Zobrazení zdroje stránky Dohertygold7655

Besides, a data augmentation method is proposed to expand the data volume and try to explore more semantic information. Extensive experiments show that our model achieves competitive performance with higher efficiency compared with other remarkable clinical NER models.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. [https://www.selleckchem.com/products/ag-1024-tyrphostin.html AG-1024] However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.The context of medical conditions is an important feature to consider when processing clinical narratives. NegEx and its extension ConText became the most well-known rule-based systems that allow determining whether a medical condition is negated, historical or experienced by someone other than the patient in English clinical text. In this paper, we present a French adaptation and enrichment of FastContext which is the most recent, n-trie engine-based implementation of the ConText algorithm. We compiled an extensive list of French lexical cues by automatic and manual translation and enrichment. To evaluate French FastContext, we manually annotated the context of medical conditions present in two types of clinical narratives (i)death certificates and (ii)electronic health records. Results show good performance across different context values on both types of clinical notes (on average 0.93 and 0.86 F1, respectively). Furthermore, French FastContext outperforms previously reported French systems for negation detection when compared on the same datasets and it is the first implementation of contextual temporality and experiencer identification reported for French. Finally, French FastContext has been implemented within the SIFR Annotator a publicly accessible Web service to annotate French biomedical text data (http//bioportal.lirmm.fr/annotator). To our knowledge, this is the first implementation of a Web-based ConText-like system in a publicly accessible platform allowing non-natural-language-processing experts to both annotate and contextualize medical conditions in clinical notes.Draft genome sequence of the glucose tolerant beta glucosidase (GT-BGL) producing rare fungus Aspergillus unguis NII 08,123 was generated through Next Generation Sequencing (NGS). The genome size of the fungus was estimated to be 37.1 Mb. A total of 3116 contigs were assembled using SPades, and 15,161 proteins were predicted using AUGUSTUS 3.1. Among them, 13,850 proteins were annotated using UniProt. Distribution of CAZyme genes specifically those encoding lignocellulose degrading enzymes were analyzed and compared with those from the industrial cellulase producer Trichoderma reesei in view of the huge differences in detectable enzyme activities between the fungi, despite the ability of A. unguis to grow on lignocellulose as sole carbon source. Full length gene sequence of the inducible GT-BGL could be identified through tracing back from peptide mass fingerprint. A total of 403 CAZymes were predicted from the genome, which includes 232 glycoside hydrolases (GHs), 12 carbohydrate esterases (CEs), 109 glycosyl transferases (GTs), 15 polysaccharide lyases (PLs), and 35 genes with auxiliary activities (AAs). The high level of zinc finger motif containing transcription factors could possibly hint a tight regulation of the cellulolytic machinery, which may also explain the low cellulase activities even when a complete repertoire of cellulase degrading enzyme genes are present in the fungus.Liver fibrosis affects millions of people worldwide and is rising vastly over the past decades. With no viable therapies available, liver transplantation is the only curative treatment for advanced diseased patients. Excessive accumulation of aberrant extracellular matrix (ECM) proteins, mostly collagens, produced by activated hepatic stellate cells (HSCs), is a hallmark of liver fibrosis. Several studies have suggested an inverse correlation between collagen-I degrading matrix metalloproteinase-1 (MMP-1) serum levels and liver fibrosis progression highlighting reduced MMP-1 levels are associated with poor disease prognosis in patients with liver fibrosis. We hypothesized that delivery of MMP-1 might potentiate collagen degradation and attenuate fibrosis development. In this study, we report a novel approach for the delivery of MMP-1 using MMP-1 decorated polymersomes (MMPsomes), as a surface-active vesicle-based ECM therapeutic, for the treatment of liver fibrosis. The storage-stable and enzymatically activeclusion, our results demonstrate an innovative approach of MMP-1 delivery, using surface-decorated MMPsomes, for alleviating liver fibrosis.