Background: Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide. However its molecular pathogenesis is incompletely characterized and clinical biomarkers remain scarce. The aims of these experiments were to identify and characterize liver protein alterations in an animal model of NAFLD and to assess novel candidate biomarkers in NAFLD patients. Methods: Liver membrane and cytosolic protein fractions from high fat fed apolipoprotein E knockout (ApoE-/-) animals were analyzed by quantitative proteomics, utilizing isobaric tags for relative and absolute quantitation (iTRAQ) combined with nano-liquid chromatography and tandem mass spectrometry (nLC-MS/MS). Differential protein expression was confirmed independently by immunoblotting and immunohistochemistry in both murine tissue and biopsies from paediatric NAFLD patients. Candidate biomarkers were analyzed by enzyme-linked immunosorbent assay in serum from adult NAFLD patients. Results: Through proteomic profiling, we identified decreased expression of hepatic glyoxalase 1 (GLO1) in a murine model of NAFLD. GLO1 protein expression was also found altered in tissue biopsies from paediatric NAFLD patients. In vitro experiments demonstrated that, in response to lipid loading in hepatocytes, GLO1 is first hyperacetylated then ubiquitinylated and degraded, leading to an increase in reactive methylglyoxal. In a cohort of 59 biopsy-confirmed adult NAFLD patients, increased serum levels of the primary methylglyoxal-derived advanced glycation endproduct, hydroimidazolone (MG-H1) were significantly correlated with body mass index (r=0.520, p
Objectives This study tested the hypothesis that shows advanced image analysis can differentiate fit and unfit patients for radical radiotherapy from standard radiotherapy planning imaging, when compared to formal lung function tests (FEV1, Forced Expiratory Volume in 1 second) and TLCO (Transfer Factor of Carbon Monoxide). Methods An apical region of interest (ROI) of lung parenchyma was extracted from a standard radiotherapy planning CT scan. Software using a grey level co-occurrence matrix (GLCM) assigned an entropy score to each voxel, based on its similarity to the voxels around it. Density and entropy scores were compared between a cohort of fit patients (defined as FEV1 and TLCO above 50% predicted value) and unfit patients (FEV1 or TLCO below 50% predicted). Results 29 fit and 32 unfit patients were included. Mean and median density and mean and median entropy were significantly different between fit and unfit patients (p= 0.0021, 0.0019, 0.0357 and 0.0363 respectively, 2 sided t-test). Conclusions Density and entropy assessment can differentiate between fit and unfit patients for radical radiotherapy, using standard CT imaging. Advances in knowledge This study shows that a novel intervention can generate further data from standard CT imaging. This data could be combined with existing studies to form a multi-organ patient fitness assessment from a single CT scan.
None of the patient-and/or tumor-related variables were significantly correlated with non-response. Without harmonization, none of the CE-CT radiomic features identified in the training/validation set had predictive power in the testing set. After ComBat harmonization, Zone Size Percentage GLZSM was significantly correlated with non-response to chemotherapy in the training set (AUC= 0.67, Se= 70%, Sp= 64%, p= 0.04) and obtained a satisfactory performance in the validation set (Se= 80%, Sp= 67%, p= 0.03).
© 2015 Elsevier B.V. All rights reserved. Existing speech source separation approaches overwhelmingly rely on acoustic pressure information acquired by using a microphone array. Little attention has been devoted to the usage of B-format microphones, by which both acoustic pressure and pressure gradient can be obtained, and therefore the direction of arrival (DOA) cues can be estimated from the received signal. In this paper, such DOA cues, together with the frequency bin-wise mixing vector (MV) cues, are used to evaluate the contribution of a specific source at each time-frequency (T-F) point of the mixtures in order to separate the source from the mixture. Based on the von Mises mixture model and the complex Gaussian mixture model respectively, a source separation algorithm is developed, where the model parameters are estimated via an expectation-maximization (EM) algorithm. A T-F mask is then derived from the model parameters for recovering the sources. Moreover, we further improve the separation performance by choosing only the reliable DOA estimates at the T-F units based on thresholding. The performance of the proposed method is evaluated in both simulated room environments and a real reverberant studio in terms of signal-to-distortion ratio (SDR) and the perceptual evaluation of speech quality (PESQ). The experimental results show its advantage over four baseline algorithms including three T-F mask based approaches and one convolutive independent component analysis (ICA) based method.
We report on the measurement of new low-lying states in the neutron-rich 81,82,83,84Zn nuclei via in-beam γ -ray spectroscopy. These include the View the MathML source41+→21+ transition in 82Zn, the View the MathML source21+→0g.s.+ and View the MathML source41+→21+ transitions in 84Zn, and low-lying states in 81,83Zn were observed for the first time. The reduced View the MathML sourceE(21+) energies and increased View the MathML sourceE(41+)/E(2+1) ratios at N=52N=52, 54 compared to those in 80Zn attest that the magicity is confined to the neutron number N=50N=50 only. The deduced level schemes are compared to three state-of-the-art shell model calculations and a good agreement is observed with all three calculations. The newly observed 2+2+ and 4+4+ levels in 84Zn suggest the onset of deformation towards heavier Zn isotopes, which has been incorporated by taking into account the upper sdg orbitals in the Ni78-II and the PFSDG-U models.
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.
This paper studies the sensitivity of a range of image texture parameters used in radiomics to: i) the number of intensity levels, ii) the method of quantisation to select the intensity levels and iii) the use of an intensity threshold. 43 commonly used texture features were studied for the gross target volume outlined on the CT component of PET/CT scans of 50 patients with non-small cell lung carcinoma (NSCLC). All cases were quantised for all values between 4 and 128 intensity levels using four commonly used quantisation methods. All results were analysed with and without a threshold range of -200 HU to 300 HU. Cases were ranked for each texture feature and for all quantisation methods with the Spearman's rank correlation coefficient determined to evaluate stability. Results showed large fluctuations in ranking, particularly for low numbers of levels, differences between quantisation methods and with the use of a threshold, with values Spearman's Rank Correlation for many parameters below 0.2. Our results demonstrated the sensitivity of radiomics features to the parameters used during analysis and highlight the risk of low reproducibility comparing studies with slightly different parameters. In terms of the lung cancer CT datasets, this study supports the use of 128 intensity levels, the same uniform quantiser applied to all scans and thresholding of the data. It also supports several of the features recommended in the literature for such studies such as skewness and kurtosis. A recommended framework is presented for curation of the data analysis process to ensure stability of results.