Transarterial chemoembolisation: effect of selectivity on tolerance, tumour response and survival

Aims To compare selective and non-selective TACE techniques in the treatment of HCC with a special emphasis on clinical and liver tolerance, tumour response and survival. Methods 184 patients with advanced HCC were retrospectively included. Three different TACE techniques were compared: non selective lipiodol-chemotherapy + non selective embolisation (TACE-technique group 1), non selective lipiodol-chemotherapy + selective embolisation (group 2), and selective lipiodol-chemotherapy + selective embolisation (group 3). Results In multivariate analysis TACE-technique group is an independently significant prognostic factor for poor clinical tolerance, poor liver tolerance and tumour response. The rate of patients with poor clinical tolerance was lower in group 3 (27.0%) than in groups 1 (64.1%, p < 10−3) or 2 (66.7%, p < 10−3). The rate of patients with poor liver tolerance was higher in group 2 (34.0%) than in groups 1 (17.6%, p = 0.050) or 3 (6.9%, p = 0.011). The rate of patients with tumour response was higher when embolisation was selective versus non-selective, i.e., group 2 + 3 (78.7%) versus group 1 (62.5%, p = 0.054). Overall survival was not significantly different between the three groups (p = 0.383). Conclusion Both selective techniques resulted in better tumour response. As for improving tolerance, our study suggests that the main technical factor is the use of selective lipiodol-chemotherapy injection

Mapping the potential energy landscape of intrinsically disordered proteins at amino acid resolution

Intrinsically disordered regions are predicted to exist in a significant fraction of proteins encoded in eukaryotic genomes. The high levels of conformational plasticity of this class of proteins endows them with unique capacities to act in functional modes not achievable by folded proteins, but also places their molecular characterization beyond the reach of classical structural biology. New techniques are therefore required to understand the relationship between primary sequence and biological function in this class of proteins. Although dependences of some NMR parameters such as chemical shifts (CSs) or residual dipolar couplings (RDCs) on structural propensity are known, so that sampling regimes are often inferred from experimental observation, there is currently no framework that allows for a statistical mapping of the available Ramachandran space of each amino acid in terms of conformational propensity. In this study we develop such an approach, combining highly efficient conformational sampling with ensemble selection to map the backbone conformational sampling of IDPs on a residue specific level. By systematically analyzing the ability of NMR data to map the conformational landscape of disordered proteins, we identify combinations of RDCs and CSs that can be used to raise conformational degeneracies inherent to different data types, and apply these approaches to characterize the conformational behavior of two intrinsically disordered proteins, the K18 domain from Tau protein and NTAIL from measles virus nucleoprotein. In both cases, we identify the enhanced populations of turn and helical regions in key regions of the proteins, as well as contiguous strands that show clear and enhanced polyproline II sampling

Brain serotonin 4 receptor binding is inversely associated with verbal memory recall

BACKGROUND: We have previously identified an inverse relationship between cerebral serotonin 4 receptor (5‐HT (4)R) binding and nonaffective episodic memory in healthy individuals. Here, we investigate in a novel sample if the association is related to affective components of memory, by examining the association between cerebral 5‐HT (4)R binding and affective verbal memory recall. METHODS: Twenty‐four healthy volunteers were scanned with the 5‐HT (4)R radioligand [(11)C]SB207145 and positron emission tomography, and were tested with the Verbal Affective Memory Test‐24. The association between 5‐HT (4)R binding and affective verbal memory was evaluated using a linear latent variable structural equation model. RESULTS: We observed a significant inverse association across all regions between 5‐HT (4)R binding and affective verbal memory performances for positive (p = 5.5 × 10(−4)) and neutral (p = .004) word recall, and an inverse but nonsignificant association for negative (p = .07) word recall. Differences in the associations with 5‐HT (4)R binding between word categories (i.e., positive, negative, and neutral) did not reach statistical significance. CONCLUSION: Our findings replicate our previous observation of a negative association between 5‐HT (4)R binding and memory performance in an independent cohort and provide novel evidence linking 5‐HT (4)R binding, as a biomarker for synaptic 5‐HT levels, to the mnestic processing of positive and neutral word stimuli in healthy humans

3D MRI of explanted sheep hearts with submillimeter isotropic spatial resolution: comparison between diffusion tensor and structure tensor imaging

OBJECTIVE: The aim of the study is to compare structure tensor imaging (STI) with diffusion tensor imaging (DTI) of the sheep heart (approximately the same size as the human heart). MATERIALS AND METHODS: MRI acquisition on three sheep ex vivo hearts was performed at 9.4 T/30 cm with a seven-element RF coil. 3D FLASH with an isotropic resolution of 150 µm and 3D spin-echo DTI at 600 µm were performed. Tensor analysis, angles extraction and segments divisions were performed on both volumes. RESULTS: A 3D FLASH allows for visualization of the detailed structure of the left and right ventricles. The helix angle determined using DTI and STI exhibited a smooth transmural change from the endocardium to the epicardium. Both the helix and transverse angles were similar between techniques. Sheetlet organization exhibited the same pattern in both acquisitions, but local angle differences were seen and identified in 17 segments representation. DISCUSSION: This study demonstrated the feasibility of high-resolution MRI for studying the myocyte and myolaminar architecture of sheep hearts. We presented the results of STI on three whole sheep ex vivo hearts and demonstrated a good correspondence between DTI and STI

Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins.

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating alpha-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts alpha-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect alpha-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available

Bayesian averaging over decision tree models: an application for estimating uncertainty in trauma severity scoring

Introduction For making reliable decisions, practitioners need to estimate uncertainties that exist in data and decision models. In this paper we analyse uncertainties of predicting survival probability for patients in trauma care. The existing prediction methodology employs logistic regression modelling of Trauma and Injury Severity Score(external) (TRISS), which is based on theoretical assumptions. These assumptions limit the capability of TRISS methodology to provide accurate and reliable predictions. Methods We adopt the methodology of Bayesian model averaging and show how this methodology can be applied to decision trees in order to provide practitioners with new insights into the uncertainty. The proposed method has been validated on a large set of 447,176 cases registered in the US National Trauma Data Bank in terms of discrimination ability evaluated with receiver operating characteristic (ROC) and precision–recall (PRC) curves. Results Areas under curves were improved for ROC from 0.951 to 0.956 (p = 3.89 × 10−18) and for PRC from 0.564 to 0.605 (p = 3.89 × 10−18). The new model has significantly better calibration in terms of the Hosmer–Lemeshow Hˆ" role="presentation"> statistic, showing an improvement from 223.14 (the standard method) to 11.59 (p = 2.31 × 10−18). Conclusion The proposed Bayesian method is capable of improving the accuracy and reliability of survival prediction. The new method has been made available for evaluation purposes as a web application

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data

We present Farseer-NMR (https://git.io/vAueU), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems’ responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension