Fluctuation analysis of lung function as a predictor of long-term response to beta2-agonists

The response to beta(2)-agonists differs between asthmatics and has been linked to subsequent adverse events, even death. Possible determinants include beta(2)-adrenoceptor genotype at position 16, lung function and airway hyperresponsiveness. Fluctuation analysis provides a simple parameter alpha measuring the complex correlation properties of day-to-day peak expiratory flow. The present study investigated whether alpha predicts clinical response to beta(2)-agonist treatment, taking into account other conventional predictors. Analysis was performed on previously published twice-daily peak expiratory flow measurements in 66 asthmatic adults over three 6-month randomised order treatment periods: placebo, salbutamol and salmeterol. Multiple linear regression was used to determine the association between alpha during the placebo period and response to treatment (change in the number of days with symptoms), taking into account other predictors namely beta(2)-adrenoceptor genotype, lung function and its variability, and airway hyperresponsiveness. The current authors found that alpha measured during the placebo period considerably improved the prediction of response to salmeterol treatment, taking into account genotype, lung function or its variability, or airway hyperresponsiveness. The present study provides further evidence that response to beta(2)-agonists is related to the time correlation properties of lung function in asthma. The current authors conclude that fluctuation analysis of lung function offers a novel predictor to identify patients who may respond well or poorly to treatment.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Kubo relations and radiative corrections for lepton number washout

The rates for lepton number washout in extensions of the Standard Model containing right-handed neutrinos are key ingredients in scenarios for baryogenesis through leptogenesis. We relate these rates to real-time correlation functions at finite temperature, without making use of any particle approximations. The relations are valid to quadratic order in neutrino Yukawa couplings and to all orders in Standard Model couplings. They take into account all spectator processes, and apply both in the symmetric and in the Higgs phase of the electroweak theory. We use the relations to compute washout rates at next-to-leading order in g, where g denotes a Standard Model gauge or Yukawa coupling, both in the non-relativistic and in the relativistic regime. Even in the non-relativistic regime the parametrically dominant radiative corrections are only suppressed by a single power of g. In the non-relativistic regime radiative corrections increase the washout rate by a few percent at high temperatures, but they are of order unity around the weak scale and in the relativistic regime.

Biochemical MRI predicts hip osteoarthritis in an experimental ovine femoroacetabular impingement model

BACKGROUND Cam-type femoroacetabular impingement (FAI) resulting from an abnormal nonspherical femoral head shape leads to chondrolabral damage and is considered a cause of early osteoarthritis. A previously developed experimental ovine FAI model induces a cam-type impingement that results in localized chondrolabral damage, replicating the patterns found in the human hip. Biochemical MRI modalities such as T2 and T2* may allow for evaluation of the cartilage biochemistry long before cartilage loss occurs and, for that reason, may be a worthwhile avenue of inquiry. QUESTIONS/PURPOSES We asked: (1) Does the histological grading of degenerated cartilage correlate with T2 or T2* values in this ovine FAI model? (2) How accurately can zones of degenerated cartilage be predicted with T2 or T2* MRI in this model? METHODS A cam-type FAI was induced in eight Swiss alpine sheep by performing a closing wedge intertrochanteric varus osteotomy. After ambulation of 10 to 14 weeks, the sheep were euthanized and a 3-T MRI of the hip was performed. T2 and T2* values were measured at six locations on the acetabulum and compared with the histological damage pattern using the Mankin score. This is an established histological scoring system to quantify cartilage degeneration. Both T2 and T2* values are determined by cartilage water content and its collagen fiber network. Of those, the T2* mapping is a more modern sequence with technical advantages (eg, shorter acquisition time). Correlation of the Mankin score and the T2 and T2* values, respectively, was evaluated using the Spearman's rank correlation coefficient. We used a hierarchical cluster analysis to calculate the positive and negative predictive values of T2 and T2* to predict advanced cartilage degeneration (Mankin ≥ 3). RESULTS We found a negative correlation between the Mankin score and both the T2 (p < 0.001, r = -0.79) and T2* values (p < 0.001, r = -0.90). For the T2 MRI technique, we found a positive predictive value of 100% (95% confidence interval [CI], 79%-100%) and a negative predictive value of 84% (95% CI, 67%-95%). For the T2* technique, we found a positive predictive value of 100% (95% CI, 79%-100%) and a negative predictive value of 94% (95% CI, 79%-99%). CONCLUSIONS T2 and T2* MRI modalities can reliably detect early cartilage degeneration in the experimental ovine FAI model. CLINICAL RELEVANCE T2 and T2* MRI modalities have the potential to allow for monitoring the natural course of osteoarthrosis noninvasively and to evaluate the results of surgical treatments targeted to joint preservation.

Characterization of the macromolecule baseline in localized (1)H-MR spectra of human brain

Short-echo-time magnetic resonance spectra of human brain contain broad contributions from macromolecules. As they are a priori of unknown shape and intensity, they pose a problem if one wants to quantitate the overlying spectral features from low-molecular-weight metabolites. On the other hand, the macromolecular contributions may provide relevant clinical information themselves, if properly evaluated. Several methods, based on T(1), T(2), or spectral shape, have previously been suggested to suppress or edit the macromolecule contributions. Here, a method is presented based on a series of saturation recovery scans and that allows for simultaneous recording of the macromolecular baseline and the fully relaxed metabolite spectrum. In comparison to an inversion recovery technique aimed at nulling signals from long-T(1) components, the saturation recovery method is less susceptible to T(1) differences inherent in signals from different metabolites or introduced by pathology. The saturation recovery method was used to quantitate the macromolecular baseline in white and/or gray matter locations of the human brain in 40 subjects. It was found that the content and composition of MR visible macromolecules depends on cerebral location, as well as the age of the investigated subject, while no gender dependence could be found.

Real-time simulation of large open quantum spin systems driven by dissipation

We consider a large quantum system with spins 12 whose dynamics is driven entirely by measurements of the total spin of spin pairs. This gives rise to a dissipative coupling to the environment. When one averages over the measurement results, the corresponding real-time path integral does not suffer from a sign problem. Using an efficient cluster algorithm, we study the real-time evolution from an initial antiferromagnetic state of the two-dimensional Heisenberg model, which is driven to a disordered phase, not by a Hamiltonian, but by sporadic measurements or by continuous Lindblad evolution.

Real-time simulation of nonequilibrium transport of magnetization in large open quantum spin systems driven by dissipation

Using quantum Monte Carlo, we study the nonequilibrium transport of magnetization in large open strongly correlated quantum spin-12 systems driven by purely dissipative processes that conserve the uniform or staggered magnetization, disregarding unitary Hamiltonian dynamics. We prepare both a low-temperature Heisenberg ferromagnet and an antiferromagnet in two parts of the system that are initially isolated from each other. We then bring the two subsystems in contact and study their real-time dissipative dynamics for different geometries. The flow of the uniform or staggered magnetization from one part of the system to the other is described by a diffusion equation that can be derived analytically.

Anatomically-Driven Soft-Tissue Simulation Strategy for Cranio-Maxillofacial Surgery Using Facial Muscle Template Model

We propose a computationally efficient and biomechanically relevant soft-tissue simulation method for cranio-maxillofacial (CMF) surgery. A template-based facial muscle reconstruction was introduced to minimize the efforts on preparing a patient-specific model. A transversely isotropic mass-tensor model (MTM) was adopted to realize the effect of directional property of facial muscles in reasonable computation time. Additionally, sliding contact around teeth and mucosa was considered for more realistic simulation. Retrospective validation study with postoperative scan of a real patient showed that there were considerable improvements in simulation accuracy by incorporating template-based facial muscle anatomy and sliding contact.

Methodical study of nitrous oxide eddy covariance measurements using quantum cascade laser spectrometery over a Swiss forest

Nitrous oxide fluxes were measured at the Lägeren CarboEurope IP flux site over the multi-species mixed forest dominated by European beech and Norway spruce. Measurements were carried out during a four-week period in October–November 2005 during leaf senescence. Fluxes were measured with a standard ultrasonic anemometer in combination with a quantum cascade laser absorption spectrometer that measured N2O, CO2, and H2O mixing ratios simultaneously at 5 Hz time resolution. To distinguish insignificant fluxes from significant ones it is proposed to use a new approach based on the significance of the correlation coefficient between vertical wind speed and mixing ratio fluctuations. This procedure eliminated roughly 56% of our half-hourly fluxes. Based on the remaining, quality checked N2O fluxes we quantified the mean efflux at 0.8±0.4 μmol m−2 h−1 (mean ± standard error). Most of the contribution to the N2O flux occurred during a 6.5-h period starting 4.5 h before each precipitation event. No relation with precipitation amount could be found. Visibility data representing fog density and duration at the site indicate that wetting of the canopy may have as strong an effect on N2O effluxes as does below-ground microbial activity. It is speculated that above-ground N2O production from the senescing leaves at high moisture (fog, drizzle, onset of precipitation event) may be responsible for part of the measured flux.

Time-dependent interactions of the two porphyrinic compounds chlorin e6 and mono-L-aspartyl-chlorin e6 with phospholipid vesicles probed by NMR spectroscopy

The distribution processes of chlorin e6 (CE) and monoaspartyl-chlorin e6 (MACE) between the outer and inner phospholipid monolayers of 1,2-dioleoyl-phosphatidylcholine (DOPC) vesicles were monitored by 1H NMR spectroscopy through analysis of chemical shifts and line widths of the DOPC vesicle resonances. Chlorin adsorption to the outer vesicle monolayer induced changes in the DOPC 1H NMR spectrum. Most pronounced was a split of the N-methyl choline resonance, allowing for separate analysis of inner and outer vesicle layers. Transbilayer distribution of the chlorin compounds was indicated by time-dependent characteristic spectral changes of the DOPC resonances. Kinetic parameters for the flip-flop processes, that is, half-lives and rate constants, were obtained from the experimental data points. In comparison to CE, MACE transbilayer movement was significantly reduced, with MACE remaining more or less attached to the outer membrane layer. The distribution coefficients for CE and MACE between the vesicular and aqueous phase were determined. Both CE and MACE exhibited a high affinity for the vesicular phase. For CE, a positive correlation was found between transfer rate and increasing molar ratio CE/DOPC. Enhanced membrane rigidity induced by increasing amounts of cholesterol into the model membrane was accompanied by a decrease of CE flip-flop rates across the membrane. The present study shows that the movement of porphyrins across membranes can efficiently be investigated by 1H NMR spectroscopy and that small changes in porphyrin structure can have large effects on membrane kinetics.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.