Postoperative cage migration and subsidence following TLIF surgery is not associated with bony fusion.

Pseudarthrosis following transforaminal interbody fusion (TLIF) is not infrequent. Although cage migration and subsidence are commonly regarded as evidence of the absence of solid fusion, there is still no evidence of the influence of cage migration and subsidence on fusion. This study aimed to evaluate cage migration and subsidence using computed tomography (CT) DICOM data following lumbar interbody fusion. The effects of cage migration and subsidence on fusion and clinical outcomes were also assessed. A postoperative CT data set of 67 patients treated with monosegmental TLIF was analyzed in terms of cage position. To assess the effects of cage migration and subsidence on fusion, 12-month postoperative CT scans were used to assess fusion status. Clinical evaluation included the visual analog scale for pain and the Oswestry Disability Index. Postoperative cage migration occurred in 85.1% of all patients, and cage subsidence was observed in 58.2%. Radiological signs of pseudarthrosis was observed in 7.5% of the patients Neither cage migration nor subsidence affected the clinical or radiographic outcomes. No correlation was found between clinical and radiographic outcomes. The incidence of cage migration was considerable. However, as cage migration and subsidence were not associated with bony fusion, their clinical significance was considered limited.

Polydopamine and gelatin coating for rapid endothelialization of vascular scaffolds.

Rapid endothelialization helps overcome the limitations of small-diameter vascular grafts. To develop biomimetic non-thrombogenic coatings supporting endothelialization, medical-grade polyurethane (PU) nanofibrous mats and tubular scaffolds with a diameter below 6 mm prepared by solution blow spinning were coated with polydopamine (PDA), or PDA and gelatin (PDA/Gel). The scaffolds were characterized by scanning electron microscopy, porosity measurement, tensile testing, wettability, Fourier Transform Infrared spectroscopy, and termogravimetric analysis, followed by the measurement of coating stability on the tubular scaffolds. The effect of coating on scaffold endothelialization and hemocompatibility was evaluated using human umbilical vein endothelial cells (HUVECs) and human platelets, showing low numbers of adhering platelets and significantly higher numbers of HUVECs on PDA- and PDA/Gel-coated mats compared to control samples. Tubular PU scaffolds and commercial ePTFE prostheses coated with PDA or PDA/Gel were colonized with HUVECs using radial magnetic cell seeding. PDA/Gel-coated samples achieved full endothelial coverage within 1-3 days post-endothelialization. Altogether, PDA and PDA/Gel coating significantly enhance the endothelialization on the flat surfaces, tubular small-diameter scaffolds, and commercial vascular prostheses. The presented approach constitutes a fast and efficient method of improving scaffold colonization with endothelial cells, expected to work equally well upon implantation.

Multivariate Count Data Models for Time Series Forecasting.

Count data appears in many research fields and exhibits certain features that make modeling difficult. Most popular approaches to modeling count data can be classified into observation and parameter-driven models. In this paper, we review two models from these classes: the log-linear multivariate conditional intensity model (also referred to as an integer-valued generalized autoregressive conditional heteroskedastic model) and the non-linear state-space model for count data. We compare these models in terms of forecasting performance on simulated data and two real datasets. In simulations, we consider the case of model misspecification. We find that both models have advantages in different situations, and we discuss the pros and cons of inference for both models in detail.

Comparison of in vitro biocompatibility of silicone and polymethyl methacrylate during the curing phase of polymerization.

Adverse events have been reported with acrylic bone cements. However, current test standards for acrylic materials fail to characterize the potentially harmful monomers released during the curing stage. In clinical applications, materials are implanted into the human body during this phase. Silicone may be a safer alternative to acrylic cements. Silicone is used in medical applications for its biocompatibility and stability characteristics. Previously, no study has been completed which compares silicone to acrylic cements. In this study, both materials were injected into the cell medium during the curing process which more accurately reflects clinical use of material. Initially, cell cultures followed ASTM standard F813-07 which fails to capture the effects of monomer released during curing. Subsequently, a modified cell culture method was employed which evaluated cytotoxicity while the materials cured. The objective of this study was to capture toxicity data during curing phase. Thus, the test method employed measured and excluded the impact of the exothermic reaction temperature of polymethyl methacrylate (PMMA) on cell growth. The concentration of PMMA monomer was measured at 1 and 24 h after injecting PMMA into culture plates in a manner consistent with established cell growth methodologies. Our results indicate current in vitro cytotoxicity assays recommended by ASTM standards are unable to reveal the real cytotoxic effect caused by methyl methacrylate monomers during polymerization. Our modified experiment can more accurately illustrate the true nature of the toxicity of materials and improve assay results. In these tests, silicone based elastomeric polymers showed excellent cytocompatibility. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 2693-2699, 2018.

The impact of latent confounders in directed network analysis in neuroscience.

In the analysis of neuroscience data, the identification of task-related causal relationships between various areas of the brain gives insights about the network of physiological pathways that are active during the task. One increasingly used approach to identify causal connectivity uses the concept of Granger causality that exploits predictability of activity in one region by past activity in other regions of the brain. Owing to the complexity of the data, selecting components for the analysis of causality as a preprocessing step has to be performed. This includes predetermined-and often arbitrary-exclusion of information. Therefore, the system is confounded by latent sources. In this paper, the effect of latent confounders is demonstrated, and paths of influence among three components are studied. While methods for analysing Granger causality are commonly based on linear vector autoregressive models, the effects of latent confounders are expected to be present also in nonlinear systems. Therefore, all analyses are also performed for a simulated nonlinear system and discussed with regard to applications in neuroscience.

Causal inference with multiple time series: principles and problems.

I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

A graphical vector autoregressive modelling approach to the analysis of electronic diary data.

BACKGROUND: In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied. METHODS: We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models. RESULTS: The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours. CONCLUSION: The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.

On Granger causality and the effect of interventions in time series.

We combine two approaches to causal reasoning. Granger causality, on the one hand, is popular in fields like econometrics, where randomised experiments are not very common. Instead information about the dynamic development of a system is explicitly modelled and used to define potentially causal relations. On the other hand, the notion of causality as effect of interventions is predominant in fields like medical statistics or computer science. In this paper, we consider the effect of external, possibly multiple and sequential, interventions in a system of multivariate time series, the Granger causal structure of which is taken to be known. We address the following questions: under what assumptions about the system and the interventions does Granger causality inform us about the effectiveness of interventions, and when does the possibly smaller system of observable times series allow us to estimate this effect? For the latter we derive criteria that can be checked graphically and are in the same spirit as Pearl's back-door and front-door criteria (Pearl 1995).

Assessing the strength of directed influences among neural signals using renormalized partial directed coherence.

Partial directed coherence is a powerful tool used to analyze interdependencies in multivariate systems based on vector autoregressive modeling. This frequency domain measure for Granger-causality is designed such that it is normalized to [0,1]. This normalization induces several pitfalls for the interpretability of the ordinary partial directed coherence, which will be discussed in some detail in this paper. In order to avoid these pitfalls, we introduce renormalized partial directed coherence and calculate confidence intervals and significance levels. The performance of this novel concept is illustrated by application to model systems and to electroencephalography and electromyography data from a patient suffering from Parkinsonian tremor.

Estimating causal dependencies in networks of nonlinear stochastic dynamical systems.

The inference of causal interaction structures in multivariate systems enables a deeper understanding of the investigated network. Analyzing nonlinear systems using partial directed coherence requires high model orders of the underlying vector-autoregressive process. We present a method to overcome the drawbacks caused by the high model orders. We calculate the corresponding statistics and provide a significance level. The performance is illustrated by means of model systems and in an application to neurological data.

Comparison of fondaparinux, low molecular-weight heparin and unfractionated heparin in preventing thrombus formation on mechanical heart valves: results of an in-vitro study.

BACKGROUND AND AIM OF THE STUDY: The study aim was to investigate the efficacy of three different anticoagulants in preventing thrombus formation on mechanical heart valves, using an in-vitro system. METHODS: Blood samples (470 ml) were taken from young and healthy male volunteers and anticoagulated with unfractionated heparin (UFH; n=18), low molecular-weight heparin (LMWH; n=18) or fondaparinux (n=16). Bileaflet mechanical heart valves were placed in a new device--the 'Thrombosis Tester'--and exposed in a continuous circulation at a rate of 80 beats per min to the anticoagulated blood samples for a total exposure time of 60 min. Results for thrombus weight were skewed distributed and presented as log-transformed values. RESULTS: The weight of each valve was measured before and after 1 h of perfusion; subsequent mean (+/-SD) thrombus weights were 0.739 +/- 0.573 g for UFH, 0.789 +/- 0.099 g for LMWH, and 0.934 +/- 0.145 g for fondaparinux (p = 0.397 for comparison of all groups by ANOVA). Electron microscopy showed concordant results with regard to thrombus formation on heart valve surfaces. CONCLUSION: Fondaparinux and LMWH were as effective as UFH in preventing thrombus formation on mechanical heart valves in vitro. The 'Thrombosis Tester' proved to be a new, unique instrument for investigating the ability of anticoagulants to prevent valve thrombosis on mechanical heart valves under in-vitro conditions.

On the evaluation of information flow in multivariate systems by the directed transfer function.

The directed transfer function (DTF) has been proposed as a measure of information flow between the components of multivariate time series. In this paper, we discuss the interpretation of the DTF and compare it with other measures for directed relationships. In particular, we show that the DTF does not indicate multivariate or bivariate Granger causality, but that it is closely related to the concept of impulse response function and can be viewed as a spectral measure for the total causal influence from one component to another. Furthermore, we investigate the statistical properties of the DTF and establish a simple significance level for testing for the null hypothesis of no information flow.

Testing for directed influences among neural signals using partial directed coherence.

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity. When applying multivariate time series analysis techniques to neural signals, detection of directed relationships, which can be described in terms of Granger-causality, is of particular interest. Partial directed coherence has been introduced for a frequency domain analysis of linear Granger-causality based on modeling the underlying dynamics by vector autoregressive processes. We discuss the statistical properties of estimates for partial directed coherence and propose a significance level for testing for nonzero partial directed coherence at a given frequency. The performance of this test is illustrated by means of linear and non-linear model systems and in an application to electroencephalography and electromyography data recorded from a patient suffering from essential tremor.

A graphical approach for evaluating effective connectivity in neural systems.

The identification of effective connectivity from time-series data such as electroencephalogram (EEG) or time-resolved function magnetic resonance imaging (fMRI) recordings is an important problem in brain imaging. One commonly used approach to inference effective connectivity is based on vector autoregressive models and the concept of Granger causality. However, this probabilistic concept of causality can lead to spurious causalities in the presence of latent variables. Recently, graphical models have been used to discuss problems of causal inference for multivariate data. In this paper, we extend these concepts to the case of time-series and present a graphical approach for discussing Granger-causal relationships among multiple time-series. In particular, we propose a new graphical representation that allows the characterization of spurious causality and, thus, can be used to investigate spurious causality. The method is demonstrated with concurrent EEG and fMRI recordings which are used to investigate the interrelations between the alpha rhythm in the EEG and blood oxygenation level dependent (BOLD) responses in the fMRI. The results confirm previous findings on the location of the source of the EEG alpha rhythm.

Partial correlation analysis for the identification of synaptic connections.

In this paper, we investigate the use of partial correlation analysis for the identification of functional neural connectivity from simultaneously recorded neural spike trains. Partial correlation analysis allows one to distinguish between direct and indirect connectivities by removing the portion of the relationship between two neural spike trains that can be attributed to linear relationships with recorded spike trains from other neurons. As an alternative to the common frequency domain approach based on the partial spectral coherence we propose a new statistic in the time domain. The new scaled partial covariance density provides additional information on the direction and the type, excitatory or inhibitory, of the connectivities. In simulation studies, we investigated the power and limitations of the new statistic. The simulations show that the detectability of various connectivity patterns depends on various parameters such as connectivity strength and background activity. In particular, the detectability decreases with the number of neurons included in the analysis and increases with the recording time. Further, we show that the method can also be used to detect multiple direct connectivities between two neurons. Finally, the methods of this paper are illustrated by an application to neurophysiological data from spinal dorsal horn neurons.