Impact of reduced cerebral perfusion pressure on outcome after severe traumatic brain injury is dependent on brain tissue oxygen pressure

INTRODUCTION. Reduced cerebral perfusion pressure (CPP) may worsen secondary damage and outcome after severe traumatic brain injury (TBI), however the optimal management of CPP is still debated. STUDY HYPOTHESIS: We hypothesized that the impact of CPP on outcome is related to brain tissue oxygen tension (PbtO2) level and that reduced CPP may worsen TBI prognosis when it is associated with brain hypoxia. DESIGN. Retrospective analysis of prospective database. METHODS. We analyzed 103 patients with severe TBI who underwent continuous PbtO2 and CPP monitoring for an average of 5 days. For each patient, duration of reduced CPP (\60 mm Hg) and brain hypoxia (PbtO2\15 mm Hg for[30 min [1]) was calculated with linear interpolation method and the relationship between CPP and PbtO2 was analyzed with Pearson's linear correlation coefficient. Outcome at 30 days was assessed with the Glasgow Outcome Score (GOS), dichotomized as good (GOS 4-5) versus poor (GOS 1-3). Multivariable associations with outcome were analyzed with stepwise forward logistic regression. RESULTS. Reduced CPP (n=790 episodes; mean duration 10.2 ± 12.3 h) was observed in 75 (74%) patients and was frequently associated with brain hypoxia (46/75; 61%). Episodes where reduced CPP were associated with normal brain oxygen did not differ significantly between patients with poor versus those with good outcome (8.2 ± 8.3 vs. 6.5 ± 9.7 h; P=0.35). In contrast, time where reduced CPP occurred simultaneously with brain hypoxia was longer in patients with poor than in those with good outcome (3.3±7.4 vs. 0.8±2.3 h; P=0.02). Outcome was significantly worse in patients who had both reduced CPP and brain hypoxia (61% had GOS 1-3 vs. 17% in those with reduced CPP but no brain hypoxia; P\0.01). Patients in whom a positive CPP-PbtO2 correlation (r[0.3) was found also were more likely to have poor outcome (69 vs. 31% in patients with no CPP-PbtO2 correlation; P\0.01). Brain hypoxia was an independent risk factor of poor prognosis (odds ratio for favorable outcome of 0.89 [95% CI 0.79-1.00] per hour spent with a PbtO2\15 mm Hg; P=0.05, adjusted for CPP, age, GCS, Marshall CT and APACHE II). CONCLUSIONS. Low CPP may significantly worsen outcome after severe TBI when it is associated with brain tissue hypoxia. PbtO2-targeted management of CPP may optimize TBI therapy and improve outcome of head-injured patients.

Therapeutic drug monitoring of imatinib: Bayesian and alternative methods to predict trough levels

Background: The imatinib trough plasma concentration (C(min)) correlates with clinical response in cancer patients. Therapeutic drug monitoring (TDM) of plasma C(min) is therefore suggested. In practice, however, blood sampling for TDM is often not performed at trough. The corresponding measurement is thus only remotely informative about C(min) exposure. Objectives: The objectives of this study were to improve the interpretation of randomly measured concentrations by using a Bayesian approach for the prediction of C(min), incorporating correlation between pharmacokinetic parameters, and to compare the predictive performance of this method with alternative approaches, by comparing predictions with actual measured trough levels, and with predictions obtained by a reference method, respectively. Methods: A Bayesian maximum a posteriori (MAP) estimation method accounting for correlation (MAP-ρ) between pharmacokinetic parameters was developed on the basis of a population pharmacokinetic model, which was validated on external data. Thirty-one paired random and trough levels, observed in gastrointestinal stromal tumour patients, were then used for the evaluation of the Bayesian MAP-ρ method: individual C(min) predictions, derived from single random observations, were compared with actual measured trough levels for assessment of predictive performance (accuracy and precision). The method was also compared with alternative approaches: classical Bayesian MAP estimation assuming uncorrelated pharmacokinetic parameters, linear extrapolation along the typical elimination constant of imatinib, and non-linear mixed-effects modelling (NONMEM) first-order conditional estimation (FOCE) with interaction. Predictions of all methods were finally compared with 'best-possible' predictions obtained by a reference method (NONMEM FOCE, using both random and trough observations for individual C(min) prediction). Results: The developed Bayesian MAP-ρ method accounting for correlation between pharmacokinetic parameters allowed non-biased prediction of imatinib C(min) with a precision of ±30.7%. This predictive performance was similar for the alternative methods that were applied. The range of relative prediction errors was, however, smallest for the Bayesian MAP-ρ method and largest for the linear extrapolation method. When compared with the reference method, predictive performance was comparable for all methods. The time interval between random and trough sampling did not influence the precision of Bayesian MAP-ρ predictions. Conclusion: Clinical interpretation of randomly measured imatinib plasma concentrations can be assisted by Bayesian TDM. Classical Bayesian MAP estimation can be applied even without consideration of the correlation between pharmacokinetic parameters. Individual C(min) predictions are expected to vary less through Bayesian TDM than linear extrapolation. Bayesian TDM could be developed in the future for other targeted anticancer drugs and for the prediction of other pharmacokinetic parameters that have been correlated with clinical outcomes.

Identification of pharmaceutical tablets by Raman spectroscopy and chemometrics

Raman spectroscopy has become an attractive tool for the analysis of pharmaceutical solid dosage forms. In the present study it is used to ensure the identity of tablets. The two main applications of this method are release of final products in quality control and detection of counterfeits. Twenty-five product families of tablets have been included in the spectral library and a non-linear classification method, the Support Vector Machines (SVMs), has been employed. Two calibrations have been developed in cascade: the first one identifies the product family while the second one specifies the formulation. A product family comprises different formulations that have the same active pharmaceutical ingredient (API) but in a different amount. Once the tablets have been classified by the SVM model, API peaks detection and correlation are applied in order to have a specific method for the identification and allow in the future to discriminate counterfeits from genuine products. This calibration strategy enables the identification of 25 product families without error and in the absence of prior information about the sample. Raman spectroscopy coupled with chemometrics is therefore a fast and accurate tool for the identification of pharmaceutical tablets.

Ambulatory assessment of 3D ground reaction force using plantar pressure distribution.

This study aimed to use the plantar pressure insole for estimating the three-dimensional ground reaction force (GRF) as well as the frictional torque (T(F)) during walking. Eleven subjects, six healthy and five patients with ankle disease participated in the study while wearing pressure insoles during several walking trials on a force-plate. The plantar pressure distribution was analyzed and 10 principal components of 24 regional pressure values with the stance time percentage (STP) were considered for GRF and T(F) estimation. Both linear and non-linear approximators were used for estimating the GRF and T(F) based on two learning strategies using intra-subject and inter-subjects data. The RMS error and the correlation coefficient between the approximators and the actual patterns obtained from force-plate were calculated. Our results showed better performance for non-linear approximation especially when the STP was considered as input. The least errors were observed for vertical force (4%) and anterior-posterior force (7.3%), while the medial-lateral force (11.3%) and frictional torque (14.7%) had higher errors. The result obtained for the patients showed higher error; nevertheless, when the data of the same patient were used for learning, the results were improved and in general slight differences with healthy subjects were observed. In conclusion, this study showed that ambulatory pressure insole with data normalization, an optimal choice of inputs and a well-trained nonlinear mapping function can estimate efficiently the three-dimensional ground reaction force and frictional torque in consecutive gait cycle without requiring a force-plate.

Local-global splitting for spatiotemporal-adaptive multiscale methods

We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations.

An iterative Multiscale Finite-Volume algorithm converging to the exact solution

The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems. The method essentially relies on the hypothesis that the (fine-scale) problem can be reasonably described by a set of local solutions coupled by a conservative global (coarse-scale) problem. In most cases, the boundary conditions assigned for the local problems are satisfactory and the approximate conservative fluxes provided by the method are accurate. In numerically challenging cases, however, a more accurate localization is required to obtain a good approximation of the fine-scale solution. In this paper we develop a procedure to iteratively improve the boundary conditions of the local problems. The algorithm relies on the data structure of the MsFV method and employs a Krylov-subspace projection method to obtain an unconditionally stable scheme and accelerate convergence. Two variants are considered: in the first, only the MsFV operator is used; in the second, the MsFV operator is combined in a two-step method with an operator derived from the problem solved to construct the conservative flux field. The resulting iterative MsFV algorithms allow arbitrary reduction of the solution error without compromising the construction of a conservative flux field, which is guaranteed at any iteration. Since it converges to the exact solution, the method can be regarded as a linear solver. In this context, the schemes proposed here can be viewed as preconditioned versions of the Generalized Minimal Residual method (GMRES), with a very peculiar characteristic that the residual on the coarse grid is zero at any iteration (thus conservative fluxes can be obtained).

Accuracy profile validation of a new method for carbon monoxide measurement in the human blood using headspace-gas chromatography-mass spectrometry (HS-GC-MS).

The aim of our study was to provide an innovative headspace-gas chromatography-mass spectrometry (HS-GC-MS) method applicable for the routine determination of blood CO concentration in forensic toxicology laboratories. The main drawback of the GC/MS methods discussed in literature for CO measurement is the absence of a specific CO internal standard necessary for performing quantification. Even if stable isotope of CO is commercially available in the gaseous state, it is essential to develop a safer method to limit the manipulation of gaseous CO and to precisely control the injected amount of CO for spiking and calibration. To avoid the manipulation of a stable isotope-labeled gas, we have chosen to generate in a vial in situ, an internal labeled standard gas ((13)CO) formed by the reaction of labeled formic acid formic acid (H(13)COOH) with sulfuric acid. As sulfuric acid can also be employed to liberate the CO reagent from whole blood, the procedure allows for the liberation of CO simultaneously with the generation of (13)CO. This method allows for precise measurement of blood CO concentrations from a small amount of blood (10 μL). Finally, this method was applied to measure the CO concentration of intoxicated human blood samples from autopsies.

A filtering method to correct time-lapse 3D ERT data and improve imaging of natural aquifer dynamics

We have developed a processing methodology that allows crosshole ERT (electrical resistivity tomography) monitoring data to be used to derive temporal fluctuations of groundwater electrical resistivity and thereby characterize the dynamics of groundwater in a gravel aquifer as it is infiltrated by river water. Temporal variations of the raw ERT apparent-resistivity data were mainly sensitive to the resistivity (salinity), temperature and height of the groundwater, with the relative contributions of these effects depending on the time and the electrode configuration. To resolve the changes in groundwater resistivity, we first expressed fluctuations of temperature-detrended apparent-resistivity data as linear superpositions of (i) time series of riverwater-resistivity variations convolved with suitable filter functions and (ii) linear and quadratic representations of river-water-height variations multiplied by appropriate sensitivity factors; river-water height was determined to be a reliable proxy for groundwater height. Individual filter functions and sensitivity factors were obtained for each electrode configuration via deconvolution using a one month calibration period and then the predicted contributions related to changes in water height were removed prior to inversion of the temperature-detrended apparent-resistivity data. Applications of the filter functions and sensitivity factors accurately predicted the apparent-resistivity variations (the correlation coefficient was 0.98). Furthermore, the filtered ERT monitoring data and resultant time-lapse resistivity models correlated closely with independently measured groundwater electrical resistivity monitoring data and only weakly with the groundwater-height fluctuations. The inversion results based on the filtered ERT data also showed significantly less inversion artefacts than the raw data inversions. We observed resistivity increases of up to 10% and the arrival time peaks in the time-lapse resistivity models matched those in the groundwater resistivity monitoring data.

Accurate calculations of intermolecular interaction energies using explicitly correlated coupled cluster wave functions and a dispersion-weighted MP2 method.

Explicitly correlated coupled-cluster calculations of intermolecular interaction energies for the S22 benchmark set of Jurecka, Sponer, Cerny, and Hobza (Chem. Phys. Phys. Chem. 2006, 8, 1985) are presented. Results obtained with the recently proposed CCSD(T)-F12a method and augmented double-zeta basis sets are found to be in very close agreement with basis set extrapolated conventional CCSD(T) results. Furthermore, we propose a dispersion-weighted MP2 (DW-MP2) approximation that combines the good accuracy of MP2 for complexes with predominately electrostatic bonding and SCS-MP2 for dispersion-dominated ones. The MP2-F12 and SCS-MP2-F12 correlation energies are weighted by a switching function that depends on the relative HF and correlation contributions to the interaction energy. For the S22 set, this yields a mean absolute deviation of 0.2 kcal/mol from the CCSD(T)-F12a results. The method, which allows obtaining accurate results at low cost, is also tested for a number of dimers that are not in the training set.

Linear relations between writhe and minimal crossing number in Conway families of ideal knots and links

We showed earlier how to predict the writhe of any rational knot or link in its ideal geometric configuration, or equivalently the average of the 3D writhe over statistical ensembles of random configurations of a given knot or link (Cerf and Stasiak 2000 Proc. Natl Acad. Sci. USA 97 3795). There is no general relation between the minimal crossing number of a knot and the writhe of its ideal geometric configuration. However, within individual families of knots linear relations between minimal crossing number and writhe were observed (Katritch et al 1996 Nature 384 142). Here we present a method that allows us to express the writhe as a linear function of the minimal crossing number within Conway families of knots and links in their ideal configuration. The slope of the lines and the shift between any two lines with the same

Linear random knots and their scaling behavior

We present here a nonbiased probabilistic method that allows us to consistently analyze knottedness of linear random walks with up to several hundred noncorrelated steps. The method consists of analyzing the spectrum of knots formed by multiple closures of the same open walk through random points on a sphere enclosing the walk. Knottedness of individual "frozen" configurations of linear chains is therefore defined by a characteristic spectrum of realizable knots. We show that in the great majority of cases this method clearly defines the dominant knot type of a walk, i.e., the strongest component of the spectrum. In such cases, direct end-to-end closure creates a knot that usually coincides with the knot type that dominates the random closure spectrum. Interestingly, in a very small proportion of linear random walks, the knot type is not clearly defined. Such walks can be considered as residing in a border zone of the configuration space of two or more knot types. We also characterize the scaling behavior of linear random knots.

A novel method for quantification of sulfolane (a metabolite of busulfan) in plasma by gas chromatography-tandem mass spectrometry.

The role of busulfan (Bu) metabolites in the adverse events seen during hematopoietic stem cell transplantation and in drug interactions is not explored. Lack of availability of established analytical methods limits our understanding in this area. The present work describes a novel gas chromatography-tandem mass spectrometric assay for the analysis of sulfolane (Su) in plasma of patients receiving high-dose Bu. Su and Bu were extracted from a single 100 μL plasma sample by liquid-liquid extraction. Bu was separately derivatized with 2,3,5,6-tetrafluorothiophenolfluorinated agent. Mass spectrometric detection of the analytes was performed in the selected reaction monitoring mode on a triple quadrupole instrument after electronic impact ionization. Bu and Su were analyzed with separate chromatographic programs, lasting 5 min each. The assay for Su was found to be linear in the concentration range of 20-400 ng/mL. The method has satisfactory sensitivity (lower limit of quantification, 20 ng/mL) and precision (relative standard deviation less than 15 %) for all the concentrations tested with a good trueness (100 ± 5 %). This method was applied to measure Su from pediatric patients with samples collected 4 h after dose 1 (n = 46), before dose 7 (n = 56), and after dose 9 (n = 54) infusions of Bu. Su (mean ± SD) was detectable in plasma of patients 4 h after dose 1, and higher levels were observed after dose 9 (249.9 ± 123.4 ng/mL). This method may be used in clinical studies investigating the role of Su on adverse events and drug interactions associated with Bu therapy.

An operator formulation of the multiscale finite-volume method with correction function

The multiscale finite-volume (MSFV) method has been derived to efficiently solve large problems with spatially varying coefficients. The fine-scale problem is subdivided into local problems that can be solved separately and are coupled by a global problem. This algorithm, in consequence, shares some characteristics with two-level domain decomposition (DD) methods. However, the MSFV algorithm is different in that it incorporates a flux reconstruction step, which delivers a fine-scale mass conservative flux field without the need for iterating. This is achieved by the use of two overlapping coarse grids. The recently introduced correction function allows for a consistent handling of source terms, which makes the MSFV method a flexible algorithm that is applicable to a wide spectrum of problems. It is demonstrated that the MSFV operator, used to compute an approximate pressure solution, can be equivalently constructed by writing the Schur complement with a tangential approximation of a single-cell overlapping grid and incorporation of appropriate coarse-scale mass-balance equations.

A Multi-SNP Locus-Association Method Reveals a Substantial Fraction of the Missing Heritability.

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.

A non-linear Bayesian approach for upscaling local-scale hydraulic conductivity measurements based on local- and regional-scale geophysical data

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed an upscaling procedure based on a Bayesian sequential simulation approach. This method is then applied to the stochastic integration of low-resolution, regional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this upscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.