Double Whammy: Concomitant acute type B aortic dissection and acute pulmonary embolism

The concomitant occurrence of acute type B aortic dissection (TBAD) and acute pulmonary embolism (PE) is a rare but challenging condition. Although anticoagulation therapy is essential in the treatment of PE, it may increase the risk of aortic rupture and bleeding complications. We herein describe a patient with acute TBAD complicated by PE, which was successfully treated with early thoracic endovascular aortic repair (TEVAR) followed by anticoagulation. The present case report demonstrates that early TEVAR not only treats the aortic pathology but also allows the safe initiation of anticoagulation therapy. Copyright © 2020, The Korean Society for Vascular Surgery

Cost-effectiveness of a population-based AAA screening program for men over 65 years old in Iran

Background: Screening program tend to recognized patients in their early stage and consequently improve health outcomes. Cost-effectiveness of the abdominal aortic aneurysm (AAA) screening program has been scarcely studied in developing countries. We sought to evaluate the cost-effectiveness of a screening program for the abdominal aortic aneurysm (AAA) in men aged over 65 years in Iran. Methods: A Markov cohort model with 11 mutually exclusive health statuses was used to evaluate the cost-effectiveness of a population-based AAA screening program compared with a no-screening strategy. Transitions between the health statuses were simulated by using 3-month cycles. Data for disease transition probabilities and quality of life outcomes were obtained from published literature, and costs were calculated based on the price of medical services in Iran and the examination of the patients� medical records. The outcomes were life-years gained, the quality-adjusted life-year (QALY), costs, and the incremental cost-effectiveness ratio (ICER). The analysis was conducted for a lifetime horizon from the payer�s perspective. Costs and effects were discounted at an annual rate of 3. Uncertainty surrounding the model inputs was tested with deterministic and probabilistic sensitivity analyses. Results: The mean incremental cost of the AAA screening strategy compared with the no-screening strategy was 140 and the mean incremental QALY gain was 0.025 QALY, resulting in an ICER of 5566 (14,656 PPP) per QALY gained. At a willingness-to-pay of 1 gross domestic product (GDP) per capita (5628) per QALY gained, the probability of the cost-effectiveness of AAA screening was about 50. However, at a willingness-to-pay of twice the GDP per capita per QALY gained, there was about a 95 probability for the AAA screening program to be cost-effective in Iran. Conclusions: The results of this study showed that at a willingness-to-pay of 1 GDP per capita per QALY gained, a 1-time AAA screening program for men aged over 65 years could not be cost-effective. Nevertheless, at a willingness-to-pay of twice the GDP per capita per QALY gained, the AAA screening program could be cost-effective in Iran. Further, AAA screening in high-risk groups could be cost-effective at a willingness-to-pay of 1 GDP per capita per QALY gained. © 2021, The Author(s)

Embodied Gesture Processing: Motor-Based Integration of Perception and Action in Social Artificial Agents

A close coupling of perception and action processes is assumed to play an important role in basic capabilities of social interaction, such as guiding attention and observation of others’ behavior, coordinating the form and functions of behavior, or grounding the understanding of others’ behavior in one’s own experiences. In the attempt to endow artificial embodied agents with similar abilities, we present a probabilistic model for the integration of perception and generation of hand-arm gestures via a hierarchy of shared motor representations, allowing for combined bottom-up and top-down processing. Results from human-agent interactions are reported demonstrating the model’s performance in learning, observation, imitation, and generation of gestures

Voluntary Exercise Prevents Lead-Induced Elevation of Oxidative Stress and Inflammation Markers in Male Rat Blood

Regular mild exercise enhances antioxidant and anti-inflammatory systems of the body. The present study investigates voluntary exercise effects on lead toxicity as a known oxidative stressor. Male Sprague-Dawley rats were randomly divided into 2 groups. Sedentary control: the animals were housed 7 weeks in the regular cages. Exercise group: the animals were housed 7 weeks in the running wheel equipped cages, that is, the animal model of voluntary exercise. During the 7th week, all animals were administered lead acetate. Blood samples were collected at the end of the 6th week and 7th week (before and after lead administrations). Glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and tumor necrosis factor (TNF-) were measured in the samples. Our results showed that lead administration reduced blood SOD, GPx and CAT and increased TNF-; in the controls, but in the exercise group, changes were not statistically significant. MDA in both groups increased after lead injections but it was significantly lower in exercise group compared to the sedentary animals. We concluded that voluntary exercise may be considered as a preventive tool against lead-induced oxidative stress and inflammation

Biomorpher: interactive evolution for parametric design

Combining graph-based parametric design with metaheuristic solvers has to date focussed solely on performance based criteria and solving clearly defined objectives. In this paper, we outline a new method for combining a parametric modelling environment with an interactive Cluster-Orientated Genetic Algorithm (COGA). In addition to performance criteria, evolutionary design exploration can be guided through choice alone, with user motivation that cannot be easily defined. As well as numeric parameters forming a genotype, the evolution of whole parametric definitions is discussed through the use of genetic programming. Visualisation techniques that enable mixing small populations for interactive evolution with large populations for performance-based optimisation are discussed, with examples from both academia and industry showing a wide range of applications

Outcomes of chronic total occlusion percutaneous coronary intervention from the RAIAN (RAjaie - Iran) registry

Objective: While most of the evidence in CTO interventions emerge from Western and Japanese studies, few data have been published up today from the Middle East. Objective of this study was to evaluate technical success rates and clinical outcomes of an Iranian population undergoing CTO PCI in a tertiary referral hospital. Moreover, we sought to evaluate the efficacy of our CTO teaching program. Methods: This is a retrospective single-center cohort study including 790 patients who underwent CTO PCI performed by operators with different volumes of CTOs PCI performed per year. According to PCI result, all patients have been divided into successful (n = 555, 70.3 %) and unsuccessful (n = 235, 29.7 %) groups. Study endpoints were Major Adverse Cardiovascular Events and Health Status Improvement evaluated using the Seattle Angina Questionnaire at one year. Results: A global success rate of 70 % for antegrade and 80 % for retrograde approach was shown despite the lack of some CTO-dedicated devices. During the enrollment period, the success rate increased significantly among operators with a lower number of CTO procedures per year. One-year MACE rate was similar in both successful and unsuccessful groups (13.5 % in successful and 10.6 % in unsuccessful group, p = 0.173). One year patients' health status improved significantly only in successful group. Conclusions: No significant differences of in-hospital and one-year MACE were found between the successful and unsuccessful groups. Angina symptoms and quality of life significantly improved after successful CTO PCI. The RAIAN registry confirmed the importance of operator expertise for CTO PCI success

Application of geographic information systems and simulation modelling to dental public health: Where next?

Public health research in dentistry has used geographic information systems since the 1960s. Since then, the methods used in the field have matured, moving beyond simple spatial associations to the use of complex spatial statistics and, on occasions, simulation modelling. Many analyses are often descriptive in nature; however, and the use of more advanced spatial simulation methods within dental public health remains rare, despite the potential they offer the field. This review introduces a new approach to geographical analysis of oral health outcomes in neighbourhoods and small area geographies through two novel simulation methods-spatial microsimulation and agent-based modelling. Spatial microsimulation is a population synthesis technique, used to combine survey data with Census population totals to create representative individual-level population datasets, allowing for the use of individual-level data previously unavailable at small spatial scales. Agent-based models are computer simulations capable of capturing interactions and feedback mechanisms, both of which are key to understanding health outcomes. Due to these dynamic and interactive processes, the method has an advantage over traditional statistical techniques such as regression analysis, which often isolate elements from each other when testing for statistical significance. This article discusses the current state of spatial analysis within the dental public health field, before reviewing each of the methods, their applications, as well as their advantages and limitations. Directions and topics for future research are also discussed, before addressing the potential to combine the two methods in order to further utilize their advantages. Overall, this review highlights the promise these methods offer, not just for making methodological advances, but also for adding to our ability to test and better understand theoretical concepts and pathways

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms