Septo-hippocampal networks in chronic epilepsy

The medial septum inhibits the appearance of interictal spikes and seizures through theta rhythm generation. We have determined that medial septal neurons increase their firing rates during chronic epilepsy and that the GABAergic neurons from both medial and lateral septal regions are highly and selectively vulnerable to the epilepsy process. Since the lateral septal region receives a strong projection from the hippocampus and its neurons are vulnerable to epilepsy, their functional properties are probably altered by this disorder. Using the pilocarpine model of temporal lobe epilepsy we examined the pilocarpine-induced functional alterations of lateral septal neurons and provided additional observations on the pilocarpine-induced functional alterations of medial septal neurons. Simultaneous extracellular recordings of septal neurons and hippocampal field potentials were obtained from chronic epileptic rats under urethane anesthesia. Our results show that: (1) the firing rates of lateral septal neurons were chronically decreased by epilepsy, (2) a subset of lateral septal neurons increased their firing rates before and during hippocampal interictal spikes, (3) the discharges of those lateral septal neurons were well correlated to the hippocampal interictal spikes, (4) in contrast, the discharges of medial septal neurons were not correlated with the hippocampal interictal spikes. We conclude that epilepsy creates dysfunctional and uncoupled septohippocampal networks. The elucidation of the roles of altered septo-hippocampal neuronal populations and networks during temporal lobe epilepsy will help design new and effective interventions dedicated to reduce or suppress epileptic activity

Using Cartesian Coordinate Systems to Create, Classify, and Retrieve Biomedical Time-Series: Applications to 24-hour Ambulatory Blood Pressure Monitoring

Background: Ambulatory Blood Pressure Measurement (ABPM) allows physicians to monitor blood pressure variability under everyday living conditions and predicts clinical outcomes better than conventional blood-pressure measurement. ABPM can demonstrate mean arterial pressure (MAP) behavior over 24 hours relevant to clinical practice, such as nocturnal hypertension or increased blood pressure variability. We hypothesized that individuals with the same cardiovascular health status would have the same MAP signal (MAPs) waveform. Methods: This study reutilizes a data subset from the IDACO Consortium to create 24-hour MAPs. We assigned all the MAPs to data matrix X, performed principal components analysis (PCA) to X, and calculated the percentage of the total variance explained by each of the 82 principal components (PC). The first three PC explained 85.03%, 9.47%, and 5.50% of the total variance. We used every MAP signal\u27s first three PC scores as their three-dimensional Euclidean Space (x, y, z) coordinates and assigned them to matrix C. Then, we calculated hierarchical clusters of the rows of C with Ward\u27s linkage minimum variance algorithm and a Euclidian metric and encoded this information on the agglomerative hierarchical cluster tree Z. We determined the gap statistic in Z to obtain the optimal number of clusters. We created seven agglomerative clusters from the linkages stored in Z, using Ward’s distance as the criterion for defining the clusters. Finally, we plotted and colored the mapped MAPs by their assigned cluster number at the locations specified by their (x, y, z) coordinates. Results: The MAPs cartesian representations show that MAPs with similar waveforms cluster in the same three-dimensional Euclidean subspace. These patterns identified individuals with dipping and non-dipping blood pressure behavior, which is relevant to clinical management. Conclusions: Mapping a set of physiological signals into a Euclidian space creates a mathematical formalism that provides a statistical framework to classify physiological signals by their waveform. By applying our method to existing electrophysiological and physiological databases, we can cluster any biomedical time-series (blood pressure, ECG, EEG, EMG, patch-clamp, single-unit recordings, etc.) by physiologic or pathological waveform, so further epidemiological and genetic studies can be conducted on the subjects or tissue samples sharing similar patterns

Study of the lamellar and micellar phases of pluronic F127: A molecular dynamics approach

In this work, we analyzed the behavior of Pluronic F127 through molecular dynamics simulations at the coarse-grain level, focusing on the micellar and lamellar phases. To this aim, two initial polymer conformations were considered, S-shape and U-shape, for both simulated phases. Through the simulations, we were able to examine the structural and mechanical properties that are difficult to access through experiments. Since no transition between S and U shapes was observed in our simulations, we inferred that all single co-polymers had memory of their initial configuration. Nevertheless, most copolymers had a more complex amorphous structure, where hydrophilic beads were part of the lamellar-like core. Finally, an overall comparison of the micellar a lamellar phases showed that the lamellar thickness was in the same order of magnitude as the micelle diameter (approx. 30 nm). Therefore, high micelle concentration could lead to lamellar formation. With this new information, we could understand lamellae as orderly packed micelles.Fil: Albano, Juan Manuel Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Grillo, Damián Alexis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Facelli, Julio C.. University of Utah; Estados UnidosFil: Ferraro, Marta Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Pickholz, Mónica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentin

Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting

Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed

A Rare Variant in ERF (rs144812092) Predisposes to Prostate and Bladder Cancers in an Extended Pedigree

Simple SummaryHere we applied a powerful predisposition candidate gene identification strategy to identify rare variants shared by two related bladder cancer cases who were members of pedigrees exhibiting a significant excess of bladder cancers. We sequenced the exomes of pairs of related bladder cancer cases belonging to high-risk bladder cancer pedigrees to identify rare, shared variants shared as candidates for predisposition. A rare, shared variant in ERF was also found to show significant association with bladder cancer risk in an independent population, was present in other prostate cancer-affected members in the pedigree, and showed evidence for altering the function of the associated protein. This evidence supports ERF (ETS2 Repressor Factor) as a bladder and prostate cancer predisposition gene.Pairs of related bladder cancer cases who belong to pedigrees with an excess of bladder cancer were sequenced to identify rare, shared variants as candidate predisposition variants. Candidate variants were tested for association with bladder cancer risk. A validated variant was assayed for segregation to other related cancer cases, and the predicted protein structure of this variant was analyzed. This study of affected bladder cancer relative pairs from high-risk pedigrees identified 152 bladder cancer predisposition candidate variants. One variant in ERF (ETS Repressing Factor) was significantly associated with bladder cancer risk in an independent population, was observed to segregate with bladder and prostate cancer in relatives, and showed evidence for altering the function of the associated protein. This finding of a rare variant in ERF that is strongly associated with bladder and prostate cancer risk in an extended pedigree both validates ERF as a cancer predisposition gene and shows the continuing value of analyzing affected members of high-risk pedigrees to identify and validate rare cancer predisposition variants

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

<p>Abstract</p> <p>Background</p> <p>Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.</p> <p>Methods</p> <p>Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.</p> <p>Results</p> <p>NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.</p> <p>Conclusions</p> <p>We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.</p