Predictors of functional decline in elderly patients undergoing transcatheter aortic valve implantation (TAVI)

Aims This study aimed to assess functional course in elderly patients undergoing transcatheter aortic valve implantation (TAVI) and to find predictors of functional decline. Methods and results In this prospective cohort, functional course was assessed in patients ≥70 years using basic activities of daily living (BADL) before and 6 months after TAVI. Baseline EuroSCORE, STS score, and a frailty index (based on assessment of cognition, mobility, nutrition, instrumental and basic activities of daily living) were evaluated to predict functional decline (deterioration in BADL) using logistic regression models. Functional decline was observed in 22 (20.8%) of 106 surviving patients. EuroSCORE (OR per 10% increase 1.18, 95% CI: 0.83-1.68, P = 0.35) and STS score (OR per 5% increase 1.64, 95% CI: 0.87-3.09, P = 0.13) weakly predicted functional decline. In contrast, the frailty index strongly predicted functional decline in univariable (OR per 1 point increase 1.57, 95% CI: 1.20-2.05, P = 0.001) and bivariable analyses (OR: 1.56, 95% CI: 1.20-2.04, P = 0.001 controlled for EuroSCORE; OR: 1.53, 95% CI: 1.17-2.02, P = 0.002 controlled for STS score). Overall predictive performance was best for the frailty index [Nagelkerke's R(2) (NR(2)) 0.135] and low for the EuroSCORE (NR(2) 0.015) and STS score (NR(2) 0.034). In univariable analyses, all components of the frailty index contributed to the prediction of functional decline. Conclusion Over a 6-month period, functional status worsened only in a minority of patients surviving TAVI. The frailty index, but not established risk scores, was predictive of functional decline. Refinement of this index might help to identify patients who potentially benefit from additional geriatric interventions after TAVI.

Functional neuroimaging of emotional learning and autonomic reactions

This article provides a selective overview of the functional neuroimaging literature with an emphasis on emotional activation processes. Emotions are fast and flexible response systems that provide basic tendencies for adaptive action. From the range of involved component functions, we first discuss selected automatic mechanisms that control basic adaptational changes. Second, we illustrate how neuroimaging work has contributed to the mapping of the network components associated with basic emotion families (fear, anger, disgust, happiness), and secondary dimensional concepts that organise the meaning space for subjective experience and verbal labels (emotional valence, activity/intensity, approach/withdrawal, etc.). Third, results and methodological difficulties are discussed in view of own neuroimaging experiments that investigated the component functions involved in emotional learning. The amygdala, prefrontal cortex, and striatum form a network of reciprocal connections that show topographically distinct patterns of activity as a correlate of up and down regulation processes during an emotional episode. Emotional modulations of other brain systems have attracted recent research interests. Emotional neuroimaging calls for more representative designs that highlight the modulatory influences of regulation strategies and socio-cultural factors responsible for inhibitory control and extinction. We conclude by emphasising the relevance of the temporal process dynamics of emotional activations that may provide improved prediction of individual differences in emotionality.

Different Prognostic Value of Functional Right Ventricular Parameters in Arrhythmogenic Right Ventricular Cardiomyopathy/Dysplasia

BACKGROUND -The value of standard two-dimensional transthoracic echocardiographic (TTE) parameters for risk stratification in patients with arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is controversial. METHODS AND RESULTS -We investigated the impact of right ventricular fractional area change (FAC) and tricuspid annulus plane systolic excursion (TAPSE) for prediction of major adverse cardiovascular events (MACE) defined as the occurrence of cardiac death, heart transplantation, survived sudden cardiac death, ventricular fibrillation, sustained ventricular tachycardia or arrhythmogenic syncope. Among 70 patients who fulfilled the 2010 ARVC/D Task Force Criteria and underwent baseline TTE, 37 (53%) patients experienced a MACE during a median follow-up period of 5.3 (IQR 1.8-9.8) years. Average values for FAC, TAPSE, and TAPSE indexed to body surface area (BSA) decreased over time (p=0.03 for FAC, p=0.03 for TAPSE and p=0.01 for TAPSE/BSA, each vs. baseline). In contrast, median right ventricular end-diastolic area (RVEDA) increased (p=0.001 vs. baseline). Based on the results of Kaplan-Meier estimates, the time between baseline TTE and experiencing MACE was significantly shorter for patients with FAC <23% (p<0.001), TAPSE <17mm (p=0.02) or right atrial (RA) short axis/BSA ≥25mm/m(2) (p=0.04) at baseline. A reduced FAC constituted the strongest predictor of MACE (hazard ratio 1.08 per 1% decrease; 95% confidence interval 1.04-1.12; p<0.001) on bivariable analysis. CONCLUSIONS -This long-term observational study indicates that TAPSE and dilation of right-sided cardiac chambers are associated with an increased risk for MACE in ARVC/D patients with advanced disease and a high risk for adverse events. However, FAC is the strongest echocardiographic predictor of adverse outcome in these patients. Our data advocate a role for TTE in risk stratification in patients with ARVC/D, although our results may not be generalizable to lower risk ARVC/D cohorts.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

Novel biomarkers for the prediction of metastasis in colorectal cancer

INTRODUCTION In patients with metastatic colorectal cancers, multimodal management and the use of biological agents such as monoclonal antibodies have had major positive effects on survival. The ability to predict which patients may be at 'high risk' of distant metastasis could have major implications on patient management. Histomorphological, immunohistochemical or molecular biomarkers are currently being investigated in order to test their potential value as predictors of metastasis. AREAS COVERED Here, the author reviews the clinical and functional data supporting the investigation of three novel promising biomarkers for the prediction of metastasis in patients with colorectal cancer: tumor budding, Raf1 kinase inhibitor protein (RKIP) and metastasis-associated in colon cancer-1 (MACC1). EXPERT OPINION The lifespan of most potential biomarkers is short as evidenced by the rare cases that have successfully made their way into daily practice such as KRAS or microsatellite instability (MSI) status. Although the three biomarkers reviewed herein have the potential to become important predictive biomarkers of metastasis, they have similar hurdles to overcome before they can be implemented into clinical management: standardization and validation in prospective patient cohorts.

IL28B expression depends on a novel TT/-G polymorphism which improves HCV clearance prediction

Approximately 3% of the world population is chronically infected with the hepatitis C virus (HCV), with potential development of cirrhosis and hepatocellular carcinoma. Despite the availability of new antiviral agents, treatment remains suboptimal. Genome-wide association studies (GWAS) identified rs12979860, a polymorphism nearby IL28B, as an important predictor of HCV clearance. We report the identification of a novel TT/-G polymorphism in the CpG region upstream of IL28B, which is a better predictor of HCV clearance than rs12979860. By using peripheral blood mononuclear cells (PBMCs) from individuals carrying different allelic combinations of the TT/-G and rs12979860 polymorphisms, we show that induction of IL28B and IFN-γ–inducible protein 10 (IP-10) mRNA relies on TT/-G, but not rs12979860, making TT/-G the only functional variant identified so far. This novel step in understanding the genetic regulation of IL28B may have important implications for clinical practice, as the use of TT/G genotyping instead of rs12979860 would improve patient management.

Prediction and prevention of psychosis: current progress and future tasks

Prevention of psychoses has been intensively investigated within the past two decades, and particularly, prediction has been much advanced. Depending on the applied risk indicators, current criteria are associated with average, yet significantly heterogeneous transition rates of ≥30 % within 3 years, further increasing with longer follow-up periods. Risk stratification offers a promising approach to advance current prediction as it can help to reduce heterogeneity of transition rates and to identify subgroups with specific needs and response patterns, enabling a targeted intervention. It may also be suitable to improve risk enrichment. Current results suggest the future implementation of multi-step risk algorithms combining sensitive risk detection by cognitive basic symptoms (COGDIS) and ultra-high-risk (UHR) criteria with additional individual risk estimation by a prognostic index that relies on further predictors such as additional clinical indicators, functional impairment, neurocognitive deficits, and EEG and structural MRI abnormalities, but also considers resilience factors. Simply combining COGDIS and UHR criteria in a second step of risk stratification produced already a 4-year hazard rate of 0.66. With regard to prevention, two recent meta-analyses demonstrated that preventive measures enable a reduction in 12-month transition rates by 54-56 % with most favorable numbers needed to treat of 9-10. Unfortunately, psychosocial functioning, another important target of preventive efforts, did not improve. However, these results are based on a relatively small number of trials; and more methodologically sound studies and a stronger consideration of individual profiles of clinical needs by modular intervention programs are required

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Functional lateralization of the anterior insula during feedback processing

Effective adaptive behavior rests on an appropriate understanding of how much responsibility we have over outcomes in the environment. This attribution of agency to ourselves or to an external event influences our behavioral and affective response to the outcomes. Despite its special importance to understanding human motivation and affect, the neural mechanisms involved in self-attributed rewards and punishments remain unclear. Previous evidence implicates the anterior insula (AI) in evaluating the consequences of our own actions. However, it is unclear if the AI has a general role in feedback evaluation (positive and negative) or plays a specific role during error processing. Using functional magnetic resonance imaging and a motion prediction task, we investigate neural responses to self- and externally attributed monetary gains and losses. We found that attribution effects vary according to the valence of feedback: significant valence × attribution interactions in the right AI, the anterior cingulate cortex (ACC), the midbrain, and the right ventral putamen. Self-attributed losses were associated with increased activity in the midbrain, the ACC and the right AI, and negative BOLD response in the ventral putamen. However, higher BOLD activity to self-attributed feedback (losses and gains) was observed in the left AI, the thalamus, and the cerebellar vermis. These results suggest a functional lateralization of the AI. The right AI, together with the midbrain and the ACC, is mainly involved in processing the salience of the outcome, whereas the left is part of a cerebello-thalamic-cortical pathway involved in cognitive control processes important for subsequent behavioral adaptations.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings.