Physiological and clinical consequences of relief of right ventricular outflow tract obstruction late after repair of congenital heart defects.

BACKGROUND: Right ventricular outflow tract obstruction (RVOTO) is a common problem after repair of congenital heart disease. Percutaneous pulmonary valve implantation (PPVI) can treat this condition without consequent pulmonary regurgitation or cardiopulmonary bypass. Our aim was to investigate the clinical and physiological response to relieving RVOTO. METHODS AND RESULTS: We studied 18 patients who underwent PPVI for RVOTO (72% male, median age 20 years) from a total of 93 who had this procedure for various indications. All had a right ventricular outflow tract (RVOT) gradient >50 mm Hg on echocardiography without important pulmonary regurgitation (less than mild or regurgitant fraction <10% on magnetic resonance imaging [MRI]). Cardiopulmonary exercise testing, tissue Doppler echocardiography, and MRI were performed before and within 50 days of PPVI. PPVI reduced RVOT gradient (51.4 to 21.7 mm Hg, P<0.001) and right ventricular systolic pressure (72.8 to 47.3 mm Hg, P<0.001) at catheterization. Symptoms and aerobic (25.7 to 28.9 mL.kg(-1).min(-1), P=0.002) and anaerobic (14.4 to 16.2 mL.kg(-1).min(-1), P=0.002) exercise capacity improved. Myocardial systolic velocity improved acutely (tricuspid 4.8 to 5.3 cm/s, P=0.05; mitral 4.7 to 5.5 cm/s, P=0.01), whereas isovolumic acceleration was unchanged. The tricuspid annular velocity was not maintained on intermediate follow-up. Right ventricular end-diastolic volume (99.9 to 89.7 mL/m2, P<0.001) fell, whereas effective stroke volume (43.7 to 48.3 mL/m2, P=0.06) and ejection fraction (48.0% to 56.8%, P=0.01) increased. Left ventricular end-diastolic volume (72.5 to 77.4 mL/m2, P=0.145), stroke volume (45.3 to 50.6 mL/m2, P=0.02), and ejection fraction (62.6% to 65.8%, P=0.03) increased. CONCLUSIONS: PPVI relieves RVOTO, which leads to an early improvement in biventricular performance. Furthermore, it reduces symptoms and improves exercise tolerance. These findings have important implications for the management of this increasingly common condition

Estimating the cumulative incidence of SARS-CoV-2 with imperfect serological tests: Exploiting cutoff-free approaches.

Large-scale serological testing in the population is essential to determine the true extent of the current SARS-CoV-2 pandemic. Serological tests measure antibody responses against pathogens and use predefined cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives and use this as a proxy for past infection. With the imperfect assays that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of the cumulative incidence and is usually corrected to account for the sensitivity and specificity. Here we use an inference method-referred to as mixture-model approach-for the estimation of the cumulative incidence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that the mixture model outperforms the methods based on cutoffs, leading to less bias and error in estimates of the cumulative incidence. We illustrate how the mixture model can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the cumulative incidence. Lastly, we show how this approach can be used to estimate the cumulative incidence of classes of infections with an unknown distribution of quantitative test measures. This is a very promising application of the mixture-model approach that could identify the elusive fraction of asymptomatic SARS-CoV-2 infections. An R-package implementing the inference methods used in this paper is provided. Our study advocates using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods at exactly the low cumulative incidence levels and test accuracies that we are currently facing in SARS-CoV-2 serosurveys

Survival-extinction phase transition in a bit-string population with mutation

A bit-string model for the evolution of a population of haploid organisms, subject to competition, reproduction with mutation and selection is studied, using mean field theory and Monte Carlo simulations. We show that, depending on environmental flexibility and genetic variability, the model exhibits a phase transtion between extinction and survival. The mean-field theory describes the infinite-size limit, while simulations are used to study quasi-stationary properties.Comment: 11 pages, 5 figure

Explicit kinetic heterogeneity: mechanistic models for interpretation of labeling data of heterogeneous cell populations

Estimation of division and death rates of lymphocytes in different conditions is vital for quantitative understanding of the immune system. Deuterium, in the form of deuterated glucose or heavy water, can be used to measure rates of proliferation and death of lymphocytes in vivo. Inferring these rates from labeling and delabeling curves has been subject to considerable debate with different groups suggesting different mathematical models for that purpose. We show that the three models that are most commonly used are in fact mathematically identical and differ only in their interpretation of the estimated parameters. By extending these previous models, we here propose a more mechanistic approach for the analysis of data from deuterium labeling experiments. We construct a model of "kinetic heterogeneity" in which the total cell population consists of many sub-populations with different rates of cell turnover. In this model, for a given distribution of the rates of turnover, the predicted fraction of labeled DNA accumulated and lost can be calculated. Our model reproduces several previously made experimental observations, such as a negative correlation between the length of the labeling period and the rate at which labeled DNA is lost after label cessation. We demonstrate the reliability of the new explicit kinetic heterogeneity model by applying it to artificially generated datasets, and illustrate its usefulness by fitting experimental data. In contrast to previous models, the explicit kinetic heterogeneity model 1) provides a mechanistic way of interpreting labeling data; 2) allows for a non-exponential loss of labeled cells during delabeling, and 3) can be used to describe data with variable labeling length

Antibiotic cycling versus mixing: the difficulty of using mathematical models to definitively quantify their relative merits.

Published PDF version deposited in accordance with SHERPA RoMEO guidelines.We ask the question Which antibiotic deployment protocols select best against drug-resistant microbes: mixing or periodic cycling? and demonstrate that the statistical distribution of the performances of both sets of protocols, mixing and periodic cycling, must have overlapping supports. In other words, it is a general, mathematical result that there must be mixing policies that outperform cycling policies and vice versa. As a result, we agree with the tenet of Bonhoefer et al. [1] that one should not apply the results of [2] to conclude that an antibiotic cycling policy that implements cycles of drug restriction and prioritisation on an ad-hoc basis can select against drug-resistant microbial pathogens in a clinical setting any better than random drug use. However, nor should we conclude that a random, per-patient drug-assignment protocol is the de facto optimal method for allocating antibiotics to patients in any general sense

Defining forgiveness: Christian clergy and general population perspectives.

The lack of any consensual definition of forgiveness is a serious weakness in the research literature (McCullough, Pargament &amp; Thoresen, 2000). As forgiveness is at the core of Christianity, this study returns to the Christian source of the concept to explore the meaning of forgiveness for practicing Christian clergy. Comparisons are made with a general population sample and social science definitions of forgiveness to ensure that a shared meaning of forgiveness is articulated. Anglican and Roman Catholic clergy (N = 209) and a general population sample (N = 159) completed a postal questionnaire about forgiveness. There is agreement on the existence of individual differences in forgiveness. Clergy and the general population perceive reconciliation as necessary for forgiveness while there is no consensus within psychology. The clergy suggests that forgiveness is limitless and that repentance is unnecessary while the general population suggests that there are limits and that repentance is necessary. Psychological definitions do not conceptualize repentance as necessary for forgiveness and the question of limits has not been addressed although within therapy the implicit assumption is that forgiveness is limitless.</p

COVID-19 infectivity profile correction

The infectivity profile of an individual with COVID-19 is attributed to the paper Temporal dynamics in viral shedding and transmissibility of COVID-19 by He et al., published in Nature Medicine in April 2020. However, the analysis within this paper contains a mistake such that the published infectivity profile is incorrect and the conclusion that infectiousness begins 2.3 days before symptom onset is no longer supported. In this document we discuss the error and compute the correct infectivity profile. We also establish confidence intervals on this profile, quantify the difference between the published and the corrected profiles, and discuss an issue of normalisation when fitting serial interval data. This infectivity profile plays a central role in policy and decision making, thus it is crucial that this issue is corrected with the utmost urgency to prevent the propagation of this error into further studies and policies. We hope that this preprint will reach all researchers and policy makers who are using the incorrect infectivity profile to inform their work.Comment: 5 pages, 2 figure

Hyperbolic planforms in relation to visual edges and textures perception

We propose to use bifurcation theory and pattern formation as theoretical probes for various hypotheses about the neural organization of the brain. This allows us to make predictions about the kinds of patterns that should be observed in the activity of real brains through, e.g. optical imaging, and opens the door to the design of experiments to test these hypotheses. We study the specific problem of visual edges and textures perception and suggest that these features may be represented at the population level in the visual cortex as a specific second-order tensor, the structure tensor, perhaps within a hypercolumn. We then extend the classical ring model to this case and show that its natural framework is the non-Euclidean hyperbolic geometry. This brings in the beautiful structure of its group of isometries and certain of its subgroups which have a direct interpretation in terms of the organization of the neural populations that are assumed to encode the structure tensor. By studying the bifurcations of the solutions of the structure tensor equations, the analog of the classical Wilson and Cowan equations, under the assumption of invariance with respect to the action of these subgroups, we predict the appearance of characteristic patterns. These patterns can be described by what we call hyperbolic or H-planforms that are reminiscent of Euclidean planar waves and of the planforms that were used in [1, 2] to account for some visual hallucinations. If these patterns could be observed through brain imaging techniques they would reveal the built-in or acquired invariance of the neural organization to the action of the corresponding subgroups.Comment: 34 pages, 11 figures, 2 table