Caged Luciferase Inhibitor-Based Bioluminescence Switching Strategy Enables Efficient Detection of Serum APN Activity and the Identification of Its Roles in Metastasis of Non-Small Cell Lung Cancer.

Bioluminogenic probes emerged as powerful tools for imaging and analysis of various bioanalyses, but traditional approaches would be limited to the low sensitivity during determine the low activity of protease in clinical specimens. Herein, we proposed a caged luciferase inhibitor-based bioluminescence-switching strategy (CLIBS) by using a cleavable luciferase inhibitor to modulate the activity of luciferase reporter to amplify the detective signals, which led to the enhancement of detection sensitivity, and enabled the determination of circulating Aminopeptidase N (APN) activity in thousands of times diluted serum. By applying the CLIBS to serum samples in non-small cell lung cancer (NSCLC) patients from two clinical cohorts, we revealed that, for the first time, higher circulating APN activities but not its concentration, were associated with more NSCLC metastasis or higher metastasis stages by subsequent clinical analysis, and can serve as an independent factor for forecasting NSCLC patients' risk of metastasis.

Numerical and Experimental Verification of a Multiple-Variable Spatiotemporal Regression Model for Grout Defect Identification in a Precast Structure.

Due to the increased service life, environmental corrosion, unreasonable construction, and other issues, local defects inevitably exist in civil structures, which affect the structural performance and can lead to structural failure. However, research on grout defect identification of precast reinforced concrete frame structures with rebars spliced by sleeves faces great challenges owing to the complexity of the problem. This study presents a multiple-variable spatiotemporal regression model algorithm to identify local defects based on structural vibration responses collected using a sensor network. First, numerical simulations were carried out on precast beam-column connection models by comparing the identification results based on a single-variable regression model, two-variable spatial regression model, and two-variable spatiotemporal regression model; furthermore, a multiple-variable spatiotemporal regression model was proposed and robustness analysis of the damage indicator was carried out. Then, to explore the validity of the proposed method, a nondestructive vibration experiment was considered on a half-scaled, two-floor, precast concrete frame structure with column rebars spliced by defective grout sleeves. The results show that local defects were successfully identified based on a multiple-variable spatiotemporal regression model.