Maternal separation (MS) has been demonstrated to up-regulate the hypothalamic vasopressin (VP) system. Intracerebrally released VP has been demonstrated to affect several types of animal behaviour, such as active/passive avoidance, social recognition, and learning and memory. However, the role of VP in spatial learning remains unclear. In the present study, we investigated the effects of an osmotic challenge and a V1b receptor-specific (V1bR) antagonist, SSR149415, on spatial learning of maternally separated and animal facility reared (AFR) adult male Wistar rats. The osmotic challenge was applied by injecting a hypertonic saline solution, 1h before the Morris water maze test (MWM). V1bR antagonist SSR149415 (5mg/kg) was injected i.p. twice (1h and 30 min) previous to the MWM. A combined treatment with both osmotic challenge and the SSR149415 was applied to the third group whereas rats for basal condition were injected with isotonic saline. Under basal condition no differences between AFR and MS groups were observed. MS rats showed severe impairment during the MWM after the osmotic challenge, but not after the administration of SSR149415. For AFR rats, the opposite phenomenon was observed. The joint application of SSR149415 and osmotic challenge restored the spatial learning ability for both groups. The differential impairment produced by osmotic stress-induced up-regulation and SSR149415 induced V1bR blockage in MS and control rats suggested that VP involvement in spatial learning depends on the individual intrinsic ligand-receptor functional state.
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http://dx.doi.org/10.1016/j.neulet.2012.09.002 | DOI Listing |
Bioinformatics
January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
January 2025
Department of Agricultural Engineering, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India.
Drought is one of the most detrimental natural calamities to the economy. Despite its significant consequences, the evolution from meteorological to agricultural and hydrological droughts still needs to be explored. A thorough investigation was carried out in India's eastern hills and plateau region to determine the extent of drought's impact through indices.
View Article and Find Full Text PDFMed Phys
January 2025
School of Computer Science and Engineering, Beihang University, Beijing, China.
Background: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.
View Article and Find Full Text PDFThe spatial arrangement of cells plays a pivotal role in shaping tissue functions in various biological systems and diseased microenvironments. However, it is still under-investigated of the topological coordinating rules among different cell types as tissue spatial patterns. Here, we introduce the Triangulation cellular community motif Neural Network (TrimNN), a bottom-up approach to estimate the prevalence of sizeable conservative cell organization patterns as Cellular Community (CC) motifs in spatial transcriptomics and proteomics.
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