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Function: _error_handler
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Menin, a product of (multiple endocrine neoplasia type 1) gene is an important regulator of tissue development and maintenance; its perturbation results in multiple tumors-primarily of the endocrine tissue. Despite its abundance in the developing central nervous system (CNS), our understanding of menin's role remains limited. Recently, we discovered menin to play an important role in cholinergic synaptogenesis in the CNS, whereas others have shown its involvement in learning, memory, depression and apoptosis. For menin to play these important roles in the CNS, its expression patterns must be corroborated with other components of the synaptic machinery imbedded in the learning and memory centers; this, however, remains to be established. Here, we report on the spatio-temporal expression patterns of menin, which we found to exhibit dynamic distribution in the murine brain from early development, postnatal period to a fully-grown adult mouse brain. We demonstrate here that menin expression is initially widespread in the brain during early embryonic stages, albeit with lower intensity, as determined by immunohistochemistry and gene expression. With the progression of development, however, menin expression became highly localized to learning, memory and cognition centers in the CNS. In addition to menin expression patterns throughout development, we provide the first direct evidence for its co-expression with nicotinic acetylcholine, glutamate and GABA (gamma aminobutyric acid) receptors-concomitant with the expression of both postsynaptic (postsynaptic density protein PSD-95) and presynaptic (synaptotagamin) proteins. This study is thus the first to provide detailed analysis of spatio-temporal patterns of menin expression from initial CNS development to adulthood. When taken together with previously published studies, our data underscore menin's importance in the cholinergic neuronal network assembly underlying learning, memory and cognition.
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http://dx.doi.org/10.3390/cells10051215 | DOI Listing |
Cell Physiol Biochem
December 2024
Joint Institute for Nuclear Research, 141980 Dubna, Russiac.
Background/aims: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early detection is crucial for timely intervention and improved patient outcomes. However, traditional diagnostic tools, such as MRI and PET scans, are costly and less accessible.
View Article and Find Full Text PDFAppl Neuropsychol Adult
December 2024
School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA.
As people live longer with HIV, reports of poor sleep and neurocognitive impairments are expected to increase. Poor sleep and neurocognitive impairments commonly occur in people living with HIV (PLWH) and some medications (e.g.
View Article and Find Full Text PDFDeep learning models are used to minimize the number of polyps that goes unnoticed by the experts and to accurately segment the detected polyps during interventions. Although state-of-the-art models are proposed, it remains a challenge to define representations that are able to generalize well and that mediate between capturing low-level features and higher-level semantic details without being redundant. Another challenge with these models is that they are computation and memory intensive, which can pose a problem with real-time applications.
View Article and Find Full Text PDFWater Res X
January 2025
Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
Sudden shocking load events featuring significant increases in inflow quantities or concentrations of wastewater treatment plants (WWTPs), are a major threat to the attainment of treated effluents to discharge quality standards. To aid in real-time decision-making for stable WWTP operations, this study developed a probabilistic deep learning model that comprises encoder-decoder long short-term memory (LSTM) networks with added capacity of producing probability predictions, to enhance the robustness of real-time WWTP effluent quality prediction under such events. The developed probabilistic encoder-decoder LSTM (P-ED-LSTM) model was tested in an actual WWTP, where bihourly effluent quality prediction of total nitrogen was performed and compared with classical deep learning models, including LSTM, gated recurrent unit (GRU) and Transformer.
View Article and Find Full Text PDFFront Behav Neurosci
December 2024
Department of Neuroscience, Developmental, and Regenerative Biology, University of Texas at San Antonio, San Antonio, TX, United States.
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