LncRNA NKILA inhibits HBV replication by repressing NF-κB signalling activation.

Hepatitis B virus (HBV) infection results in liver cirrhosis and hepatocellular carcinoma (HCC). HBx/nuclear factor (NF)-κB pathway plays a role in HBV replication. However, whether NF-κB-interacting long noncoding RNA (NKILA), a suppressor of NF-κB activation, regulates HBV replication remains largely unknown. In this study, gain-and-loss experiments showed that NKILA inhibited HBV replication by inhibiting NF-κB activity. In turn, HBV infection down-regulated NKILA expression. In addition, expression levels of NKILA were lower in the peripheral blood-derived monocytes (PBMCs) of HBV-positive patients than in healthy individuals, which were correlated with HBV viral loads. And a negative correlation between NKILA expression level and HBV viral loads was observed in blood serum from HBV-positive patients. Lower levels of endogenous NKILA were also observed in HepG2 cells expressing a 1.3-fold HBV genome, HBV-infected HepG2-NTCP cells, stable HBV-producing HepG2.2.15 and HepAD38 â€‹cells, compared to those HBV-negative cells. Furthermore, HBx was required for NKILA-mediated inhibition on HBV replication. NKILA decreased HBx-induced NF-κB activation by interrupting the interaction between HBx and p65, whereas NKILA mutants lack of essential domains for NF-ĸB inhibition, lost the ability to inhibit HBV replication. Together, our data demonstrate that NKILA may serve as a suppressor of HBV replication via NF-ĸB signalling.

Breast cancer pathological image classification based on deep learning.

The automatic classification of breast cancer pathological images has important clinical application value. However, to develop the classification algorithm using the artificially extracted image features faces several challenges including the requirement of professional domain knowledge to extract and compute highiquality image features, which are often time-consuming, laborious, and difficult. For overcoming these challenges, this study developed and applied an improved deep convolutional neural network model to perform automatic classification of breast cancer using pathological images. Specifically, in this study, data enhancement and migration learning methods are used to effectively avoid the overfitting problems with deep learning models when they are limited by training image sample size. Experimental results show that a 91% recognition rate or accuracy when applying this improved deep learning model to a publicly available dataset of BreaKHis. Comparing with other previously used models, the new model yields good robustness and generalization.

Extraction and purification of a lectin from red kidney bean and preliminary immune function studies of the lectin and four Chinese herbal polysaccharides.

Reversed micelles were used to extract lectin from red kidney beans and factors affecting reverse micellar systems (pH value, ionic strength and extraction time) were studied. The optimal conditions were extraction at pH 4-6, back extraction at pH 9-11, ion strength at 0.15 M NaCl, extraction for 4-6 minutes and back extraction for 8 minutes. The reverse micellar system was compared with traditional extraction methods and demonstrated to be a time-saving method for the extraction of red kidney bean lectin. Mitogenic activity of the lectin was reasonably good compared with commercial phytohemagglutinin (extracted from Phaseolus vulgaris) Mitogenic properties of the lectin were enhanced when four Chinese herbal polysaccharides were applied concurrently, among which 50 µg/mL Astragalus mongholicus polysaccharides (APS) with 12.5 µg/mL red kidney bean lectin yielded the highest mitogenic activity and 100 mg/kg/bw APS with 12.5 mg/kg/bw red kidney bean lectin elevated mouse nonspecific immunity.