RGS4 inhibits angiotensin II signaling and macrophage localization during renal reperfusion injury independent of vasospasm.

Vascular inflammation is a major contributor to the severity of acute kidney injury. In the context of vasospasm-independent reperfusion injury we studied the potential anti-inflammatory role of the Gα-related RGS protein, RGS4. Transgenic RGS4 mice were resistant to 25 min injury, although post-ischemic renal arteriolar diameter was equal to the wild type early after injury. A 10 min unilateral injury was performed to study reperfusion without vasospasm. Eighteen hours after injury, blood flow was decreased in the inner cortex of wild-type mice with preservation of tubular architecture. Angiotensin II levels in the kidneys of wild-type and transgenic mice were elevated in a sub-vasoconstrictive range 12 and 18 h after injury. Angiotensin II stimulated pre-glomerular vascular smooth muscle cells (VSMCs) to secrete the macrophage chemoattractant RANTES, a process decreased by angiotensin II R2 (AT2) inhibition. However, RANTES increased when RGS4 expression was suppressed implicating Gα protein activation in an AT2-RGS4-dependent pathway. RGS4 function, specific to VSMC, was tested in a conditional VSMC-specific RGS4 knockout showing high macrophage density by T2 MRI compared with transgenic and non-transgenic mice after the 10 min injury. Arteriolar diameter of this knockout was unchanged at successive time points after injury. Thus, RGS4 expression, specific to renal VSMC, inhibits angiotensin II-mediated cytokine signaling and macrophage recruitment during reperfusion, distinct from vasomotor regulation.

Polymer property prediction and optimization using neural networks.

Prediction and optimization of polymer properties is a complex and highly nonlinear problem with no easy method to predict polymer properties directly and accurately. The problem is especially complicated with high molecular weight polymers such as engineering plastics which have the greatest use in industry. The effect of modifying a monomer (polymer repeat unit) on polymerization and the resulting polymer properties is not easy to investigate experimentally given the large number of possible changes. This severely curtails the design of new polymers with specific end-use properties. In this paper, we show how properties of modified monomers can be predicted using neural networks. We utilize a database of polymer properties and employ a variety of networks ranging from backpropagation networks to unsupervised self-associating maps. We select particular networks that accurately predict specific polymer properties. These networks are classified into groups that range from those that provide quick training to those that provide excellent generalization. We also show how the available polymer database can be used to accurately predict and optimize polymer properties.