It is known that natural killer (NK) cell function is downregulated in chronic hepatitis B (CHB)-infected patients and in hepatic carcinoma (HCC) patients, but the mechanisms underlying this functional downregulation are largely unclear. In this study, microRNA (miR)-146a expression increased in NK cells from CHB and HCC patients compared with NK cells from healthy donors, and miR-146a levels were negatively correlated to NK cell functions. Overexpression of miR-146a reduced NK cell-mediated cytotoxicity and the production of interferon (IFN)-γ and tumor necrosis factor-α, which were reversed upon inhibition of miR-146a. In NK cells, miR-146a expression was induced by interleukin (IL)-10 and transforming growth factor-β, but reduced after treatment with interleukin-12, IFN-α and IFN-β. We further revealed that miR-146a regulated NK cell functions by targeting STAT1. Taken together, upregulated miR-146a expression, at least partially, attributes to NK cell dysfunction in CHB and HCC patients. Therefore, miR-146a may become a therapeutic target with great potential to ameliorate NK cell functions in liver disease.

Although crack inspection is a routine practice in civil infrastructure management (especially for highway bridge structures), it is time-consuming and safety-concerning to trained engineers and costly to the stakeholders. To automate this in the near future, the algorithmic challenge at the onset is to detect and localize cracks in imagery data with complex scenes. The rise of deep learning (DL) sheds light on overcoming this challenge through learning from imagery big data. However, how to exploit DL techniques is yet to be fully explored. One primary component of practical crack inspection is that it is not merely detection via visual recognition. To evaluate the potential risk of structural failure, it entails quantitative characterization, which usually includes crack width measurement. To further facilitate the automation of machine-vision-based concrete crack inspection, this article proposes a DL-enabled quantitative crack width measurement method. In the detection and mapping phase, dual-scale convolutional neural networks are designed to detect cracks in complex scene images with validated high accuracy. Subsequently, a novel crack width estimation method based on the use of Zernike moment operator is further developed for thin cracks. The experimental results based on a laboratory loading test agree well with the direct measurements, which substantiates the effectiveness of the proposed method for quantitative crack detection.

Cyclophilin A (CypA) is a member of cyclophilins, a family of the highly homologous peptidyl prolyl cis-trans isomerases (PPIases), which can bind to cyclosporin A (CsA). CypA plays critical roles in various biological processes, including protein folding, assembly, transportation, regulation of neuron growth, and HIV replication. The discovery of CypA inhibitor is now of a great special interest in the treatment of immunological disorders. In this study, a series of novel small molecular CypA inhibitors have been discovered by using structure-based virtual screening in conjunction with chemical synthesis and bioassay. The SPECS_1 database containing 85,000 small molecular compounds was searched by virtual screening against the crystal structure of human CypA. After SPR-based binding affinity assay, 15 compounds were found to show binding affinities to CypA at submicro-molar or micro-molar level (compounds 1-15). Seven compounds were selected as the starting point for the further structure modification in considering binding activity, synthesis difficulty, and structure similarity. We thus synthesized 40 new small molecular compounds (1-6, 15, 16a-q, 17a-d, and 18a-l), and four of which (compounds 16b, 16h, 16k, and 18g) showed high CypA PPIase inhibition activities with IC50s of 2.5-6.2 microM. Pharmacological assay indicated that these four compounds demonstrated somewhat inhibition activities against the proliferation of spleen cells.