On the Feasibility of Ultrasound Imaging Enrichment by Medium-Temperature Changes.

We describe the concept of a new imaging modality based on the tracking and dynamic modeling of local intensity changes (ICs) observed in conventional ultrasound images collected during a medium-temperature change. We computed the pixel-by-pixel IC from averaged B-mode images that exhibited different behaviors with varying temperature resulting from changes in the speed of sound, which consequently induce changes in the backscattered energy. Moreover, for each pixel, a first-order polynomial model was adjusted to the different temperature-dependent ICs. The representation of the polynomial angular parameter in 2D pixel space was used to obtain a parametric image. The results obtained by simulations and with real B-mode images indicated that this new ultrasound imaging modality was able to enhance the contrast and highlight structures that were poorly visible or even undetected in conventional images. A temperature change of 3°C was found to be sufficient to generate appropriate images with the proposed method. In addition, if a temperature change of 6°C was considered, the thermal dose, measured as the cumulative number of equivalent minutes at 43°C (CEM43°C), was 2.4 CEM43°C, which is a value that is considered safe according to the literature. We provide a proof-of-concept of a new imaging modality that opens new opportunities for the enhancement of ultrasound images and consequently contributes to improvements in ultrasound-based diagnoses. Our approach is based on images returned by commercial ultrasound scanners. Therefore, it can be implemented in any ultrasound system and is independent of specific ultrasound hardware and software data acquisition characteristics.

A knowledge discovery methodology from EEG data for cyclic alternating pattern detection.

BACKGROUND: Detection and quantification of cyclic alternating patterns (CAP) components has the potential to serve as a disease bio-marker. Few methods exist to discriminate all the different CAP components, they do not present appropriate sensitivities, and often they are evaluated based on accuracy (AC) that is not an appropriate measure for imbalanced datasets. METHODS: We describe a knowledge discovery methodology in data (KDD) aiming the development of automatic CAP scoring approaches. Automatic CAP scoring was faced from two perspectives: the binary distinction between A-phases and B-phases, and also for multi-class classification of the different CAP components. The most important KDD stages are: extraction of 55 features, feature ranking/transformation, and classification. Classification is performed by (i) support vector machine (SVM), (ii) k-nearest neighbors (k-NN), and (iii) discriminant analysis. We report the weighted accuracy (WAC) that accounts for class imbalance. RESULTS: The study includes 30 subjects from the CAP Sleep Database of Physionet. The best alternative for the discrimination of the different A-phase subtypes involved feature ranking by the minimum redundancy maximum relevance algorithm (mRMR) and classification by SVM, with a WAC of 51%. Concerning the binary discrimination between A-phases and B-phases, k-NN with mRMR ranking achieved the best WAC of 80%. CONCLUSIONS: We describe a KDD that, to the best of our knowledge, was for the first time applied to CAP scoring. In particular, the fully discrimination of the three different A-phases subtypes is a new perspective, since past works tried multi-class approaches but based on grouping of different sub-types. We also considered the weighted accuracy, in addition to simple accuracy, resulting in a more trustworthy performance assessment. Globally, better subtype sensitivities than other published approaches were achieved.

Evaluation of lung ventilation distribution in chronic obstructive pulmonary disease patients using the global inhomogeneity index.

The global inhomogeneity (GI) index is a electrical impedance tomography (EIT) parameter that quantifies the tidal volume distribution within the lung. In this work the global inhomogeneity index was computed for twenty subjects in order to evaluate his potential use in the detection and follow up of chronic obstructive pulmonary disease (COPD) patients. EIT data of 17 subjects were acquired: 14 patients with the main diagnoses of COPD and 3 healthy subjects which served as a control group. Two or three datasets of around 30 seconds were acquired at 33 scans/s and analysed for each subject. After reconstruction, a tidal EIT image was computed for each breathing cycle and a GI index calculated from it. Results have shown significant differences in GI values between the two groups (0.745 ± 0.007 for COPD and 0.668 ± 0.006 for lung-healthy subject, p <; 0.005). The GI values obtained for each subject have shown small variance between them, which is a good indication of stability. The results suggested that the GI may be useful for the identification and follow up of ventilation problems in patients with COPD.

Slow modulations of high-frequency activity (40-140-Hz) discriminate preictal changes in human focal epilepsy.

Recent evidence suggests that some seizures are preceded by preictal changes that start from minutes to hours before an ictal event. Nevertheless an adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence of preictal changes in long-term intracranial recordings from 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe a measure of brain excitability based on the slow modulation of high-frequency gamma activities (40-140 Hz) in ensembles of intracranial contacts. In prospective tests, we found that this index identified preictal changes at levels above chance in 13.2% of the patients (7/53), suggesting that results may be significant for the whole group (p < 0.05). These results provide a demonstration that preictal states can be detected prospectively from EEG data. They advance understanding of the network dynamics leading to seizure and may help develop novel seizure prediction algorithms.

Feasibility of non-invasive temperature estimation by the assessment of the average gray-level content of B-mode images.

This paper assesses the potential of the average gray-level (AVGL) from ultrasonographic (B-mode) images to estimate temperature changes in time and space in a non-invasive way. Experiments were conducted involving a homogeneous bovine muscle sample, and temperature variations were induced by an automatic temperature regulated water bath, and by therapeutic ultrasound. B-mode images and temperatures were recorded simultaneously. After data collection, regions of interest (ROIs) were defined, and the average gray-level variation computed. For the selected ROIs, the AVGL-Temperature relation were determined and studied. Based on uniformly distributed image partitions, two-dimensional temperature maps were developed for homogeneous regions. The color-coded temperature estimates were first obtained from an AVGL-Temperature relation extracted from a specific partition (where temperature was independently measured by a thermocouple), and then extended to the other partitions. This procedure aimed to analyze the AVGL sensitivity to changes not only in time but also in space. Linear and quadratic relations were obtained depending on the heating modality. We found that the AVGL-Temperature relation is reproducible over successive heating and cooling cycles. One important result was that the AVGL-Temperature relations extracted from one region might be used to estimate temperature in other regions (errors inferior to 0.5 °C) when therapeutic ultrasound was applied as a heating source. Based on this result, two-dimensional temperature maps were developed when the samples were heated in the water bath and also by therapeutic ultrasound. The maps were obtained based on a linear relation for the water bath heating, and based on a quadratic model for the therapeutic ultrasound heating. The maps for the water bath experiment reproduce an acceptable heating/cooling pattern, and for the therapeutic ultrasound heating experiment, the maps seem to reproduce temperature profiles consistent with the pressure field of the transducer, and in agreement with temperature maps developed by COMSOL®MultiPhysics simulations.

Robust features for detection of crackles: an exploratory study.

Crackles are adventitious and explosive respiratory sounds that can be classified as fine or coarse. These sounds are usually associated with cardiopulmonary diseases such as the chronic obstructive pulmonary disease. In this work seven different features were tested with the objective to identify the best subset of features that allows a robust detection of coarse crackles. Some of the features used in this study are new, namely those based on the local entropy, on the Teager energy and on the residual fit of a Generalized Autoregressive Conditional Heteroskedasticity process. The best features as a function of the number of features used in classification were identified having into account the Matthews correlation coefficient. The best individual feature was based on the local entropy. A significant improvement in the performance was obtained by using the feature based on local entropy and the feature based on the wavelet packed stationary transform - no stationary transform. The addition of more features only allows a smaller improvement.

Composição química e atividade inseticida do extrato acetônico de Piper alatabaccum Trel & Yuncker (Piperaceae) sobre Hypothenemus hampei Ferrari / Chemical constituents and insecticidal activity of the crude acetonic extract of Piper alatabaccum Trel & Yuncker (Piperaceae) on Hypothenemus hampei Ferrari

O presente trabalho teve como objetivo investigar a composição química e avaliar a atividade inseticida do extrato acetônico das raízes de Piper alatabaccum sobre Hypothenemus hampei. As raízes foram coletadas no campus da Universidade Federal de Rondônia, em Porto Velho-RO. Diluições de 100,0, 50,0, 25,0, 5,0 e 0,5 mg mL-1 foram utilizadas para exposição na superfície contaminada e aplicação tópica. No teste de repelência, utilizou-se as diluições de 25,0 e 100,0 mg mL-1. As avaliações ocorreram durante 48 horas após a exposição ao extrato. O extrato foi eficiente na mortalidade em aplicação tópica e superfície contaminada, não sendo eficiente no efeito de repelência. Estes resultados apontam para o elevado potencial inseticida do extrato de P. alatabaccum no controle de H. hampei. Identificou-se cinco compostos, sendo três amidas, dihydropiplartine piperovatina e piplartina, e duas flavona, 3',4',5,5',7 pentamethoxyflavone e 5,5',7-trimetóxi-3',4'-methylenedioxyflavone.

The purpose of this work was to investigate the chemical composition and to evaluate the insecticidal activity of the acetonic extract of Piper alatabaccum roots on Hypothenemus hampei. Roots of P. alatabaccum were collected at the University of Rondônia in Porto Velho-RO. Dilutions of 100.0, 50.0, 25.0, 5.0 and 0.5 mg mL-1 were used for exposition in the contaminated surface and for topical application. For the repellence test, 25.0 and 100.0 mg mL-1 dilutions were used. The evaluations occurred during 48 hours after exposure to the extract. The extract was efficient in terms of mortality, but no repellence was observed in the bioassays. These results pointed out the high insecticidal potential of the P. alatabaccum extract to control H. hampei. Five compounds, three amids, piperovatine, dihydropiplartine and piplartine, and two flavones, 3',4',5,5',7-pentamethoxyflavone and 5,5',7-trimethoxy-3',4' methylenedioxyflavone were identified.

Composição química e atividade inseticida do óleo essencial de Schinus terebinthifolius Raddi (Anacardiaceae) sobre a broca-do-café (Hypothenemus hampei) Ferrari / Chemical composition and insecticidal activity of the essential oil of Schinus terebinthifolius Raddi (Anacardiaceae) on coffee berry borer (Hypothenemus hampei) Ferrari

O óleo essencial das folhas de Schinus terebinthifolius foi obtido por hidrodestilação e analisado por CG-EM. O rendimento do óleo foi de 0,8%, sendo identificados 37 constituintes químicos. Os componentes principais foram germacreno D (25,0%), (E)-ß-cariofileno (17,5%) e δ -elemeno (10,5%). O óleo essencial foi diluído em acetona nas concentrações de 10-2 a 10-8 e aplicado aos insetos (Hypothenemus hampei) por aplicação tópica e exposição em superfície contaminada. As taxas de mortalidade foram avaliadas após 24 e 48 horas do início do experimento. A aplicação em superfície contaminada resultou em 25% de mortalidade no controle, enquanto nas diluições de 10-2 a 10-8 foram observados 100,0 a 30,0% de mortalidade. Na aplicação tópica, observou-se 27,5% de mortalidade no controle e 97,5 a 77,5% nas diluições de 10-2 a 10-8.

The essential oil from the leaves of Schinus terebinthifolius was obtained by hydrodistillation and was analyzed by GC-MS. The oil yield was 0.8%, and 37 chemical constituents were identified. The main components were germacrene D (25.0%), (E)-ß-cariophyllene (17.5%) and δ-elemene (10.5%). The essential oil was diluted in acetone in concentrations of 10-2 to 10-8 and it was applied to insects (Hypothenemus hampei) by topical application and exposure to contaminated surface. Mortality rates were evaluated after 24 and 48 hours. The contaminated surface application resulted in 25.0% of mortality in control while the dilutions of 10-2 to 10-8 resulted in 100% to 30.0% of mortality respectively. In the topical application, it was observed 27.5% of mortality in control and 97.5 to 77.5% of mortality in dilutions of 10-2 to 10-8

EPILAB: a software package for studies on the prediction of epileptic seizures.

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

Paracrine-mediated osteoclastogenesis by the osteosarcoma MG63 cell line: is RANKL/RANK signalling really important?

Although in the past little attention has been paid to the influence of osteosarcoma cells in osteoclast function, recent studies suggest a close relationship between osteosarcoma aggressiveness and osteoclastic activity. The present study addresses the paracrine effects of MG63 cells, a human osteosarcoma-derived cell line, on the differentiation of peripheral blood osteoclast precursor cells (PBMC). PBMC were cultured for 21 days in the presence of conditioned media from MG63 cell cultures (CM) collected at 48 h (CM_MG1), 7 days (CM_MG2) and 14 days (CM_MG3). MG63 cell cultures displayed the expression of ALP and BMP-2 and, also, the osteoclastogenic genes M-CSF and RANKL, although with a low expression of RANKL. PBMC cultures supplemented with CM presented an evident osteoclastogenic behavior, which was dependent on the culture period of the MG63 cells. The inductive effect appeared to be more relevant for the differentiation and activation genes, c-myc and c-src, and lower for genes associated with osteoclast function. In addition, PBMC cultures displayed increased functional parameters, including calcium phosphate resorbing activity. Assessment of the PBMC cultures in the presence of U0126, PDTC, and indomethacin suggested that in addition to MEK and NFkB pathways, other signaling mechanisms, probably not involving RANKL/RANK interaction, might be activated in the presence of conditioned medium from MG63. In conclusion, MG63 cell line appears to induce a significant paracrine-mediated osteoclastogenic response. Understanding the mechanisms underlying the interaction of osteosarcoma cells and osteoclasts may contribute to the development of new potential approaches in the treatment of such bone metabolic diseases.

A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

On the possibility of non-invasive multilayer temperature estimation using soft-computing methods.

OBJECTIVE AND MOTIVATION: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e.g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. NOVELTY ASPECTS: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. MATERIALS AND METHODS: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar-agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic algorithm (MOGA). In this work 40 operating conditions were considered, i.e. five 5-mm spaced spatial points and eight therapeutic intensities (I(SATA)): 0.3, 0.5, 0.7, 1.0, 1.3, 1.5, 1.7 and 2.0W/cm(2). Models were trained and selected to estimate temperature at only four intensities, then during the validation phase, the best-fitted models were analyzed in data collected at the eight intensities. This procedure leads to a more realistic evaluation of the generalisation level of the best-obtained structures. RESULTS AND DISCUSSION: At the end of the identification phase, 82 (preferable) estimator models were achieved. The majority of them present an average maximum absolute error (MAE) inferior to 0.5 degrees C. The best-fitted estimator presents a MAE of only 0.4 degrees C for both the 40 operating conditions. This means that the gold-standard maximum error (0.5 degrees C) pointed for hyperthermia was fulfilled independently of the intensity and spatial position considered, showing the improved generalisation capacity of the identified estimator models. As the majority of the preferable estimator models, the best one presents 6 inputs and 11 neurons. In addition to the appropriate error performance, the estimator models present also a reduced computational complexity and then the possibility to be applied in real-time. CONCLUSIONS: A non-invasive temperature estimation model, based on soft-computing technique, was proposed for a three-layered phantom. The best-achieved estimator models presented an appropriate error performance regardless of the spatial point considered (inside or at the interface of the layers) and of the intensity applied. Other methodologies published so far, estimate temperature only in homogeneous media. The main drawback of the proposed methodology is the necessity of a-priory knowledge of the temperature behavior. Data used for training and optimisation should be representative, i.e., they should cover all possible physical situations of the estimation environment.

Restless leg syndrome, sleep quality and fatigue in multiple sclerosis patients.

We have tested the hypothesis that restless leg syndrome (RLS) is related to quality of sleep, fatigue and clinical disability in multiple sclerosis (MS). The diagnosis of RLS used the four minimum criteria defined by the International Restless Legs Syndrome Study Group. Fatigue was assessed by the Fatigue Severity Scale (FSS >27), quality of sleep by the Pittsburgh Sleep Quality Index (PSQI >6), excessive daytime sleepiness by the Epworth Sleepiness Scale (ESS >10) and clinical disability by the Expanded Disability Status Scale (EDSS). Forty-four patients (32 women) aged 14 to 64 years (43 +/- 14) with disease from 0.4 to 23 years (6.7 +/- 5.9) were evaluated. Thirty-five were classified as relapsing-remitting, 5 as primary progressive and 4 as secondary progressive. EDSS varied from 0 to 8.0 (3.6 +/- 2.0). RLS was detected in 12 cases (27%). Patients with RLS presented greater disability (P = 0.01), poorer sleep (P = 0.02) and greater levels of fatigue (P = 0.03). Impaired sleep was present in 23 (52%) and excessive daytime sleepiness in 3 cases (6.8%). Fatigue was present in 32 subjects (73%) and was associated with clinical disability (P = 0.000) and sleep quality (P = 0.002). Age, gender, disease duration, MS pattern, excessive daytime sleepiness and the presence of upper motor neuron signs were not associated with the presence of RLS. Fatigue was best explained by clinical disability and poor sleep quality. Awareness of RLS among health care professionals may contribute to improvement in MS management.

Restless leg syndrome, sleep quality and fatigue in multiple sclerosis patients

We have tested the hypothesis that restless leg syndrome (RLS) is related to quality of sleep, fatigue and clinical disability in multiple sclerosis (MS). The diagnosis of RLS used the four minimum criteria defined by the International Restless Legs Syndrome Study Group. Fatigue was assessed by the Fatigue Severity Scale (FSS >27), quality of sleep by the Pittsburgh Sleep Quality Index (PSQI >6), excessive daytime sleepiness by the Epworth Sleepiness Scale (ESS >10) and clinical disability by the Expanded Disability Status Scale (EDSS). Forty-four patients (32 women) aged 14 to 64 years (43 ± 14) with disease from 0.4 to 23 years (6.7 ± 5.9) were evaluated. Thirty-five were classified as relapsing-remitting, 5 as primary progressive and 4 as secondary progressive. EDSS varied from 0 to 8.0 (3.6 ± 2.0). RLS was detected in 12 cases (27 percent). Patients with RLS presented greater disability (P = 0.01), poorer sleep (P = 0.02) and greater levels of fatigue (P = 0.03). Impaired sleep was present in 23 (52 percent) and excessive daytime sleepiness in 3 cases (6.8 percent). Fatigue was present in 32 subjects (73 percent) and was associated with clinical disability (P = 0.000) and sleep quality (P = 0.002). Age, gender, disease duration, MS pattern, excessive daytime sleepiness and the presence of upper motor neuron signs were not associated with the presence of RLS. Fatigue was best explained by clinical disability and poor sleep quality. Awareness of RLS among health care professionals may contribute to improvement in MS management.

CLN2/TPP1 deficiency: the novel mutation IVS7-10A>G causes intron retention and is associated with a mild disease phenotype.

The classical form of late infantile neuronal ceroid lipofuscinosis (LINCL) is a childhood hereditary neurodegenerative disease usually fatal in the first decade of life. The underlying gene, CLN2, encodes the lysosomal soluble enzyme tripeptidyl-peptidase 1 (TPP1). In a Portuguese patient with juvenile form of the disease, the histochemical study revealed the presence of curvilinear inclusions typical of LINCL. In vitro TPP1 activity was deficient in patient's cells. CLN2 gene analysis revealed the transition IVS7-10A>G (g.4196A>G) in both alleles. In silico analysis suggested that A-to-G change in the A-rich region of intron 7 could cause aberrant splicing of exon 8 by creating a novel acceptor splice site. However, because the wild-type acceptor of intron 7 is weak and it was not apparently affected, the severity of this mutation could not be established through sequencing data of gDNA. Normal level of spliced CLN2/mRNA was observed in patient's fibroblasts. In the cDNA, the 9-nt retention of intronic sequence (c.886_887ins9) was observed. The mutation is predicted to result in a protein with three extra amino acids between proline 295 and glycine 296. In patient's fibroblasts the level of mutant CLN2p was reduced to about 60% but the migration pattern was similar to the wild-type protein, suggesting that it was correctly targeted to the lysosomes. Taken together, these findings suggest that the first "ag" is selected for splicing and the mutant protein must retain some residual catalytic activity, thus explaining the late onset and the delayed progression of the disease.

Non-invasive temperature prediction of in vitro therapeutic ultrasound signals using neural networks.

In this paper, a novel black-box modelling scheme applied to non-invasive temperature prediction in a homogeneous medium subjected to therapeutic ultrasound is presented. It is assumed that the temperature in a point of the medium is non-linearly related to some spectral features and one temporal feature, extracted from the collected RF-lines. The black-box models used are radial basis functions neural networks (RBFNNs), where the best-fitted models were selected from the space of model structures using a genetic multi-objective strategy. The best-fitted predictive model presents a maximum absolute error less than 0.4 degrees C in a prediction horizon of approximately 2 h, in an unseen data sequence. This work demonstrates that this type of black-box model is well-suited for punctual and non-invasive temperature estimation, achieving, for a single point estimation, better results than the ones presented in the literature, encouraging research on multi-point non-invasive temperature estimation.

Gene expression profiling in vLINCL CLN6-deficient fibroblasts: Insights into pathobiology.

The CLN6 vLINCL is caused by molecular defects in CLN6 gene coding for an ER resident transmembrane protein whose function is unknown. In the present study gene expression profiling of CLN6-deficient fibroblasts using cDNA microarray was undertaken in order to provide novel insights into the molecular mechanisms underlying this neurodegenerative fatal disease. Data were validated by qRT-PCR. Statistically significant alterations of expression were observed for 12 transcripts. The two most overexpressed genes, versican and tissue factor pathway inhibitor 2, are related to extracellular matrix (ECM), predicting changes in ECM-related proteins in CLN6-deficient cells. Transcript profiling also suggested alterations in signal transduction pathways, apoptosis and the immune/inflammatory response. Up-regulated genes related to steroidogenesis or signalling, and the relationship between cholesterol dynamics and glycosphingolipid sorting, led to investigation of free cholesterol and gangliosides in CLN6-deficient fibroblasts. Cholesterol accumulation in lysosomes suggests a homeostasis block as a result of CLN6p deficiency. The cholesterol imbalance may affect structure/function of caveolae and lipid rafts, disrupting signalling transduction pathways and sorting cell mechanisms. Alterations in protein/lipid intracellular trafficking would affect the composition and function of endocytic compartments, including lysosomes. Dysfunctional endosomal/lysosomal vesicles may act as one of the triggers for apoptosis and cell death, and for a secondary protective inflammatory response. In conclusion, the data reported provide novel clues into molecular pathophysiological mechanisms of CLN6-deficiency, and may also help in developing disease biomarkers and therapies for this and other neurodegenerative diseases.

Two novel CLN5 mutations in a Portuguese patient with vLINCL: insights into molecular mechanisms of CLN5 deficiency.

The neuronal ceroid-lipofuscinoses are the most common neurodegenerative disorders in childhood characterized by progressive blindness, epilepsy, brain atrophy, and premature death. Based on the age at onset, disease progression and ultrastructural features three classical (infantile, late-infantile, and juvenile) and three variant late-infantile forms are generally distinguished (Finnish variant, Costa Rican variant, and epilepsy with progressive motor retardation). The Finnish variant late-infantile form has been associated with CLN5 gene defects, with only five mutations described to date. We report a patient with vLINCL/CLN5 who represents the first evidence of the disease in the Portuguese population. Mutational screening revealed the previously described missense mutation c.835G>A (D279N) inherited from the mother, and two novel mutations, c.565C>T (Q189X) and c.335G>C (R112P) from paternal and maternal inheritance, respectively. Based on data here reported: (i) the number of possible mutations in CLN5 gene is now 7; (ii) the CLN5 Portuguese case represents the third description of the disease outside northern Europe; (iii) the CLN5/mRNA expression level reduced to 45% supports the existence of one mRNA non-producing allele, further noticeable at the protein level; (iv) Western blotting data using a specific antibody to human CLN5p provided evidence for the presence of four integral membrane isoforms in human fibroblasts; (v) data from differential expression of CLN2, CLN3, and CLN5 suggest down-regulation of CLN3 gene expression in CLN2 and CLN5-deficient human patients and this observation strengths the hypothesis of functional redundancy of the CLN system.

Temperature models of a homogeneous medium under therapeutic ultrasound

A modelagem da temperatura em tecidos humanos, quando os mesmos são sujeitos a ultra-som de terapia, é um aspecto essencial para um correto controle e calibração da instrumentação de terapia. A existência de modelos precisos possibilitaria um uso mais seguro e eficiente das terapias térmicas. O objetivo principal deste trabalho é a comparação entre a performance de um modelo linear e de um modelo não linear, na estimação pontual da temperatura num meio homogêneo. O objetivo final do trabalho é a construção de modelos para estimação in-vivo da temperatura. Os modelos lineares aplicados foram "autoregressive models with exogenous inputs" (ARX), enquanto que os modelos não-lineares aplicados foram "radial basis functions neural networks" (RBFNN). As melhores estruturas para as RBFNN foram selecionadas usando o "multi-objective genetic algoritm" (MOGA). A melhor estrutura RBFNN apresentou um erro máximo absoluto de 0,2ºC, que é inferior em uma ordem de grandeza ao erro cometido pelo melhor modelo ARX.

Ultrassom de terapia / Ultrasound therapy

Temperature modeling of human tissue subjected to ultrasound for therapeutic use is essential for an accurate instrumental assessment and calibration. Prior studies with a homogeneous medium are hereby reported. Nonlinear punctual temperature modeling is proposed by means of Radial Basis Functions Neural Network (RBFNN) structures...