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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. 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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.