[Rapid Diagnosis of Sound, Yellow and Citrus Greening Leaves with Hyperspectral Imaging].

The feasibility was investigated for identifying sound, yellow and citrus greening leaves of navel orange trees based on hyperspectral imaging combined with correlation analysis and discriminant partial least square (DPLS) methods. The hyperspectral data of sound, yellow and citrus greening leaves were recorded in the wavelength range of 374.28～1 016.89 nm. Two regions of interest (ROI) were marked symmetrically on both sides along main veins with an ellipse of major axis of 60 pixels and minor axis of 30 pixels. The average reflectance spectrum was extracted from ROI regions. A pair wavelengths of 502.79 and 374.28 nm were chosen with correlation analysis method in the wavelength range of 374.28～1 016.89 nm. The classification model was developed with the peak ratio of the pair wavelengths. This model was effective to sound leaves with the recognition accuracy of 1.7% but yellow and citrus greening leaves. The DPLS model was employed with the preprocessing spectra of second derivative and Savitzky-Golay smoothing. The recognition accuracy of this model was 100% for citrus greening leaves and yellow ones. The number of latent variables (LVs) was optimized with the leave one out cross validation method. The optimal LVs, correlation coefficient and standard error of calibration of the DPLS model were 17, 0.96 and 0.13, respectively. The correction classification rate of the DPLS model was 100% for yellow leaves and citrus greening ones. Two-step method of the peak ratio models combination with the DPLS was proposed for identifying sound, yellow and citrus greening leaves. The new samples were applied to evaluation the classification ability of the two-step method, which included sound leaves of 10, citrus greening leaves of 10 and yellow leaves of 10. The correction classification rate reached 96.7%. Experimental results showed that it was feasible to identify sound, yellow and citrus greening leaves by hyperspectral imaging coupled with the peak ratio and DPLS models.

miR-34 is associated with poor prognosis of patients with gallbladder cancer through regulating telomere length in tumor stem cells.

miR-34a has been identified as a tumor suppressor in several tumors, but its involvement in gallbladder cancer (GBC) has not been reported. In this study, the miR-34a level and telomere length were measured in 77 gallbladder adenocarcinomas and 36 peritumoral tissues by real-time PCR. Forced miR-34a expression was established by an adenovirus carrying a miR-34a expression cassette. The colony-forming ability of isolated CD44+CD133+ GBC tumor stem-like cells was measured by matrigel colony assay. The xenograft tumor models were established by inoculating nude mice with CD44+CD133+cells. Results showed that significantly lower miR-34a expression and longer telomere length were observed in gallbladder adenocarcinoma tissues, which correlated with poor prognosis of GBC patients. Forced overexpression of miR-34a inhibited the colony-forming ability of CD44+CD133+ GBC tumor stem-like cells in vitro and xenograft tumor growth in vivo. Injection of Ad-miR-34a downregulated PNUTS expression and reduced telomere length in xenograft GBC tumor cells. In conclusion, miR-34a is a tumor suppressor in gallbladder cancer. Both low miR-34a expression and long telomere length are markers for poor prognosis of patients with gallbladder adenocarcinoma. Our study also suggests that the miR-34a gene could be a target for targeting therapy of GBC.