Adenosine is known to exert dual actions on the afferent arteriole, eliciting vasoconstriction, by activating A1 receptors, and vasodilation at higher concentrations, by activating lower-affinity A2 receptors. We could demonstrate both of these known adenosine responses in the in vitro perfused hydronephrotic rat kidney. Thus, 1.0 microM adenosine elicited a transient vasoconstriction blocked by 8-cyclopentyl-1,3-dipropylxanthine (DPCPX), and 10-30 microM adenosine reversed KCl-induced vasoconstriction. However, when we examined the effects of adenosine on pressure-induced afferent arteriolar vasoconstriction, we observed a third action. In this setting, a high-affinity adenosine vasodilatory response was observed at concentrations of 10-300 nM. This response was blocked by both 4-(2-[7-amino-2-(2-furyl)[1,2,4]triazolo[2,3-a][1,3, 5]triazin-5-yl-amino]ethyl)phenol (ZM-241385) and glibenclamide and was mimicked by 2-phenylaminoadenosine (CV-1808) (IC50 of 100 nM), implicating adenosine A2a receptors coupled to ATP-sensitive K channels (KATP). Like adenosine, 5'-N-ethylcarboxamidoadenosine (NECA) elicited both glibenclamide-sensitive and glibenclamide-insensitive vasodilatory responses. The order of potency for the glibenclamide-sensitive component was NECA > adenosine = CV-1808. Our findings suggest that, in addition to the previously described adenosine A1 and low-affinity A2b receptors, the renal microvasculature is also capable of expressing high-affinity adenosine A2a receptors. This renal adenosine receptor elicits afferent arteriolar vasodilation at submicromolar adenosine levels by activating KATP.

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

Matrix metalloproteinases (MMPs) are hypothesized to play an important role in the pathogenesis of several central nervous system disorders. Increased levels of expression of MMP-9 (gelatinase B) and MMP-2 (gelatinase A) have been observed in Alzheimer's disease, stroke, multiple sclerosis, and amyotrophic lateral sclerosis. This suggests an aberrant regulation of MMPs that could lead to inappropriate expression of MMP activity. To allow us to evaluate the effect of increased levels of active MMP-9 in the central nervous system, mutant forms of the enzyme were designed to autocatalytically remove the pro domain, yielding active enzyme. This was accomplished by modifying residues in the cysteine switch autoinhibitor region of the propeptide. Stable cell lines and transgenic mice that express G100L and D103N autoactive forms of human MMP-9 were developed to study the role of dysregulation of MMP-9 in disease.

Abstract. Elderly patients face the problems of morbidity and mortality due to age-mediated disabilities. The purpose of the present study was to investigate the expression of thrombospondin-1 (TSP-1) and transforming growth factor-β (TGF-β) in aging mice, and its probable mechanism in the pathological changes of aging myocardium. The aging model group (AM) comprised 30-month-old mice, while the control group comprised 2-month-old mice. The pathological changes were explored by H&E staining, and the contents of superoxide dismulase (SOD) and malondialdehyde (MDA) in the hearts were determined by xanthine oxidation or TBA colorimetry. TSP-1 and TGF-β expression in the left ventricular myocardium was also measured by immunohistochemistry. The results showed that the activities of SOD decreased and the MDA content increased markedly in the hearts of the AM group compared to the control group. H&E staining showed that the control group myocardial cells lined up in order with clear structure and stained equably, while the AM group myocardial cells lined up in disorder with an augmented cell body and the appearance of many granules and interstitial fibrosis. Compared to the control group, in the hearts of the AM group, TSP-1 and TGF-β protein expression in myocardial cells showed a significant increase (P<0.01). TSP-1 and TGF-β expression increased in the myocardium, which may be related to pathological changes of age-related heart diseases, such as hypertrophy, fibrosis of myocardial cells and microvessel dissepiment thickening.