Publications by authors named "Chengzhi Xiang"

Background: Pancreatic cancer (PC) is a lethal disease, especially metastatic PC. And it can be divided into two types: head pancreatic cancer (H-PC) and body and tail pancreatic cancer (BT-PC). Prior studies have proved that they have different overall survival (OS) and should be regarded as two different categories of PC.

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An asymmetric Michael/Friedel-Crafts cascade reaction with Morita-Baylis-Hillman (MBH) nitroallylic esters and 3-pyrrolyloxindoles has been developed for the stereoselective construction of spirooxindole-containing tetrahydroindolizines. A range of tetracyclic scaffolds possessing three consecutive chiral centers, including an all-carbon quaternary stereocenter, were generated in 53-85% isolated yields with high diastereoselectivities and enantiopurities (≥3:1 dr, 50-98% ee). A newly synthesized bifunctional secondary amine/squaramide organocatalyst was demonstrated to exhibit better stereochemical control than their tertiary analogues.

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Postoperative pancreatic fistula (POPF) is a troublesome complication after pancreatic surgeries, and grade C POPF is the most serious situation among pancreatic fistulas. At present, the incidence of grade C POPF varies from less than 1% to greater than 9%, with an extremely high postoperative mortality rate of 25.7%.

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The non-destructive and rapid estimation of the crop's leaf nitrogen concentration (LNC) is significant for the quality evaluation and precise management of nitrogen (N) fertilizer. First derivative can be applied to reduce the noise in the spectral analysis, which is suited to estimate leaf N and chlorophyll concentration with different fertilization levels. In this study, the first-derivative fluorescence spectrum (FDFS) was calculated in terms of the laser-induced fluorescence (LIF) spectra and was combined with different regression algorithms, including principal component analysis (PCA), partial least-square regression (PLSR), random forest (RF), radial basic function neural network (RBF-NN), and back-propagation neural network (BPNN) for paddy rice LNC estimation.

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Atmospheric CO₂ plays an important role in controlling climate change and its effect on the carbon cycle. However, detailed information on the dynamics of CO₂ vertical mixing remains lacking, which hinders the accurate understanding of certain key features of the carbon cycle. Differential absorption lidar (DIAL) is a promising technology for CO₂ detection due to its characteristics of high precision, high time resolution, and high spatial resolution.

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High-accuracy on-line wavelength stabilization is required for differential absorption lidar (DIAL), which is ideal for precisely measuring atmospheric CO(2) concentration. Using a difference-frequency laser, we developed a ground-based 1.57-μm pulsed DIAL for performing atmospheric CO(2) measurements.

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