Will the real ventricular architecture please stand up?

Ventricular twisting, essential for cardiac function, is attributed to the contraction of myocardial helical fibers. The exact relationship between ventricular anatomy and function remains to be determined, but one commonly used explanatory model is the helical ventricular myocardial band (HVMB) model of Torrent-Guasp. This model has been successful in explaining many aspects of ventricular function, (Torrent-Guasp et al. Eur. J. Cardiothorac. Surg., 25, 376, 2004; Buckberg et al. Eur. J. Cardiothorac. Surg., 47, 587, 2015; Buckberg et al. Eur. J. Cardiothorac. Surg. 47, 778, 2015) but the model ignores important aspects of ventricular anatomy and should probably be replaced. The purpose of this review is to compare the HVMB model with a different model (nested layers). A complication when interpreting experimental observations that relate anatomy to function is that, in the myocardium, shortening does not always imply activation and lengthening does not always imply inactivation

Knowledge-guided multi-scale independent component analysis for biomarker identification

<p>Abstract</p> <p>Background</p> <p>Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.</p> <p>Results</p> <p>Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.</p> <p>Conclusion</p> <p>We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.</p

Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.Comment: 7 pages, 7 figure

Serum antibodies in first-degree relatives of patients with IBD: A marker of disease susceptibility? A follow-up pilot-study after 7 years

Introduction: Various disease-specific serum antibodies were described in patients with inflammatory bowel disease and their yet healthy first-degree relatives. In the latter, serum antibodies are commonly regarded as potential markers of disease susceptibility. The present long-term follow-up study evaluated the fate of antibody-positive first-degree relatives. Patients and Methods: 25 patients with Crohn's disease, 19 patients with ulcerative colitis and 102 first-degree relatives in whom presence of ASCA, pANCA, pancreatic- and goblet-cell antibodies had been assessed were enrolled. The number of incident cases with inflammatory bowel disease was compared between antibody-positive and antibody-negative first-degree relatives 7 years after storage of serum samples. Results: 34 of 102 (33%) first-degree relatives were positive for at least one of the studied serum antibodies. In the group of first-degree relatives, one case of Crohn's disease and one case of ulcerative colitis were diagnosed during the follow-up period. However, both relatives did not display any of the investigated serum antibodies (p = 1). Discussion: The findings of our pilot study argue against a role of serum antibodies as a marker of disease susceptibility in first-degree relatives of patients with inflammatory bowel disease. However, these data have to await confirmation in larger ideally prospective multicenter studies before definite conclusions can be drawn

Serum antibodies in first-degree relatives of patients with IBD: A marker of disease susceptibility? A follow-up pilot-study after 7 years

Introduction: Various disease-specific serum antibodies were described in patients with inflammatory bowel disease and their yet healthy first-degree relatives. In the latter, serum antibodies are commonly regarded as potential markers of disease susceptibility. The present long-term follow-up study evaluated the fate of antibody-positive first-degree relatives. Patients and Methods: 25 patients with Crohn's disease, 19 patients with ulcerative colitis and 102 first-degree relatives in whom presence of ASCA, pANCA, pancreatic- and goblet-cell antibodies had been assessed were enrolled. The number of incident cases with inflammatory bowel disease was compared between antibody-positive and antibody-negative first-degree relatives 7 years after storage of serum samples. Results: 34 of 102 (33%) first-degree relatives were positive for at least one of the studied serum antibodies. In the group of first-degree relatives, one case of Crohn's disease and one case of ulcerative colitis were diagnosed during the follow-up period. However, both relatives did not display any of the investigated serum antibodies (p = 1). Discussion: The findings of our pilot study argue against a role of serum antibodies as a marker of disease susceptibility in first-degree relatives of patients with inflammatory bowel disease. However, these data have to await confirmation in larger ideally prospective multicenter studies before definite conclusions can be drawn

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

Suprasellar cysts: clinical presentation, surgical indications, and optimal surgical treatment

<p>Abstract</p> <p>Background</p> <p>To describe the clinical presentation of suprasellar cysts (SSCs) and surgical indications, and compare the treatment methods of endoscopic ventriculocystostomy (VC) and ventriculocystocisternotomy (VCC).</p> <p>Methods</p> <p>We retrospectively reviewed the records of 73 consecutive patients with SSC who were treated between June 2002 and September 2009. Twenty-two patients were treated with VC and 51 with VCC. Outcome was assessed by clinical examination and magnetic resonance imaging.</p> <p>Results</p> <p>The patients were divided into five groups based on age at presentation: age less than 1 year (n = 6), 1-5 years (n = 36), 6-10 years (n = 15), 11-20 years (n = 11), and 21-53 years (n = 5). The main clinical presentations were macrocrania (100%), motor deficits (50%), and gaze disturbance (33.3%) in the age less than 1 year group; macrocrania (75%), motor deficits (63.9%), and gaze disturbance (27.8%) in the 1-5 years group; macrocrania (46.7%), symptoms of raised intracranial pressure (ICP) (40.0%), endocrine dysfunction (40%), and seizures (33.3%) in the 6-10 years group; symptoms of raised ICP (54.5%), endocrine dysfunction (54.5%), and reduced visual field or acuity (36.4%) in the 11-20 years group; and symptoms of raised ICP (80.0%) and reduced visual field or acuity (40.0%) in the 21-53 years group. The overall success rate of endoscopic fenestration was 90.4%. A Kaplan-Meier curve for long-term efficacy of the two treatment modalities showed better results for VCC than for VC (p = 0.008).</p> <p>Conclusions</p> <p>Different age groups with SSCs have different main clinical presentations. VCC appears to be more efficacious than VC.</p