Capsaicin- resistant arterial baroreceptors

BACKGROUND: Aortic baroreceptors (BRs) comprise a class of cranial afferents arising from major arteries closest to the heart whose axons form the aortic depressor nerve. BRs are mechanoreceptors that are largely devoted to cardiovascular autonomic reflexes. Such cranial afferents have either lightly myelinated (A-type) or non-myelinated (C-type) axons and share remarkable cellular similarities to spinal primary afferent neurons. Our goal was to test whether vanilloid receptor (TRPV1) agonists, capsaicin (CAP) and resiniferatoxin (RTX), altered the pressure-discharge properties of peripheral aortic BRs. RESULTS: Periaxonal application of 1 μM CAP decreased the amplitude of the C-wave in the compound action potential conducting at <1 m/sec along the aortic depressor nerve. 10 μM CAP eliminated the C-wave while leaving intact the A-wave conducting in the A-δ range (<12 m/sec). These whole nerve results suggest that TRPV1 receptors are expressed along the axons of C- but not A-conducting BR axons. In an aortic arch – aortic nerve preparation, intralumenal perfusion with 1 μM CAP had no effect on the pressure-discharge relations of regularly discharging, single fiber BRs (A-type) – including the pressure threshold, sensitivity, frequency at threshold, or maximum discharge frequency (n = 8, p > 0.50) but completely inhibited discharge of an irregularly discharging BR (C-type). CAP at high concentrations (10–100 μM) depressed BR sensitivity in regularly discharging BRs, an effect attributed to non-specific actions. RTX (≤ 10 μM) did not affect the discharge properties of regularly discharging BRs (n = 7, p > 0.18). A CAP-sensitive BR had significantly lower discharge regularity expressed as the coefficient of variation than the CAP-resistant fibers (p < 0.002). CONCLUSION: We conclude that functional TRPV1 channels are present in C-type but not A-type (A-δ) myelinated aortic arch BRs. CAP has nonspecific inhibitory actions that are unlikely to be related to TRV1 binding since such effects were absent with the highly specific TRPV1 agonist RTX. Thus, CAP must be used with caution at very high concentrations

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Isolation of a euryhaline microalgal strain, Tetraselmis sp CTP4, as a robust feedstock for biodiesel production

Bioprospecting for novel microalgal strains is key to improving the feasibility of microalgae-derived biodiesel production. Tetraselmis sp. CTP4 (Chlorophyta, Chlorodendrophyceae) was isolated using fluorescence activated cell sorting (FACS) in order to screen novel lipid-rich microalgae. CTP4 is a robust, euryhaline strain able to grow in seawater growth medium as well as in non-sterile urban wastewater. Because of its large cell size (9-22 mu m), CTP4 settles down after a six-hour sedimentation step. This leads to a medium removal efficiency of 80%, allowing a significant decrease of biomass dewatering costs. Using a two-stage system, a 3-fold increase in lipid content (up to 33% of DW) and a 2-fold enhancement in lipid productivity (up to 52.1 mg L-1 d(-1)) were observed upon exposure to nutrient depletion for 7 days. The biodiesel synthesized from the lipids of CTP4 contained high levels of oleic acid (25.67% of total fatty acids content) and minor amounts of polyunsaturated fatty acids with >= 4 double bonds (< 1%). As a result, this biofuel complies with most of the European (EN14214) and American (ASTM D6751) specifications, which commonly used microalgal feedstocks are usually unable to meet. In conclusion, Tetraselmis sp. CTP4 displays promising features as feedstock with lower downstream processing costs for biomass dewatering and biodiesel refining

Predicting P-Glycoprotein-Mediated Drug Transport Based On Support Vector Machine and Three-Dimensional Crystal Structure of P-glycoprotein

Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening

Toxin-Based Models to Investigate Demyelination and Remyelination.

Clinical myelin diseases, and our best experimental approximations, are complex entities in which demyelination and remyelination proceed unpredictably and concurrently. These features can make it difficult to identify mechanistic details. Toxin-based models offer lesions with predictable spatiotemporal patterns and relatively discrete phases of damage and repair: a simpler system to study the relevant biology and how this can be manipulated. Here, we discuss the most widely used toxin-based models, with a focus on lysolecithin, ethidium bromide, and cuprizone. This includes an overview of their respective mechanisms, strengths, and limitations and step-by-step protocols for their use

Biophysical Characterization of the Strong Stabilization of the RNA Triplex poly(U)•poly(A)*poly(U) by 9-O-(ω-amino) Alkyl Ether Berberine Analogs

Background: Binding of two 9-O-(v-amino) alkyl ether berberine analogs BC1 and BC2 to the RNA triplex poly(U)Npoly(A)*poly(U) was studied by various biophysical techniques. Methodology/Principal Findings: Berberine analogs bind to the RNA triplex non-cooperatively. The affinity of binding was remarkably high by about 5 and 15 times, respectively, for BC1 and BC2 compared to berberine. The site size for the binding was around 4.3 for all. Based on ferrocyanide quenching, fluorescence polarization, quantum yield values and viscosity results a strong intercalative binding of BC1 and BC2 to the RNA triplex has been demonstrated. BC1 and BC2 stabilized the Hoogsteen base paired third strand by about 18.1 and 20.5uC compared to a 17.5uC stabilization by berberine. The binding was entropy driven compared to the enthalpy driven binding of berbeine, most likely due to additional contacts within the grooves of the triplex and disruption of the water structure by the alkyl side chain. Conclusions/Significance: Remarkably higher binding affinity and stabilization effect of the RNA triplex by the amino alkyl berberine analogs was achieved compared to berberine. The length of the alkyl side chain influence in the triplex stabilization phenomena

Gut microbiota and diabetes: from pathogenesis to therapeutic perspective

More than several hundreds of millions of people will be diabetic and obese over the next decades in front of which the actual therapeutic approaches aim at treating the consequences rather than causes of the impaired metabolism. This strategy is not efficient and new paradigms should be found. The wide analysis of the genome cannot predict or explain more than 10–20% of the disease, whereas changes in feeding and social behavior have certainly a major impact. However, the molecular mechanisms linking environmental factors and genetic susceptibility were so far not envisioned until the recent discovery of a hidden source of genomic diversity, i.e., the metagenome. More than 3 million genes from several hundreds of species constitute our intestinal microbiome. First key experiments have demonstrated that this biome can by itself transfer metabolic disease. The mechanisms are unknown but could be involved in the modulation of energy harvesting capacity by the host as well as the low-grade inflammation and the corresponding immune response on adipose tissue plasticity, hepatic steatosis, insulin resistance and even the secondary cardiovascular events. Secreted bacterial factors reach the circulating blood, and even full bacteria from intestinal microbiota can reach tissues where inflammation is triggered. The last 5 years have demonstrated that intestinal microbiota, at its molecular level, is a causal factor early in the development of the diseases. Nonetheless, much more need to be uncovered in order to identify first, new predictive biomarkers so that preventive strategies based on pre- and probiotics, and second, new therapeutic strategies against the cause rather than the consequence of hyperglycemia and body weight gain

Search for Dark Matter and Supersymmetry with a Compressed Mass Spectrum in the Vector Boson Fusion Topology in Proton-Proton Collisions at root s=8 TeV

Peer reviewe