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.

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.

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.

Early axonal loss accompanied by impaired endocytosis, abnormal axonal transport, and decreased microtubule stability occur in the model of Krabbe's disease

In Krabbe's disease (KD), a leukodystrophy caused by β-galactosylceramidase deficiency, demyelination and a myelin-independent axonopathy contributes to the severe neuropathology. Beyond axonopathy, we show that in Twitcher mice, a model of KD, a decreased number of axons both in the PNS and in the CNS, and of neurons in dorsal root ganglia (DRG), occurred before the onset of demyelination. Despite the early axonal loss, and although in vitro Twitcher neurites degenerated over time, Twitcher DRG neurons displayed an initial neurite overgrowth and, following sciatic nerve injury, Twitcher axons were regeneration-competent, at a time point where axonopathy was already ongoing. Psychosine, the toxic substrate that accumulates in KD, induced lipid raft clustering. At the mechanistic level, TrkA recruitment to lipid rafts was dysregulated in Twitcher neurons, and defective activation of the ERK1/2 and AKT pathways was identified. Besides defective recruitment of signaling molecules to lipid rafts, the early steps of endocytosis and the transport of endocytic and synaptic vesicles were impaired in Twitcher DRG neurons. Defects in axonal transport, specifically in the retrograde component, correlated with decreased levels of dynein, abnormal levels of post-translational tubulin modifications and decreased microtubule stability. The identification of the axonal defects that precede demyelination in KD, together with the finding that Twitcher axons are regeneration-competent when axonopathy is already installed, opens new windows of action to effectively correct the neuropathology that characterizes this disorder.

Primary bone marrow mesenchymal stromal cells rescue the axonal phenotype of Twitcher mice

Krabbe's disease (KD) is a demyelinating disorder caused by the deficiency of lysosomal galactocerebrosidase (GALC), affecting both the central (CNS) and the peripheral nervous system (PNS). A current therapy, hematopoietic stem cell transplantation (HSCT), is ineffective at correcting the PNS pathology. We have previously shown that systemic delivery of immortalized bone marrow-derived murine mesenchymal stromal cells (BM-MSCs) diminishes the neuropathology of transplanted Twitcher mice, a murine model of KD. In this study, to move one step closer to clinical application, the effectiveness of a systematic delivery of primary BM-MSCs to promote recovery of the Twitcher PNS was assessed. Primary BM-MSCs grafted to the Twitcher sciatic nerve led to increased GALC activity that was not correlated to decreased psychosine (the toxic GALC substrate) accumulation. Nevertheless, BM-MSC transplantation rescued the axonal phenotype of Twitcher mice in the sciatic nerve, with an increased density of both myelinated and unmyelinated axons in transplanted animals. Whereas no increase in myelination was observed, upon transplantation an increased proliferation of Schwann cell precursors occurred. Supporting these findings, in vitro, BM-MSCs promoted neurite outgrowth of Twitcher sensory neurons and proliferation of Twitcher Schwann cells. Moreover, BM-MSCs expressed nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF) and promoted increased BDNF synthesis by neighboring Schwann cells. Besides their action in neurons and glia, BM-MSCs led to macrophage activation in Twitcher sciatic nerves. In summary, primary BM-MSCs diminish the neuropathology of Twitcher sciatic nerves by coordinately affecting neurons, glia, and macrophages.

Advances and pitfalls of cell therapy in metabolic leukodystrophies

Leukodystrophies are a group of disorders characterized by myelin dysfunction, either at the level of myelin formation or maintenance, that affect the central nervous system (CNS) and also in some cases, to a lesser extent, the peripheral nervous system (PNS). Although these genetic-based disorders are generally rare, all together they have a significant impact in the society, with an estimated overall incidence of 1 in 7,663 live births. Currently, there is no cure for leukodystrophies, and the development of effective treatments remains challenging. Not only leukodystrophies generally progress very fast, but also most are multifocal needing the simultaneous targeting at multiple sites. Moreover, as the CNS is affected, the blood-brain barrier (BBB) limits the efficacy of treatment. Recently, interest on cell therapy has increased, and the leukodystrophies for which metabolic correction is needed have become first-choice candidates for cell-based clinical trials. In this review, we present and discuss the available cell transplantation therapies in metabolic leukodystrophies including fucosidosis, X-linked adrenoleukodystrophy, metachromatic leukodystrophy, Canavan disease, and Krabbe's disease. We will discuss the latest advances of cell therapy and its pitfalls in this group of disorders, taking into account, among others, the limitations imposed by reduced cell migration in multifocal conditions, the need to achieve corrective enzyme threshold levels, and the growing awareness that not only myelin but also the associated axonopathy needs to be targeted in some leukodystrophies.

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.

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.

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.

Retrovirus-mediated transfer and expression of beta-hexosaminidase alpha-chain cDNA in human fibroblasts from G(M2)-gangliosidosis B1 variant

Mutations in the alpha-chain of lysosomal hexosaminidase (EC 3.2.1.52) underlie two distinct biochemical phenotypes known as variant B and variant B1 of G(M2) gangliosidosis. This paper shows that the transduction of human B1-type fibroblasts (producing catalytically inactive alpha-chains) with a retroviral vector encoding the human hexosaminidase alpha-chain leads to a complete correction of HexA (alpha beta dimer) activity with both synthetic and natural substrates. The alpha-subunit overexpression leads to a partial HexB (beta beta dimer) depletion corresponding to about 10% of control HexB activity. The newly synthesized enzyme is correctly processed and targeted to the lysosomes in transduced cells. The high levels of recombinant enzyme correctly produced the metabolic defect, enabling the cells efficiently to degrade the accumulated storage product present in lysosomes. The transduced fibroblasts are also able to secrete HexA efficiently into the culture medium. Moreover, transfer of the human transgene product to B1-type deficient fibroblasts lead to an increase of activity against 4MUGS, the alpha-chain specific synthetic substrate, up to 30% of the control mean activity level. This level of activity might be sufficient to restore the normal ganglioside G(M2) metabolism in recipient cells. The data obtained demonstrate that B1-type phenotype can be efficiently corrected by retrovirus-mediated gene transfer.

Epilepsia partialis continua (Koshevnikov): a preliminary case report

We report on the preliminary clinical and electrophysiological aspects of an in-patient possibly presenting epilepsia partialis continua (Koshevnikov). We discuss the different etiologies and emphasize on the possible idiopathic form in this case.

Amyotrophic lateral sclerosis. Clinical analysis of 78 cases from Fortaleza (northeastern Brazil)

We report on the clinical characteristics of amyotrophic lateral sclerosis (ALS) in Fortaleza (Northeastern Brazil). For this, we analyzed retrospectively (from 1980 to 1999) 78 cases of ALS from the Service of Neurology of the University Hospital of Fortaleza diagnosed clinically and laboratorially (EMG, muscle biopsy, myelography, blood biochemistry, muscle enzymes and cranio-cervical X-ray). The results showed that they were mostly sporadic ALS (76/78), and they were divided into definite (n = 36), probable (n = 20), possible (n = 15) and suspected (n = 7), according to the level of diagnostic certainty. They were also subdivided into juvenile (n = 17), early-onset adult (n = 18), age-specific (n = 39) and late-onset (n = 4) groups. Clinically, they presented as initials symptoms, principally, asymmetrical (30/78) and symmetrical (24/78) weakness of extremities, besides bulbar signs, fasciculations, and atrophy. Curiously, pain as first symptom occurred in an expressive fashion (17/78). The predominant initial anatomic site, in this series, was the spinal cord, and mainly affecting the arms. As to the symptom accrual from region to region, this occurs more quickly in contiguous areas, and fasciculations are predominant when bulbar region was associated.