Casebasse5852

The results obtained in the Naglu-/- cells were in accordance with the results found in the mouse model of MPS IIIB. It suggests that the presented murine Naglu-/- cell lines might be a convenient in vitro model of MPS IIIB. The first x-ray structures of flaviviral proteases defined two conformational states, open and closed, depending on the relative position of NS2B with respect to NS3, a feature that affects the shape of the binding site. The degree of flexibility in the active site was limited to changes in the fold of NS2B rather than NS3 and an induced-fit mechanism was regarded as the main factor for ligand binding. A minor degree of conformational plasticity in NS3 is observed in the two protein chains in the asymmetric unit for the structure of Zika protease with a dipeptide boronate, synthesized in our group. We hypothesize that the NS3 fold has a crucial influence on the shape of the binding site and that a reevaluation of the induced-fit interpretation is warranted. A comparison of flaviviral protease structures identifies conformational dynamics of NS3 and their unexpected role in controlling the depth of the, otherwise shallow, active site. The structural changes of NS3 are mediated by conserved residues and reveal a subpocket, which we denote as subpocket B, extending beyond the catalytic aspartate 75 towards the allosteric binding site, providing a unique connection between the orthosteric and allosteric sites in the protease. The structural evidence supports a molecular recognition based primarily on conformational selection and population shift rather than induced-fit. Besides the implications on protease studies and drug development, this hypothesis provides an interpretation for the alternate binding modes with respect to the catalytic serine, which are observed for recently developed beta-lactam inhibitors incorporating benzyloxyphenylglycine. The development of machine learning solutions in medicine is often hindered by difficulties associated with sharing patient data. Distributed learning aims to train machine learning models locally without requiring data sharing. However, the utility of distributed learning for rare diseases, with only a few training examples at each contributing local center, has not been investigated. The aim of this work was to simulate distributed learning models by ensembling with artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) and evaluate them using four medical datasets. Distributed learning by ensembling locally trained agents improved performance compared to models trained using the data from a single institution, even in cases where only a very few training examples are available per local center. Distributed learning improved when more locally trained models were added to the ensemble. Local class imbalance reduced distributed SVM performance but did not impact distributed RF and ANN classification. Our results suggest that distributed learning by ensembling can be used to train machine learning models without sharing patient data and is suitable to use with small datasets. Adverse Drug Reactions (ADRs) are extremely hazardous to patients. ADR Detection aims to automatically determine whether a sentence is related to an ADR, which is a fundamental study for public health monitoring tasks, particularly for pharmacovigilance. Benchmark corpora are mostly sampled from biomedical literature or social media, but most of them are on small scales. Correspondingly, existing ADR detection models are either trained with additional corpora that are annotated manually or jointly trained with the ADR detection and the entity mention extraction task. However, directly training a method with additional corpora sampled from different sources may introduce noises and impact the performance of neural networks. Besides, jointly training a method with different tasks requires the annotation for other tasks, which still increases the annotation workload. To address the above issues, we formulate ADR detection as a text classification task and introduce an adversarial transfer learning framework into ADR detection. Our method focuses on exploiting a source corpus to improve the performance on small target corpora which only contain hundreds of training instances. Also, adversarial learning is applied to prevent corpus-specific features from being introduced into shared space so that corpora from different sources can be leveraged with minimum extra noises. Experimental results on three different benchmark corpora show that our proposed method consistently outperforms other state-of-the-art methods, especially on small corpora. Adverse events caused by drug-drug interaction (DDI) not only pose a serious threat to health, but also increase additional medical care expenditure. However, despite the emergence of many excellent text mining-based DDI classification methods, achieving a balance between using simpler method and better model performance is still unsatisfactory. In this article, we present a deep learning method of stacked bidirectional Gated Recurrent Unit (GRU)- convolutional neural network (SGRU-CNN) model which apply stacked bidirectional GRU (BiGRU) network and convolutional neural network (CNN) on lexical information and entity position information respectively to conduct DDIs extraction task. Furthermore, SGRU-CNN model assigns the weights of each word feature to improve performance with one attentive pooling layer. On the condition that other values are not inferior to other algorithms, experimental results on the DDI Extraction 2013 corpus show that our model achieves a 1.54% improvement in recall value. MM3122 order And the proposed SGRU-CNN model reaches great performance (F1-score 0.75) with the fewest features, indicating an excellent balance between avoiding redundant preprocessing task and higher accuracy in relation extraction on biomedical literature using our method. Many reports describe an association between preconceptional paternal exposure to environmental chemicals, including the persistent organic pollutant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) with an increased number of female offspring. We chronically treated wild-type C57BL/6 male mice with TCDD to investigate a role for the aryl hydrocarbon receptor (AHR) transcription factor. These mice had a 14% lower malefemale sex ratio than control mice, which was not observed in TCDD-treated Ahr knock out mice. AHR target genes Cyp1a1 and Ahrr were upregulated in the liver and testis of WT mice and Ahr expression was higher in the epididymis (2-fold) and liver (18-fold) than in whole testis tissue. The AHR protein was localized to round spermatids, elongating spermatids, and Leydig cells in the testis of WT mice. These studies demonstrate AHR involvement in the sex ratio distortion of TCDD-exposed males and the need for evaluating the molecular and genetic mechanism of this process. Accumulating evidences have pointed out that neuroinflammation is involved in Parkinson's disease (PD) pathogenesis. Toll-like receptor 3 (TLR3), as a member of pattern-recognition receptors (PRRs), is known to play a pivotal role in inflammatory responses and immune responses. It was recently suggested that TLR3 was increased in the animal models of PD. The present study aimed to evaluate whether TLR3 gene (rs3775290) polymorphism was associated with PD susceptibility. We genotyped the single-nucleotide polymorphism (SNP) of TLR3 gene (rs3775290) using polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP) from 380 PD patients and 380 control subjects in Chinese Han population. Our data demonstrated that rs3775290 T allele carriers were associated with a reduced risk of PD between early-onset PD(EOPD)group and its healthy-matched control subgroup (OR = 0.571, 95 %CI = 0.366-0.891, P =  0.013 for TT + TC vs CC). Moreover, there were significant differences in genotype and allele distribution between EOPD group and the late-onset PD (LOPD) group (P = 0.024 and P  = 0.008, respectively). Therefore, our study suggested a possible association between TLR3 (rs3775290) gene polymorphism and PD susceptibility, indicating that T allele of rs3775290 might be a protective factor for sporadic PD in Han Chinese population. Hyperthyroidism may cause cognitive decline and increases the risk of Alzheimer's disease (AD), the major form of dementia; however, the underlying mechanism of this relationship is unclear. AD is associated with increased serum levels of tau. In this study, we investigated the relationship between serum thyroid hormones (THs) and tau. Fifty participants diagnosed with hyperthyroidism and fifty euthyroid counterparts were included and received clinical examinations. Serum concentrations of thyroid-stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3) and tau protein were assessed. The total tau protein level was significantly higher in hyperthyroidism participants than in their euthyroid counterparts. The level of circulating total tau had a significant positive association with the serum concentrations of FT3 and FT4. Total tau level was increased in the low TSH group and the serum THs decreased with the increase of age. These findings reveal that peripheral THs are associated with the serum concentration of tau, which may be involved in the pathogenesis of Alzheimer's disease (AD), suggesting a potential therapeutic target of AD via hyperthyroidism therapy. V.Post-translational modification of Tau, a microtubule-associated protein in the neuronal cell, plays a major role in Alzheimer's disease. Tau is an axonal protein expressed in mature neurons that promote the self-assembly of tubulin into microtubules and its stabilization in neurons. Phosphorylation of Tau makes it prone to aggregation at the intra-neuronal region leading to impaired neurotransmission and dementia. Tau aggregates undergo trans-cellular propagation by the release of fibrillar species into the extra-cellular environment from damaged and infected neurons that can be internalized by neighbouring neuronal and glia cells and promotes aggregation in healthy cells. G-protein coupled receptors, the largest group of seven transmembrane receptors, are involved in neuronal signal transduction in response to various signals such as hormones and neurotransmitters. In Alzheimer's disease, GPCRs are involved in phosphorylation of Tau through various downstream kinases such as GSK-3β, CDK-5 and ERKs signalling cascade. Several neuronal GPCRs that are involved in Tau phosphorylation are elaborated in this review. The astrocytic GPCR, Tau phosphorylation mediated by CaS receptors and its propagation by exosomes are also elaborated. In the microglia, the extra-cellular Tau binding to a chemokine GPCR, CX3CR1 triggers its internalization, whereas Tau phosphorylation at specific sites decreases its binding affinity to this receptor. Here we highlight the role of GPCRs in Tau phosphorylation and Tau interaction in different cells of the nervous system. Hence, the role of GPCRs are attaining more attention in the therapeutic field of Alzheimer's disease. Specific agonists/antagonists and allosteric modulators could be the potential target for therapy against GPCR-mediated Tau phosphorylation in Alzheimer's disease.