Measurement of salivary cortisol – effects of replacing polyester with cotton and switching antibody

Stable performance between‐runs are essential in longitudinal studies and when different studies are being compared. However, changes in analytical kits and laboratory material occur and have the potential to threaten analytical stability. In the present case, we examined how salivary cortisol measurements in our laboratory were affected by: 1) changes in the tampon material and 2) changes in the antibody of the analytical kit. In study 1, saliva from healthy subjects (n = 19) was split and spiked to Salivette^® polyester and cotton tampons, respectively, and treated as ordinary samples before being analysed for cortisol using a Spectria RIA kit for cortisol. In study 2, 68 anonymous saliva samples were analysed with the Spectria Cortisol RIA kit both before and after the manufacturer changed the antibody. The change from polyester to cotton tampons reduced the measured concentration of salivary cortisol by 62?%. A difference of 12?% between the two runs with different antibodies could not be attributed to differences in storage or in thawing and freezing of samples. To conclude, both a change in the material of the Salivette used for collecting saliva samples as well as a change of antibody in a kit can have a major impact on measurements, as illustrated here for concentrations of cortisol in saliva. It is therefore recommended always to check that the analysis stays in statistical control in one's own laboratory when changes are made, even if the manufacturer reports that the changes should have no effects.     

Collection,verification, sharing and dissemination of data: the CONTRAST experience

The scientific community is charged with growing demands regarding the management of project data and outputs and the dissemination of key results to various stakeholders. We discuss experiences and lessons from CONTRAST, a multidisciplinary alliance that had been funded by the European Commission over a 4-year period, in order to optimize schistosomiasis control and transmission surveillance in sub-Saharan Africa. From the start, project partners from Europe and Africa set out an ambitious goal: to sample data following standard protocols at all field sites and then sharing the data in a way that would enable all project partners to have access through a password-protected Internet-based data portal. This required anonymous agreement on several common standardized sample forms, ranging from the mundane but important issue of using the same units of measurement to more complex challenges, for instance agreeing on the same protocols for double-treatment of praziquantel in different settings. With the experiences gained by the CONTRAST project, this paper discusses issues of data management and sharing in research projects in the light of the current donor demand, and offers advice and specific suggestions for similar interdisciplinary research projects.     

Potential impact of PD-L1 (SP-142) immunohistochemical heterogeneity in clear cell renal cell carcinoma immunotherapy

Intratumor heterogeneity (ITH) detection remains a challenge in modern oncology because it can have a direct impact on the success of new therapies. Anti-PD-1/PD-L1 immunotherapy is an emerging treatment modality that is showing great promise for clear cell renal cell carcinoma (CCRCC) patients with advanced disease. Patient selection for such therapy relies upon the immunohistochemical detection of PD-1/PD-L1, however the degree of ITH for these markers among tumor cells and/or inflammatory mononuclear infiltrates remains unknown. Therefore, we analyzed PD-L1 (SP-142) expression in the tumor inflammatory cells of 22 CCRCC cases with the aim to define the pattern of PD-L1 expression, and to compare the reliability of current tumor sampling protocols (RS) with a multisite tumor sampling strategy (MSTS). While the RS protocol identified 5/22 (22.7%) of cases that were positive for PD-L1 expression, MSTS identified 10/22 (45.45%) of cases. This suggests that RS may miss a proportion of CCRCC patients that might benefit from immunotherapy. In addition, MSTS demonstrated that positive and negative regions of PD-L1 expression are very variable within each tumor.     

From the Cover: Bandit solutions provide unified ethical models for randomized clinical trials and comparative effectiveness research

As electronic medical records enable increasingly ambitious studies of treatment outcomes, ethical issues previously important only to limited clinical trials become relevant to unlimited whole populations. For randomized clinical trials, adaptive assignment strategies are known to expose substantially fewer patients to avoidable treatment failures than strategies with fixed assignments (e.g., equal sample sizes). An idealized adaptive case—the two-armed Bernoulli bandit problem—can be exactly optimized for a variety of ethically motivated cost functions that embody principles of duty-to-patient, but the solutions have been thought computationally infeasible when the numbers of patients in the study (the “horizon”) is large. We report numerical experiments that yield a heuristic approximation that applies even to very large horizons, and we propose a near-optimal strategy that remains valid even when the horizon is unknown or unbounded, thus applicable to comparative effectiveness studies on large populations or to standard-of-care recommendations. For the case in which the economic cost of treatment is a parameter, we give a heuristic, near-optimal strategy for determining the superior treatment (whether more or less costly) while minimizing resources wasted on any inferior, more expensive, treatment. Key features of our heuristics can be generalized to more complicated protocols.     

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

Dynamic systems, represented as a set of ordinary differential equations (ODEs), are commonly used to model behaviors in scientific domains, such as gene regulation (1), biological rhythms (2), spread of disease (3), ecology (4), etc. We focus on models specified by a set of ODEsx˙(t)=dx(t)dt=f(x(t),θ,t), t∈[0,T],[1]where the vector x(t) contains the system outputs that evolve over time t and θ is the vector of model parameters to be estimated from experimental/observational data. When f is nonlinear, solving x(t) given initial conditions x(0) and θ generally requires a numerical integration method, such as Runge–Kutta.Historically, ODEs have mainly been used for conceptual or theoretical understanding rather than data fitting as experimental data were limited. Advances in experimental and data collection techniques have increased the capacity to follow dynamic systems closer to real time. Such data will generally be recorded at discrete times and subject to measurement error. Thus, we assume that we observe y(τ)=x(τ)+ϵ(τ) at a set of observation time points τ with error ϵ governed by noise level σ. Our focus here is inference of θ given y(τ), with emphasis on nonlinear f where specialized methods that exploit a linear structure (e.g., refs. 5 and 6), are not generally applicable. We shall present a coherent, statistically principled framework for dynamic system inference with the help of Gaussian processes (GPs). The key to our method is to restrict the GPs on a manifold that satisfies the ODE system: Thus, we name our method MAGI (manifold-constrained Gaussian process inference). Placing a GP on x(t) facilitates inference of θ without numerical integration, and our explicit manifold constraint is the key idea that addresses the conceptual incompatibility between the GP and the specification of the ODE model, as we shall discuss shortly when overviewing our method. We show that the resulting parameter inference is computationally efficient, statistically principled, and effective in a variety of practical scenarios. MAGI particularly works in the cases when some system component(s) is/are unobserved. To the best of our knowledge, none of the current available software packages that do not use numerical integration can analyze systems with unobserved component(s).     

The development of theoretical sampling in practice

Problem

Theoretical sampling is a key research process within grounded theory. However, whilst methodological texts provide a definition, it is difficult to find examples of how theoretical sampling is undertaken as a study develops. The lack of clear exemplars has caused confusion amongst researchers, with many grounded theory studies providing no evidence of theoretical sampling.