Oral administration of the KATP channel opener diazoxide ameliorates disease progression in a murine model of multiple sclerosis

Background Multiple Sclerosis (MS) is an acquired inflammatory demyelinating disorder of the central nervous system (CNS) and is the leading cause of nontraumatic disability among young adults. Activated microglial cells are important effectors of demyelination and neurodegeneration, by secreting cytokines and others neurotoxic agents. Previous studies have demonstrated that microglia expresses ATP-sensitive potassium (KATP) channels and its pharmacological activation can provide neuroprotective and anti-inflammatory effects. In this study, we have examined the effect of oral administration of KATP channel opener diazoxide on induced experimental autoimmune encephalomyelitis (EAE), a mouse model of MS. Methods Anti-inflammatory effects of diazoxide were studied on lipopolysaccharide (LPS) and interferon gamma (IFNy)-activated microglial cells. EAE was induced in C57BL/6J mice by immunization with myelin oligodendrocyte glycoprotein peptide (MOG35-55). Mice were orally treated daily with diazoxide or vehicle for 15 days from the day of EAE symptom onset. Treatment starting at the same time as immunization was also assayed. Clinical signs of EAE were monitored and histological studies were performed to analyze tissue damage, demyelination, glial reactivity, axonal loss, neuronal preservation and lymphocyte infiltration. Results Diazoxide inhibited in vitro nitric oxide (NO), tumor necrosis factor alpha (TNF-¿) and interleukin-6 (IL-6) production and inducible nitric oxide synthase (iNOS) expression by activated microglia without affecting cyclooxygenase-2 (COX-2) expression and phagocytosis. Oral treatment of mice with diazoxide ameliorated EAE clinical signs but did not prevent disease. Histological analysis demonstrated that diazoxide elicited a significant reduction in myelin and axonal loss accompanied by a decrease in glial activation and neuronal damage. Diazoxide did not affect the number of infiltrating lymphocytes positive for CD3 and CD20 in the spinal cord. Conclusion Taken together, these results demonstrate novel actions of diazoxide as an anti-inflammatory agent, which might contribute to its beneficial effects on EAE through neuroprotection. Treatment with this widely used and well-tolerated drug may be a useful therapeutic intervention in ameliorating MS disease.

Multifunctional ligands for application in Alzheimer’s Disease: molecular modelling and biological evaluation

Projecte de recerca elaborat a partir d’una estada a la University of British Columbia, Canadà, entre 2010 i 2012 La malaltia d'Alzheimer (MA) representa avui la forma més comuna de demència en la població envellida. Malgrat fa 100 anys que va ser descoberta, encara avui no existeix cap tractament preventiu i/o curatiu ni cap agent de diagnòstic que permeti valorar quantitativament l'evolució d'aquesta malaltia. L'objectiu en el que s'emmarca aquest treball és contribuir a aportar solucions al problema de la manca d'agents terapèutics i de diagnosi, unívocs i rigorosos, per a la MA. Des del camp de la química bioinorgànica és fàcil fixar-se en l'excessiva concentració d'ions Zn(II) i Cu(II) en els cervells de malalts de MA, plantejar-se la seva utilització com a dianes terapèutica i, en conseqüència, cercar agents quelants que evitin la formació de plaques senils o contribueixin a la seva dissolució. Si bé aquest va ser el punt de partida d’aquest projecte, els múltiples factors implicats en la patogènesi de la MA fan que el clàssic paradigma d’ ¨una molècula, una diana¨ limiti la capacitat de la molècula de combatre aquesta malaltia tan complexa. Per tant, un esforç considerable s’ha dedicat al disseny d’agentsmultifuncionals que combatin els múltiples factors que caracteritzen el desenvolupament de la MA. En el present treball s’han dissenyat agents multifuncionals inspirats en dos esquelets moleculars ben establers i coneguts en el camp de la química medicinal: la tioflavina-T (ThT) i la deferiprona (DFP). La utilització de tècniques in silico que inclouen càlculs farmacocinètics i modelatge molecular ha estat un procés cabdal per a l’avaluació dels millors candidats en base als següents requeriments: (a) compliment de determinades propietats farmacocinètiques que estableixin el seu possible ús com a fàrmac (b) hidrofobicitat adequada per travessar la BBB i (c) interacció amb el pèptid Aen solució.

Estimation of the viscoelastic properties of vessel walls using a computational model and Doppler ultrasound

Human arteries affected by atherosclerosis are characterized by altered wall viscoelastic properties. The possibility of noninvasively assessing arterial viscoelasticity in vivo would significantly contribute to the early diagnosis and prevention of this disease. This paper presents a noniterative technique to estimate the viscoelastic parameters of a vascular wall Zener model. The approach requires the simultaneous measurement of flow variations and wall displacements, which can be provided by suitable ultrasound Doppler instruments. Viscoelastic parameters are estimated by fitting the theoretical constitutive equations to the experimental measurements using an ARMA parameter approach. The accuracy and sensitivity of the proposed method are tested using reference data generated by numerical simulations of arterial pulsation in which the physiological conditions and the viscoelastic parameters of the model can be suitably varied. The estimated values quantitatively agree with the reference values, showing that the only parameter affected by changing the physiological conditions is viscosity, whose relative error was about 27% even when a poor signal-to-noise ratio is simulated. Finally, the feasibility of the method is illustrated through three measurements made at different flow regimes on a cylindrical vessel phantom, yielding a parameter mean estimation error of 25%.

@neurIST infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services

The increasing volume of data describing humandisease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the@neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system’s architecture is generic enough that it could be adapted to the treatment of other diseases.Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers cliniciansthe tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medicalresearchers gain access to a critical mass of aneurysm related data due to the system’s ability to federate distributed informationsources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access andwork on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand forperforming computationally intensive simulations for treatment planning and research.

A nuclear defect in the 4p16 region predisposes to multiple mitochondrial DNA deletions in families with Wolfram syndrome.

Wolfram syndrome is a progressive neurodegenerative disorder transmitted in an autosomal recessive mode. We report two Wolfram syndrome families harboring multiple deletions of mitochondrial DNA. The deletions reached percentages as high as 85-90% in affected tissues such as the central nervous system of one patient, while in other tissues from the same patient and from other members of the family, the percentages of deleted mitochondrial DNA genomes were only 1-10%. Recently, a Wolfram syndrome gene has been linked to markers on 4p16. In both families linkage between the disease locus and 4p16 markers gave a maximum multipoint lod score of 3.79 at theta = 0 (Pi<0.03) with respect to D4S431. In these families, the syndrome was caused by mutations in this nucleus-encoded gene which deleteriously interacts with the mitochondrial genome. This is the first evidence of the implication of both genomes in a recessive disease.

A nuclear defect in the 4p16 region predisposes to multiple mitochondrial DNA deletions in families with Wolfram syndrome.

Wolfram syndrome is a progressive neurodegenerative disorder transmitted in an autosomal recessive mode. We report two Wolfram syndrome families harboring multiple deletions of mitochondrial DNA. The deletions reached percentages as high as 85-90% in affected tissues such as the central nervous system of one patient, while in other tissues from the same patient and from other members of the family, the percentages of deleted mitochondrial DNA genomes were only 1-10%. Recently, a Wolfram syndrome gene has been linked to markers on 4p16. In both families linkage between the disease locus and 4p16 markers gave a maximum multipoint lod score of 3.79 at theta = 0 (Pi<0.03) with respect to D4S431. In these families, the syndrome was caused by mutations in this nucleus-encoded gene which deleteriously interacts with the mitochondrial genome. This is the first evidence of the implication of both genomes in a recessive disease.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

An efficient algorithm to perform multiple testing in epistasis screening

Background: Research in epistasis or gene-gene interaction detection for human complex traits has grown over the last few years. It has been marked by promising methodological developments, improved translation efforts of statistical epistasis to biological epistasis and attempts to integrate different omics information sources into the epistasis screening to enhance power. The quest for gene-gene interactions poses severe multiple-testing problems. In this context, the maxT algorithm is one technique to control the false-positive rate. However, the memory needed by this algorithm rises linearly with the amount of hypothesis tests. Gene-gene interaction studies will require a memory proportional to the squared number of SNPs. A genome-wide epistasis search would therefore require terabytes of memory. Hence, cache problems are likely to occur, increasing the computation time. In this work we present a new version of maxT, requiring an amount of memory independent from the number of genetic effects to be investigated. This algorithm was implemented in C++ in our epistasis screening software MBMDR-3.0.3. We evaluate the new implementation in terms of memory efficiency and speed using simulated data. The software is illustrated on real-life data for Crohn’s disease. Results: In the case of a binary (affected/unaffected) trait, the parallel workflow of MBMDR-3.0.3 analyzes all gene-gene interactions with a dataset of 100,000 SNPs typed on 1000 individuals within 4 days and 9 hours, using 999 permutations of the trait to assess statistical significance, on a cluster composed of 10 blades, containing each four Quad-Core AMD Opteron(tm) Processor 2352 2.1 GHz. In the case of a continuous trait, a similar run takes 9 days. Our program found 14 SNP-SNP interactions with a multiple-testing corrected p-value of less than 0.05 on real-life Crohn’s disease (CD) data. Conclusions: Our software is the first implementation of the MB-MDR methodology able to solve large-scale SNP-SNP interactions problems within a few days, without using much memory, while adequately controlling the type I error rates. A new implementation to reach genome-wide epistasis screening is under construction. In the context of Crohn’s disease, MBMDR-3.0.3 could identify epistasis involving regions that are well known in the field and could be explained from a biological point of view. This demonstrates the power of our software to find relevant phenotype-genotype higher-order associations.

Ischemic heart disease and primary care: identifying gender-related differences. An observational study

Background: Gender-related differences are seen in multiple aspects of both health and illness. Ischemic heart disease (IHD) is a pathology in which diagnostic, treatment and prognostic differences are seen between sexes, especially in the acute phase and in the hospital setting. The objective of the present study is to analyze whether there are differences between men and women when examining associated cardiovascular risk factors and secondary pharmacological prevention in the primary care setting. Methods: Retrospective descriptive observational study from January to December of 2006, including 1907 patients diagnosed with ischemic heart disease in the city of Lleida, Spain. The clinical data were obtained from computerized medical records and pharmaceutical records of medications dispensed in pharmacies with official prescriptions. Data was analyzed using bivariate descriptive statistical analysis as well as logistic regression. Results: There were no gender-related differences in screening percentages for arterial hypertension, diabetes, obesity, dyslipemia, and smoking. A greater percentage of women were hypertensive, obese and diabetic compared to men. However, men showed a tendency to achieve control targets more easily than women, with no statistically significant differences. In both sexes cardiovascular risk factors control was inadequate, between 10 and 50%. For secondary pharmaceutical prevention, the percentages of prescriptions were greater in men for anticoagulants, beta-blockers, lipid-lowering agents and angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, with age group variations up to 10%. When adjusting by age and specific diagnoses, differences were maintained for anticoagulants and lipid-lowering agents. Conclusion: Screening of cardiovascular risk factors was similar in men and women with IHD. Although a greater percentage of women were hypertensive, diabetic or obese, their management of risk factors tended to be worse than men. Overall, a poor control of cardiovascular risk factors was noted. Taken as a whole, more men were prescribed secondary prevention drugs, with differences varying by age group and IHD diagnosis.

Multiple Myeloma with primary manifestation in the mandible: a case report

Multiple myeloma is a monoclonal malignant proliferation of plasma cells that causes osteolytic lesions in the vertebrae, ribs, pelvic bone, skull and jaw. We report on a clinical case of an 81-year-old male patient who presented with a tumefaction in the mandibular symphysis region, which had evolved over the previous seven months. In the radiographic examination, an extensive osteolytic lesion was observed in the region mentioned above. An incisional biopsy was performed and a histopathological study revealed a malignant hematopoietic neoplasm formed by plasmacytoid cells. During the bone gammagraphy a dissemination of the disease was detected in the scapula, clavicle and ribs. The diagnosis was multiple myeloma. Knowledge about the maxillofacial manifestations of multiple myeloma is important for the early diagnosis of the disease, since its primary form can manifest itself in the jaw. In the clinical case presented here, we highlight the interdisciplinarity needed to obtain a diagnosis and treatment of multiple myeloma

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

A hybrid feature selection approach for the early diagnosis of Alzheimer's disease

Objective. Recently, significant advances have been made in the early diagnosis of Alzheimer’s disease from EEG. However, choosing suitable measures is a challenging task. Among other measures, frequency Relative Power and loss of complexity have been used with promising results. In the present study we investigate the early diagnosis of AD using synchrony measures and frequency Relative Power on EEG signals, examining the changes found in different frequency ranges. Approach. We first explore the use of a single feature for computing the classification rate, looking for the best frequency range. Then, we present a multiple feature classification system that outperforms all previous results using a feature selection strategy. These two approaches are tested in two different databases, one containing MCI and healthy subjects (patients age: 71.9 ± 10.2, healthy subjects age: 71.7 ± 8.3), and the other containing Mild AD and healthy subjects (patients age: 77.6 ± 10.0; healthy subjects age: 69.4± 11.5). Main Results. Using a single feature to compute classification rates we achieve a performance of 78.33% for the MCI data set and of 97.56 % for Mild AD. Results are clearly improved using the multiple feature classification, where a classification rate of 95% is found for the MCI data set using 11 features, and 100% for the Mild AD data set using 4 features. Significance. The new features selection method described in this work may be a reliable tool that could help to design a realistic system that does not require prior knowledge of a patient's status. With that aim, we explore the standardization of features for MCI and Mild AD data sets with promising results.