Cardiovascular care of patients with stroke and high risk of stroke: The need for interdisciplinary action: A consensus report from the European Society of Cardiology Cardiovascular Round Table.

Comprehensive stroke care is an interdisciplinary challenge. Close collaboration of cardiologists and stroke physicians is critical to ensure optimum utilisation of short- and long-term care and preventive measures in patients with stroke. Risk factor management is an important strategy that requires cardiologic involvement for primary and secondary stroke prevention. Treatment of stroke generally is led by stroke physicians, yet cardiologists need to be integrated care providers in stroke units to address all cardiovascular aspects of acute stroke care, including arrhythmia management, blood pressure control, elevated levels of cardiac troponins, valvular disease/endocarditis, and the general management of cardiovascular comorbidities. Despite substantial progress in stroke research and clinical care has been achieved, relevant gaps in clinical evidence remain and cause uncertainties in best practice for treatment and prevention of stroke. The Cardiovascular Round Table of the European Society of Cardiology together with the European Society of Cardiology Council on Stroke in cooperation with the European Stroke Organisation and partners from related scientific societies, regulatory authorities and industry conveyed a two-day workshop to discuss current and emerging concepts and apparent gaps in stroke care, including risk factor management, acute diagnostics, treatments and complications, and operational/logistic issues for health care systems and integrated networks. Joint initiatives of cardiologists and stroke physicians are needed in research and clinical care to target unresolved interdisciplinary problems and to promote the best possible outcomes for patients with stroke

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Phenothiourea Sensitizes Zebrafish Cranial Neural Crest and Extraocular Muscle Development to Changes in Retinoic Acid and IGF Signaling

1-phenyl 2-thiourea (PTU) is a tyrosinase inhibitor commonly used to block pigmentation and aid visualization of zebrafish development. At the standard concentration of 0.003% (200 µM), PTU inhibits melanogenesis and reportedly has minimal other effects on zebrafish embryogenesis. We found that 0.003% PTU altered retinoic acid and insulin-like growth factor (IGF) regulation of neural crest and mesodermal components of craniofacial development. Reduction of retinoic acid synthesis by the pan-aldehyde dehydrogenase inhibitor diethylbenzaldehyde, only when combined with 0.003% PTU, resulted in extraocular muscle disorganization. PTU also decreased retinoic acid-induced teratogenic effects on pharyngeal arch and jaw cartilage despite morphologically normal appearing PTU-treated controls. Furthermore, 0.003% PTU in combination with inhibition of IGF signaling through either morpholino knockdown or pharmacologic inhibition of tyrosine kinase receptor phosphorylation, disrupted jaw development and extraocular muscle organization. PTU in and of itself inhibited neural crest development at higher concentrations (0.03%) and had the greatest inhibitory effect when added prior to 22 hours post fertilization (hpf). Addition of 0.003% PTU between 4 and 20 hpf decreased thyroxine (T4) in thyroid follicles in the nasopharynx of 96 hpf embryos. Treatment with exogenous triiodothyronine (T3) and T4 improved, but did not completely rescue, PTU-induced neural crest defects. Thus, PTU should be used with caution when studying zebrafish embryogenesis as it alters the threshold of different signaling pathways important during craniofacial development. The effects of PTU on neural crest development are partially caused by thyroid hormone signaling

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

Inhibition of IGF-1 Signalling Enhances the Apoptotic Effect of AS602868, an IKK2 Inhibitor, in Multiple Myeloma Cell Lines

Multiple myeloma (MM) is a B cell neoplasm characterized by bone marrow infiltration with malignant plasma cells. IGF-1 signalling has been explored as a therapeutic target in this disease. We analyzed the effect of the IKK2 inhibitor AS602868, in combination with a monoclonal antibody targeting IGF-1 receptor (anti-IGF-1R) in human MM cell lines. We found that anti-IGF-1R potentiated the apoptotic effect of AS602868 in LP1 and RPMI8226 MM cell lines which express high levels of IGF-1R. Anti-IGF-1R enhanced the inhibitory effect of AS602868 on NF-κB pathway signalling and potentiated the disruption of mitochondrial membrane potential caused by AS602868. These results support the role of IGF-1 signalling in MM and suggest that inhibition of this pathway could sensitize MM cells to NF-κB inhibitors

Bypassing cellular EGF receptor dependence through epithelial-to-mesenchymal-like transitions

Over 90% of all cancers are carcinomas, malignancies derived from cells of epithelial origin. As carcinomas progress, these tumors may lose epithelial morphology and acquire mesenchymal characteristics which contribute to metastatic potential. An epithelial-to-mesenchymal transition (EMT) similar to the process critical for embryonic development is thought to be an important mechanism for promoting cancer invasion and metastasis. Epithelial-to-mesenchymal transitions have been induced in vitro by transient or unregulated activation of receptor tyrosine kinase signaling pathways, oncogene signaling and disruption of homotypic cell adhesion. These cellular models attempt to mimic the complexity of human carcinomas which respond to autocrine and paracrine signals from both the tumor and its microenvironment. Activation of the epidermal growth factor receptor (EGFR) has been implicated in the neoplastic transformation of solid tumors and overexpression of EGFR has been shown to correlate with poor survival. Notably, epithelial tumor cells have been shown to be significantly more sensitive to EGFR inhibitors than tumor cells which have undergone an EMT-like transition and acquired mesenchymal characteristics, including non-small cell lung (NSCLC), head and neck (HN), bladder, colorectal, pancreas and breast carcinomas. EGFR blockade has also been shown to inhibit cellular migration, suggesting a role for EGFR inhibitors in the control of metastasis. The interaction between EGFR and the multiple signaling nodes which regulate EMT suggest that the combination of an EGFR inhibitor and other molecular targeted agents may offer a novel approach to controlling metastasis