Transforming growth factor 31 (TGFj81) -

Three gap junction proteins have been identified in mammalian cardiac myocytes: connexin43 (Cx43), connexin45 (Cx45), and connexin40 (Cx40). These proteins form channels with different electrophysiological properties and have different distributions in cardiac tissues with disparate conduction properties. We characterized the expression, phosphorylation, turnover, and subcellular distribution of these connexins in primary cultures of neonatal rat ventricular myocytes. Cx43, Cx45, and Cx40 mRNA were specifically detected in RNA blots. Immunofluorescent staining with antibodies specific for Cx43 and Cx45 revealed punctate labeling at appositional membranes, but no immunoreactive Cx40 was detected. Double-label immunofluorescence confocal microscopy of cultured myocytes revealed colocalization of Cx43 and Cx45. Cx43 and Cx45 were both identified by immunoprecipitation from [35S]methionine-labeled cultures, but anti-Cx40 antibodies did not precipitate any radiolabeled protein. Phosphorylated forms of both Cx45 and Cx43 were immunoprecipitated from cultures metabolically labeled with [32P]orthophosphate. Phosphoamino acid analysis demonstrated that Cx45 was modified on serine residues, and Cx43 was phosphorylated on serine and threonine residues. Pulse-chase labeling experiments demonstrated that the half-lives of Cx43 and Cx45 were 1.9 and 2.9 hours, respectively. Thus, both Cx43 and Cx45 turn over relatively rapidly, suggesting that myocardial gap junctions have the potential for dynamic remodeling. The results implicate multiple mechanisms of gap junction regulation that may differ for different connexins.

Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcomes and decreasing the cost of healthcare delivery in the United States. Patient readmission rates are relatively high for conditions like heart failure (HF) despite the implementation of high-quality healthcare delivery operation guidelines created by regulatory authorities. Multiple predictive models are currently available to evaluate potential 30-day readmission rates of patients. Most of these models are hypothesis driven and repetitively assess the predictive abilities of the same set of biomarkers as predictive features. In this manuscript, we discuss our attempt to develop a data-driven, electronic-medical record-wide (EMR-wide) feature selection approach and subsequent machine learning to predict readmission probabilities. We have assessed a large repertoire of variables from electronic medical records of heart failure patients in a single center. The cohort included 1,068 patients with 178 patients were readmitted within a 30-day interval (16.66% readmission rate). A total of 4,205 variables were extracted from EMR including diagnosis codes (n=1,763), medications (n=1,028), laboratory measurements (n=846), surgical procedures (n=564) and vital signs (n=4). We designed a multistep modeling strategy using the Naïve Bayes algorithm. In the first step, we created individual models to classify the cases (readmitted) and controls (non-readmitted). In the second step, features contributing to predictive risk from independent models were combined into a composite model using a correlation-based feature selection (CFS) method. All models were trained and tested using a 5-fold cross-validation method, with 70% of the cohort used for training and the remaining 30% for testing. Compared to existing predictive models for HF readmission rates (AUCs in the range of 0.6–0.7), results from our EMR-wide predictive model (AUC=0.78; Accuracy=83.19%) and phenome-wide feature selection strategies are encouraging and reveal the utility of such data-driven machine learning. Fine tuning of the model, replication using multi-center cohorts and prospective clinical trial to evaluate the clinical utility would help the adoption of the model as a clinical decision system for evaluating readmission status.

No abstract

Electrical activation of the heart requires transfer of current from one discrete cardiac myocyte to another, a process that occurs at gap junctions. Recent advances in knowledge have established that, like most differentiated cells, individual cardiac myocytes express multiple gap junction channel proteins that are members of a multigene family of channel proteins called connexins. These proteins form channels with unique biophysical properties. Furthermore, functionally distinct cardiac tissues such as the nodes and bundles of the conduction system and atrial and ventricular muscle express different combinations of connexins. Myocytes in these tissues are interconnected by gap junctions that differ in tissue-specific manner in terms of their number, size, and three-dimensional distribution. These observations suggest that both molecular and structural aspects of gap junctions are critical determinants of the anisotropic conduction properties of different cardiac tissues. Expression of multiple connexins also creates the possibility that "hybrid" channels composed of more than one connexin protein type can form, thus greatly increasing the potential for fine control of intercellular ion flow and communication within the heart.