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Membrane proteins are the major component of plasma membranes, and they play crucial roles in all organisms. To understand the influence of the presence of KcsA channel on cell membrane electroporation induced by picosecond pulse trains (psPT), in this paper, the electroporation of KcsA membrane protein system and bare lipid bilayer system (POPC) with the applied psPT are simulated using molecular dynamics (MD) method. First, we find that the average pore formation time of the KcsA system is longer than the bare system with the applied psPT. In the KcsA system, water protrusions appear more slowly. Then, the system size effects of psPT in the MD simulations are investigated. When the system size decreases, the average pore formation time of small KcsA membrane protein system is shorter than the bare system with the applied psPT. It is found that the psPT makes the protein fluctuation of small system increase greatly; meanwhile the instability of protein disturbs the water and then affects the water protrusion appearance time. Furthermore, it shows that the protein fluctuation of constant electric field is smaller than that of psPT and no field, and protein fluctuation increases with the psPT repetition frequency increasing. [ABSTRACT FROM AUTHOR]

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of 'big data'. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital's EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com ) suitable for visualization of both human-designed and data-mined processes which can then be used for 'what-if' analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively 'deep dive' into big data. [ABSTRACT FROM AUTHOR]

Purpose: The macular degeneration quality of life (MacDQoL) instrument is a validated condition-specific measure of quality of life in patients with macular degeneration. This paper presents the first mapping algorithm to predict EQ-5D from responses to the MacDQoL instrument.Methods: Responses to the MacDQoL and EQ-5D-3L instruments from 482 patients were collected from the IVAN multicentre trial of two alternative drug treatments for neovascular age-related macular degeneration. Regression specifications were estimated using OLS, censored least absolute deviation, Tobit and two-part models. Their predictive performance was assessed using mean squared error. An internal validation sample based on a random selection of 25 % of patients was used to assess the performance of the model estimated on the remaining 75 % of patients.Results: A two-part model had the best predictive performance on the full sample. The covariates of this model include responses and weighted impact scores for all 23 condition-specific domains of the MacDQoL, and responses to a general MacDQoL quality of life question. The selected models were successful at predicting means and standard deviations of target populations, but prediction is weaker at the upper and lower extremes of the EQ-5D-3L distribution.Conclusion: The mapping algorithms provide a means of predicting EQ-5D-3L index scores from MacDQoL scores, and could facilitate cost-effectiveness analyses when the latter but not the former are available to researchers. Further validation of the performance of the algorithms using external data would provide a means of establishing the robustness of the algorithms. [ABSTRACT FROM AUTHOR]

Background: Patient complexity is often operationalized by counting multiple chronic conditions (MCC) without considering contextual factors that can affect patient risk for adverse outcomes.Objective: Our objective was to develop a conceptual model of complexity addressing gaps identified in a review of published conceptual models.Data Sources: We searched for English-language MEDLINE papers published between 1 January 2004 and 16 January 2014. Two reviewers independently evaluated abstracts and all authors contributed to the development of the conceptual model in an iterative process.Results: From 1606 identified abstracts, six conceptual models were selected. One additional model was identified through reference review. Each model had strengths, but several constructs were not fully considered: 1) contextual factors; 2) dynamics of complexity; 3) patients' preferences; 4) acute health shocks; and 5) resilience. Our Cycle of Complexity model illustrates relationships between acute shocks and medical events, healthcare access and utilization, workload and capacity, and patient preferences in the context of interpersonal, organizational, and community factors.Conclusions/implications: This model may inform studies on the etiology of and changes in complexity, the relationship between complexity and patient outcomes, and intervention development to improve modifiable elements of complex patients. [ABSTRACT FROM AUTHOR]

Background/objectives: Insects have been the subject of recent attention as a potentially environmentally sustainable and nutritious alternative to traditional protein sources. The purpose of this paper is to test the hypothesis that insects are nutritionally preferable to meat, using two evaluative tools that are designed to combat over- and under-nutrition. Subjects/methods: We selected 183 datalines of publicly available data on the nutrient composition of raw cuts and offal of three commonly consumed meats (beef, pork and chicken), and six commercially available insect species, for energy and 12 relevant nutrients. We applied two nutrient profiling tools to this data: The Ofcom model, which is used in the United Kingdom, and the Nutrient Value Score (NVS), which has been used in East Africa. We compared the median nutrient profile scores of different insect species and meat types using non-parametric tests and applied Bonferroni adjustments to assess for statistical significance in differences. Results: Insect nutritional composition showed high diversity between species. According to the Ofcom model, no insects were significantly 'healthier' than meat products. The NVS assigned crickets, palm weevil larvae and mealworm a significantly healthier score than beef (P<0.001) and chicken (P<0.001). No insects were statistically less healthy than meat. Conclusions: Insect nutritional composition is highly diverse in comparison with commonly consumed meats. The food category 'insects' contains some foods that could potentially exacerbate diet-related public health problems related to over-nutrition, but may be effective in combating under-nutrition. [ABSTRACT FROM AUTHOR]

Purpose: To build a mathematical model which could calculate the desired laminoplasty opening size (LOS) based on the target sagittal canal diameter (SCD) before single-door cervical laminoplasty (SDCL) when taking the effects of surgery drill into consideration.Methods: The model was based on geometric analysis on deformation of spinal canal; the formula was derived and characterized as: y (mm) = 2 [Formula: see text] × sin(β/2) = c - d (y is the size of LOS, [Formula: see text] the size of transverse canal diameter, β the size of laminoplasty opening size, c the size of mini-plate and d the diameter of the drill bit used during the surgery operation). The parameters of pre- and postoperative computed tomography scans of 20 patients who had undergone SDCL were measured by the picture archiving and communication system (PACS) software and a new instrument named as Lei's ruler, respectively.Results: The effects of surgery SDCL were very significant; for each patient, the SCD was enlarged dramatically after the surgery (P < 0.01). The differences between the data obtained by PACS and Lei's ruler were no statistically significant (P > 0.05). According to the derived formula, the 95% confidence intervals of SCD after the surgery were within the range of 14 mm and 14.5 mm.Conclusion: Applying the mathematical model and derived formula, the desired LOS could be calculated according to the target SCD which could help the surgeon select an optimum mini-plate before SDCL. At the same time, a new measuring device named Lei's ruler is designed for the convenience of the derived formula. These slides can be retrieved under Electronic Supplementary Material. [ABSTRACT FROM AUTHOR]

Purpose: Recent reviews have questioned the efficacy of selective serotonin reuptake inhibitors (SSRIs) above placebo response, and their working mechanisms remain unclear. New approaches to understanding the effects of SSRIs are necessary to enhance their efficacy. The aim of this study was to explore the possibilities of using cross-sectional network analysis to increase our understanding of symptom connectivity before and after SSRI treatment.Methods: In two randomized controlled trials (total N = 178), we estimated Gaussian graphical models among 20 symptoms of the Beck Depression Inventory-II before and after 8 weeks of treatment with the SSRI paroxetine. Networks were compared on connectivity, community structure, predictability (proportion explained variance), and strength centrality (i.e., connectedness to other symptoms in the network).Results: Symptom severity for all individual BDI-II symptoms significantly decreased over 8 weeks of SSRI treatment, whereas interconnectivity and predictability of the symptoms significantly increased. At baseline, three communities were detected; five communities were detected at week 8.Conclusions: Findings suggest the effects of SSRIs can be studied using the network approach. The increased connectivity, predictability, and communities at week 8 may be explained by the decrease in depressive symptoms rather than specific effects of SSRIs. Future studies with larger samples and placebo controls are needed to offer insight into the effects of SSRIs.Trial Registration: The trials described in this manuscript were funded by the NIMH. Pennsylvania/Vanderbilt study: 5 R10 MH55877 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=6186633&icde=28344168&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&MMOpt= ). Washington study: R01 MH55502 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=2034618&icde=28344217&ddparam=&ddvalue=&ddsub=&cr=5&csb=default&cs=ASC ). [ABSTRACT FROM AUTHOR]

This study explored the use of dynamical systems modeling techniques to evaluate self- and co-regulation of affect in couples' interactions before and after the transition to parenthood, and the impact of the Family Foundations program on these processes. Thirty-four heterosexual couples, randomized to intervention and control conditions, participated in videotaped dyadic interaction tasks at pretest (during pregnancy) and posttest (1 year after birth). Husbands' and wives' positivity and negativity were micro-coded throughout interactions. Individual negativity set-points, self-regulation, and partner co-regulatory processes during interactions were examined using a coupled oscillators model. Regarding self-regulatory processes, men exhibited amplification of negativity at the prenatal assessment that did not change at the postnatal assessment; women demonstrated no significant damping or amplification at pretest and a marginally significant change towards greater amplification at the postnatal assessment. In terms of partner-influenced regulatory dynamics, men's positive behaviors changed from damping to amplifying women's negative behaviors in the control group following the transition to parenthood, but exerted an even stronger damping effect on women's negative behaviors in the intervention group. The study highlights the advantages of dynamic modeling approaches in testing specific hypotheses in the study of self- and co-regulatory couple dynamics and demonstrates the potential of studying dynamic processes to further understanding of developmental and intervention-related change mechanisms. [ABSTRACT FROM AUTHOR]

Context: Statistical models employed in analysing patient-level cost and effectiveness data need to be flexible enough to adjust for any imbalanced covariates, account for correlations between key parameters, and accommodate potential skewed distributions of costs and/or effects. We compare prominent statistical models for cost-effectiveness analysis alongside randomised controlled trials (RCTs) and covariate adjustment to assess their performance and accuracy using data from a large RCT.Method: Seemingly unrelated regressions, linear regression of net monetary benefits, and Bayesian generalized linear models with various distributional assumptions were used to analyse data from the TASMINH2 trial. Each model adjusted for covariates prognostic of costs and outcomes.Results: Cost-effectiveness results were notably sensitive to model choice. Models assuming normally distributed costs and effects provided a poor fit to the data, and potentially misleading inference. Allowing for a beta distribution captured the true incremental difference in effects and changed the decision as to which treatment is preferable.Conclusions: Our findings suggest that Bayesian generalized linear models which allow for non-normality in estimation offer an attractive tool for researchers undertaking cost-effectiveness analyses. The flexibility provided by such methods allows the researcher to analyse patient-level data which are not necessarily normally distributed, while at the same time it enables assessing the effect of various baseline covariates on cost-effectiveness results. [ABSTRACT FROM AUTHOR]

Purpose: Alcohol intake may be associated with cancer risk, but epidemiologic evidence for prostate cancer is inconsistent. We aimed to prospectively investigate the association between midlife alcohol intake and drinking patterns with future prostate cancer risk and mortality in a population-based cohort of Finnish twins.Methods: Data were drawn from the Older Finnish Twin Cohort and included 11,372 twins followed from 1981 to 2012. Alcohol consumption was assessed by questionnaires administered at two time points over follow-up. Over the study period, 601 incident cases of prostate cancer and 110 deaths from prostate cancer occurred. Cox regression was used to evaluate associations between weekly alcohol intake and binge drinking patterns with prostate cancer risk and prostate cancer-specific mortality. Within-pair co-twin analyses were performed to control for potential confounding by shared genetic and early environmental factors.Results: Compared to light drinkers (≤3 drinks/week; non-abstainers), heavy drinkers (>14 drinks/week) were at a 1.46-fold higher risk (HR 1.46; 95 % CI 1.12, 1.91) of prostate cancer, adjusting for important confounders. Among current drinkers, binge drinkers were at a significantly increased risk of prostate cancer (HR 1.28; 95 % CI 1.06, 1.55) compared to non-binge drinkers. Abstainers were at a 1.90-fold higher risk (HR 1.90; 95 % CI 1.04, 3.47) of prostate cancer-specific mortality compared to light drinkers, but no other significant associations for mortality were found. Co-twin analyses suggested that alcohol consumption may be associated with prostate cancer risk independent of early environmental and genetic factors.Conclusion: Heavy regular alcohol consumption and binge drinking patterns may be associated with increased prostate cancer risk, while abstinence may be associated with increased risk of prostate cancer-specific mortality compared to light alcohol consumption. [ABSTRACT FROM AUTHOR]

Purpose: Research studies that measure health-related quality of life (HRQOL) in both children and adults and longitudinal studies that follow children into adulthood need measures that can be compared across these age groups. This study links the PROMIS pediatric and adult emotional distress measures using data from participants with diverse health conditions and disabilities.Methods: Analyses were conducted and compared in two separate samples to confirm the stability of results. One sample (n = 874) included individuals aged 14-20 years with special health care needs and who require health services. The other sample (n = 641) included individuals aged 14-25 years who have a physical or cognitive disability. Participants completed both PROMIS pediatric and adult measures. Item response theory-based scores were linked using the linear approximation to calibrated projection.Results: The estimated latent-variable correlation between pediatric and adult PROMIS measures ranged from 0.87 to 0.94. Regression coefficients β 0 (intercept) and β 1 (slope), and mean squared error are provided to transform scores from the pediatric to the adult measures, and vice versa.Conclusions: This study used a relatively new linking method, calibrated projection, to link PROMIS pediatric and adult measure scores, thus expanding the use of PROMIS measures to research that includes both populations. [ABSTRACT FROM AUTHOR]

In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery. [ABSTRACT FROM AUTHOR]

Purpose: Equations for blood oxyhemoglobin (HbO2) and carbaminohemoglobin (HbCO2) dissociation curves that incorporate nonlinear biochemical interactions of oxygen and carbon dioxide with hemoglobin (Hb), covering a wide range of physiological conditions, are crucial for a number of practical applications. These include the development of physiologically-based computational models of alveolar-blood and blood-tissue O2–CO2 transport, exchange, and metabolism, and the analysis of clinical and in vitro data.Methods and Results: To this end, we have revisited, simplified, and extended our previous models of blood HbO2 and HbCO2 dissociation curves (Dash and Bassingthwaighte, Ann Biomed Eng 38:1683–1701, 2010), validated wherever possible by available experimental data, so that the models now accurately fit the low HbO2 saturation (SHbO2) range over a wide range of values of PCO2, pH, 2,3-DPG, and temperature. Our new equations incorporate a novel PO2-dependent variable cooperativity hypothesis for the binding of O2 to Hb, and a new equation for P50 of O2 that provides accurate shifts in the HbO2 and HbCO2 dissociation curves over a wide range of physiological conditions. The accuracy and efficiency of these equations in computing PO2 and PCO2 from the SHbO2 and SHbCO2 levels using simple iterative numerical schemes that give rapid convergence is a significant advantage over alternative SHbO2 and SHbCO2 models.Conclusion: The new SHbO2 and SHbCO2 models have significant computational modeling implications as they provide high accuracy under non-physiological conditions, such as ischemia and reperfusion, extremes in gas concentrations, high altitudes, and extreme temperatures. [ABSTRACT FROM AUTHOR]

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses. [ABSTRACT FROM AUTHOR]

We describe an algorithm to detect cardiac quiescence within a heartbeat using nonlinear filtering and boundary detection techniques in echocardiography images. The motivation for detection of these quiescent phases is to provide improved cardiac gating to obtain motion-artifact-free images of the heart at cardiac computed tomography (CT). Currently, cardiac gating is provided through electrocardiography (ECG), which does not provide information about the instantaneous mechanical state of the heart. Our goal is to test if information about the actual mechanical motion of the heart obtained from B-mode echocardiographic data could potentially be used for gating purposes. The nonlinear filtering algorithm presented involves anisotropic diffusion to smoothen the homogeneous regions of the B-mode images while preserving image edges that represent myocardial boundaries. Following this, we detect the boundary of a particular region of interest (ROI) using a thresholding step. The positional changes of this ROI are then observed for quiescent phases over multiple cardiac cycles using the ECG's R-R interval. In a pilot study, seven subjects were imaged in the apical, four-chamber view, and quiescence of the interventricular septum was primarily observed in the diastolic region of the ECG signal. However, the position and length of quiescence vary across multiple heartbeats for the same individual and for different individuals as well. The center of quiescence for the seven patients ranged from 51 to 84 % and did not show a trend with heart rates, which ranged from 54 to 83 beats per minute. The gating intervals based on such analysis of echocardiographic signals could potentially optimize cardiac CT gating. [ABSTRACT FROM AUTHOR]