The CKD Stress Scale: Development and identification of patients at risk.

AIMS: Develop a novel chronic kidney disease (CKD)-specific stress scale and examine associations with patient characteristics. MATERIALS AND METHODS: Adults with CKD stages 1 - 5 enrolled in a cross-sectional survey. Eight questions assessed patients' thoughts and feelings of stress related to CKD (CKD Stress Scale). Patients also reported their knowledge of CKD, barriers to CKD health, and demographics. The scale was evaluated using exploratory factor analysis and Cronbach's alpha. Associations were examined via linear regression. RESULTS: 245 participant enrolled with a mean age of 60 years and a mean estimated glomerular filtration rate (eGFR) of 34 mL/min/1.73m2; 49% were women (match percentage in Table 1), 74% White, 14% African American. A one-factor model of CKD Stress exhibited high internal consistency (α = 0.89). In bivariate analyses, higher CKD Stress was associated with lower eGFR, younger age, African American race (compared to White), and having a high school education or some college (compared to college degree or higher). Adjusting for these characteristics, as well as income and knowledge about CKD, only lower eGFR (b = -0.01; 95% CI [-0.01, -0.001]), younger age (b = -0.01; 95% CI [-0.01, -0.003]), African American race (b = 0.35, 95% CI [0.10, 0.60]), and receiving a high school education or some college (b = 0.20, 95% CI [0.01, 0.39]) were independently associated with more CKD-specific stress. Concurrent validity was supported by associations between stress and perceived barriers to care. CONCLUSION: Our CKD Stress Scale exhibits excellent internal reliability and identified where future educational interventions may benefit from tailoring for at-risk patients.

Patient Electronic Health Record Portal Use and Patient-Centered Outcomes in CKD.

RATIONALE & OBJECTIVE: Electronic health record portals are increasingly emphasized in chronic kidney disease (CKD). However, associations of portal use with clinical and patient-centered outcomes remain unknown. STUDY DESIGN: Cross-sectional survey (April 2015 to March 2018). SETTING & PARTICIPANTS: Nondialysis patients with CKD from nephrology clinics within 1 academic medical center. EXPOSURES: Patient demographics (age, sex, race, ethnicity, education, and income), kidney function. OUTCOMES: Association between portal use as an outcome and exposures. Additionally, associations of portal use and patient demographics with 4 patient-centered outcomes (CKD-specific knowledge, stress, and 2 self-ratings of health). ANALYTIC APPROACH: Logistic regression to examine associations between patient portal use, demographics, and kidney function. Linear regression to examine associations between portal use and patient-centered outcomes. RESULTS: Of 245 participants, mean age was 60 ± 17 (SD) years, 182 (77%) were White, 121 (49%) were women, 230 (96%) had a high school education or higher, and 96 (45%) had <$50,000 annual income. Examining portal use, 159 (65%) used the portal as follows: checking laboratory test results, 157 (99%); managing appointments, 133 (84%); messaging providers, 131 (82%); viewing medical history, 127 (80%); reviewing educational resources, 113 (71%); and renewing prescriptions, 98 (62%). African Americans (OR, 0.34; 95% CI, 0.16-0.72 vs White patients), patients with less formal education (OR, 0.06; 95% CI, 0.01-0.36), and those with lower income (OR, 0.28; 95% CI, 0.13-0.60; and OR, 0.26; 95% CI, 0.12-0.54 comparing income < $25,000 and $25,000-$50,000, respectively, with ≥$50,000) had lower odds of using the portal. In adjusted analysis, only lower income predicted lower portal use. Examining patient-centered outcomes in univariable analysis, portal users had higher knowledge (ß = 4.89; P = 0.02), higher ratings of current health (ß = 0.28; P = 0.03), and lower CKD-related stress (ß = -0.18; P = 0.05). In adjusted analysis, only patient demographics and/or kidney function remained independent predictors of patient-centered outcomes. LIMITATIONS: Cross-sectional study design, cannot determine causality. CONCLUSIONS: Interventions are needed to ensure that all patients have access to portals to mitigate disparities in care.

Inkjet-printed micro-calibration standards for ultraquantitative Raman spectral cytometry.

Herein we report the development of a cytometric analysis platform for measuring the contents of individual cells in absolute (picogram) scales; this study represents the first report of Raman-based quantitation of the absolute mass - or the total amount - of multiple endogenous biomolecules within single-cells. To enable ultraquantitative calibration, we engineered single-cell-sized micro-calibration standards of known composition by inkjet-printer deposition of biomolecular components in microarrays across the surface of silicon chips. We demonstrate clinical feasibility by characterizing the compositional phenotype of human skin fibroblast and porcine alveolar macrophage cell populations in the respective contexts of Niemann-Pick disease and drug-induced phospholipidosis: two types of lipid storage disorders. We envision this microanalytical platform as the foundation for many future biomedical applications, ranging from diagnostic assays to pathological analysis to advanced pharmaco/toxicokinetic research studies.

Effect of Silver Diamine Fluoride Treatment on Microbial Profiles of Plaque Biofilms from Root/Cervical Caries Lesions.

PURPOSE: To assess the effect of silver diamine fluoride (SDF) on microbial profiles present in plaque from root/cervical carious lesions, and its association with caries lesion arrest. MATERIALS AND METHODS: Twenty patients with at least one soft cavitated root/cervical carious lesion were included. One lesion/patient was randomly selected and treated with 38% SDF. Supragingival plaque samples were harvested at preintervention and 1 month postintervention. Using an MiSeq platform, 16S rDNA sequencing of the V3-V4 regions was used to determine bacterial profiles. Clinical evaluation of lesion hardness was used to evaluate arrest. t tests, principal component analysis (PCA), multidimensional scaling (MDS), and generalized linear models (GLMs) tests were used for statistical comparisons. RESULTS: From a total of 40 plaque samples, 468 probe targets were observed. Although 60% of lesions became hard postintervention, PCA and MDS tests showed no distinct pre- and postintervention groups. In addition, pre- and postintervention differences in diversity (Shannon index) of microbial profiles between patients with and without lesion arrest were not statistically different. A likelihood ratio test for pre- versus postintervention differences within patients, i.e., adjusting for differences between patients using negative binomial GLMs, showed 17 bacterial taxa with significant differences (FDR <0.05). CONCLUSION: Although 60% of lesions hardened after SDF treatment, this was not directly due to either overall statistically significant differences in microbial profiles or differences in microbial diversity. Nevertheless, there was a trend with some acid-producing species in that their relative abundance was reduced postintervention. The negative binomial GLMs showed 17 bacterial taxa that were significantly different after SDF treatment.

Dynamic Recruitment of Single RNAs to Processing Bodies Depends on RNA Functionality.

Cellular RNAs often colocalize with cytoplasmic, membrane-less ribonucleoprotein (RNP) granules enriched for RNA-processing enzymes, termed processing bodies (PBs). Here we track the dynamic localization of individual miRNAs, mRNAs, and long non-coding RNAs (lncRNAs) to PBs using intracellular single-molecule fluorescence microscopy. We find that unused miRNAs stably bind to PBs, whereas functional miRNAs, repressed mRNAs, and lncRNAs both transiently and stably localize within either the core or periphery of PBs, albeit to different extents. Consequently, translation potential and 3' versus 5' placement of miRNA target sites significantly affect the PB localization dynamics of mRNAs. Using computational modeling and supporting experimental approaches, we show that partitioning in the PB phase attenuates mRNA silencing, suggesting that physiological mRNA turnover occurs predominantly outside of PBs. Instead, our data support a PB role in sequestering unused miRNAs for surveillance and provide a framework for investigating the dynamic assembly of RNP granules by phase separation at single-molecule resolution.

An Expandable Mechanopharmaceutical Device (3): a Versatile Raman Spectral Cytometry Approach to Study the Drug Cargo Capacity of Individual Macrophages.

PURPOSE: To improve cytometric phenotyping abilities and better understand cell populations with high interindividual variability, a novel Raman-based microanalysis was developed to characterize macrophages on the basis of chemical composition, specifically to measure and characterize intracellular drug distribution and phase separation in relation to endogenous cellular biomolecules. METHODS: The microanalysis was developed for the commercially-available WiTec alpha300R confocal Raman microscope. Alveolar macrophages were isolated and incubated in the presence of pharmaceutical compounds nilotinib, chloroquine, or etravirine. A Raman data processing algorithm was specifically developed to acquire the Raman signals emitted from single-cells and calculate the signal contributions from each of the major molecular components present in cell samples. RESULTS: Our methodology enabled analysis of the most abundant biochemicals present in typical eukaryotic cells and clearly identified "foamy" lipid-laden macrophages throughout cell populations, indicating feasibility for cellular lipid content analysis in the context of different diseases. Single-cell imaging revealed differences in intracellular distribution behavior for each drug; nilotinib underwent phase separation and self-aggregation while chloroquine and etravirine accumulated primarily via lipid partitioning. CONCLUSIONS: This methodology establishes a versatile cytometric analysis of drug cargo loading in macrophages requiring small numbers of cells with foreseeable applications in toxicology, disease pathology, and drug discovery.

A computational approach to studying ageing at the individual level.

The ageing process is actively regulated throughout an organism's life, but studying the rate of ageing in individuals is difficult with conventional methods. Consequently, ageing studies typically make biological inference based on population mortality rates, which often do not accurately reflect the probabilities of death at the individual level. To study the relationship between individual and population mortality rates, we integrated in vivo switch experiments with in silico stochastic simulations to elucidate how carefully designed experiments allow key aspects of individual ageing to be deduced from group mortality measurements. As our case study, we used the recent report demonstrating that pheromones of the opposite sex decrease lifespan in Drosophila melanogaster by reversibly increasing population mortality rates. We showed that the population mortality reversal following pheromone removal was almost surely occurring in individuals, albeit more slowly than suggested by population measures. Furthermore, heterogeneity among individuals due to the inherent stochasticity of behavioural interactions skewed population mortality rates in middle-age away from the individual-level trajectories of which they are comprised. This article exemplifies how computational models function as important predictive tools for designing wet-laboratory experiments to use population mortality rates to understand how genetic and environmental manipulations affect ageing in the individual.

Connecting the dots: the effects of macromolecular crowding on cell physiology.

The physicochemical properties of cellular environments with a high macromolecular content have been systematically characterized to explain differences observed in the diffusion coefficients, kinetics parameters, and thermodynamic properties of proteins inside and outside of cells. However, much less attention has been given to the effects of macromolecular crowding on cell physiology. Here, we review recent findings that shed some light on the role of crowding in various cellular processes, such as reduction of biochemical activities, structural reorganization of the cytoplasm, cytoplasm fluidity, and cellular dormancy. We conclude by presenting some unresolved problems that require the attention of biophysicists, biochemists, and cell physiologists. Although it is still underappreciated, macromolecular crowding plays a critical role in life as we know it.

Optimal experimental design to estimate statistically significant periods of oscillations in time course data.

We investigated commonly used methods (Autocorrelation, Enright, and Discrete Fourier Transform) to estimate the periodicity of oscillatory data and determine which method most accurately estimated periods while being least vulnerable to the presence of noise. Both simulated and experimental data were used in the analysis performed. We determined the significance of calculated periods by applying these methods to several random permutations of the data and then calculating the probability of obtaining the period's peak in the corresponding periodograms. Our analysis suggests that the Enright method is the most accurate for estimating the period of oscillatory data. We further show that to accurately estimate the period of oscillatory data, it is necessary that at least five cycles of data are sampled, using at least four data points per cycle. These results suggest that the Enright method should be more widely applied in order to improve the analysis of oscillatory data.

Unravelling the impact of obstacles in diffusion and kinetics of an enzyme catalysed reaction.

Lattice gas automata simulations of diffusion-limited reactions in heterogeneous media exhibit fractal-like kinetics, which is a generalised mass action kinetics with time-dependent rate constants. We develop a two dimensional lattice gas automata simulation of the Michaelis-Menten mechanism in diffusion-limited conditions to investigate the effect of density and size of obstacles on reactant diffusion and rate coefficients. In order to simulate more physicochemical realistic conditions, reactants rotate and interact according to their specific orientation. We also model weak interaction forces between reactants and obstacles. Our results show that obstacle density and size affect diffusion, first- and second-order rates. We also find that particle rotations and weak force interactions among particles lead to a significant decay in the fractal-like kinetic exponent h. These results suggest that the effects of fractal-like kinetics disappear under less restricted conditions than previously believed in lattice based simulations.

Circadian rhythms regulate amelogenesis.

Ameloblasts, the cells responsible for making enamel, modify their morphological features in response to specialized functions necessary for synchronized ameloblast differentiation and enamel formation. Secretory and maturation ameloblasts are characterized by the expression of stage-specific genes which follows strictly controlled repetitive patterns. Circadian rhythms are recognized as key regulators of the development and diseases of many tissues including bone. Our aim was to gain novel insights on the role of clock genes in enamel formation and to explore the potential links between circadian rhythms and amelogenesis. Our data shows definitive evidence that the main clock genes (Bmal1, Clock, Per1 and Per2) oscillate in ameloblasts at regular circadian (24 h) intervals both at RNA and protein levels. This study also reveals that the two markers of ameloblast differentiation i.e. amelogenin (Amelx; a marker of secretory stage ameloblasts) and kallikrein-related peptidase 4 (Klk4, a marker of maturation stage ameloblasts) are downstream targets of clock genes. Both, Amelx and Klk4 show 24h oscillatory expression patterns and their expression levels are up-regulated after Bmal1 over-expression in HAT-7 ameloblast cells. Taken together, these data suggest that both the secretory and the maturation stages of amelogenesis might be under circadian control. Changes in clock gene expression patterns might result in significant alterations of enamel apposition and mineralization.

A graphical user interface for a method to infer kinetics and network architecture (MIKANA).

One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).

Macroscopic simulations of microtubule dynamics predict two steady-state processes governing array morphology.

Microtubule polymers typically function through their collective organization into a patterned array. The formation of the pattern, whether it is a relatively simple astral array or a highly complex mitotic spindle, relies on controlled microtubule nucleation and the basal dynamics parameters governing polymer growth and shortening. We have investigated the interaction between the microtubule nucleation and dynamics parameters, using macroscopic Monte Carlo simulations, to determine how these parameters contribute to the underlying microtubule array morphology (i.e. polymer density and length distribution). In addition to the well-characterized steady state achieved between free tubulin subunits and microtubule polymer, we propose that microtubule nucleation and extinction constitute a second, interdependent steady state process. Our simulation studies show that the magnitude of both nucleation and extinction additively impacts the final steady state free subunit concentration. We systematically varied individual microtubule dynamics parameters to survey the effects on array morphology and find specific sensitivity to perturbations of catastrophe frequency. Altering the cellular context for the microtubule array, we find that nucleation template number plays a defining role in shaping the microtubule length distribution and polymer density.

Enzyme catalyzed reactions: from experiment to computational mechanism reconstruction.

The traditional experimental practice in enzyme kinetics involves the measurement of substrate or product concentrations as a function of time. Advances in computing have produced novel approaches for modeling enzyme catalyzed reactions from time course data. One example of such an approach is the selection of appropriate chemical reactions that best fit the data. A common limitation of this approach resides in the number of chemical species considered. The number of possible chemical reactions grows exponentially with the number of chemical species, which makes difficult to select reactions that uniquely describe the data and diminishes the efficiency of the methods. In addition, a method's performance is also dependent on several quantitative and qualitative properties of the time course data, of which we know very little. This information is important to experimentalists as it could allow them to setup their experiments in ways that optimize the network reconstruction. We have previously described a method for inferring reaction mechanisms and kinetic rate parameters from time course data. Here, we address the limitations in the number of chemical reactions by allowing the introduction of information about chemical interactions. We also address the unknown properties of the input data by determining experimental data properties that maximize our method's performance. We investigate the following properties: initial substrate-enzyme concentration ratios; initial substrate-enzyme concentration variation ranges; number of data points; number of different experiments (time courses); and noise. We test the method using data generated in silico from the Michaelis-Menten and the Hartley-Kilby reaction mechanisms. Our results demonstrate the importance of experimental design for time course assays that has not been considered in experimental protocols. These considerations can have far reaching implications for the computational mechanism reconstruction process.