRP loss leads to abnormal synaptic activity and hyperexcitability in neurons, but effective treatments have yet to be found due to translation issues from animal models to humans.
- A new high-resolution all-optical electrophysiology platform has been developed to create a sensitive assay that measures FMRP re-expression and healthy neuron restoration, which can be used to identify potential new therapies for FXS.
Article Synopsis
- - Delirium is an acute neurological syndrome affecting cognition, impacting over 20% of medical patients and 50% of critically ill individuals, leading to significant distress, morbidity, and socioeconomic costs, particularly in the aging population.
- - The understanding of delirium's underlying mechanisms is limited, with a focus on neuronal dysfunction, but recent research highlights the role of glial cells—especially astrocytes, microglia, and oligodendrocytes—in its pathophysiology.
- - This review discusses how astrocyte-related issues, like disrupted brain metabolism and blood-brain barrier dysfunction, contribute to delirium, while also addressing the challenges of studying these mechanisms and the potential of advanced neuroimaging techniques for further insights
Hungry animals need compensatory mechanisms to maintain flexible brain function, while modulation reconfigures circuits to prioritize resource seeking. In Drosophila, hunger inhibits aversively reinforcing dopaminergic neurons (DANs) to permit the expression of food-seeking memories. Multitasking the reinforcement system for motivation potentially undermines aversive learning.
View Article and Find Full Text PDFBackground: Lyme disease is caused by the bacteria Borreliella burgdorferi sensu lato (Bb) transmitted to humans from the bite of an infected Ixodes tick. Current diagnostics for Lyme disease are insensitive at the early disease stage and they cannot differentiate between active infections and people with a recent history of antibiotic-treated Lyme disease.
Methods: Machine learning technology was utilized to improve the prediction of acute Lyme disease and identify sialic acid and galactose sugar structures (N-glycans) on immunoglobulins associated specifically at time points during acute Lyme disease time.