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.

Comparison study of procedures for aspiration of biopsy samples

Introduction: The samples obtained from fine needle aspiration in liquid base cytology (FNAC) are often limited by scarce cellularity compared to the amount of colloid and presence of blood. Accordingly, it was important to test alternative technical procedures so as to maximize the cellularity of each sample. Objective: To compare the morphological features and cellularity of the three procedures in the FNAC cytodiagnosis of the thyroid. Methods: A total of 31 cases were each subjected to a cell block and ThinPrep preparation as well as a routine smear. The observation and analysis was performed using an optical microscope. Cytological diagnosis of each cell block case was objectively analysed for cellularity, presence of background and cellular preservation. Each smear and ThinPrep case was analysed for the presence or absence of cells. The data was analysed with Microsoft Excel (Office 2010) and SPSS (Statistical Package of Social Science) version 15.0 for Windows. Results: Of 31 cases, only 20 had thyroid cells in the cell block and ThinPrep preparations, however, all smear cases contained thyroid cells. Some background was found in 30 Cell block cases with only 5 of these containing well preserved cells for cytodiagnosis. Conclusions: As indicated by the results, smear is the most appropriate procedure for FNAC of the thyroid.