[A fluorescence immunochromatography method for detection of human papillomavirus type 16 E6 and L1 proteins].

This study aims to establish a time-resolved fluorescence immunochromatography method for simultaneous determination of human papillomavirus (HPV) type 16 E6 and L1 protein concentrations. The amount of lanthanide microsphere-labeled antibodies, the concentration of coated antibodies, and the reaction time were optimized, and then a test strip for the simultaneous determination of the protein concentrations was prepared. The performance of the detection method was evaluated based on the concordance of the results from clinical practice.

MLN4924 alleviates autoimmune myocarditis by promoting Act1 degradation and blocking Act1-mediated mRNA stability.

Background: Prolonged exposure to interleukin-17A (IL-17A) can induce autoimmune myocarditis, and MLN4924, an inhibitor of NEDD8 activating enzyme (NAE), has been reported to effectively suppress various inflammatory reactions. However, the effects of MLN4924 in IL-17A-mediated inflammation associated with autoimmune myocarditis remain uncertain.

Methods: An experimental autoimmune myocarditis (EAM) model was established and treated with MLN4924.

Metabolic analysis of Schizochytrium sp. mutants with high EPA content achieved with ARTP mutagenesis screening.

Eicosapentaenoic acid (EPA) belonged to the ω-3 series of polyunsaturated fatty acids and had physiological functions lipid as regulating blood lipid and preventing cardiovascular diseases. Schizochytrium sp. was considered to be a potential industrial fermentation strain of EPA because of its fast growth, high oil content, and simple fatty acid composition.

Improving acetoin production through construction of a genome-scale metabolic model.

Acetoin was widely used in food, medicine, and other industries, because of its unique fragrance. Bacillus amyloliquefaciens was recognized as a safe strain and a promising acetoin producer in fermentation. However, due to the complexity of its metabolic network, it had not been fully utilized.

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks.

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results.