Ultrasonic characterization of ultrasound contrast agents

The main constituent of an ultrasound contrast agent (UCA) is gas-filled microbubbles. An average UCA contains billions per ml. These microbubbles are excellent ultrasound scatterers due to their high compressibility. In an ultrasound field they act as resonant systems, resulting in harmonic energy in the backscattered ultrasound signal, such as energy at the subharmonic, ultraharmonic and higher harmonic frequencies. This harmonic energy is exploited for contrast enhanced imaging to discriminate the contrast agent from surrounding tissue. The amount of harmonic energy that the contrast agent bubbles generate depends on the bubble characteristics in combination with the ultrasound field applied. This paper summarizes different strategies to characterize the UCAs. These strategies can be divided into acoustic and optical methods, which focus on the linear or nonlinear responses of the contrast agent bubbles. In addition, the characteristics of individual bubbles can be determined or the bubbles can be examined when they are part of a population. Recently, especially optical methods have proven their value to study individual bubbles. This paper concludes by showing some examples of optically observed typical behavior of contrast bubbles in ultrasound fields

Preclinical animal research on therapy dosimetry with dual isotopes

Preclinical research into radionuclide therapies based on radiation dosimetry will enable the use of any LET-equivalent radionuclide. Radiation dose and dose rate have significant influence on dose effects in the tumour depending on its radiation sensitivity, possibilities for repair of sublethal damage, and repopulation during or after the therapy. Models for radiation response of preclinical tumour models after peptide receptor radionuclide therapy based on the linear quadratic model are presented. The accuracy of the radiation dose is very important for observation of dose-effects. Uncertainties in the radiation dose estimation arise from incomplete assay of the kinetics, low accuracy in volume measurements and absorbed dose S-values for stylized models instead of the actual animal geometry. Normal dose uncertainties in the order of 20% might easily make the difference between seeing a dose-effect or missing it altogether. This is true for the theoretical case of a homogeneous tumour type behaving in vivo in the same way as its cells do in vitro. Heterogeneity of tumours induces variations in clonogenic cell density, radiation sensitivity, repopulation capacity and repair kinetics. The influence of these aspects are analysed within the linear quadratic model for tumour response to radionuclide therapy. Preclinical tumour models tend to be less heterogenic than the clinical conditions they should represent. The results of various preclinical radionuclide therapy experiments for peptide receptor radionuclide therapy are compared to the outcome of theoretical models and the influence of increased heterogeneity is analysed when the results of preclinical research is transferred to the clinic. When the radiation dose and radiobiology of the tumour response is known well enough it may be possible to leave the current phenomenological approach in preclinical radionuclide therapy and start basing these experiments on radiation dose. Then the use of a gamma ray-emitting radionuclides for a chemically comparable beta-particle-emitting paired isotope for therapy evaluation would be feasible

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

Shot noise in mesoscopic systems

This is a review of shot noise, the time-dependent fluctuations in the electrical current due to the discreteness of the electron charge, in small conductors. The shot-noise power can be smaller than that of a Poisson process as a result of correlations in the electron transmission imposed by the Pauli principle. This suppression takes on simple universal values in a symmetric double-barrier junction (suppression factor 1/2), a disordered metal (factor 1/3), and a chaotic cavity (factor 1/4). Loss of phase coherence has no effect on this shot-noise suppression, while thermalization of the electrons due to electron-electron scattering increases the shot noise slightly. Sub-Poissonian shot noise has been observed experimentally. So far unobserved phenomena involve the interplay of shot noise with the Aharonov-Bohm effect, Andreev reflection, and the fractional quantum Hall effect.Comment: 37 pages, Latex, 10 figures (eps). To be published in "Mesoscopic Electron Transport," edited by L. P. Kouwenhoven, G. Schoen, and L. L. Sohn, NATO ASI Series E (Kluwer Academic Publishing, Dordrecht

Quantitative imaging of 124I and 86Y with PET

The quantitative accuracy and image quality of positron emission tomography (PET) measurements with 124I and 86Y is affected by the prompt emission of gamma radiation and positrons in their decays, as well as the higher energy of the emitted positrons compared to those emitted by 18F. PET scanners cannot distinguish between true coincidences, involving two 511-keV annihilation photons, and coincidences involving one annihilation photon and a prompt gamma, if the energy of this prompt gamma is within the energy window of the scanner. The current review deals with a number of aspects of the challenge this poses for quantitative PET imaging. First, the effect of prompt gamma coincidences on quantitative accuracy of PET images is discussed and a number of suggested corrections are described. Then, the effect of prompt gamma coincidences and the increased singles count rates due to gamma radiation on the count rate performance of PET is addressed, as well as possible improvements based on modification of the scanner’s energy windows. Finally, the effect of positron energy on spatial resolution and recovery is assessed. The methods presented in this overview aim to overcome the challenges associated with the decay characteristics of 124I and 86Y. Careful application of the presented correction methods can allow for quantitatively accurate images with improved image contrast

Mapping genetic determinants of host susceptibility to Pseudomonas aeruginosa lung infection in mice.

Background: P. aeruginosa is one of the top three causes of opportunistic human bacterial infections. The remarkable variability in the clinical outcomes of this infection is thought to be associated with genetic predisposition. However, the genes underlying host susceptibility to P. aeruginosa infection are still largely unknown. Results: As a step towards mapping these genes, we applied a genome wide linkage analysis approach to a mouse model. A large F2 intercross population, obtained by mating P. aeruginosa-resistant C3H/HeOuJ, and susceptible A/J mice, was used for quantitative trait locus (QTL) mapping. The F2 progenies were challenged with a P. aeruginosa clinical strain and monitored for the survival time up to 7 days post-infection, as a disease phenotype associated trait. Selected phenotypic extremes of the F2 distribution were genotyped with high-density single nucleotide polymorphic (SNP) markers, and subsequently QTL analysis was performed. A significant locus was mapped on chromosome 6 and was named P. aeruginosa infection resistance locus 1 (Pairl1). The most promising candidate genes, including Dok1, Tacr1, Cd207, Clec4f, Gp9, Gata2, Foxp1, are related to pathogen sensing, neutrophils and macrophages recruitment and inflammatory processes. Conclusions: We propose a set of genes involved in the pathogenesis of P. aeruginosa infection that may be explored to complement human studie

GP-initiated preconception counselling in a randomised controlled trial does not induce anxiety

BACKGROUND: Preconception counselling (PCC) can reduce adverse pregnancy outcome by addressing risk factors prior to pregnancy. This study explores whether anxiety is induced in women either by the offer of PCC or by participation with GP-initiated PCC. METHODS: Randomised trial of usual care versus GP-initiated PCC for women aged 18–40, in 54 GP practices in the Netherlands. Women completed the six-item Spielberger State Trait Anxiety Inventory (STAI) before PCC (STAI-1) and after (STAI-2). After pregnancy women completed a STAI focusing on the first trimester of pregnancy (STAI-3). RESULTS: The mean STAI-1-score (n = 466) was 36.4 (95% CI 35.4 – 37.3). Following PCC there was an average decrease of 3.6 points in anxiety-levels (95% CI, 2.4 – 4.8). Mean scores of the STAI-3 were 38.5 (95% CI 37.7 – 39.3) in the control group (n = 1090) and 38.7 (95% CI 37.9 – 39.5) in the intervention group (n = 1186). CONCLUSION: PCC from one's own GP reduced anxiety after participation, without leading to an increase in anxiety among the intervention group during pregnancy. We therefore conclude that GPs can offer PCC to the general population without fear of causing anxiety. Trial Registration: ISRCTN5394291

Efficacy of Combined Therapy with Amantadine, Oseltamivir, and Ribavirin In Vivo against Susceptible and Amantadine-Resistant Influenza A Viruses

The limited efficacy of existing antiviral therapies for influenza – coupled with widespread baseline antiviral resistance – highlights the urgent need for more effective therapy. We describe a triple combination antiviral drug (TCAD) regimen composed of amantadine, oseltamivir, and ribavirin that is highly efficacious at reducing mortality and weight loss in mouse models of influenza infection. TCAD therapy was superior to dual and single drug regimens in mice infected with drug-susceptible, low pathogenic A/H5N1 (A/Duck/MN/1525/81) and amantadine-resistant 2009 A/H1N1 influenza (A/California/04/09). Treatment with TCAD afforded >90% survival in mice infected with both viruses, whereas treatment with dual and single drug regimens resulted in 0% to 60% survival. Importantly, amantadine had no activity as monotherapy against the amantadine-resistant virus, but demonstrated dose-dependent protection in combination with oseltamivir and ribavirin, indicative that amantadine's activity had been restored in the context of TCAD therapy. Furthermore, TCAD therapy provided survival benefit when treatment was delayed until 72 hours post-infection, whereas oseltamivir monotherapy was not protective after 24 hours post-infection. These findings demonstrate in vivo efficacy of TCAD therapy and confirm previous reports of the synergy and broad spectrum activity of TCAD therapy against susceptible and resistant influenza strains in vitro