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.

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.

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.

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.