Development and Acceptability of a Kidney Therapy Decision Aid for Patients Aged 75 Years and Older: A Design-Based Research Involving Patients, Caregivers, and a Multidisciplinary Team.

Rationale & Objective: Many older adults prefer quality of life over longevity, and some prefer conservative kidney management (CKM) over dialysis. There is a lack of patient-decision aids for adults aged 75 years or older facing kidney therapy decisions, which not only include information on dialysis and CKM but also encourage end-of-life planning. We iteratively developed a paper-based patient-decision aid for older people with low literacy and conducted surveys to assess its acceptability. Study Design: Design-based research. Setting and Participants: Informed by design-based research principles and theory of behavioral activation, a multidisciplinary team of experts created a first version of the patient-decision aid containing 2 components: (1) educational material about kidney therapy options such as CKM, and (2) a question prompt list relevant to kidney therapy and end-of-life decision making. On the basis of the acceptability input of patients and caregivers, separate qualitative interviews of 35 people receiving maintenance dialysis, and with the independent feedback of educated layperson, we further modified the patient-decision aid to create a second version. Analytical Approach: We used descriptive statistics to present the results of acceptability surveys and thematic content analyses for patients' qualitative interviews. Results: The mean age of patients (n=21) who tested the patient-decision aid was 80 years and the mean age of caregivers (n=9) was 70 years. All respondents held positive views about the educational component and would recommend the educational component to others (100% patients and caregivers). Most of the patients reported that the question prompt list helped them put concerns into words (80% patients and 88% caregivers) and would recommend the question prompt list to others (95% patients and 100% caregivers). Limitations: Single-center study. Conclusions: Both components of the patient-decision aid received high acceptability ratings. We plan to launch a larger effectiveness study to test the outcomes of a decision-supporting intervention combining the patient-decision aid with palliative care-based decision coaching.

Atomistic Simulation Informs Interface Engineering of Nanoscale LiCoO2.

Lithium-ion batteries continue to be a critical part of the search for enhanced energy storage solutions. Understanding the stability of interfaces (surfaces and grain boundaries) is one of the most crucial aspects of cathode design to improve the capacity and cyclability of batteries. Interfacial engineering through chemical modification offers the opportunity to create metastable states in the cathodes to inhibit common degradation mechanisms. Here, we demonstrate how atomistic simulations can effectively evaluate dopant interfacial segregation trends and be an effective predictive tool for cathode design despite the intrinsic approximations. We computationally studied two surfaces, {001} and {104}, and grain boundaries, Σ3 and Σ5, of LiCoO2 to investigate the segregation potential and stabilization effect of dopants. Isovalent and aliovalent dopants (Mg2+, Ca2+, Sr2+, Sc3+, Y3+, Gd3+, La3+, Al3+, Ti4+, Sn4+, Zr4+, V5+) were studied by replacing the Co3+ sites in all four of the constructed interfaces. The segregation energies of the dopants increased with the ionic radius of the dopant. They exhibited a linear dependence on the ionic size for divalent, trivalent, and quadrivalent dopants for surfaces and grain boundaries. The magnitude of the segregation potential also depended on the surface chemistry and grain boundary structure, showing higher segregation energies for the Σ5 grain boundary compared with the lower energy Σ3 boundary and higher for the {104} surface compared to the {001}. Lanthanum-doped nanoparticles were synthesized and imaged with scanning transmission electron microscopy-electron energy loss spectroscopy (STEM-EELS) to validate the computational results, revealing the predicted lanthanum enrichment at grain boundaries and both the {001} and the {104} surfaces.

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities.

In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

Geometry of behavioral spaces: A computational approach to analysis and understanding of agent based models and agent behaviors.

Systems with non-linear dynamics frequently exhibit emergent system behavior, which is important to find and specify rigorously to understand the nature of the modeled phenomena. Through this analysis, it is possible to characterize phenomena such as how systems assemble or dissipate and what behaviors lead to specific final system configurations. Agent Based Modeling (ABM) is one of the modeling techniques used to study the interaction dynamics between a system's agents and its environment. Although the methodology of ABM construction is well understood and practiced, there are no computational, statistically rigorous, comprehensive tools to evaluate an ABM's execution. Often, a human has to observe an ABM's execution in order to analyze how the ABM functions, identify the emergent processes in the agent's behavior, or study a parameter's effect on the system-wide behavior. This paper introduces a new statistically based framework to automatically analyze agents' behavior, identify common system-wide patterns, and record the probability of agents changing their behavior from one pattern of behavior to another. We use network based techniques to analyze the landscape of common behaviors in an ABM's execution. Finally, we test the proposed framework with a series of experiments featuring increasingly emergent behavior. The proposed framework will allow computational comparison of ABM executions, exploration of a model's parameter configuration space, and identification of the behavioral building blocks in a model's dynamics.