An International Survey of Peritoneal Dialysis Exercise Practices and Perceptions.

Introduction: Low activity levels and poor physical function are associated with technique failure and mortality in people receiving peritoneal dialysis (PD). Adequate levels of physical function are required to maintain independence for people choosing this predominantly home-based therapy. The objective of this study was to identify the exercise-related perceptions and practices of PD clinicians globally. Methods: We conducted a cross-sectional survey of PD clinicians from English-, Thai-, Spanish-, and Portuguese-speaking PD-prevalent countries exploring clinicians' perceptions and practices of swimming, activity following PD catheter insertion, lifting, and falls prevention. This study was convened by the International Society of Peritoneal Dialysis and Global Renal Exercise Network between July and December 2021. Results: Of 100 of the highest PD-prevalent countries, 85 responded and were represented in the findings. A total of 1125 PD clinicians (448 nephrologists, 558 nephrology nurses, 59 dietitians, and 56 others) responded from 61% high-income, 32% upper middle-income and 7% lower middle-income countries. The majority (n = 1054, 94%) agreed that structured exercise programs would be beneficial for people receiving PD. Most respondents believed people on PD could perform more exercise (n = 907, 81%) and that abdominal strengthening exercises could be safely performed (n = 661, 59%). Compared to clinicians in high-income countries, clinicians from lower middle-income status (odds ratio [OR], 5.57; 1.64 to 18.9) are more likely to promote participation in physical activity. Conclusion: Clinicians know the importance of physical activity in people receiving PD. Exercise counseling and structured exercise plans could be included in the standard care of people receiving PD to maintain independence.

Physical activity and exercise in peritoneal dialysis: International Society for Peritoneal Dialysis and the Global Renal Exercise Network practice recommendations.

Life participation requiring physical activity and physical function is a key patient-reported outcome for people receiving peritoneal dialysis (PD). Clinician guidance is required from multidisciplinary sources regarding exercise and activity advice to address the specific needs of this group. From August 2020 through to June 2021, the Global Renal Exercise Network and the International Society for Peritoneal Dialysis reviewed the published literature and international clinical experience to develop a set of clinical practice points. A set of questions relevant to physical activity and exercise were developed from the perspective of a person receiving PD and were the basis for the practice point development. The GRADE framework was used to evaluate the quality of evidence and to guide clinical practice points. The review of the literature found sparse quality evidence, and thus the clinical practice points are generally based on the expert consensus of people receiving PD, PD exercise expert clinicians and experienced PD exercise researchers. Clinical practice points address timing of exercise and activity (post-catheter insertion, peritoneal space empty or full), the uptake of specific activities (work, sex, swimming, core exercise), potential adverse outcomes related to activity and exercise (exit site care, perspiration, cardiovascular compromise, fatigue, intra-abdominal pressure), the effect of exercise and activity on conditions of interest (mental health, obesity, frailty, low fitness) and exercise nutrition.

Acoustic Ejection Mass Spectrometry for High-Throughput Analysis.

We describe a mass spectrometry (MS) analytical platform resulting from the novel integration of acoustic droplet ejection (ADE) technology, an open-port interface (OPI), and electrospray ionization (ESI)-MS that creates a transformative system enabling high-speed sampling and label-free analysis. The ADE technology delivers nanoliter droplets in a touchless manner with high speed, precision, and accuracy. Subsequent sample dilution within the OPI, in concert with the capabilities of modern ESI-MS, eliminates the laborious sample preparation and method development required in current approaches. This platform is applied to a variety of experiments, including high-throughput (HT) pharmacology screening, label-free in situ enzyme kinetics, in vitro absorption, distribution, metabolism, elimination, pharmacokinetic and biomarker analysis, and HT parallel medicinal chemistry.

An Exercise Program for Peritoneal Dialysis Patients in the United States: A Feasibility Study.

BACKGROUND: People with end-stage kidney disease receiving peritoneal dialysis (PD) are generally physically inactive and frail. Exercise studies in PD are scarce and currently there are no PD exercise programs in the United States. The primary objective of this study was to test the feasibility of a combined resistance and cardiovascular exercise program for PD patients under the care of a dedicated home dialysis center in the United States. STUDY DESIGN: Parallel randomized controlled feasibility study. SETTING & PARTICIPANTS: PD patients were recruited from a single center and randomly assigned to the intervention (exercise; n = 18) or control (nonexercise; n = 18) group. INTERVENTION: The intervention group received monthly exercise physiologist consultation, exercise prescription (resistance and aerobic exercise program using exercise bands), and 4 exercise support telephone calls over 12 weeks. The control group received standard care. OUTCOMES: The primary outcome was study feasibility as measured by eligibility rates, recruitment rates, retention rates, adherence rates, adverse events, and sustained exercise rates. Secondary outcome measures were changes in physical function (sit-to-stand test, timed-up-and-go test, and pinch-strength tests) and patient-reported outcome measures. RESULTS: From a single center with 75 PD patients, 57 (76%) were deemed eligible, resulting in a recruitment rate of 36 (63%) patients. Participants were randomly assigned into 2 groups of 18 (1:1). 10 patients discontinued the study (5 in each arm), resulting in 26 (72%) patients, 13 in each arm, completing the study. 10 of 13 (77%) intervention patients were adherent to the exercise program. A t test analysis of covariance found a difference between the treatment groups for the timed-up-and-go test (P = 0.04) and appetite (P = 0.04). No serious adverse events caused by the exercise program were reported. LIMITATIONS: Single center, no blinded assessors. CONCLUSIONS: A resistance and cardiovascular exercise program appears feasible and safe for PD patients. We recommend that providers of PD therapy consider including exercise programs coordinated by exercise professionals to reduce the physical deterioration of PD patients. FUNDING: None. TRIAL REGISTRATION: NCT03980795.

Exercise and physical activity for people receiving peritoneal dialysis: Why not?

People with end-stage kidney disease (ESKD) receiving peritoneal dialysis (PD) are physically inactive leading to low physical function and poor health outcomes. Guidelines recommend that nephrologists encourage PD patients to increase their activity levels; however, PD patients are often discouraged from participating in exercise programs because of perceived barriers and a lack of precision about the appropriate exercise regimen. This review suggests ways forward to assist nephrology professionals to encourage PD patients to exercise, instead of creating barriers. The paper draws on the literature in addition to the experience of programs in France, the United States, and Australia to demonstrate the possibilities when considering increasing physical activity in this group.

VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDY1.

The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodologically, we develop the "RelATive cEntrality" (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We illustrate RATE through Bayesian Gaussian process regression, but the methodological innovations apply to other "black box" methods. It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and two real data association mapping studies, we show that applying RATE enables an explanation for this improved performance.

Evaluation of Cannabinoid and Terpenoid Content: Cannabis Flower Compared to Supercritical CO2 Concentrate.

A recent cannabis use survey revealed that 60% of cannabis users rely on smelling the flower to select their cannabis. Olfactory indicators in plants include volatile compounds, principally represented by the terpenoid fraction. Currently, medicinal- and adult-use cannabis is marketed in the United States with relatively little differentiation between products other than by a common name, association with a species type, and Δ-9 tetrahydrocannabinol/cannabidiol potency. Because of this practice, how terpenoid compositions may change during an extraction process is widely overlooked. Here we report on a comparative study of terpenoid and cannabinoid potencies of flower and supercritical fluid CO2 (SC-CO2) extract from six cannabis chemovars grown in Washington State. To enable this comparison, we employed a validated high-performance liquid chromatography/diode array detector methodology for quantification of seven cannabinoids and developed an internal gas chromatography-mass spectrometry method for quantification of 42 terpenes. The relative potencies of terpenoids and cannabinoids in flower versus concentrate were significantly different. Cannabinoid potency increased by factors of 3.2 for Δ-9 tetrahydrocannabinol and 4.0 for cannabidiol in concentrates compared to flower. Monoterpenes were lost in the extraction process; a ketone increased by 2.2; an ether by 2.7; monoterpene alcohols by 5.3, 7 and 9.4; and sesquiterpenes by 5.1, 4.2, 7.7, and 8.9. Our results demonstrate that the product of SC-CO2 extraction may have a significantly different chemotypic fingerprint from that of cannabis flower. These results highlight the need for more complete characterization of cannabis and associated products, beyond cannabinoid content, in order to further understand health-related consequences of inhaling or ingesting concentrated forms.

Discriminative variable subsets in Bayesian classification with mixture models, with application in flow cytometry studies.

We discuss the evaluation of subsets of variables for the discriminative evidence they provide in multivariate mixture modeling for classification. The novel development of Bayesian classification analysis presented is partly motivated by problems of design and selection of variables in biomolecular studies, particularly involving widely used assays of large-scale single-cell data generated using flow cytometry technology. For such studies and for mixture modeling generally, we define discriminative analysis that overlays fitted mixture models using a natural measure of concordance between mixture component densities, and define an effective and computationally feasible method for assessing and prioritizing subsets of variables according to their roles in discrimination of one or more mixture components. We relate the new discriminative information measures to Bayesian classification probabilities and error rates, and exemplify their use in Bayesian analysis of Dirichlet process mixture models fitted via Markov chain Monte Carlo methods as well as using a novel Bayesian expectation-maximization algorithm. We present a series of theoretical and simulated data examples to fix concepts and exhibit the utility of the approach, and compare with prior approaches. We demonstrate application in the context of automatic classification and discriminative variable selection in high-throughput systems biology using large flow cytometry datasets.

Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples.

Flow cytometry is the prototypical assay for multi-parameter single cell analysis, and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies (0.1% or less). Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts, a process that is subjective and often difficult to reproduce. An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion. Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment, and the ability to align cell subsets across multiple data samples for comparative analysis. In this manuscript, we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model (DPGMM) approach we have previously described for cell subset identification, and show that the hierarchical DPGMM (HDPGMM) naturally generates an aligned data model that captures both commonalities and variations across multiple samples. HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously. We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell (PBMC) samples with known frequencies of antigen-specific T cells. These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding. We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations. We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses.

Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.

Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling approaches in many areas of biomedical research and application. In studies of immune profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays in which each marker is targeted with a probe that is labeled with two or more fluorescent tags. The use of several colors enables the identification of, in principle, combinatorially increasingly numbers of subtypes of cells, each identified by a subset of colors. This represents a major advance in the ability to characterize variation in immune responses involving larger numbers of functionally differentiated cell subtypes. We describe novel classes of Markov chain Monte Carlo methods for model fitting that exploit distributed GPU (graphics processing unit) implementation. We discuss issues of cellular subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments discuss broader questions in applications in immunology, and aspects of statistical computation.

Model-controlled flooding with applications to image reconstruction and segmentation.

We discuss improved image reconstruction and segmentation in a framework we term model-controlled flooding (MCF). This extends the watershed transform for segmentation by allowing the integration of a priori information about image objects into flooding simulation processes. Modeling the initial seeding, region growing, and stopping rules of the watershed flooding process allows users to customize the simulation with user-defined or default model functions incorporating prior information. It also extends a more general class of transforms based on connected attribute filters by allowing the modification of connected components of a grayscale image, thus providing more flexibility in image reconstruction. MCF reconstruction defines images with desirable features for further segmentation using existing methods and can lead to substantial improvements. We demonstrate the MCF framework using a size transform that extends grayscale area opening and attribute thickening/thinning, and give examples from several areas: concealed object detection, speckle counting in biological single cell studies, and analyses of benchmark microscopic image data sets. MCF achieves benchmark error rates well below those reported in the recent literature and in comparison with other algorithms, while being easily adapted to new imaging contexts.

Development of a high-speed, multiplexed sample-delivery instrument for LC-MS/MS bioanalysis.

BACKGROUND: The number of new chemical entities and types of in vitro and in vivo samples that require bioanalysis in drug discovery is large and diverse. In addition, method development time is limited as data turnaround is the highest priority. These circumstances require that a well-defined set of bioanalysis options be available in short timeframes to triage samples for analysis. METHOD: The Apricot Designs Dual Arm (ADDA) instrument is an LC-MS/MS sample delivery system that features a flexible hardware design coupled with software automation to enhance throughput in LC-MS/MS bioanalysis drug discovery. The instrument can perform high-throughput LC-MS/MS (8-10 s/sample) for screening and in vitro bioanalysis, as well as multiplexed LC for traditional gradient or isocratic LC approaches. The instrument control software is designed to integrate with DiscoveryQuant™ software (AB Sciex) and a global database of MS/MS conditions. CONCLUSION: Development of the sample delivery platform and its application in high-throughput and gradient LC will be described.

Computation of steady-state probability distributions in stochastic models of cellular networks.

Cellular processes are "noisy". In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry.

Efficient Classification-Based Relabeling in Mixture Models.

Effective component relabeling in Bayesian analyses of mixture models is critical to the routine use of mixtures in classification with analysis based on Markov chain Monte Carlo methods. The classification-based relabeling approach here is computationally attractive and statistically effective, and scales well with sample size and number of mixture components concordant with enabling routine analyses of increasingly large data sets. Building on the best of existing methods, practical relabeling aims to match data:component classification indicators in MCMC iterates with those of a defined reference mixture distribution. The method performs as well as or better than existing methods in small dimensional problems, while being practically superior in problems with larger data sets as the approach is scalable. We describe examples and computational benchmarks, and provide supporting code with efficient computational implementation of the algorithm that will be of use to others in practical applications of mixture models.

Origin of bistability underlying mammalian cell cycle entry.

Precise control of cell proliferation is fundamental to tissue homeostasis and differentiation. Mammalian cells commit to proliferation at the restriction point (R-point). It has long been recognized that the R-point is tightly regulated by the Rb-E2F signaling pathway. Our recent work has further demonstrated that this regulation is mediated by a bistable switch mechanism. Nevertheless, the essential regulatory features in the Rb-E2F pathway that create this switching property have not been defined. Here we analyzed a library of gene circuits comprising all possible link combinations in a simplified Rb-E2F network. We identified a minimal circuit that is able to generate robust, resettable bistability. This minimal circuit contains a feed-forward loop coupled with a mutual-inhibition feedback loop, which forms an AND-gate control of the E2F activation. Underscoring its importance, experimental disruption of this circuit abolishes maintenance of the activated E2F state, supporting its importance for the bistability of the Rb-E2F system. Our findings suggested basic design principles for the robust control of the bistable cell cycle entry at the R-point.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80.

To the Editor: We would like to retract our article, "A Genomic Strategy to Refine Prognosis in Early-Stage Non-Small-Cell Lung Cancer,"(1) which was published in the Journal on August 10, 2006. Using a sample set from a study by the American College of Surgeons Oncology Group (ACOSOG) and a collection of samples from a study by the Cancer and Leukemia Group B (CALGB), we have tried and failed to reproduce results supporting the validation of the lung metagene model described in the article. We deeply regret the effect of this action on the work of other investigators.

Bayesian learning from marginal data in bionetwork models.

In studies of dynamic molecular networks in systems biology, experiments are increasingly exploiting technologies such as flow cytometry to generate data on marginal distributions of a few network nodes at snapshots in time. For example, levels of intracellular expression of a few genes, or cell surface protein markers, can be assayed at a series of interim time points and assumed steady-states under experimentally stimulated growth conditions in small cellular systems. Such marginal data on a small number of cellular markers will typically carry very limited information on the parameters and structure of dynamic network models, though experiments will typically be designed to expose variation in cellular phenotypes that are inherently related to some aspects of model parametrization and structure. Our work addresses statistical questions of how to integrate such data with dynamic stochastic models in order to properly quantify the information-or lack of information-it carries relative to models assumed. We present a Bayesian computational strategy coupled with a novel approach to summarizing and numerically characterizing biological phenotypes that are represented in terms of the resulting sample distributions of cellular markers. We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation.

The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.