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Autophagy is believed to be important in tumorigenesis and tumor progression. However, the role of autophagy in hepatocellular carcinoma (HCC), and especially the prognostic value of autophagic proteins, has not been investigated. Our studies described here show decreased basal expression of autophagic genes and their corresponding autophagic activity under conditions of starvation in HCC cell lines, and the autophagy defect correlated well with the highly malignant phenotype of HCC. In addition, in a tissue microarray study of HCC patients who underwent resection, the expression of the autophagy-related protein Beclin 1 was extremely low in tumors, where Beclin 1 could predict the prognosis of HCC patients only in a Bcl-X(L)-positive expression background. Based on our results, we propose that autophagy defects that synergize with altered apoptotic activity might facilitate tumor progression and poor prognosis of HCC, due to the fact that autophagy may interact with apoptosis in the regulation of HCC.

Our data indicate that Tim-3 expression on NK cells is regulated by T-bet, and that Tim-3 levels correlate with advanced stages of gastric cancer.

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A graph G is called a fractional (g, f, n , m)-critical deleted graph if after deleting any n vertices of G the remaining graph is a fractional (g, f, m)-deleted graph. A graph G is called a fractional ID-(g, f, m)-deleted graph if after deleting any independent set I of G the remaining graph is a fractional (g, f, m)-deleted graph. In this paper, we give some sharp degree conditions for a graph to be a fractional (g, f, n , m)-critical deleted graph and a fractional ID-(g, f, m)-deleted graph. The tight degree conditions for fractional (a, b, n , m)-critical deleted graphs and fractional ID-(a, b, m)-deleted graphs are also considered.

The role of leptin and its receptors (OBRs) in the pathogenesis of various primary human malignancies has been demonstrated. However, their expression and clinicopathological significance in papillary thyroid cancer (PTC) is not fully understood. In this study, we examined the expression of leptin and OBRs in 76 PTC samples using immunohistochemistry, and their associations with clinicopathological parameters were evaluated. The expression of OBRs was observed in the tumor cell membrane and/or cytoplasm, with a positive rate of 73.7% (56/76), while leptin was expressed in the tumor cell cytoplasm in 55 of 76 cases (72.4%). The expression of either protein was associated with greater tumor size (P=0.016 for leptin and P=0.002 for OBRs). In addition, the expression levels of leptin and OBRs were associated with each other. Neither leptin nor OBR expression levels were associated with other parameters, including age, body weight, postmenopausal state, multifocality and lymph node metastasis. These data suggest that the expression of leptin and/or OBRs in PTC is associated with tumor size and may be a potential target in PTC.

Grayscale image colorization, especially for ethnic costume images, is highly challenging due to its rich and complex color features. The existing image colorization methods usually take the costume image as a whole in practical applications that lead to the ignorance of the semantic information of different parts of the costume. It is known that each part's color distribution of the ethnic costume is different. So, the color mapping of other parts is also diverse, which is determined by distinctive ethnic characteristics. This study introduces fine‐grained level semantic information and proposes a high‐resolution image colorization model for ethnic costumes targeting enhancement, inspired by semantic‐level colorization. The semantic information of different regions of ethnic costumes has a significant impact on the performance of the coloring task. Using Pix2PixHD as the backbone network, we create a new network architecture that maintains color distribution correspondence and spatial consistency of costume images using fine‐grained semantic information. In our network, we take the splice result of fine‐grained semantic for ethnic costume and grayscale image as the conditions and then feed them into the generative adversarial networks. We also discuss and analyze the influences of the grayscale channel and fine‐grained semantics on discriminator. Extensive experiments demonstrate that our method performs well compared with other state‐of‐the‐art automatic colorization methods.