Characterization of neonatal aortic cannula jet flow regimes for improved cardiopulmonary bypass.

<p>During pediatric and neonatal cardiopulmonary bypass (CPB), tiny aortic outflow cannulae (2-3 mm inner diameter), with micro-scale blood-wetting features transport relatively large blood volumes (0.3 to 1.0 L/min) resulting in high blood flow velocities (2 to 5 m/s). These severe flow conditions are likely to complement platelet activation, release pro-inflammatory cytokines, and further result in vascular and blood damage. Hemodynamically efficient aortic outflow cannulae are required to provide high blood volume flow rates at low exit force. In addition, optimal aortic insertion strategies are necessary in order to alleviate hemolytic risk, post-surgical neurological complications and developmental defects, by improving cerebral perfusion in the young patient. The methodology and results presented in this study serve as a baseline for design of superior aortic outflow cannulae. In this study, direct numerical simulation (DNS) computational fluid dynamics (CFD) was employed to delineate baseline hemodynamic performance of jet wakes emanating from microCT scanned state-of-the-art pediatric cannula tips in a cuboidal test rig operating at physiologically relevant laminar and turbulent Reynolds numbers (Re: 650-2150 , steady inflow). Qualitative and quantitative validation of CFD simulated device-specific jet wakes was established using time-resolved flow visualization and particle image velocimetry (PIV). For the standard end-hole cannula tip design, blood damage indices were further numerically assessed in a subject-specific cross-clamped neonatal aorta model for different cannula insertion configurations. Based on these results, a novel diffuser type cannula tip is proposed for improved jet flow-control, decreased blood damage and exit force and increased permissible flow rates. This study also suggests that surgically relevant cannula orientation parameters such as outflow angle and insertion depth may be important for improved hemodynamic performance. The jet flow design paradigm demonstrated in this study represents a philosophical shift towards cannula flow control enabling favorable pressure-drop versus outflow rate characteristics.</p

Computational hemodynamic optimization predicts dominant aortic arch selection is driven by embryonic outflow tract orientation in the chick embryo.

In the early embryo, a series of symmetric, paired vessels, the aortic arches, surround the foregut and distribute cardiac output to the growing embryo and fetus. During embryonic development, the arch vessels undergo large-scale asymmetric morphogenesis to form species-specific adult great vessel patterns. These transformations occur within a dynamic biomechanical environment, which can play an important role in the development of normal arch configurations or the aberrant arch morphologies associated with congenital cardiac defects. Arrested migration and rotation of the embryonic outflow tract during late stages of cardiac looping has been shown to produce both outflow tract and several arch abnormalities. Here, we investigate how changes in flow distribution due to a perturbation in the angular orientation of the embryonic outflow tract impact the morphogenesis and growth of the aortic arches. Using a combination of in vivo arch morphometry with fluorescent dye injection and hemodynamics-driven bioengineering optimization-based vascular growth modeling, we demonstrate that outflow tract orientation significantly changes during development and that the associated changes in hemodynamic load can dramatically influence downstream aortic arch patterning. Optimization reveals that balancing energy expenditure with diffusive capacity leads to multiple arch vessel patterns as seen in the embryo, while minimizing energy alone led to the single arch configuration seen in the mature arch of aorta. Our model further shows the critical importance of the orientation of the outflow tract in dictating morphogenesis to the adult single arch and accurately predicts arch IV as the dominant mature arch of aorta. These results support the hypothesis that abnormal positioning of the outflow tract during early cardiac morphogenesis may lead to congenital defects of the great vessels due to altered hemodynamic loading.</p

Left atrial ligation alters intracardiac flow patterns and the biomechanical landscape in the chick embryo.

<p>BACKGROUND: Hypoplastic left heart syndrome (HLHS) is a major human congenital heart defect that results in single ventricle physiology and high mortality. Clinical data indicate that intracardiac blood flow patterns during cardiac morphogenesis are a significant etiology. We used the left atrial ligation (LAL) model in the chick embryo to test the hypothesis that LAL immediately alters intracardiac flow streams and the biomechanical environment, preceding morphologic and structural defects observed in HLHS.</p> <p>RESULTS: Using fluorescent dye injections, we found that intracardiac flow patterns from the right common cardinal vein, right vitelline vein, and left vitelline vein were altered immediately following LAL. Furthermore, we quantified a significant ventral shift of the right common cardinal and right vitelline vein flow streams. We developed an in silico model of LAL, which revealed that wall shear stress was reduced at the left atrioventricular canal and left side of the common ventricle.</p> <p>CONCLUSIONS: Our results demonstrate that intracardiac flow patterns change immediately following LAL, supporting the role of hemodynamics in the progression of HLHS. Sites of reduced WSS revealed by computational modeling are commonly affected in HLHS, suggesting that changes in the biomechanical environment may lead to abnormal growth and remodeling of left heart structures.</p

Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis

CD4(+) T cells are central mediators of autoimmune pathology; however, defining their key effector functions in specific autoimmune diseases remains challenging. Pathogenic CD4+ T cells within affected tissues may be identified by expression of markers of recent activation(1). Here, we used mass cytometry to evaluate activated T cells in joint tissue from patients with rheumatoid arthritis (RA), a chronic immune-mediated arthritis that affects up to 1% of the population(2). This approach revealed a strikingly expanded population of PD-1(hi) CXCR5(-) CD4(+) T cells in RA synovium. These cells are not exhausted. Rather, multidimensional cytometry, transcriptomics, and functional assays define a population of PD-1(hi) CXCR5(-) ‘peripheral helper’ T (Tph) cells that express factors enabling B cell help, including IL-21, CXCL13, ICOS, and MAF. Like PD-1(hi) CXCR5(+) T follicular helper (Tfh) cells, Tph cells induce plasma cell differentiation in vitro via IL-21 and SLAMF5-interactions(3,4). However, global transcriptomics robustly separate Tph cells from Tfh cells, with altered expression of Bcl6 and Blimp-1 and unique expression of chemokine receptors that direct migration to inflamed sites, such as CCR2, CX3CR1, and CCR5, in Tph cells. Tph cells appear uniquely poised to promote B cell responses and antibody production within pathologically inflamed non-lymphoid tissues

Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes

To define potentially causal variants for autoimmune disease, we fine-mapped(1,2) 76 rheumatoid arthritis (11,475 cases,15,870 controls)(3) and type 1 diabetes loci (9,334 cases, 11,111 controls)(4). After sequencing 799 1-kilobase regulatory (H3K4me3) regions within these loci in 568 individuals, we observed accurate imputation for 89% of common variants. We defined credible sets o

Immune cell profiling to guide therapeutic decisions in rheumatic diseases.

Biomarkers are needed to guide treatment decisions for patients with rheumatic diseases. Although the phenotypic and functional analysis of immune cells is an appealing strategy for understanding immune-mediated disease processes, immune cell profiling currently has no role in clinical rheumatology. New technologies, including mass cytometry, gene expression profiling by RNA sequencing (RNA-seq) and multiplexed functional assays, enable the analysis of immune cell function with unprecedented detail and promise not only a deeper understanding of pathogenesis, but also the discovery of novel biomarkers. The large and complex data sets generated by these technologies—big data—require specialized approaches for analysis and visualization of results. Standardization of assays and definition of the range of normal values are additional challenges when translating these novel approaches into clinical practice. In this Review, we discuss technological advances in the high-dimensional analysis of immune cells and consider how these developments might support the discovery of predictive biomarkers to benefit the practice of rheumatology and improve patient care