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Congenital cataracts is the most common cause of childhood visual impairment and blindness worldwide. It is reported that about one quarter of congenital cataracts caused by genetic defects. Various gene mutations have been identified in hereditary cataracts so far. The purpose of the present study was to investigate the relationship between gap junction protein alpha 8 (

) gene mutation and congenital cataract.

A pedigree with autosomal dominant congenital cataract was investigated and the peripheral venous blood was extracted from 18 family members. After the high-throughput targeted capture and whole exome sequencing for the proband, bioinformatics analysis was performed. By combining the proband clinical symptoms, candidate variations were eliminated which were significantly not consistent with the clinical phenotype. And disease-causing variant was identified.

Gene sequencing revealed the heterozygous missense mutation in exon 2 of the

gene (c.178G>A), which co-segregated with the disease phenotype in the family and resulted in the substitution of glycine to serine at position 178 (p.G60S). This missense mutation was located in the hotspot mutation region, and might be harmful.

This study reports a novel disease-causing sequence variant in the gap junctional protein encoding genes causing autosomal dominant congenital cataract in the Chinese population, caused by the missense mutation of

(c.178G>A). Our data expand the spectrum of

variants and associated phenotypes, facilitate clinical diagnosis and support the presence of relationship between genetic basis and human disease.

A). Our data expand the spectrum of GJA8 variants and associated phenotypes, facilitate clinical diagnosis and support the presence of relationship between genetic basis and human disease.

Lung cancer causes more deaths worldwide than any other cancer. For early-stage patients, low-dose computed tomography (LDCT) of the chest is considered to be an effective screening measure for reducing the risk of mortality. The accuracy and efficiency of cancer screening would be enhanced by an intelligent and automated system that meets or surpasses the diagnostic capabilities of human experts.

Based on the artificial intelligence (AI) technique, i.e., deep neural network (DNN), we designed a framework for lung cancer screening. First, a semi-automated annotation strategy was used to label the images for training. Then, the DNN-based models for the detection of lung nodules (LNs) and benign or malignancy classification were proposed to identify lung cancer from LDCT images. Finally, the constructed DNN-based LN detection and identification system was named as DeepLN and confirmed using a large-scale dataset.

A dataset of multi-resolution LDCT images was constructed and annotated by a multidisciplinar work efficiency of radiologists in lung cancer screening. The effectiveness of the proposed system was verified through retrospective clinical evaluation. Thus, the future application of this system is expected to help patients and society.In this paper formulas are derived for the analytic center of the solution set of linear matrix inequalities (LMIs) defining passive transfer functions. The algebraic Riccati equations that are usually associated with such systems are related to boundary points of the convex set defined by the solution set of the LMI. It is shown that the analytic center is described by closely related matrix equations, and their properties are analyzed for continuous- and discrete-time systems. Numerical methods are derived to solve these equations via steepest descent and Newton methods. It is also shown that the analytic center has nice robustness properties when it is used to represent passive systems. The results are illustrated by numerical examples.Interferon lambda (IFN-λ) is an antiviral naturally produced in response to viral infections, with activity on cells of epithelial origin and located in the mucosal surfaces. This localized activity results in reduced toxicity compared to type I IFNs, whose receptors are ubiquitously expressed. IFN-λ has been effective in the therapy of respiratory viral infections, playing a crucial role in potentiating adaptive immune responses that initiate at mucosal surfaces. Human IFN-λ has polymorphisms that may cause differences in the interaction with the specific receptor in the human population. Interestingly, bovine IFN-λ3 has an in silico-predicted higher affinity for the human receptor than its human counterparts, with high identity with different human IFN-λ variants, making it a suitable antiviral therapeutic candidate for human health. Here, we demonstrate that a recombinant bovine IFN-λ (rbIFN-λ) produced in HEK-293 cells is effective in preventing SARS-CoV-2 infection of VERO cells, with an inhibitory concentration 50% (IC50) between 30 and 50 times lower than that of human type I IFN tested here (α2b and β1a). We also demonstrated the absence of toxicity of rbIFN-λ in human PBMCs and the lack of proinflammatory activity on these cells. Altogether, our results show that rbIFN-λ is as an effective antiviral potentially suitable for COVID-19 therapy. Among other potential applications, rbIFN-λ could be useful to preclude virus dispersion to the lungs and/or to reduce transmission from infected people. Moreover, and due to the non-specific activity of this IFN, it can be potentially effective against other respiratory viruses that may be circulating together with SARS-CoV-2.The gut microbiome provides important metabolic functions for the host animal. Bacterial dysbiosis as a result of bacterial, viral, and parasitic gastrointestinal infections can adversely affect the metabolism, productivity, and overall health. The objective of this study is to characterize the commensal microbiome present in the lumen and the mucosal surface of the duodenum of cattle, as we hypothesize that due to metabolic processes and or host proprieties, there are differences in the natural microbiota present in the mucosal surface and luminal contents of the bovine duodenum. Duodenal lumen contents and mucosal biopsies were collected from six dairy crossbred yearling steers. A flexible video-endoscope was used to harvest biopsy samples via a T shaped intestinal cannula. In order to assess as much environmental and individual steer microbiota variation as possible, each animal was sampled three times over a 6 week period. selleck kinase inhibitor The DNA was extracted from the samples and submitted for16S rRNA gene Ion Torrent PGM bacterial sequencing.