Irbesartan, an angiotensin type 1 receptor inhibitor, regulates markers of inflammation in patients with premature atherosclerosis

AbstractOBJECTIVESThis study assessed the role of angiotensin II type 1 (AT1) receptor antagonists on inflammatory mechanisms involved in atherogenesis. Specific inflammatory markers included solubilized tumor necrosis factor-alpha receptor II (sTNF-αRII), vascular cell adhesion molecule-1 (VCAM-1) and superoxide. In addition, the AT1receptor blocker irbesartan was evaluated for its ability to suppress these markers in individuals with atherosclerosis.BACKGROUNDMechanisms involved in the complex process of atherogenesis include alterations in the inflammatory responses. The use of compounds that suppress these responses may reduce the degree of damage seen in atherosclerosis.METHODSWith a cross-sectional study design, 33 normotensive patients with stable coronary artery disease (CAD) were treated with irbesartan for a 24-week period. These patients were compared against a control population with no known coronary atherosclerosis. Marker levels were measured by enzyme-linked immunosorbent assay technique and lucigenin chemiluminescence assay and statistically evaluated by two-way repeated measures analysis of variance.RESULTSAll patients with coronary artery disease had increased levels of inflammatory molecules over those of control patients. Treatment with irbesartan in these patients significantly reduced levels of inflammatory molecules measured. Soluble VCAM-1 levels were reduced by 36%; soluble TNF-alpha levels were reduced by 54% and superoxide level decreased by 52%. Maximal suppression of inflammatory markers by irbesartan therapy in patients with CAD was seen at 12 weeks.CONCLUSIONSThe effect of irbesartan on each inflammatory marker is significant. Our results show that use of irbesartan may retard the inflammatory process seen in premature forms of atherosclerosis

GuiTope: an application for mapping random-sequence peptides to protein sequences

BACKGROUND: Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. RESULTS: GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. CONCLUSIONS: GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net

Blood transcriptomic discrimination of bacterial and viral infections in the emergency department: a multi-cohort observational validation study

Background: There is an urgent need to develop biomarkers that stratify risk of bacterial infection in order to support antimicrobial stewardship in emergency hospital admissions. / Methods: We used computational machine learning to derive a rule-out blood transcriptomic signature of bacterial infection (SeptiCyte™ TRIAGE) from eight published case-control studies. We then validated this signature by itself in independent case-control data from more than 1500 samples in total, and in combination with our previously published signature for viral infections (SeptiCyte™ VIRUS) using pooled data from a further 1088 samples. Finally, we tested the performance of these signatures in a prospective observational cohort of emergency department (ED) patients with fever, and we used the combined SeptiCyte™ signature in a mixture modelling approach to estimate the prevalence of bacterial and viral infections in febrile ED patients without microbiological diagnoses. / Results: The combination of SeptiCyte™ TRIAGE with our published signature for viral infections (SeptiCyte™ VIRUS) discriminated bacterial and viral infections in febrile ED patients, with a receiver operating characteristic area under the curve of 0.95 (95% confidence interval 0.90–1), compared to 0.79 (0.68–0.91) for WCC and 0.73 (0.61–0.86) for CRP. At pre-test probabilities 0.35 and 0.72, the combined SeptiCyte™ score achieved a negative predictive value for bacterial infection of 0.97 (0.90–0.99) and 0.86 (0.64–0.96), compared to 0.90 (0.80–0.94) and 0.66 (0.48–0.79) for WCC and 0.88 (0.69–0.95) and 0.60 (0.31–0.72) for CRP. In a mixture modelling approach, the combined SeptiCyte™ score estimated that 24% of febrile ED cases receiving antibacterials without a microbiological diagnosis were due to viral infections. Our analysis also suggested that a proportion of patients with bacterial infection recovered without antibacterials. / Conclusions: Blood transcriptional biomarkers offer exciting opportunities to support precision antibacterial prescribing in ED and improve diagnostic classification of patients without microbiologically confirmed infections

Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors

Individual pitch control (IPC) provides an important means of attenuating harmful fatigue and extreme loads upon the load bearing structures of a wind turbine. Conventional IPC architectures determine the additional pitch demand signals required for load mitigation in response to measurements of the flap-wise blade-root bending moments. However, the performance of such architectures is fundamentally limited by bandwidth constraints imposed by the blade dynamics. Seeking to overcome this problem, we present a simple solution based upon a local blade inflow measurement on each blade. Importantly, this extra measurement enables the implementation of an additional cascaded feedback controller that overcomes the existing IPC performance limitation and hence yields significantly improved load reductions. Numerical demonstration upon a high-fidelity and nonlinear wind turbine model reveals (i) 60% reduction in the amplitude of the dominant 1P fatigue loads, and (ii) 59% reduction in the amplitude of extreme wind shear induced blade loads, compared to a conventional IPC controller with the same robust stability margin. This paper therefore represents a significant alternative to wind turbine IPC load mitigation as compared to LiDAR-based feedforward control approaches

Antibody Based Strategies For Multiplexed Diagnostics

Peptide microarrays are to proteomics as sequencing is to genomics. As microarrays become more content-rich, higher resolution proteomic studies will parallel deep sequencing of nucleic acids. Antigen-antibody interactions can be studied at a much higher resolution using microarrays than was possible only a decade ago. My dissertation focuses on testing the feasibility of using either the Immunosignature platform, based on non-natural peptide sequences, or a pathogen peptide microarray, which uses bioinformatically-selected peptides from pathogens for creating sensitive diagnostics. Both diagnostic applications use relatively little serum from infected individuals, but each approaches diagnosis of disease differently. The first project compares pathogen epitope peptide (life-space) and non-natural (random-space) peptide microarrays while using them for the early detection of Coccidioidomycosis (Valley Fever). The second project uses NIAID category A, B and C priority pathogen epitope peptides in a multiplexed microarray platform to assess the feasibility of using epitope peptides to simultaneously diagnose multiple exposures using a single assay. Cross-reactivity is a consistent feature of several antigen-antibody based immunodiagnostics. This work utilizes microarray optimization and bioinformatic approaches to distill the underlying disease specific antibody signature pattern. Circumventing inherent cross-reactivity observed in antibody binding to peptides was crucial to achieve the goal of this work to accurately distinguishing multiple exposures simultaneously

Iterative data-driven load control for flexible wind turbine rotors

Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is possible to reduce the cost of energy by upscaling wind turbines, it is believed that we may be approaching a plateau in turbine size. Beyond this plateau, the material costs associated with the high dynamic turbine loads would outweigh the benefits of increased energy capture. To postpone this plateau, research is currently being carried out in the active control of loads for lightweight, flexible rotors. Traditional control for wind turbines involves the use of fixed-structure low order controllers, the gains of which are often hand-tuned separately for each turbine class. However, for the increasingly multivariable plant, such time-invariant approaches may no longer yield good performance. As such, the thesis focusses specifically on datadriven control for these flexible turbines. First, different data-driven approaches in the literature are evaluated and categorised as two-step approaches; which involves distinct online identification and control steps; and direct approaches, which uses data to iteratively tune fixed-structure controller gains. The approaches need to be modified to be made tractable in real time for implementation on wind turbines. For time-varying plants, such as wind turbines, it is often interesting to performidentification repeatedly over time for the two-step data-driven approach. Conventional recursive identification is extended in this thesis through the use of the nuclear norm. The benefit of the nuclear norm is evident in that it increases responsiveness of the algorithm, through the suppression of the effect of external noise. Identification can be readily combined with repetitive control for reducing periodic loads in the Subspace Predictive Repetitive Control (SPRC) technique. SPRC can be performed in a restricted basis function subspace, thus reducing the computational complexity and providing smooth control signals. The control law is stabilising and performs well as long as the identification converges to relatively good estimates, and the system dynamics change slowly. For varying wind speed, the approach above would require continuous reïdentification. As an alternative, a direct data-driven approach, Iterative Feedback Tuning (IFT) has been extended to gain-schedule tuning and for designing a Linear Parameter- Varying (LPV) controller for an LPV plant. This requires an exponential increase in the number of tuning experiments per iteration; however, structure can be used to reduce computational complexity. IFT-LPV converges to a locally optimal low-order controller. These data-driven approaches are evaluated for the load control of flexible rotors. A review of the state of the art shows that, for the low-frequency region of the load spectrum, full-span pitch control has demonstrable control authority. For higher frequencies, among the new actuators, it is found that trailing-edge flaps have the highest level of technological maturity. Aeroservoelastic simulations are carried out to show the potential of the data-driven approaches. SPRC is able to adaptively tune itself to achieve average blade load reductions close to those achieved by conventional approaches under similar conditions. For these load reductions the actuator duty is roughly half of that with the conventional approach. IFT-LPV has been used to tune a feedforward controller that works on similar basis functions scheduled on the azimuth. It can provide the correct control action irrespective of wind conditions. To expand the load control design space, pitch control is designed to stabilise an upwind turbine in yaw, without the yaw drive. This approach enables a trade-off between blade and support structure loads. SPRC is then investigated with wind tunnel experiments for pitch control of a scaled wind turbine. It reduces deterministic loads by over 60%with strict control over the pitch activity, and can also compensate for asymmetric blade control authority and changed operating conditions adaptively. Further, on this setup, the concept of IPC has been shown to perform yaw stabilisation for an upwind turbine for the first time. The setup blades are then redesigned to include free-floating trailing-edge flaps. First-principles models are set up for the system, and it is found that the system shows a low wind-speed form of flutter; this is validated experimentally. Recursive identification, using the nuclear norm, is able to track the unstable mode damping, and detect flutter twice as fast as conventional methods. Finally, a feedforward controller is tuned using IFT for combined pitch and flap control; the load peaks at 1P and 2P are almost entirely attenuated. IFT is also able to tune an linear gain schedule for operation across a range of wind speeds. It is concluded that iterative methods for data-driven control perform well for the highly uncertain control problem of flexible rotor load alleviation. For this, use has to be made of the structure of the problem. The two-step approach (such as SPRC), with combined recursive identification and control law synthesis, provides a convex first approximation of the desired controller. With the help of direct approaches, (like IFT-LPV), the controller structure can be reduced and fine-tuned to improve the control performance. Such quasi-feedforward data-driven approaches can complement the existing turbine control structure and achieve enhanced load control performance for flexible rotors.Numerics for Control & Identificatio