Brachial artery vasodilatory response and wall shear rate determined by multi-gate Doppler in a healthy young cohort

This is the author accepted manuscript. The final version is available from American Physiological Society via the DOI in this record.Wall shear rate (WSR) is an important stimulus for the brachial artery flow-mediated dilation (FMD) response. However, WSR estimation near the arterial wall by conventional Doppler is inherently difficult. To overcome this limitation, we utilised multi-gate Doppler to accurately determine the WSR stimulus near the vessel wall simultaneously with the FMD response using an integrated FMD system [Ultrasound Advanced Open Platform (ULA-OP)]. Using the system, we aimed to perform a detailed analysis of WSR-FMD response and establish novel WSR parameters in a healthy young population. Data from 33 young healthy individuals (27.5±4.9yrs, 19F) were analysed. FMD was assessed with reactive hyperemia using ULA-OP. All acquired raw data were post-processed using custom-designed software to obtain WSR and diameter parameters. The acquired velocity data revealed that non-parabolic flow-profiles within the cardiac cycle and under different flow-states, with heterogeneity between participants. We also identified seven WSR magnitude and four WSR time-course parameters. Among them, WSR area under the curve until its return to baseline was the strongest predictor of the absolute (R2 =0.25) and percentage (R2 =0.31) diameter changes in response to reactive hyperemia. For the first time, we identified mono- and biphasic WSR stimulus patterns within our cohort that produced different magnitudes of FMD response [absolute diameter change: 0.24±0.10mm (monophasic) vs 0.17±0.09mm (biphasic), p<0.05]. We concluded that accurate and detailed measurement of the WSR stimulus is important to comprehensively understand the FMD response and that this advance in current FMD technology could be important to better understand vascular physiology and pathology.This study was supported by the European Union’s Seventh Framework Programme (FP7/2007-2013) for the Innovative Medicine Initiative under grant agreement number IMI/115006 (the SUMMIT consortium), in part by the National Institute of Health Research (NIHR) Exeter Clinical Research Facility, and by the Italian Ministry of University and Research (MIUR, Project PRIN 2010-2011)

Microstructural Characterisation of Resistance Artery Remodelling in Diabetes Mellitus

This is the final version. Available on open access from Karger Publishers via the DOI in this recordIntroduction: Microvascular remodelling is a symptom of cardiovascular disease. Despite the mechanical environment being recognised as a major contributor to the remodelling process, it is currently only understood in a rudimentary way. Objective: Amorphological and mechanicalevaluation of the resistance vasculature in health and diabetes mellitus.Methods: The cells and extracellular matrix of human subcutaneous resistance arteriesfrom abdominal fat biopsieswere imagedusing two-photon fluorescence and second harmonic generationat varying transmural pressure.The results informed a two-layer mechanical model.Results: Diabetic resistance arteries reducedin wall area as pressure was increased. This was attributed to the presence of thick, straight collagen fibre bundles that bracedthe outer wall.The abnormal mechanical environment caused theinternal elastic lamina and endothelial and vascular smooth muscle cellarrangementsto twist. Conclusions: Our resultssuggest diabetic microvascular remodelling is likely to be stress-driven, comprisingat least two stages: 1. Laying down of adventitial bracing fibres that limit outward distension, and 2. Deposition of additional collagen in the media, likely due to the significantly altered mechanical environment. This work represents a step towards elucidating the local stress environment of cells, which iscrucial to build accurate models of mechanotransduction in disease.British Heart FoundationMedical Research Council (MRC)National Institute for Health Research (NIHR

Evaluating predictive pharmacogenetic signatures of adverse events in colorectal cancer patients treated with fluoropyrimidines

The potential clinical utility of genetic markers associated with response to fluoropyrimidine treatment in colorectal cancer patients remains controversial despite extensive study. Our aim was to test the clinical validity of both novel and previously identified markers of adverse events in a broad clinical setting. We have conducted an observational pharmacogenetic study of early adverse events in a cohort study of 254 colorectal cancer patients treated with 5-fluorouracil or capecitabine. Sixteen variants of nine key folate (pharmacodynamic) and drug metabolising (pharmacokinetic) enzymes have been analysed as individual markers and/or signatures of markers. We found a significant association between TYMP S471L (rs11479) and early dose modifications and/or severe adverse events (adjusted OR = 2.02 [1.03; 4.00], p = 0.042, adjusted OR = 2.70 [1.23; 5.92], p = 0.01 respectively). There was also a significant association between these phenotypes and a signature of DPYD mutations (Adjusted OR = 3.96 [1.17; 13.33], p = 0.03, adjusted OR = 6.76 [1.99; 22.96], p = 0.002 respectively). We did not identify any significant associations between the individual candidate pharmacodynamic markers and toxicity. If a predictive test for early adverse events analysed the TYMP and DPYD variants as a signature, the sensitivity would be 45.5 %, with a positive predictive value of just 33.9 % and thus poor clinical validity. Most studies to date have been under-powered to consider multiple pharmacokinetic and pharmacodynamic variants simultaneously but this and similar individualised data sets could be pooled in meta-analyses to resolve uncertainties about the potential clinical utility of these markers

Microbial ligand costimulation drives neutrophilic steroid-refractory asthma

Funding: The authors thank the Wellcome Trust (102705) and the Universities of Aberdeen and Cape Town for funding. This research was also supported, in part, by National Institutes of Health GM53522 and GM083016 to DLW. KF and BNL are funded by the Fonds Wetenschappelijk Onderzoek, BNL is the recipient of an European Research Commission consolidator grant and participates in the European Union FP7 programs EUBIOPRED and MedALL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule

AIMS/HYPOTHESIS: Microalbuminuria is common in type 1 diabetes and is associated with an increased risk of renal and cardiovascular disease. We aimed to develop and validate a clinical prediction rule that estimates the absolute risk of microalbuminuria. METHODS: Data from the European Diabetes Prospective Complications Study (n = 1115) were used to develop the prediction rule (development set). Multivariable logistic regression analysis was used to assess the association between potential predictors and progression to microalbuminuria within 7 years. The performance of the prediction rule was assessed with calibration and discrimination (concordance statistic [c-statistic]) measures. The rule was validated in three other diabetes studies (Pittsburgh Epidemiology of Diabetes Complications [EDC] study, Finnish Diabetic Nephropathy [FinnDiane] study and Coronary Artery Calcification in Type 1 Diabetes [CACTI] study). RESULTS: Of patients in the development set, 13% were microalbuminuric after 7 years. Glycosylated haemoglobin, AER, WHR, BMI and ever smoking were found to be the most important predictors. A high-risk group (n = 87 [8%]) was identified with a risk of progression to microalbuminuria of 32%. Predictions showed reasonable discriminative ability, with c-statistic of 0.71. The rule showed good calibration and discrimination in EDC, FinnDiane and CACTI (c-statistic 0.71, 0.79 and 0.79, respectively). CONCLUSIONS/INTERPRETATION: We developed and validated a clinical prediction rule that uses relatively easily obtainable patient characteristics to predict microalbuminuria in patients with type 1 diabetes. This rule can help clinicians to decide on more frequent check-ups for patients at high risk of microalbuminuria in order to prevent long-term chronic complication

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Systemic 7-methylxanthine in retarding axial eye growth and myopia progression: a 36-month pilot study

The adenosine antagonist 7-methylxanthine (7-mx) works against myopia in animal models. In a clinical trial, 68 myopic children (mean age 11.3 years) received either placebo or 7-mx tablets for 12 months. All participants subsequently received 7-mx for another 12 months, after which treatment was stopped. Axial length was measured with Zeiss IOL-Master and cycloplegic refraction with Nikon Retinomax at −6, 0, 12, 24, and 36 months. Axial growth was reduced among children treated with 7-mx for 24 months compared with those only treated for the last 12 months. Myopia progression and axial eye growth slowed down in periods with 7-mx treatment, but when the treatment was stopped, both myopia progression and axial eye growth continued with invariable speed. The results indicate that 7-mx reduces eye elongation and myopia progression in childhood myopia. The treatment is safe and without side effects and may be continued until 18–20 years of age when myopia progression normally stops

Progression to microalbuminuria in patients with type 1 diabetes: a seven-year prospective study

<p>Abstract</p> <p>Background</p> <p>The presence of microalbuminuria can be associated with overt nephropathy and cardiovascular disease in patients with type 1 diabetes (T1D). We aimed to determine the incidence and evaluate the baseline predictors for the development of microalbuminuria in patients with T1D.</p> <p>Methods</p> <p>This study is a longitudinal cohort study of 122 normoalbuminuric patients with T1D who were receiving routine clinical care at baseline. A detailed medical history was taken, and a physical examination was performed at baseline. All of the patients were regularly examined for diabetes-associated complications. An analysis of predictors was performed using the Cox regression.</p> <p>Results</p> <p>Over 6.81 (3.59-9.75) years of follow-up, 50 (41%) of the patients developed microalbuminuria. The incidence density was 6.79/100 people per year (95% CI 5.04-8.95), and the microalbuminuria developed after 5.9 (2.44-7.76) and 11 (5-15) years of follow-up and diabetes duration, respectively. After an individual Cox regression, the baseline variables associated with the development of microalbuminuria were age, age at diagnosis, duration of diabetes, systolic and diastolic blood pressure, fasting glycemia, body mass index (BMI), total cholesterol and triglycerides levels, cholesterol/HDL ratio and a family history of type 2 diabetes.After a multivariate Cox regression, the only independent factors associated with the development of microalbuminuria were BMI [HR 1.12 (1.03-1.21)] and cholesterol/HDL ratio [HR 1.32 (1.05-1.67)].</p> <p>Conclusions</p> <p>A higher BMI and cholesterol/HDL ratio increased the risk of developing microalbuminuria in young patients with T1D after a short follow-up. Both risk factors are modifiable and should be identified early and followed closely.</p

A new framework for cortico-striatal plasticity: behavioural theory meets In vitro data at the reinforcement-action interface

Operant learning requires that reinforcement signals interact with action representations at a suitable neural interface. Much evidence suggests that this occurs when phasic dopamine, acting as a reinforcement prediction error, gates plasticity at cortico-striatal synapses, and thereby changes the future likelihood of selecting the action(s) coded by striatal neurons. But this hypothesis faces serious challenges. First, cortico-striatal plasticity is inexplicably complex, depending on spike timing, dopamine level, and dopamine receptor type. Second, there is a credit assignment problem—action selection signals occur long before the consequent dopamine reinforcement signal. Third, the two types of striatal output neuron have apparently opposite effects on action selection. Whether these factors rule out the interface hypothesis and how they interact to produce reinforcement learning is unknown. We present a computational framework that addresses these challenges. We first predict the expected activity changes over an operant task for both types of action-coding striatal neuron, and show they co-operate to promote action selection in learning and compete to promote action suppression in extinction. Separately, we derive a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then show this model produces the predicted activity changes necessary for learning and extinction in an operant task, a remarkable convergence of a bottom-up data-driven plasticity model with the top-down behavioural requirements of learning theory. Moreover, we show the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. Validating the model, we show it can account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. By bridging the levels between the single synapse and behaviour, our model shows how striatum acts as the action-reinforcement interface