Rho kinase and protein kinase C involvement in vascular smooth muscle myofilament calcium sensitization in arteries from diabetic rats.

BACKGROUND AND PURPOSE: Diabetes mellitus (DM) causes multiple dysfunctions including circulatory disorders such as cardiomyopathy, angiopathy, atherosclerosis and arterial hypertension. Rho kinase (ROCK) and protein kinase C (PKC) regulate vascular smooth muscle (VSM) Ca(2+) sensitivity, thus enhancing VSM contraction, and up-regulation of both enzymes in DM is well known. We postulated that in DM, Ca(2+) sensitization occurs in diabetic arteries due to increased ROCK and/or PKC activity. EXPERIMENTAL APPROACH: Rats were rendered hyperglycaemic by i.p. injection of streptozotocin. Age-matched control tissues were used for comparison. Contractile responses to phenylephrine (Phe) and different Ca(2+) concentrations were recorded, respectively, from intact and chemically permeabilized vascular rings from aorta, tail and mesenteric arteries. KEY RESULTS: Diabetic tail and mesenteric arteries demonstrated markedly enhanced sensitivity to Phe while these changes were not observed in aorta. The ROCK inhibitor HA1077, but not the PKC inhibitor chelerythrine, caused significant reduction in sensitivity to agonist in diabetic vessels. Similar changes were observed for myofilament Ca(2+) sensitivity, which was again enhanced in DM in tail and mesenteric arteries, but not in aorta, and could be reduced by both the ROCK and PKC blockers. CONCLUSIONS AND IMPLICATIONS: We conclude that in DM enhanced myofilament Ca(2+) sensitivity is mainly manifested in muscular-type blood vessels and thus likely to contribute to the development of hypertension. Both PKC and, in particular, ROCK are involved in this phenomenon. This highlights their potential usefulness as drug targets in the pharmacological management of DM-associated vascular dysfunction.

Functional innervation of Guinea-pig bladder interstitial cells of cajal subtypes: neurogenic stimulation evokes in situ calcium transients

Several populations of interstitial cells of Cajal (ICC) exist in the bladder, associated with intramural nerves. Although ICC respond to exogenous agonists, there is currently no evidence of their functional innervation. The objective was to determine whether bladder ICC are functionally innervated. Guinea-pig bladder tissues, loaded with fluo-4AM were imaged with fluorescent microscopy and challenged with neurogenic electrical field stimulation (EFS). All subtypes of ICC and smooth muscle cells (SMC) displayed spontaneous Ca2+-oscillations. EFS (0.5Hz, 2Hz, 10Hz) evoked tetrodotoxin (1µM)-sensitive Ca2+-transients in lamina propria ICC (ICC-LP), detrusor ICC and perivascular ICC (PICC) associated with mucosal microvessels. EFS responses in ICC-LP were significantly reduced by atropine or suramin. SMC and vascular SMC (VSM) also responded to EFS. Spontaneous Ca2+-oscillations in individual ICC-LP within networks occurred asynchronously whereas EFS evoked coordinated Ca2+-transients in all ICC-LP within a field of view. Non-correlated Ca2+-oscillations in detrusor ICC and adjacent SMC pre-EFS, contrasted with simultaneous neurogenic Ca2+ transients evoked by EFS. Spontaneous Ca2+-oscillations in PICC were little affected by EFS, whereas large Ca2+-transients were evoked in pre-EFS quiescent PICC. EFS also increased the frequency of VSM Ca2+-oscillations. In conclusion, ICC-LP, detrusor ICC and PICC are functionally innervated. Interestingly, Ca2+-activity within ICC-LP networks and between detrusor ICC and their adjacent SMC were synchronous under neural control. VSM and PICC Ca2+-activity was regulated by bladder nerves. These novel findings demonstrate functional neural control of bladder ICC. Similar studies should now be carried out on neurogenic bladder to elucidate the contribution of impaired nerve-ICC communication to bladder pathophysiology.

Application of citrate-stabilized gold-coated ferric oxide composite nanoparticles for biological separations

Gold-coated magnetic nanoparticles were synthesized with size ranging from 15 to 40 nm using sodium citrates as the reducing agent. Oxidized magnetites (Fe3O4) fabricated by co-precipitation of Fe2+ and Fe3+ in strong alkaline solution were used as magnetic cores. The structures of gold (Au) shell and magnetic core (Au–Fe) were studied by transmission electron microscopy (TEM) image and energy dispersive spectroscopy (EDS) spectrum. Results from high-resolution X-ray diffraction (HR XRD) show that the Au–Fe oxide nanoparticles have a face-centered cubic shape with the crystalline faces of {1 1 1}. The Au-coated magnetic nanoparticles exhibited a surface plasmon resonance peak at 528 nm. The nanoparticles are well dispersed in distilled water. A 3000 G permanent magnet was successfully used for the separation of the functionalized nanoparticles. Magnetic properties of the nanoparticles were determined by magnetic force microscope (MFM) in nanometric resolution and vibrating sample magnetometer (VSM). Magnetic separation of biological molecules using Au-coated magnetic oxide composite nanoparticles was examined after attachment of protein immunoglobulin G (IgG) through electrostatic interactions. Using this method, separation was achieved with a maximum yield of 35% at an IgG concentration of 400 ng/ml.

Documents and Dependencies: an Exploration of Vector Space Models for Semantic Composition

In most previous research on distributional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntactic/semantic types of words (e.g., dependency parse links of a word in sentences), but not both. In this paper, we explore the utility of combining these two representations to build VSM for the task of semantic composition of adjective-noun phrases. Through extensive experiments on benchmark datasets, we find that even though a type-based VSM is effective for semantic composition, it is often outperformed by a VSM built using a combination of topic- and type-based statistics. We also introduce a new evaluation task wherein we predict the composed vector representation of a phrase from the brain activity of a human subject reading that phrase. We exploit a large syntactically parsed corpus of 16 billion tokens to build our VSMs, with vectors for both phrases and words, and make them publicly available.

A Compositional and Interpretable Semantic Space

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task.