Serum amyloid A levels in patients with liver diseases

BACKGROUND Serum amyloid A(SAA)is an acute phase protein mainly synthesized by the liver.SAA induces inflammatory phenotype and promotes cell proliferation in activated hepatic stellate cells,the major scar forming cells in the liver.However,few studies have reported on the serum levels of SAA in human liver disease and its clinical significance in various liver diseases.AIM To investigate the serum levels of SAA in patients with different liver diseases and analyze the factors associated with the alteration of SAA levels in chronic hepatitis B(CHB)patients.METHODS Two hundred and seventy-eight patients with different liver diseases and 117 healthy controls were included in this study.The patients included 205 with CHB,22 with active autoimmune liver disease(AILD),21 with nonalcoholic steatohepatitis(NASH),14 with drug-induced liver injury(DILI),and 16 with pyogenic liver abscess.Serum levels of SAA and other clinical parameters were collected for the analysis of the factors associated with SAA level.Mann-Whitney U test was used to compare the serum SAA levels of patients with various liver diseases with those of healthy controls.Bonferroni test was applied for post hoc comparisons to control the probability of type 1 error(alpha=0.05/6=0.008).For statistical tests of other variables,P<0.05 was considered statistically significant.Statistically significant factors determined by single factor analysis were further analyzed by binary multivariate logistic regression analysis.RESULTS All patients with active liver diseases had higher serum SAA levels than healthy controls and the inactive CHB patients,with the highest SAA level found in patients with pyogenic liver abscess(398.4±246.8 mg/L).Patients with active AILD(19.73±24.81 mg/L)or DILI(8.036±5.685 mg/L)showed higher SAA levels than those with active CHB(6.621±6.776 mg/L)and NASH(6.624±4.891 mg/L).Single(P<0.001)and multivariate logistic regression analyses(P=0.039)for the CHB patients suggested that patients with active CHB were associated with an SAA serum level higher than 6.4 mg/L.Serum levels of SAA and CRP(C-reactive protein)were positively correlated in patients with CHB(P<0.001),pyogenic liver abscess(P=0.045),and active AILD(P=0.02).Serum levels of SAA(0.80-871.0 mg/L)had a broader fluctuation range than CRP(0.30-271.3 mg/L).CONCLUSION Serum level of SAA is a sensitive biomarker for inflammatory activity of pyogenic liver abscess.It may also be a weak marker reflecting milder inflammatory status in the liver of patients with CHB and other active liver diseases.

Statistical Inference of Partially Specified Spatial Autoregressive Model

This paper studies estimation of a partially specified spatial autoregressive model with heteroskedas- ticity error term. Under the assumption of exogenous regressors and exogenous spatial weighting matrix, the unknown parameter is estimated by applying the instrumental variable estimation. Under certain sufficient conditions, the proposed estimator for the finite dimensional parameters is shown to be root-n consistent and asymptotically normally distributed; The proposed estimator for the unknown function is shown to be consis- tent and asymptotically distributed as well, though at a rate slower than root-n. Consistent estimators for the asymptotic variance-covariance matrices of both estimators are provided. Monte Carlo simulations suggest that the proposed procedure has some practical value.

Window Detection in Facades Using Heatmap Fusion

Window detection is a key component in many graphics and vision applications related to 3D city modeling and scene visualization.We present a novel approach for learning to recognize windows in a colored facade image.Rather than predicting bounding boxes or performing facade segmentation,our system locates keypoints of windows,and learns keypoint relationships to group them together into windows.A further module provides extra recognizable information at the window center.Locations and relationships of keypoints are encoded in different types of heatmaps,which are learned in an end-to-end network.We have also constructed a facade dataset with 3418 annotated images to facilitate research in this field.It has richly varying facade structures,occlusion,lighting conditions,and angle of view.On our dataset,our method achieves precision of 91.4%and recall of 91.0%under 50%IoU(intersection over union).We also make a quantitative comparison with state-of-the-art methods to verify the utility of our proposed method.Applications based on our window detector are also demonstrated,such as window blending.