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Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph. The performance of GENET is evaluated and compared with existing methods. The named entity recognition model has achieved an overall precision of 94.23% (4835/5131; 93.56–94.84%), recall of 97.72% (4835/4948; 97.27–98.10%), and an F1 score of 95.94%. The relation extraction model has demonstrated an overall precision of 91.63% (2593/2830; 90.55–92.59%), recall of 89.17% (2593/2908; 87.99–90.25%), and an F1 score of 90.38%. GENET significantly outperforms existing methods in extracting molecular relationships (P < 0.001). Additionally, GENET has successfully predicted WNT family member 4 regulates insulin‐like growth factor 2 via signal transducer and activator of transcription 3 in colon cancer. With RNA sequencing data and multiple immunofluorescence, the authenticity of this prediction is validated, supporting the promising feasibility of GENET. [ABSTRACT FROM AUTHOR]

A metabonomics study using RRLC-Q-TOF/MS was performed to obtain a systematic view of the osteoporosis and anti-osteoporosis effects of velvet collagen hydrolysate. Combined with multivariate statistics, the urinary difference was determined between the model group and control group and between the treatment group and model group. Biochemical and pathway analyses were also performed to validate the osteoporosis model and investigate the anti-osteoporosis effect of velvet collagen hydrolysate. A clear separation trend was observed in the PCA scores plot. A total of five compounds were identified as significantly changed metabolites in the urine samples and may be potential biomarkers. This study demonstrates that the action mechanism of velvet collagen hydrolysate on osteoporosis may involve the regulation of amino acid metabolism and phospholipid metabolism. [ABSTRACT FROM PUBLISHER]

Abstract: Single-step preparation of SBA-15 materials functionalized with both propylsulfonic acid groups and aluminum species (AlSBA-15–SO3H) was carried out by hydrothermal treatment of a mixture of aluminum isopropoxide, 3-mercaptopropyltriethoxysilane, tetraethoxysilane, and triblock copolymer surfactant. At Si/Al molar ratio of 11–96, the materials exhibited well-ordered hexagonally arranged mesopores with pore diameter of ca. 9nm, BET surface area of 546.9–666.0m2 g−1, and pore volume of 0.82–1.03cm3 g−1. As-prepared AlSBA-15–SO3H was successfully used in the Claisen–Schmidt condensation reaction of benzaldehyde with acetophenone to produce chalcone under solvent-free condition, and the influence of the reaction parameters including temperatures, molar ratios of BZD to APN, and aluminum loadings were considered during the chalcone synthesis procedure. It showed that AlSBA-15–SO3H exhibited significantly high catalytic activity and selectivity, outperforming the reference catalysts such as sulfuric acid, ZSM-5, and acidic MCM-49. In addition, the catalytic stability and regeneration of AlSBA-15–SO3H was studied. [Copyright &y& Elsevier]