Greenelamb1673

Moreover, activity of histamine metabolizing enzymes was present in mouse cardiac samples as well as in human atrial samples. Thus, drugs used for other indication (e.g. antidepressants) can alter histamine levels in the heart. Our results deepen our understanding of the physiological role of histamine in the mouse and human heart. Our findings might be clinically relevant because we show enzyme targets for drugs to modify the beating rate and force of the human heart.Abnormal amyloid beta (Aβ) clearance is a distinctive pathological mechanism for Alzheimer's disease (AD). ATP-binding cassette transporter A1 (ABCA1), which mediates the lipidation of apolipoprotein E, plays a critical role in Aβ clearance. As an environmental factor for AD, dichlorodiphenyltrichloroethane (DDT) can decrease ATP-binding cassette transporter A1 (ABCA1) expression and disrupt Aβ clearance. Liver X receptor α (LXRα) is an autoregulatory transcription factor for ABCA1 and a target of some environmental pollutants, such as organophosphate pesticides. In this study, we aimed to investigate whether DDT could affect Aβ clearance by targeting LXRα. The DDT-pretreated H4 human neuroglioma cells and immortalized astrocytes were incubated with exogenous Aβ to evaluate Aβ consumption. Meanwhile, cytotoxicity and LXRα expression were determined in the DDT-treated cells. Subsequently, the antagonism of DDT on LXRα agonist T0901317 was determined in vitro. The interaction between DDT and LXRα was predicted by molecular docking and molecular dynamics simulation technology. PF-04620110 We observed that DDT could inhibit Aβ clearance and decrease the levels of LXRα mRNA and LXRα protein. Moreover, DDT is supposed to strongly bind to LXRα and exert antagonistic effects on LXRα. In conclusion, this study firstly presented that DDT could inhibit LXRα expression, which would contribute to Aβ clearance decline in vitro. It provides an experimental basis to search for potential therapeutic targets of AD.This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.In Alzheimer's disease (AD), Amyloid β (Aβ) impairs synaptic function by inhibiting long-term potentiation (LTP), and by facilitating long-term depression (LTD). There is now evidence from AD models that Aβ provokes this shift toward synaptic depression by triggering the access to and accumulation of PTEN in the postsynaptic terminal of hippocampal neurons. Here we quantified the PTEN in 196,138 individual excitatory dentate gyrus synapses from AD patients at different stages of the disease and from controls with no neuropathological findings. We detected a gradual increase of synaptic PTEN in AD brains as the disease progresses, in conjunction with a significant decrease in synaptic density. The synapses that remain in symptomatic AD patients are more likely to be smaller and exhibit fewer AMPA receptors (AMPARs). Hence, a high Aβ load appears to strongly compromise human hippocampal synapses, as reflected by an increase in PTEN, inducing a loss of AMPARs that may eventually provoke synaptic failure and loss.Semaphorins, originally discovered as guidance cues for developing axons, are involved in many processes that shape the nervous system during development, from neuronal proliferation and migration to neuritogenesis and synapse formation. Interestingly, the expression of many Semaphorins persists after development. For instance, Semaphorin 3A is a component of perineuronal nets, the extracellular matrix structures enwrapping certain types of neurons in the adult CNS, which contribute to the closure of the critical period for plasticity. Semaphorin 3G and 4C play a crucial role in the control of adult hippocampal connectivity and memory processes, and Semaphorin 5A and 7A regulate adult neurogenesis. This evidence points to a role of Semaphorins in the regulation of adult neuronal plasticity. In this review, we address the distribution of Semaphorins in the adult nervous system and we discuss their function in physiological and pathological processes.With the rapid development of artificial intelligence, Cybernetics, and other High-tech subject technology, robots have been made and used in increasing fields. And studies on robots have attracted growing research interests from different communities. The knowledge graph can act as the brain of a robot and provide intelligence, to support the interaction between the robot and the human beings. Although the large-scale knowledge graphs contain a large amount of information, they are still incomplete compared with real-world knowledge. Most existing methods for knowledge graph completion focus on entity representation learning. However, the importance of relation representation learning is ignored, as well as the cross-interaction between entities and relations. In this paper, we propose an encoder-decoder model which embeds the interaction between entities and relations, and adds a gate mechanism to control the attention mechanism. Experimental results show that our method achieves better link prediction performance than state-of-the-art embedding models on two benchmark datasets, WN18RR and FB15k-237.