Thistedweinstein7388
The main causes for morbidity and mortality in von Hippel-Lindau (VHL) disease are central nervous system hemangioblastoma and clear cell renal cell carcinoma, but the effect of VHL-related pancreatic neuroendocrine tumors (PNET) on patient outcome is unclear. We assessed the impact of PNET diagnosis in patients with VHL on all-cause mortality (ACM) risk.
We used the Surveillance, Epidemiology, and End Results database. Of 16 344 patients, 170 had VHL based on clinical diagnostic criteria, and 510 patients had PNET (91 VHL-related and 419 sporadic).
Survival analysis demonstrated a lower ACM among patients with VHL-related PNET compared to patients with sporadic PNET (log-rank test, P= .011). Among patients with VHL, ACM risk was higher with vs without PNET (P= .029). The subgroup analysis revealed a higher ACM risk with metastatic PNET (sporadic P= .0031 and VHL-related P= .08) and a similar trend for PNET diameter ≥3 cm (P= .06 and P= 0.1 in sporadic and VHL-related PNET, respectively). In a multivariable analysis of patients with VHL, diagnosis with PNET by itself was associated with a trend of lower risk for ACM, while presence of metastatic PNET was independently associated with increased ACM risk.
Diagnosis with PNET is not associated with a higher ACM risk in VHL by itself. The independent association of advanced PNET stage with higher mortality risk emphasizes the importance of active surveillance for detecting high-risk PNET at an early stage to allow timely intervention.
Diagnosis with PNET is not associated with a higher ACM risk in VHL by itself. The independent association of advanced PNET stage with higher mortality risk emphasizes the importance of active surveillance for detecting high-risk PNET at an early stage to allow timely intervention.
Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed.
We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives (1) provide functely; and (2) generate and test hypotheses across their own and other datasets.Named entity recognition (NER) is a fundamental task in Chinese natural language processing (NLP) tasks. Recently, Chinese clinical NER has also attracted continuous research attention because it is an essential preparation for clinical data mining. The prevailing deep learning method for Chinese clinical NER is based on long short-term memory (LSTM) network. However, the recurrent structure of LSTM makes it difficult to utilize GPU parallelism which to some extent lowers the efficiency of models. Besides, when the sentence is long, LSTM can hardly capture global context information. To address these issues, we propose a novel and efficient model completely based on convolutional neural network (CNN) which can fully utilize GPU parallelism to improve model efficiency. Moreover, we construct multi-level CNN to capture short-term and long-term context information. We also design a simple attention mechanism to obtain global context information which is conductive to improving model performance in sequence labeling tasks. 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. 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. https://www.selleckchem.com/products/rgfp966.html 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.Most infectious agents use mucosal tissues as entry portals, thus, mucosae are frequently defined as a first line of defense against pathogens. Mucosal protection generally operates through antibody-mediated and cytotoxic T-cell responses which can be triggered by mucosal vaccines. Sublingual vaccination provides many advantages such as systemic and mucosal responses (both locally and at remote mucosal sites), besides being a needle-free administration route with high patient compliance and limited adverse effects. Buccal mucosa complexity nonetheless represents a challenge for vaccine administration, hence, many efforts were recently deployed to improve vaccine components, mucoadhesion and/or penetration. Several innovative approaches indeed confirmed that a robust and protective immunity can be achieved by sublingual vaccines. This review will then specify the most recent delivery systems and improvements developed to increase sublingual vaccines efficiency. We will focus our description on the immune mechanisms involved and the requirements for optimal sublingual immunization and mucosal protection.In the last decade, cellular forces in three-dimensional hydrogels that mimic the extracellular matrix have been calculated by means of Traction Force Microscopy (TFM). However, characterizing the accuracy limits of a traction recovery method is critical to avoid obscuring physiological information due to traction recovery errors. So far, 3D TFM algorithms have only been validated using simplified cell geometries, bypassing image processing steps or arbitrarily simulating focal adhesions. Moreover, it is still uncertain which of the two common traction recovery methods, i.e., forward and inverse, is more robust against the inherent challenges of 3D TFM. In this work, we established an advanced in silico validation framework that is applicable to any 3D TFM experimental setup and that can be used to correctly couple the experimental and computational aspects of 3D TFM. Advancements relate to the simultaneous incorporation of complex cell geometries, simulation of microscopy images of varying bead densities and different focal adhesion sizes and distributions.
