Facilitators and barriers of adaptation to diabetes: experiences of Iranian patients

Background: Diabetes mellitus is one of the most challenging and burdensome chronic diseases of the 21st century and More than 1% of the Iranian urban population older than 20 years develops Type 2 diabetes each year. Living with diabetes mellitus has been described as a dynamic personal transitional adaptation, based on restructuring of the illness perceived experience and management of the self. Adaptation to Type 2 Diabetes mellitus is an integral part of diabetes care.This study explored the experiences of facilitators and barriers adaptation to Type 2 Diabetes by Iranian patients.Methods: This study was conducted by using qualitative content analysis. Data were collected via in-depth, semi-structured and face to face interviews with 15 patients with type2 diabetes.Results: Three themes emerged from collected data, including a) individual context with Beliefs, personal background, and previous experience subthemes. b) supportive system with Family, Society and Health organizations subthemes and c) self-comparison with comparison with other diabetes and comparison with other diseases subthemes.Conclusions: Identifying and managing Facilitators and Barriers adaptation to Type 2 Diabetes mellitus are an integral part of diabetes care. This study provides a better understanding of the factors from perspective of patients and it can be utilized by health care providers to adapt their health care and education contents to better meet the needs of people with diabetes. © 2014 Karimi Moonaghi et al.; licensee BioMed Central Ltd

Struggling towards diagnosis: Experiences of Iranian diabetes

Background: Healthcare-seeking behavior is one of the factors determining the uptake and outcome of healthcare. However, few studies have discussed how and why diabetics seek healthcare assistance before meeting a physician. Objectives: In this study, we explored the subjective experiences of healthcare-seeking behavior among Iranian patients with type 2 diabetes mellitus. Patients and Methods: A qualitative approach was adopted using a conventional content analysis of semi-structured interviews carried out in the Diabetes Association in Tabriz (Iran) with 15 participants suffering from type 2 diabetes. Participants were recruited by the purposeful sampling method. Results: Five themes emerged from the study: 1) warning by physical signs; 2) personal processing; 3) self-remedy and its outcomes; 4) seeking information, and; 5) diagnosis and verification of information by healthcare staff. Conclusions: Individual social context plays an important role in the decision-making process when seeking healthcare for diabetes. The results of this study can be utilized by healthcare providers to facilitate interventions to increase diabetics' active involvement in their healthcare, and encourage a wider knowledge of its symptoms and outcomes to facilitate appropriate healthcare-seeking and service use. © 2014, Iranian Red Crescent Medical Journal

Multi-Fidelity Cost-Aware Bayesian Optimization

Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive low-fidelity data. The overall premise of this strategy is to reduce the overall sampling costs by querying inexpensive low-fidelity sources whose data are correlated with high-fidelity samples. Here, we propose a multi-fidelity cost-aware BO framework that dramatically outperforms the state-of-the-art technologies in terms of efficiency, consistency, and robustness. We demonstrate the advantages of our framework on analytic and engineering problems and argue that these benefits stem from our two main contributions: (1) we develop a novel acquisition function for multi-fidelity cost-aware BO that safeguards the convergence against the biases of low-fidelity data, and (2) we tailor a newly developed emulator for multi-fidelity BO which enables us to not only simultaneously learn from an ensemble of multi-fidelity datasets, but also identify the severely biased low-fidelity sources that should be excluded from BO

On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization

Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas including materials design. In real world applications, acquiring high-fidelity (HF) data through physical experiments or HF simulations is the major cost component of BO. To alleviate this bottleneck, multi-fidelity (MF) methods are used to forgo the sole reliance on the expensive HF data and reduce the sampling costs by querying inexpensive low-fidelity (LF) sources whose data are correlated with HF samples. However, existing multi-fidelity BO (MFBO) methods operate under the following two assumptions that rarely hold in practical applications: (1) LF sources provide data that are well correlated with the HF data on a global scale, and (2) a single random process can model the noise in the fused data. These assumptions dramatically reduce the performance of MFBO when LF sources are only locally correlated with the HF source or when the noise variance varies across the data sources. In this paper, we dispense with these incorrect assumptions by proposing an MF emulation method that (1) learns a noise model for each data source, and (2) enables MFBO to leverage highly biased LF sources which are only locally correlated with the HF source. We illustrate the performance of our method through analytical examples and engineering problems on materials design

Multi-fidelity Design of Porous Microstructures for Thermofluidic Applications

As modern electronic devices are increasingly miniaturized and integrated, their performance relies more heavily on effective thermal management. Two-phase cooling methods enhanced by porous surfaces, which capitalize on thin-film evaporation atop structured porous surfaces, are emerging as potential solutions. In such porous structures, the optimum heat dissipation capacity relies on two competing objectives that depend on mass and heat transfer. The computational costs of evaluating these objectives, the high dimensionality of the design space which a voxelated microstructure representation, and the manufacturability constraints hinder the optimization process for thermal management. We address these challenges by developing a data-driven framework for designing optimal porous microstructures for cooling applications. In our framework we leverage spectral density functions (SDFs) to encode the design space via a handful of interpretable variables and, in turn, efficiently search it. We develop physics-based formulas to quantify the thermofluidic properties and feasibility of candidate designs via offline simulations. To decrease the reliance on expensive simulations, we generate multi-fidelity data and build emulators to find Pareto-optimal designs. We apply our approach to a canonical problem on evaporator wick design and obtain fin-like topologies in the optimal microstructures which are also characteristics often observed in industrial applications.Comment: 24 pages, 10 figure

Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets

In many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced via data fusion or multi-fidelity (MF) modeling where one leverages inexpensive low-fidelity (LF) sources to reduce the reliance on expensive high-fidelity (HF) data. In this paper, we employ neural networks (NNs) for data fusion in scenarios where data is very scarce and obtained from an arbitrary number of sources with varying levels of fidelity and cost. We introduce a unique NN architecture that converts MF modeling into a nonlinear manifold learning problem. Our NN architecture inversely learns non-trivial (e.g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution. This probabilistic manifold quantifies model form uncertainties such that LF sources with small bias are encoded close to the HF source. Additionally, we endow the output of our NN with a parametric distribution not only to quantify aleatoric uncertainties, but also to reformulate the network's loss function based on strictly proper scoring rules which improve robustness and accuracy on unseen HF data. Through a set of analytic and engineering examples, we demonstrate that our approach provides a high predictive power while quantifying various sources uncertainties

Comparing the Effect of Two Methods of Distraction on the Pain Intensity Venipuncture in School-age Children: A Randomized Clinical Trial

Background Children undergo painful procedures during care and treatment. This study aimed to determine the effect of distractionon the intensityof pain in children aged 6 to 12 years old. Materials and Methods This clinical trial was conducted on the school-age children, who referred to Imam Hossein Hospital, Iran, Heris city, East Azarbaijan province (Iran) in 2017. In total 48 patients were selected through convenience sampling technique and were randomly divided into three groups of 16 cases.  In all three groups, pain was measured using the Oucher  self-report scale, 3 minute before and after the venipuncture. One minute before venipuncture, in the "deep breathing with blowing paper whirligigs" groups after spinning the paper whirligigs and exhalation, in the "deep breathing" groups after exhalation, numbers were counted up to 10 spins or 10 breaths. In the control group, no intervention was performed. The data analysis was performed in the SPSS software (version 13.0). Results: The results showed that "deep breathing with blowing paper whirligigs" (Mean + standard deviation [SD]: 2.69±0.79) and "deep breathing" (Mean + SD: 2.63±1.31) reported less pain intensity than the control group (Mean + SD: 5.25±1.00), and the "deep breathing with blowing paper whirligigs" method had the least pain intensity. The results of ANOVA test showed that there was a significant difference among the groups in terms of pain intensity after intervention (P ≥0.001). Conclusion The findings showed that both methods of distraction in this study (deep breathing with blowing paper whirligigs and deep breathing) can effectively decrease the venipuncture pain

DNA repair systems and the pathogenesis of Mycobacterium tuberculosis: varying activities at different stages of infection

Mycobacteria, including most of all MTB (Mycobacterium tuberculosis), cause pathogenic infections in humans and, during the infectious process, are exposed to a range of environmental insults, including the host's immune response. From the moment MTB is exhaled by infected individuals, through an active and latent phase in the body of the new host, until the time they reach the reactivation stage, MTB is exposed to many types of DNA-damaging agents. Like all cellular organisms, MTB has efficient DNA repair systems, and these are believed to play essential roles in mycobacterial pathogenesis. As different stages of infection have great variation in the conditions in which mycobacteria reside, it is possible that different repair systems are essential for progression to specific phases of infection. MTB possesses homologues of DNA repair systems that are found widely in other species of bacteria, such as nucleotide excision repair, base excision repair and repair by homologous recombination. MTB also possesses a system for non-homologous end-joining of DNA breaks, which appears to be widespread in prokaryotes, although its presence is sporadic within different species within a genus. However, MTB does not possess homologues of the typical mismatch repair system that is found in most bacteria. Recent studies have demonstrated that DNA repair genes are expressed differentially at each stage of infection. In the present review, we focus on different DNA repair systems from mycobacteria and identify questions that remain in our understanding of how these systems have an impact upon the infection processes of these important pathogens

Microfluidic manufacture of lipid-based nanomedicines

Nanoparticulate technologies have revolutionised drug delivery allowing for passive and active targeting, altered biodistribution, control drug release (temporospatial or triggered), enhanced sta-bility, improved solubilisation capacity, reduction of dose and adverse effects. However, their manufacture remains immature, and challenges exist in industrial scale due to high batch-to-batch variability hindering their clinical translation. Lipid-based nanomedicines remain the most-widely approved nanomedicines and their current manufacturing methods remain discontinuous and face several problems such as high batch-to-batch variability affecting the critical quality attributes (CQAs) of the product, laborious multi-step processes, need for an expert workforce and are not easily amenable to industrial scale-up involving typically a complex process control. Several tech-niques have emerged in recent years for nanomedicine manufacture, but a paradigm shift occurred when microfluidic strategies able to mix fluids in channels with dimensions of tens of micrometers and small volumes of liquid reagents in a highly controlled manner to form nanoparticles with tunable and reproducible structure are employed. In this review, we summarize the recent ad-vancements in the manufacturing of lipid-based nanomedicines using microfluidics with particular emphasis on the parameters that govern the control of CQAs of final nanomedicines. The impact of microfluidic environments on formation dynamics of nanomaterials, and the application of mi-crodevices as platforms for nanomaterial screening were also discussed