[Correlation of behavioral performance with expressions of substance P and neurokinin-1 receptor in the L5－S2 spinal cord of chronic prostatitis rats].

OBJECTIVE: To investigate the correlation between the behavioral performance and the expressions of substance P (SP) and neurokinin-1 receptor (NK-1R) in the posterior horn of the L5－S2 spinal cord in rats with chronic prostatitis (CP). METHODS: A CP model was made in 30 adult male SD rats by intraperitoneal injection of 0.5 ml dyphtheria pertussis tetanus (DPT) vaccine and mixed solution of 1 ml prostatein extract and complete adjuvant in a 1∶1 ratio, and another 10 rats were injected with the same volume of normal saline as controls. At 45 (n = 10), 60 (n = 10) and 90 days (n = 10) after modeling, the behavioral changes of the rats were observed by open-field and sucrose consumption tests, the prostatic indexes and levels of serum TNF-α, IL-1ß, IL-2 and IL-10 were obtained, and the expressions of SP and NK1-R in the L5－S2 spinal cord were determined by immunohistochemistry. RESULTS: Compared with the controls, the CP model rats showed obviously decreased horizontal and vertical movement scores and sucrose consumption, particularly in the 90 d group (P < 0.05), significantly reduced prostatic indexes in the 45 d, 60 d and 90 d groups (all P < 0.05), even lower in the 90 d than in the 45 d and 60 d groups (P < 0.05). Edema and lymphocytes were increased in the prostatic tissue with the prolonged time of modeling. The levels of serum TNF-α, IL-1ß, IL-2 and IL-10 were markedly elevated in all the CP rats as compared with those in the controls (P < 0.05), and so were the expressions of SP and NK-1R in the L5－S2 spinal cord (P < 0.05), even more significantly in the 90 d than in the 45 d and 60 d groups (P < 0.05). CONCLUSIONS: Rats with chronic prostatitis are characterized by behavioral manifestation of depression, increased levels of serum TNF-α, IL-1ß, IL-2 and IL-10, and a time-dependent upregulation of the expressions of SP and NK-1R in the posterior horn of the L5-S2 spinal cord, which suggests a correlation between the behavioral performance and the expressions of SP and NK-1R in the L5－S2 spinal cord of the rats.

Two-hierarchical nonnegative matrix factorization distinguishing the fluorescent targets from autofluorescence for fluorescence imaging.

BACKGROUND: Nonnegative matrix factorization (NMF) has been used in blind fluorescence unmixing for multispectral in-vivo fluorescence imaging, which decomposes a mixed source data into a set of constituent fluorescence spectra and corresponding concentrations. However, most classical NMF algorithms have ill convergence problems and they always fail to unmix multiple fluorescent targets from background autofluorescence for the sparse acquisition of multispectral fluorescence imaging, which introduces incomplete measurements and severe discontinuities in multispectral fluorescence emissions across the multiple spectral bands. METHODS: Observing the spatial distinction between the diffusive autofluorescence and the sparse fluorescent targets, we propose to separate the mixed sparse multispectral data into equality constrained two-hierarchical updating within NMF framework by dividing the concentration matrix of entire endmembers into two hierarchies: the fluorescence targets and the background autofluorescence. Specifically, when updating concentrations of multiple fluorescent targets in the two-hierarchical NMF, we assume that the concentration of autofluorescence is fixed and known, and vice versa. Furthermore, a sparsity constraint is imposed on the concentration matrix components of fluorescence targets only. RESULTS: Synthetic data sets, in vivo fluorescence imaging data are employed to demonstrate and validate the performance of our approach. The proposed algorithm can achieve more satisfying results of spectral unmixing and autofluorescence removal compared to other state-of-the-art methods, especially for the sparse multispectral fluorescence imaging. CONCLUSIONS: The proposed algorithm can successfully tackle the sparse acquisition and ill-posed problems in the NMF-based fluorescence unmixing through equality constraint along with partial sparsity constraint during two-hierarchical NMF optimization, at which fixing sparsity constrained target fluorescence can make the update of autofluorescence as accurate as possible and vice versa.

Reducing Poisson noise and baseline drift in X-ray spectral images with bootstrap Poisson regression and robust nonparametric regression.

X-ray spectral imaging provides quantitative imaging of trace elements in a biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of x-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing. Then, we apply the Poisson regression through its maximum likelihood estimation on this area to estimate the true photon counts from the Poisson noise corrupted data. Unlike other denoising methods based on regression analysis, we use the bootstrap resampling method to ensure the accuracy of regression estimation. Finally, we use a robust local nonparametric regression method to estimate the baseline and subsequently subtract it from the x-ray spectral data to further improve the SNR of the data. Experiments on several real samples show that the proposed method performs better than some state-of-the-art approaches to ensure accuracy and precision for quantitative analysis of the different trace elements in a standard reference biological sample.