Impact of Temporary Portocaval Shunting and Initial Arterial Reperfusion in Orthotopic Liver Transplantation.

The use of a temporary portocaval shunt (TPCS) as well as the order of reperfusion (initial arterial reperfusion [IAR] versus initial portal reperfusion) in orthotopic liver transplantation (OLT) is controversial and, therefore, still under debate. The aim of this study was to evaluate outcome for the 4 possible combinations (temporary portocaval shunt with initial arterial reperfusion [A+S+], temporary portocaval shunt with initial portal reperfusion, no temporary portocaval shunt with initial arterial reperfusion, and no temporary portocaval shunt with initial portal reperfusion) in a center-based cohort study, including liver transplantations (LTs) from both donation after brain death and donation after circulatory death (DCD) donors. The primary outcome was the perioperative transfusion of red blood cells (RBCs), and the secondary outcomes were operative time and patient and graft survival. Between January 2005 and May 2017, all first OLTs performed in our institution were included in the 4 groups mentioned. With IAR and TPCS, a significantly lower perioperative transfusion of RBCs was seen (P < 0.001) as well as a higher number of recipients without any transfusion of RBCs (P < 0.001). A multivariate analysis showed laboratory Model for End-Stage Liver Disease (MELD) score (P < 0.001) and IAR (P = 0.01) to be independent determinants of the transfusion of RBCs. When comparing all groups, no statistical difference was seen in operative time or in 1-year patient and graft survival rates despite more LTs with a liver from a DCD donor in the A+S+ group (P = 0.005). In conclusion, next to a lower laboratory MELD score, the use of IAR leads to a significantly lower need for perioperative blood transfusion. There was no significant interaction between IAR and TPCS. Furthermore, the use of a TPCS and/or IAR does not lead to increased operative time and is therefore a reasonable alternative surgical strategy.

The Effect of Histidine-tryptophan-ketoglutarate Solution and University of Wisconsin Solution: An Analysis of the Eurotransplant Registry.

BACKGROUND: Both University of Wisconsin (UW) and histidine-tryptophan-ketoglutarate (HTK) solutions are currently used in the Eurotransplant region for preservation of liver allografts. Previous studies on their effect have led to a lot of discussion. This study aims to compare the effect of HTK and UW on graft survival. METHODS: First liver transplantations in recipients 18 years or older from January 1, 2007, until December 31, 2016, were included. Graft survival was compared for livers preserved with HTK and UW at 30 days, 1, 3, and 5 years. Multivariable analysis of risk factors was performed and outcome was adjusted for important confounders. RESULTS: Of all 10 628 first liver transplantations, 8176 (77%) and 2452 (23%) were performed with livers preserved with HTK and UW, respectively. Kaplan-Meier curves showed significant differences in graft survival between HTK and UW at 30 days (89% vs 93%, P=<0.001), 1 year (75% vs 82%, P=<0.001), 3 years (67% vs 72%, P<0.001), and at 5 years (60% vs 67%, P<0.001). No significant differences in outcome were observed in separate analyses of Germany or non-German countries. In multivariable analysis, UW was associated with a decreased risk of graft loss at 30 days (HR 0.772, P=0.002) and at 1 year (0.847 (0.757-0.947). When adjusted for risk factors, no differences in long term outcome could be detected. CONCLUSIONS: Because the use of preservation fluids is clustered geographically, differences in outcome by preservation fluids are strongly affected by regional differences in donor and recipient characteristics. When adjusted for risk factors, no differences in graft survival exist between transplantations performed with livers preserved with either HTK or UW.

Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods.

Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a "good" tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user's preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a quantitative structure activity relationship (QSAR) data set are evaluated using PLS and RR.

Sparse methods in spectroscopy: an introduction, overview, and perspective.

Multivariate calibration methods such as partial least-squares build calibration models that are not parsimonious: all variables (either wavelengths or samples) are used to define a calibration model. In high-dimensional or large sample size settings, interpretable analysis aims to reduce model complexity by finding a small subset of variables that significantly influences the model. The term "sparsity", as used here, refers to calibration models having many zero-valued regression coefficients. Only the variables associated with non-zero coefficients influence the model. In this paper, we briefly review the regression problems associated with sparse models and discuss their spectroscopic applications. We also discuss how one can re-appropriate sparse modeling algorithms that perform wavelength selection for purposes of sample selection. In particular, we highlight specific sparse modeling algorithms that are easy to use and understand for the spectroscopist, as opposed to the overly complex "black-box" algorithms that dominate much of the statistical learning literature. We apply these sparse modeling approaches to three spectroscopic data sets.

The shuttling scaffold model for prevention of yeast pheromone pathway misactivation.

The molecular scaffold in the yeast pheromone pathway, Ste5, shuttles continuously between the nucleus and the cytoplasm. Ste5 undergoes oligomerization reaction in the nucleus. Upon pheromone stimulation, the Ste5 dimer is rapidly exported out of the nucleus and recruited to the plasma membrane for pathway activation. This clever device on part of the yeast cell is thought to prevent pathway misactivation at high enough levels of Ste5 in the absence of pheromone. We have built a spatiotemporal model of signaling in this pathway to describe its regulation. Our present work underscores the importance of spatial modeling of cell signaling networks to understand their control and functioning.

Spectral multivariate calibration with wavelength selection using variants of Tikhonov regularization.

Tikhonov regularization (TR) is a general method that can be used to form a multivariate calibration model and numerous variants of it exist, including ridge regression (RR). This paper reports on the unique flexibility of TR to form a model using full wavelengths (RR), individually selected wavelengths, or multiple bands of selected wavelengths. Of these three TR variants, the one based on selection of wavelength bands is found to produce lower prediction errors. As with most wavelength selection algorithms, the model vector magnitude indicates that this error reduction comes with a potential increase in prediction uncertainty. Results are presented for near-infrared, ultraviolet-visible, and synthetic spectral data sets. While the focus of this paper is wavelength selection, the TR methods are generic and applicable to other variable-selection situations.

Model updating for spectral calibration maintenance and transfer using 1-norm variants of Tikhonov regularization.

In this study, calibration maintenance confronts the problem of updating a model developed in the primary condition to accurately predict the calibrated analyte in samples measured in new secondary conditions. Calibration transfer refers to updating a model based on a primary instrument to predict samples measured on new secondary instruments. A 2-norm variant of Tikhonov regularization (TR) has been used with spectroscopic data to perform calibration maintenance and transfer where just a few samples measured in the secondary condition/instrument are augmented to the primary calibration data to update the primary model. To achieve improved predictions, in this paper we report on 1-norm penalties to create two novel variants of TR for model updating. To solve the multiple penalty minimization numerical problems involved with the new TR variants, data transformation processes are applied, allowing a least absolute shrinkage and selection operator type algorithm to be implemented. Near-infrared spectra measured under two different temperatures represent the calibration maintenance application, and near-infrared spectra measured on two instruments denote the calibration transfer situation. Compared to TR in the recently developed 2-norm penalty mode, validation sample prediction errors are reduced when the 1-norm TR variant that selects wavelengths is used.

Calibration maintenance and transfer using Tikhonov regularization approaches.

Maintaining multivariate calibrations is essential and involves keeping models developed on an instrument applicable to predicting new samples over time. Sometimes a primary instrument model is needed to predict samples measured on secondary instruments. This situation is referred to as calibration transfer. This paper reports on using a Tikhonov regularization (TR) based method in both cases. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for a small set of new (transfer or standardization) samples augmented to the full set of calibration samples. Because straight application of basic TR theory is not always possible with calibration maintenance and transfer, this paper develops a generic solution to always enable application of TR. Harmonious (bias/variance tradeoff) and parsimonious (effective rank) considerations for TR are compared with the same TR format applied to partial least squares (PLS), showing that both approaches are viable solutions to the calibration maintenance and transfer problems.

Methods for kinetic modeling of temporally resolved hyperspectral confocal fluorescence images.

Elucidating kinetic information (rate constants) from temporally resolved hyperspectral confocal fluorescence images offers some very important opportunities for the interpretation of spatially resolved hyperspectral confocal fluorescence images but also presents significant challenges, these being (1) the massive amount of data contained in a series of time-resolved spectral images (one time course of spectral data for each pixel) and (2) unknown concentrations of the reactants and products at time = 0, a necessary precondition normally required by traditional kinetic fitting approaches. This paper describes two methods for solving these problems: direct nonlinear (DNL) estimation of all parameters and separable least squares (SLS). The DNL method can be applied to reactions of any rate law, while the SLS method is restricted to first-order reactions. In SLS, the inherently linear and nonlinear parameters of first-order reactions are solved in separate linear and nonlinear steps, respectively. The new methods are demonstrated using simulated data sets and an experimental data set involving photobleaching of several fluorophores. This work demonstrates that both DNL and SLS hard-modeling methods applied to the kinetic modeling of temporally resolved hyperspectral images can outperform traditional soft-modeling and hard/soft-modeling methods which use multivariate curve resolution-alternating least squares (MCRALS) methods. In addition, the SLS method is much faster and is able to analyze much larger data sets than the DNL method.

Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.

Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction.

To determine whether gene expression profiling could improve risk classification and outcome prediction in older acute myeloid leukemia (AML) patients, expression profiles were obtained in pretreatment leukemic samples from 170 patients whose median age was 65 years. Unsupervised clustering methods were used to classify patients into 6 cluster groups (designated A to F) that varied significantly in rates of resistant disease (RD; P < .001), complete response (CR; P = .023), and disease-free survival (DFS; P = .023). Cluster A (n = 24), dominated by NPM1 mutations (78%), normal karyotypes (75%), and genes associated with signaling and apoptosis, had the best DFS (27%) and overall survival (OS; 25% at 5 years). Patients in clusters B (n = 22) and C (n = 31) had the worst OS (5% and 6%, respectively); cluster B was distinguished by the highest rate of RD (77%) and multidrug resistant gene expression (ABCG2, MDR1). Cluster D was characterized by a "proliferative" gene signature with the highest proportion of detectable cytogenetic abnormalities (76%; including 83% of all favorable and 34% of unfavorable karyotypes). Cluster F (n = 33) was dominated by monocytic leukemias (97% of cases), also showing increased NPM1 mutations (61%). These gene expression signatures provide insights into novel groups of AML not predicted by traditional studies that impact prognosis and potential therapy.

Secondary cardiac tumour originating from laryngeal carcinoma: case report and review of the literature.

Although most malignant tumours metastasize to the heart they rarely produce clinical symptoms. Cardiac metastasis of tumours arising from head and neck and particulary of the larynx are extremely rare. Herein we report an unusual case of successfully treated, advanced laryngeal carcinoma with subsequent late symptomatic metastatic disease to the heart. The patient discussed, developed a right ventricular intracavitary tumour with pseudo-infarct pattern on the ECG. Current litterature regarding incidence, diagnostic techniques, treatment modalities and survival rates of secondary heart tumours are reviewed.

Oxygen uptake efficiency slope, a new submaximal parameter in evaluating exercise capacity in chronic heart failure patients.

BACKGROUND: The oxygen uptake efficiency slope (OUES) is a new submaximal parameter which objectively predicts the maximal exercise capacity in children and healthy subjects. However, the usefulness of OUES in adult patients with and without advanced heart failure remains undetermined. The present study investigates the stability and the usefulness of OUES in adult cardiac patients with and without heart failure. METHODS: Forty-five patients with advanced heart failure (group A) and 35 patients with ischemic heart disease but normal left ventricular ejection fraction (group B) performed a maximal exercise test. PeakVO2 and percentage of predicted peakVO2 were markers of maximal exercise capacity, whereas OUES, ventilatory anaerobic threshold (VAT), and slope VE/VCO2 were calculated as parameters of submaximal exercise. RESULTS: Group A patients had lower peakVO2 (P < .001), lower percentage of predicted peakVO2 (P = .001), lower VAT (P < .05), steeper slope VE/VCO2 (P < .001), and lower OUES (P < .02). Within group A, significant differences were found for VAT, slope VE/VCO2, and OUES (all P < .01) between patients with peakVO2 above and below 14 mL O2/kg/min. Of all the submaximal parameters, VAT correlated best with peakVO2 (r =.814, P < .01) followed by OUES/kg (r = .781, P < .01), and slope VE/VCO2 (r = -.492, P < .001). However, VAT could not be determined in 18 (23%) patients. CONCLUSIONS: OUES remains stable over the entire exercise duration and is significantly correlated with peakVO2 in adult cardiac patients with and without impaired LVEF. Therefore, OUES could be helpful to assess exercise performance in advanced heart failure patients unable to perform a maximal exercise test. Further studies are needed to confirm our hypothesis.

Arritmias cardiacas nos anos 90: um ponto de vista sobre mecanismos eletrofisiologicos, diagnostico eletrocardiografico e tratamento / Cardiac arrhythmias in the 90's: a point of view on the eletrocardiographic mechanisms and treatment

Tanto quanto a cardiologia, o estudo dos disturbios do ritmo cardiaco prosperou enormemente na ultima decada. Entretanto, dado o imenso numero de publicacoes, medicos nao envolvidos diretamente com eletrofisiologia clinica em sua pratica diaria, perderam a seguranca em tratar pacientes portadores de arritmias cardiacas. Esta revisao tenta resgatar conceitos basicos relacionados ao mecanismo, diagnostico e tratamento das arritmias cardiacas, dentro de um ponto de vista eminentemente pratico.