Global and local detection of liver steatosis from ultrasound

Liver steatosis is a common disease usually associated with social and genetic factors. Early detection and quantification is important since it can evolve to cirrhosis. Steatosis is usually a diffuse liver disease, since it is globally affected. However, steatosis can also be focal affecting only some foci difficult to discriminate. In both cases, steatosis is detected by laboratorial analysis and visual inspection of ultrasound images of the hepatic parenchyma. Liver biopsy is the most accurate diagnostic method but its invasive nature suggest the use of other non-invasive methods, while visual inspection of the ultrasound images is subjective and prone to error. In this paper a new Computer Aided Diagnosis (CAD) system for steatosis classification and analysis is presented, where the Bayes Factor, obatined from objective intensity and textural features extracted from US images of the liver, is computed in a local or global basis. The main goal is to provide the physician with an application to make it faster and accurate the diagnosis and quantification of steatosis, namely in a screening approach. The results showed an overall accuracy of 93.54% with a sensibility of 95.83% and 85.71% for normal and steatosis class, respectively. The proposed CAD system seemed suitable as a graphical display for steatosis classification and comparison with some of the most recent works in the literature is also presented.

Data mining framework for fatty liver disease classification in ultrasound: a hybrid feature extraction paradigm

PURPOSE: Fatty liver disease (FLD) is an increasing prevalent disease that can be reversed if detected early. Ultrasound is the safest and ubiquitous method for identifying FLD. Since expert sonographers are required to accurately interpret the liver ultrasound images, lack of the same will result in interobserver variability. For more objective interpretation, high accuracy, and quick second opinions, computer aided diagnostic (CAD) techniques may be exploited. The purpose of this work is to develop one such CAD technique for accurate classification of normal livers and abnormal livers affected by FLD. METHODS: In this paper, the authors present a CAD technique (called Symtosis) that uses a novel combination of significant features based on the texture, wavelet transform, and higher order spectra of the liver ultrasound images in various supervised learning-based classifiers in order to determine parameters that classify normal and FLD-affected abnormal livers. RESULTS: On evaluating the proposed technique on a database of 58 abnormal and 42 normal liver ultrasound images, the authors were able to achieve a high classification accuracy of 93.3% using the decision tree classifier. CONCLUSIONS: This high accuracy added to the completely automated classification procedure makes the authors' proposed technique highly suitable for clinical deployment and usage.

The usefulness of ultrasound in the classification of chronic liver disease

Chronic Liver Disease is a progressive, most of the time asymptomatic, and potentially fatal disease. In this paper, a semi-automatic procedure to stage this disease is proposed based on ultrasound liver images, clinical and laboratorial data. In the core of the algorithm two classifiers are used: a k nearest neighbor and a Support Vector Machine, with different kernels. The classifiers were trained with the proposed multi-modal feature set and the results obtained were compared with the laboratorial and clinical feature set. The results showed that using ultrasound based features, in association with laboratorial and clinical features, improve the classification accuracy. The support vector machine, polynomial kernel, outperformed the others classifiers in every class studied. For the Normal class we achieved 100% accuracy, for the chronic hepatitis with cirrhosis 73.08%, for compensated cirrhosis 59.26% and for decompensated cirrhosis 91.67%.

Chronic liver disease staging classification based on ultrasound, clinical and laboratorial data

In this work the identification and diagnosis of various stages of chronic liver disease is addressed. The classification results of a support vector machine, a decision tree and a k-nearest neighbor classifier are compared. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. The classifiers training is performed by using a population of 97 patients at six different stages of chronic liver disease and a leave-one-out cross-validation strategy. The best results are obtained using the support vector machine with a radial-basis kernel, with 73.20% of overall accuracy. The good performance of the method is a promising indicator that it can be used, in a non invasive way, to provide reliable information about the chronic liver disease staging.

Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Fatty liver characterization and classification by ultrasound

Steatosis, also known as fatty liver, corresponds to an abnormal retention of lipids within the hepatic cells and reflects an impairment of the normal processes of synthesis and elimination of fat. Several causes may lead to this condition, namely obesity, diabetes, or alcoholism. In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis from ultrasound images. The features are selected in order to catch the same characteristics used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The algorithm, designed in a Bayesian framework, computes two images: i) a despeckled one, containing the anatomic and echogenic information of the liver, and ii) an image containing only the speckle used to compute the textural features. These images are computed from the estimated RF signal generated by the ultrasound probe where the dynamic range compression performed by the equipment is taken into account. A Bayes classifier, trained with data manually classified by expert clinicians and used as ground truth, reaches an overall accuracy of 95% and a 100% of sensitivity. The main novelties of the method are the estimations of the RF and speckle images which make it possible to accurately compute textural features of the liver parenchyma relevant for the diagnosis.

Ecosapentanoic acid (EPA) does not affect cell kinetics in peripheral lymphocites from patients with Crohn's disease (CD) according to IL6 174G/C polymorphism

Rationale: Omega 3 fatty acids have been shown to be of potential benefit in patients with CD. The aim of the present study was to evaluate whether EPA can modulate the inflammatory response according to different genotypes of IL6G174G/C polymorphism. Methods: Peripheral blood cells were collected from CD patients with different genotypes for IL6 174G/C (GG, n = 16, GC, n = 8, CC, n = 7), and lymphocytes were established in culture media. Replicates with the addition of EPA (25 mM) were analysed in a period of 24h, 48h and 72h. Expression of IL6 e a PGE2 was assessed by ELISA. Apoptosis and cellular proliferation was determined by flow cytometry.

Cholesterol 24S-Hydroxylase overexpression inhibits the liver X receptor (LXR) pathway by activating small guanosine triphosphate-binding proteins (sGTPases) in neuronal cells

The neuronal-specific cholesterol 24S-hydroxylase (CYP46A1) is important for brain cholesterol elimination. Cyp46a1 null mice exhibit severe deficiencies in learning and hippocampal long-term potentiation, suggested to be caused by a decrease in isoprenoid intermediates of the mevalonate pathway. Conversely, transgenic mice overexpressing CYP46A1 show an improved cognitive function. These results raised the question of whether CYP46A1 expression can modulate the activity of proteins that are crucial for neuronal function, namely of isoprenylated small guanosine triphosphate-binding proteins (sGTPases). Our results show that CYP46A1 overexpression in SH-SY5Y neuroblastoma cells and in primary cultures of rat cortical neurons leads to an increase in 3-hydroxy-3-methyl-glutaryl-CoA reductase activity and to an overall increase in membrane levels of RhoA, Rac1, Cdc42 and Rab8. This increase is accompanied by a specific increase in RhoA activation. Interestingly, treatment with lovastatin or a geranylgeranyltransferase-I inhibitor abolished the CYP46A1 effect. The CYP46A1-mediated increase in sGTPases membrane abundance was confirmed in vivo, in membrane fractions obtained from transgenic mice overexpressing this enzyme. Moreover, CYP46A1 overexpression leads to a decrease in the liver X receptor (LXR) transcriptional activity and in the mRNA levels of ATP-binding cassette transporter 1, sub-family A, member 1 and apolipoprotein E. This effect was abolished by inhibition of prenylation or by co-transfection of a RhoA dominant-negative mutant. Our results suggest a novel regulatory axis in neurons; under conditions of membrane cholesterol reduction by increased CYP46A1 expression, neurons increase isoprenoid synthesis and sGTPase prenylation. This leads to a reduction in LXR activity, and consequently to a decrease in the expression of LXR target genes.

Heterogeneous kinetics of the reduction of chromium (VI) by elemental iron

Zero valent iron (ZVI) has been extensively used as a reactive medium for the reduction of Cr(VI) to Cr(III) in reactive permeable barriers. The kinetic rate depends strongly on the superficial oxidation of the iron particles used and the preliminary washing of ZVI increases the rate. The reaction has been primarily modelled using a pseudo-first-order kinetics which is inappropriate for a heterogeneous reaction. We assumed a shrinking particle type model where the kinetic rate is proportional to the available iron surface area, to the initial volume of solution and to the chromium concentration raised to a power ˛ which is the order of the chemical reaction occurring at surface. We assumed α= 2/3 based on the likeness to the shrinking particle models with spherical symmetry. Kinetics studies were performed in order to evaluate the suitability of this approach. The influence of the following parameters was experimentally studied: initial available surface area, chromium concentration, temperature and pH. The assumed order for the reaction was confirmed. In addition, the rate constant was calculated from data obtained in different operating conditions. Digital pictures of iron balls were periodically taken and the image treatment allowed for establishing the time evolution of their size distribution.

Do fatigue, physical activity, handgrip strength and quality of life change between pre and post liver transplant?

Analisar: níveis de fadiga, força de preensão, HRQoL, níveis de actividade física. Será que se alteram em doentes PAF após o transplante de fígado? Dado que os níveis de actividade física se encontram abaixo dos valores mínimos recomendados deveria ser encontrada uma estratégia de aumento do tempo dispendido na actividade física leve a moderada idealmente no PRÉ TRANSPLANTE.

Automatic diagnosis of liver steatosis by ultrasound using autoregressive tissue characterization

Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

Adsorption kinetics of removal of yellow lanasol dyestuff using gallinaceous feathers

Gallinaceous feathers are an abundant solid waste from the poultry processing industries, which poses disposal problems. A kinetic study dealing with the adsorption process of wool reactive dye, Yellow Lanasol 4G (CI Reactive Yellow 39), on gallinaceous (Gallus gallus, Cobb 500) feathers was carried out. The main research goals of this work were to evaluate the viability of using this waste as adsorbent and to study the kinetics of the adsorption process, using a synthetic effluent. The characterization of feathers was performed by scanning electron microscopy, mercury porosimetry and B.E.T. method. The study of several factors (stirring, particles size, initial dye concentration and temperature) showed their influence over the adsorption process. An adapted version of the Schumckler and Goldstein´s unreacted core model fitted the experimental data. The best fit was obtained when the rate-limiting step was the diffusion through the reacted layer, which was expected considering the size of the dyestuff molecules. The comparison with the granular activated carbon (GAC) Sutcliffe GAC 10-30 indicate that in spite of the high adsorption capacities shown by feathers the GAC presented higher values, the values obtained were respectively 150 and 219 mg g-1, for an initial concentration of 500 mg L-1. The results obtained might open future perspectives both to the valorization of feathers and to the economical treatment of textile wastewaters.

Experimental design methodologies for the identification of Michaelis-Menten type kinetics

The main objective of this work was to investigate the application of experimental design techniques for the identification of Michaelis-Menten kinetic parameters. More specifically, this study attempts to elucidate the relative advantages/disadvantages of employing complex experimental design techniques in relation to equidistant sampling when applied to different reactor operation modes. All studies were supported by simulation data of a generic enzymatic process that obeys to the Michaelis-Menten kinetic equation. Different aspects were investigated, such as the influence of the reactor operation mode (batch, fed-batch with pulse wise feeding and fed-batch with continuous feeding) and the experimental design optimality criteria on the effectiveness of kinetic parameters identification. The following experimental design optimality criteria were investigated: 1) minimization of the sum of the diagonal of the Fisher information matrix (FIM) inverse (A-criterion), 2) maximization of the determinant of the FIM (D-criterion), 3) maximization of the smallest eigenvalue of the FIM (E-criterion) and 4) minimization of the quotient between the largest and the smallest eigenvalue (modified E-criterion). The comparison and assessment of the different methodologies was made on the basis of the Cramér-Rao lower bounds (CRLB) error in respect to the parameters vmax and Km of the Michaelis-Menten kinetic equation. In what concerns the reactor operation mode, it was concluded that fed-batch (pulses) is better than batch operation for parameter identification. When the former operation mode is adopted, the vmax CRLB error is lowered by 18.6 % while the Km CRLB error is lowered by 26.4 % when compared to the batch operation mode. Regarding the optimality criteria, the best method was the A-criterion, with an average vmax CRLB of 6.34 % and 5.27 %, for batch and fed-batch (pulses), respectively, while presenting a Km’s CRLB of 25.1 % and 18.1 %, for batch and fed-batch (pulses), respectively. As a general conclusion of the present study, it can be stated that experimental design is justified if the starting parameters CRLB errors are inferior to 19.5 % (vmax) and 45% (Km), for batch processes, and inferior to 42 % and to 50% for fed-batch (pulses) process. Otherwise equidistant sampling is a more rational decision. This conclusion clearly supports that, for fed-batch operation, the use of experimental design is likely to largely improve the identification of Michaelis-Menten kinetic parameters.

Fatty liver automatic diagnosis from ultrasound images

In this paper an automatic classification algorithm is proposed for the diagnosis of the liver steatosis, also known as, fatty liver, from ultrasound images. The features, automatically extracted from the ultrasound images used by the classifier, are basically the ones used by the physicians in the diagnosis of the disease based on visual inspection of the ultrasound images. The main novelty of the method is the utilization of the speckle noise that corrupts the ultrasound images to compute textural features of the liver parenchyma relevant for the diagnosis. The algorithm uses the Bayesian framework to compute a noiseless image, containing anatomic and echogenic information of the liver and a second image containing only the speckle noise used to compute the textural features. The classification results, with the Bayes classifier using manually classified data as ground truth show that the automatic classifier reaches an accuracy of 95% and a 100% of sensitivity.

Reactivity of syringyl and guaiacyl lignin units and delignification kinetics in the kraft pulping of eucalyptus globulus wood using Py-Gc-Ms/FID

Eucalyptus globulus sapwood and heartwood showed no differences in lignin content (23.0% vs. 23.7%) and composition: syringyl-lignin (17.9% vs. 18.0%) and guaiacyl-lignin (4.8% vs. 5.2%). Delignification kinetics of S- and G-units in heartwood and sapwood was investigated by Py-GC–MS/FID at 130, 150 and 170 °C and modeled as double first-order reactions. Reactivity differences between S and G-units were small during the main pulping phase and the higher reactivity of S over G units was better expressed in the later pulping stage. The residual lignin composition in pulps was different from wood or from samples in the initial delignification stages, with more G and H-units. S/G ratio ranged from 3 to 4.5 when pulp residual lignin was higher than 10%, decreasing rapidly to less than 1. The S/H was initially around 20 (until 15% residual lignin), decreasing to 4 when residual lignin was about 3%.