Novel smart hydrogel fibers were prepared from a blend of hydrolyzed-polyacrylonitrile (H-PAN) and Soy Protein (SP). The dynamic and static elongation/contraction behaviors were studied. It was discovered that H-PAN/ SP hydrogel fibers consisted of a multiporous structure. With an increase in the content of SP, the hydrogel fibers exhibited excellent reversible pH-sensitive behavior. When the weight ratio of H-PAN to SP was 4 : 6, the hydrogel fibers showed the best response times at 1.35 s (elongation) and 0.63 s (contraction). Discontinuous volume phase transitions and hysteresis loops of the hydrogel fibers induced by changes in pH value were found. These results were found to be explicable in terms of the surface morphology and polyampholytic properties of H-PAN/SP hydrogel fibers.

Yiqi Huoxue Recipe (YHR) is commonly used in China to treat diseases such as heart failure (HF). It has been reported that YHR can treat HF and has a certain protective effect on myocardial cell damage. The purpose of this study is to determine the cardioprotective effects of YHR on HFinduced apoptosis and to clarify its mechanism of action. Oxygen glucose deprivation/recovery (OGD/R) induces H9C2 cell apoptosis model. Ligation of the left anterior descending artery (LAD) coronary artery can induce an animal model of HF. We found that YHR protected H9C2 cells from OGD/R-induced apoptosis, reduced the level of reactive oxygen species (ROS) in H9C2 cells, and increased the mitochondrial membrane potential in H9C2 cells. The results of in vivo animal experiments showed that in the HF model, YHR could reduce infarct area of heart tissue and cardiomyocyte apoptosis rate. YHR regulated the expression of key apoptotic molecules, including increasing the ratio of Bcl-2 and Bax, and reducing the expression of Kelch-like ECH-associated protein 1 (Keap1) and caspase-3. Interestingly, YHR also regulates the expression of NF-E2-related factor 2 (Nrf2) in the nucleus. In summary, YHR may provide cardioprotective effects in heart failure through inhibiting the Keap1/Nrf2/HIF-1α apoptosis pathway.

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Rapid technological advances have allowed for molecular profiling across multiple omics domains from a single sample for clinical decision making in many diseases, especially cancer. As tumor development and progression are dynamic biological processes involving composite genomic aberrations, key challenges are to effectively assimilate information from these domains to identify genomic signatures and biological entities that are druggable, develop accurate risk prediction profiles for future patients, and identify novel patient subgroups for tailored therapy and monitoring. We propose integrative probabilistic frameworks for high-dimensional multiple-domain cancer data that coherently incorporate dependence within and between domains to accurately detect tumor subtypes, thus providing a catalogue of genomic aberrations associated with cancer taxonomy. We propose an innovative, flexible and scalable Bayesian nonparametric framework for simultaneous clustering of both tumor samples and genomic probes. We describe an efficient variable selection procedure to identify relevant genomic aberrations that can potentially reveal underlying drivers of a disease. Although the work is motivated by several investigations related to lung cancer, the proposed methods are broadly applicable in a variety of contexts involving high-dimensional data. The success of the methodology is demonstrated using artificial data and lung cancer omics profiles publicly available from The Cancer Genome Atlas.

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