Browsing by Author "Zhang, Linlin"

Now showing 1 - 7 of 7
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    Accurate Quantification of Disease Markers in Human Serum Using Iron Oxide Nanoparticle-linked Immunosorbent Assay
    (Ivyspring, 2016) Zhang, Linlin; Tong, Sheng; Zhou, Jun; Bao, Gang; Bioengineering
    Accurate and reliable quantification of biomarkers in the blood is essential in disease screening and diagnosis. Here we describe an iron oxide nanoparticle (IONP)-linked immunosorbent assay (ILISA) for detecting biomolecules in human serum. Sandwich ILISA was optimized for the detection of four important serological markers, IgA, IgG, IgM, and C-reactive protein (CRP), and assessed with normal sera, simulated disease-state sera and the serum samples from patients infected with West Nile virus (WNV) or human herpes virus (HHV). Our study shows that using the detection assay formulated with 18.8 nm wüstite nanocrystals, ILISA can achieve sub-picomolar detection sensitivity, and all four markers can be accurately quantified over a large dynamic range. In addition, ILISA is not susceptible to variations in operating procedures and shows better linearity and higher stability compared with ELISA, which facilitates its integration into detection methods suitable for point of care. Our results demonstrate that ILISA is a simple and versatile nanoplatform for highly sensitive and reliable detection of serological biomarkers in biomedical research and clinical applications.
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    Bayesian models for functional magnetic resonance imaging data analysis
    (Wiley, 2015) Zhang, Linlin; Guindani, Michele; Vannucci, Marina
    Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This article provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatiotemporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as electroencephalography/magnetoencephalography (EEG/MEG) and diffusion tensor imaging (DTI) data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics. 
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    Bayesian nonparametric models for functional magnetic resonance imaging (fMRI) data
    (2015-04-24) Zhang, Linlin; Guindani, Michele; Vannucci, Marina; Schweinberger, Michael; Cox, Steven
    In this research work, I propose Bayesian nonparametric approaches to model functional magnetic resonance imaging (fMRI) data. Due to the complex spatial and temporal correlation structure as well as the high dimensionality of fMRI data, statistical methods play a crucial role in the analysis of fMRI data. My research focuses on developing novel methods that incorporate both temporal and spatial correlations into a single modeling framework and simultaneously capture brain connectivity via appropriate priors. First, I propose a spatio-temporal nonparametric Bayesian variable selection model of single-subject fMRI data. The method provides a joint analytical framework that allows to detect activated brain regions in response to a stimulus and infer the clustering of spatially remote voxels that exhibit fMRI time series with similar characteristics. I show good performance of the model on inference through simulations, and demonstrate via synthetic data analysis that the model outperforms methods implemented in the SPM8, a standard software for fMRI data analysis. I also apply the model to a fMRI study on attention to visual motion, and illustrate the results of activation detection and clustering. Then I propose a Bayesian modeling approach to the analysis of multiple-subject fMRI data. The proposed method provides a unified, single stage, and probabilistically coherent Bayesian framework for the inference of task-related brain activity. Furthermore, I employ with advanced Bayesian nonparametric priors to tie the activation strengths within and across subjects, and graphical network priors to model the complex spatio-temporal correlation structure observed in fMRI scans from multiple subjects. I develop a variational Bayesian method for inference, in addition to a Markov Chain Monte Carlo (MCMC) method. I investigate the performance of the proposed model on simulated data, and compare its performance to competing methods on synthetic data. In an application to data from a fMRI study on breast cancer survivors, the model demonstrates the excellent estimation performance.
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    Integrated microheater array for efficient and localized heating of magnetic nanoparticles at microwave frequencies
    (2025-02-11) Fan, Yingying; Zhang, Qingbo; Zhang, Linlin; Bao, Gang; Chi, Taiyun; Rice University; United States Patent and Trademark Office
    An microheater array system includes an integrated microheater array configured to generate a localized heat and having a plurality of pixels. Each pixel includes: an inductor; a stacked oscillator configured to generate a magnetic field at microwave frequencies with tunable intensity and frequency; and an electro-thermal loop. The microheater array system may further include a plurality of magnetic nanoparticles (MNPs).
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    Magnetic forces enable controlled drug delivery by disrupting endothelial cell-cell junctions
    (Springer Nature, 2017) Qiu, Yongzhi; Tong, Sheng; Zhang, Linlin; Sakurai, Yumiko; Myers, David R.; Hong, Lin; Lam, Wilbur A.; Bao, Gang; Bioengineering
    The vascular endothelium presents a major transport barrier to drug delivery by only allowing selective extravasation of solutes and small molecules. Therefore, enhancing drug transport across the endothelial barrier has to rely on leaky vessels arising from disease states such as pathological angiogenesis and inflammatory response. Here we show that the permeability of vascular endothelium can be increased using an external magnetic field to temporarily disrupt endothelial adherens junctions through internalized iron oxide nanoparticles, activating the paracellular transport pathway and facilitating the local extravasation of circulating substances. This approach provides a physically controlled drug delivery method harnessing the biology of endothelial adherens junction and opens a new avenue for drug delivery in a broad range of biomedical research and therapeutic applications.
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    A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses
    (Elsevier, 2014) Zhang, Linlin; Guindani, Michele; Versace, Francesco; Vannucci, Marina
    In this paper we present a novel wavelet-based Bayesian nonparametric regression model for the analysis of functional magnetic resonance imaging (fMRI) data. Our goal is to provide a joint analytical framework that allows to detect regions of the brain which exhibit neuronal activity in response to a stimulus and, simultaneously, infer the association, or clustering, of spatially remote voxels that exhibit fMRI time series with similar characteristics. We start by modeling the data with a hemodynamic response function (HRF) with a voxel-dependent shape parameter. We detect regions of the brain activated in response to a given stimulus by using mixture priors with a spike at zero on the coefficients of the regression model. We account for the complex spatial correlation structure of the brain by using a Markov random field (MRF) prior on the parameters guiding the selection of the activated voxels, therefore capturing correlation among nearby voxels. In order to infer association of the voxel time courses, we assume correlated errors, in particular long memory, and exploit the whitening properties of discrete wavelet transforms. Furthermore, we achieve clustering of the voxels by imposing a Dirichlet process (DP) prior on the parameters of the long memory process. For inference, we use Markov Chain Monte Carlo (MCMC) sampling techniques that combine Metropolis–Hastings schemes employed in Bayesian variable selection with sampling algorithms for nonparametric DP models. We explore the performance of the proposed model on simulated data, with both block- and event-related design, and on real fMRI data.
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    A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
    (Project Euclid, 2016) Zhang, Linlin; Guindani, Michele; Versace, Francesco; Engelmann, Jeffrey M.; Vannucci, Marina
    In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented.