Monte Carlo Simulations of Diffusion Weighted MRI in Myocardium: Validation and Sensitivity Analysis

A model of cardiac microstructure and diffusion MRI is presented, and compared with experimental data from ex vivo rat hearts. The model includes a simplified representation of individual cells, with physiologically correct cell size and orientation, as well as intra- to extracellular volume ratio. Diffusion MRI is simulated using a Monte Carlo model and realistic MRI sequences. The results show good correspondence between the simulated and experimental MRI signals. Similar patterns are observed in the eigenvalues of the diffusion tensor, the mean diffusivity (MD), and the fractional anisotropy (FA). A sensitivity analysis shows that the diffusivity is the dominant influence on all three eigenvalues of the diffusion tensor, the MD, and the FA. The area and aspect ratio of the cell cross-section affect the secondary and tertiary eigenvalues, and hence the FA. Within biological norms, the cell length, volume fraction of cells, and rate of change of helix angle play a relatively small role in influencing tissue diffusion. Results suggest that the model could be used to improve understanding of the relationship between cardiac microstructure and diffusion MRI measurements, as well as in testing and refinement of cardiac diffusion MRI protocols

Resolving Fine Cardiac Structures in Rats with High-Resolution Diffusion Tensor Imaging

Cardiac architecture is fundamental to cardiac function and can be assessed non-invasively with diffusion tensor imaging (DTI). Here, we aimed to overcome technical challenges in ex vivo DTI in order to extract fine anatomical details and to provide novel insights in the 3D structure of the heart. An integrated set of methods was implemented in ex vivo rat hearts, including dynamic receiver gain adjustment, gradient system scaling calibration, prospective adjustment of diffusion gradients, and interleaving of diffusion-weighted and non-diffusion-weighted scans. Together, these methods enhanced SNR and spatial resolution, minimised orientation bias in diffusion-weighting, and reduced temperature variation, enabling detection of tissue structures such as cell alignment in atria, valves and vessels at an unprecedented level of detail. Improved confidence in eigenvector reproducibility enabled tracking of myolaminar structures as a basis for segmentation of functional groups of cardiomyocytes. Ex vivo DTI facilitates acquisition of high quality structural data that complements readily available in vivo cardiac functional and anatomical MRI. The improvements presented here will facilitate next generation virtual models integrating micro-structural and electro-mechanical properties of the heart

Evaluation of non‐Gaussian diffusion in cardiac MRI

Purpose: The diffusion tensor model assumes Gaussian diffusion and is widely applied in cardiac diffusion MRI. However, diffusion in biological tissue deviates from a Gaussian profile as a result of hindrance and restriction from cell and tissue microstructure, and may be quantified better by non‐Gaussian modeling. The aim of this study was to investigate non‐Gaussian diffusion in healthy and hypertrophic hearts. Methods: Thirteen rat hearts (five healthy, four sham, four hypertrophic) were imaged ex vivo. Diffusion‐weighted images were acquired at b‐values up to 10,000 s/mm2. Models of diffusion were fit to the data and ranked based on the Akaike information criterion. Results: The diffusion tensor was ranked best at b‐values up to 2000 s/mm2 but reflected the signal poorly in the high b‐value regime, in which the best model was a non‐Gaussian “beta distribution” model. Although there was considerable overlap in apparent diffusivities between the healthy, sham, and hypertrophic hearts, diffusion kurtosis and skewness in the hypertrophic hearts were more than 20% higher in the sheetlet and sheetlet‐normal directions. Conclusion: Non‐Gaussian diffusion models have a higher sensitivity for the detection of hypertrophy compared with the Gaussian model. In particular, diffusion kurtosis may serve as a useful biomarker for characterization of disease and remodeling in the heart

Gene silencing in tick cell lines using small interfering or long double-stranded RNA

Gene silencing by RNA interference (RNAi) is an important research tool in many areas of biology. To effectively harness the power of this technique in order to explore tick functional genomics and tick-microorganism interactions, optimised parameters for RNAi-mediated gene silencing in tick cells need to be established. Ten cell lines from four economically important ixodid tick genera (Amblyomma, Hyalomma, Ixodes and Rhipicephalus including the sub-species Boophilus) were used to examine key parameters including small interfering RNA (siRNA), double stranded RNA (dsRNA), transfection reagent and incubation time for silencing virus reporter and endogenous tick genes. Transfection reagents were essential for the uptake of siRNA whereas long dsRNA alone was taken up by most tick cell lines. Significant virus reporter protein knockdown was achieved using either siRNA or dsRNA in all the cell lines tested. Optimum conditions varied according to the cell line. Consistency between replicates and duration of incubation with dsRNA were addressed for two Ixodes scapularis cell lines; IDE8 supported more consistent and effective silencing of the endogenous gene subolesin than ISE6, and highly significant knockdown of the endogenous gene 2I1F6 in IDE8 cells was achieved within 48 h incubation with dsRNA. In summary, this study shows that gene silencing by RNAi in tick cell lines is generally more efficient with dsRNA than with siRNA but results vary between cell lines and optimal parameters need to be determined for each experimental system

Scaling up community mobilisation through women's groups for maternal and neonatal health: experiences from rural Bangladesh

Background: Program coverage is likely to be an important determinant of the effectiveness of community interventions to reduce neonatal mortality. Rigorous examination and documentation of methods to scale-up interventions and measure coverage are scarce, however. To address this knowledge gap, this paper describes the process and measurement of scaling-up coverage of a community mobilisation intervention for maternal, child and neonatal health in rural Bangladesh and critiques this real-life experience in relation to available literature on scaling-up.Methods: Scale-up activities took place in nine unions in rural Bangladesh. Recruitment and training of those who deliver the intervention, communication and engagement with the community and other stakeholders and active dissemination of intervention activities are described. Process evaluation and population survey data are presented and used to measure coverage and the success of scale-up.Results: The intervention was scaled-up from 162 women's groups to 810, representing a five-fold increase in population coverage. The proportion of women of reproductive age and pregnant women who were engaged in the intervention increased from 9% and 3%, respectively, to 23% and 29%.Conclusions: Examination and documentation of how scaling-up was successfully initiated, led, managed and monitored in rural Bangladesh provide a deeper knowledge base and valuable lessons.Strong operational capabilities and institutional knowledge o

A UMLS-based spell checker for natural language processing in vaccine safety

BACKGROUND: The Institute of Medicine has identified patient safety as a key goal for health care in the United States. Detecting vaccine adverse events is an important public health activity that contributes to patient safety. Reports about adverse events following immunization (AEFI) from surveillance systems contain free-text components that can be analyzed using natural language processing. To extract Unified Medical Language System (UMLS) concepts from free text and classify AEFI reports based on concepts they contain, we first needed to clean the text by expanding abbreviations and shortcuts and correcting spelling errors. Our objective in this paper was to create a UMLS-based spelling error correction tool as a first step in the natural language processing (NLP) pipeline for AEFI reports. METHODS: We developed spell checking algorithms using open source tools. We used de-identified AEFI surveillance reports to create free-text data sets for analysis. After expansion of abbreviated clinical terms and shortcuts, we performed spelling correction in four steps: (1) error detection, (2) word list generation, (3) word list disambiguation and (4) error correction. We then measured the performance of the resulting spell checker by comparing it to manual correction. RESULTS: We used 12,056 words to train the spell checker and tested its performance on 8,131 words. During testing, sensitivity, specificity, and positive predictive value (PPV) for the spell checker were 74% (95% CI: 74–75), 100% (95% CI: 100–100), and 47% (95% CI: 46%–48%), respectively. CONCLUSION: We created a prototype spell checker that can be used to process AEFI reports. We used the UMLS Specialist Lexicon as the primary source of dictionary terms and the WordNet lexicon as a secondary source. We used the UMLS as a domain-specific source of dictionary terms to compare potentially misspelled words in the corpus. The prototype sensitivity was comparable to currently available tools, but the specificity was much superior. The slow processing speed may be improved by trimming it down to the most useful component algorithms. Other investigators may find the methods we developed useful for cleaning text using lexicons specific to their area of interest

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples

Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models

New fluorescent perylene bisimide indicators—a platform for broadband pH optodes

Asymmetric perylene bisimide (PBI) dyes are prepared and are shown to be suitable for the preparation of fluorescence chemosensors for pH. They carry one amino-functional substituent which introduces pH sensitivity via photoinduced electron transfer (PET) while the other one increases solubility. The luminescence quantum yields for the new indicators exceed 75% in the protonated form. The new indicators are non-covalently entrapped in polyurethane hydrogel D4 and poly(hydroxyalkylmethacrylates). Several PET functions including aliphatic and aromatic amino groups were successfully used to tune the dynamic range of the sensor. Because of their virtually identical spectral properties, various PBIs with selected PET functions can easily be integrated into a single sensor with enlarged dynamic range (over 4 pH units). PBIs with two different substitution patterns in the bay position are investigated and possess variable spectral properties. Compared with their tetrachloro analogues, tetra-tert-butyl-substituted PBIs yield more long-wave excitable sensors which feature excellent photostability. Cross-sensitivity to ionic strength was found to be negligible. The practical applicability of the sensors may be compromised by the long response times (especially in case of tetra-tert-butyl-substituted PBIs)