Multiparametric Echocardiography Scores for the Diagnosis of Cardiac Amyloidosis

OBJECTIVES: This study aimed to investigate the accuracy of a broad range of echocardiographic variables to develop multiparametric scores to diagnose CA in patients with proven light chain (AL) amyloidosis or those with increased heart wall thickness who had amyloid was suspected. We also aimed to further characterize the structural and functional changes associated with amyloid infiltration. BACKGROUND: Cardiac amyloidosis (CA) is a serious but increasingly treatable cause of heart failure. Diagnosis is challenging and frequently unclear at echocardiography, which remains the most often used imaging tool. METHODS: We studied 1,187 consecutive patients evaluated at 3 referral centers for CA and analyzed morphological, functional, and strain-derived echocardiogram parameters with the aim of developing a score-based diagnostic algorithm. Cardiac amyloid burden was quantified by using extracellular volume measurements at cardiac magnetic resonance. RESULTS: A total of 332 patients were diagnosed with AL amyloidosis and 339 patients with transthyretin CA. Concentric remodeling and strain-derived parameters displayed the best diagnostic performance. A multivariable logistic regression model incorporating relative wall thickness, E wave/e' wave ratio, longitudinal strain, and tricuspid annular plane systolic excursion had the greatest diagnostic performance in AL amyloidosis (area under the curve: 0.90; 95% confidence interval: 0.87 to 0.92), whereas the addition of septal apical-to-base ratio yielded the best diagnostic accuracy in the increased heart wall thickness group (area under the curve: 0.80; 95% confidence interval: 0.85 to 0.90). CONCLUSIONS: Specific functional and structural parameters characterize different burdens of CA deposition with different diagnostic performances and enable the definition of 2 scores that are sensitive and specific tools with which diagnose or exclude CA

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Many applications, related to autonomous mobile robots, require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue and handle cooperatively some distributed targets. This problem is formulated by first defining an optimization model and then considering two sub-problems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit a certain number of robots to deal with the found target together. For this latter purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization, and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete the mission. Simulation results indicate that the firefly-based strategy usually provides superior performance and can reduce the wastage of energy, especially in complex scenarios

Modeling the Adaptive Role of Negative Signaling in Honey Bee Intraspecific Competition

Collective decision making in the social insects often proceeds via feedback cycles based on positive signaling. Negative signals have, however, been found in a few contexts in which costs exist for paying attention to no longer useful information. Here we incorporate new research on the specificity and context of the negative stop signal into an agent based model of honey bee foraging to explore the adaptive basis of negative signaling in the dance language. Our work suggests that the stop signal, by acting as a counterbalance to the waggle dance, allows colonies to rapidly shut down attacks on other colonies. This could be a key adaptation, as the costs of attacking a colony strong enough to defend itself are significant

A Ribosomal S-6 Kinase–Mediated Signal to C/EBP-β Is Critical for the Development of Liver Fibrosis

BACKGROUND: In response to liver injury, hepatic stellate cell (HSC) activation causes excessive liver fibrosis. Here we show that activation of RSK and phosphorylation of C/EBPbeta on Thr217 in activated HSC is critical for the progression of liver fibrosis. METHODOLOGY/PRINCIPAL FINDINGS: Chronic treatment with the hepatotoxin CCl(4) induced severe liver fibrosis in C/EBPbeta(+/+) mice but not in mice expressing C/EBPbeta-Ala217, a non-phosphorylatable RSK-inhibitory transgene. C/EBPbeta-Ala217 was present within the death receptor complex II, with active caspase 8, and induced apoptosis of activated HSC. The C/EBPbeta-Ala217 peptides directly stimulated caspase 8 activation in a cell-free system. C/EBPbeta(+/+) mice with CCl(4)-induced severe liver fibrosis, while continuing on CCl(4), were treated with a cell permeant RSK-inhibitory peptide for 4 or 8 weeks. The peptide inhibited RSK activation, stimulating apoptosis of HSC, preventing progression and inducing regression of liver fibrosis. We found a similar activation of RSK and phosphorylation of human C/EBPbeta on Thr266 (human phosphoacceptor) in activated HSC in patients with severe liver fibrosis but not in normal livers, suggesting that this pathway may also be relevant in human liver fibrosis. CONCLUSIONS/SIGNIFICANCE: These data indicate that the RSK-C/EBPbeta phosphorylation pathway is critical for the development of liver fibrosis and suggest a potential therapeutic target

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques