Enhancing the compatibility of BioCaRGOS silica sol-gel technology with ctDNA extraction and droplet digital PCR (ddPCR) analysis.

Previously, our group had demonstrated long term stabilization of protein biomarkers using BioCaRGOS, a silica sol-gel technology. Herein, we describe workflow modifications to allow for extraction of cell free DNA (cfDNA) from primary samples containing working concentrations of BioCaRGOS, as well as the compatibility of BioCaRGOS with droplet digital PCR (ddPCR) analysis for pancreatic cancer biomarkers i.e., KRAS circulating tumor DNA (ctDNA). Preliminary attempts to extract ctDNA from BioCaRGOS containing samples demonstrated interference in the extraction of primary samples and the interference with ddPCR analysis when BioCaRGOS was directly introduced to stabilize sample extracts. In our modified technique, we have minimized the interference caused by methanol with ddPCR by complete removal of methanol from the activated BioCaRGOS formulation prior to addition to the biospecimen or ctDNA extract. Interference of the silica matrix present in BioCaRGOS with ctDNA extraction was eliminated through the introduction of invert filtration of the sample prior to extraction. These modifications to the workflow of BioCaRGOS containing samples allow for use of BioCaRGOS for stabilization of trace quantities of nucleic acid biomarkers such as plasma ctDNA, while retaining the capability to extract the biomarker and quantify based on ddPCR.

A microfluidic impedance platform for real-time, in vitro characterization of endothelial cells undergoing fluid shear stress.

We introduce a microfluidic impedance platform to electrically monitor in real-time, endothelium monolayers undergoing fluid shear stress. Our platform incorporates sensing electrodes (SEs) that measure cell behavior and cell-free control electrodes that measure cell culture media resistance simultaneously but independently from SEs. We evaluated three different cellular subpopulations sizes through 50, 100, and 200 µm diameter SEs. We tested their utility in measuring the response of human umbilical vein endothelial cells (HUVECs) at static, constant (17.6 dyne per cm2), and stepped (23.7-35-58.1 dyne per cm2) shear stress conditions. For 14 hours, we collected the impedance spectra (100 Hz-1 MHz) of sheared cells. Using equivalent circuit models, we extracted monolayer permeability (RTER), cell membrane capacitance, and cell culture media resistance. Platform evaluation concluded that: (1) 50 µm SEs (∼2 cells) suffered interfacial capacitance and reduced cell measurement sensitivity, (2) 100 µm SEs (∼6 cells) was limited to measuring cell behavior only and cannot measure cell culture media resistance, and (3) 200 µm SEs (∼20 cells) detected cell behavior with accurate prediction of cell culture media resistance. Platform-based shear stress studies indicated a shear magnitude dependent increase in RTER at the onset of acute flow. Consecutive stepped shear conditions did not alter RTER in the same magnitude after shear has been applied. Finally, endpoint staining of VE-cadherin on the actual SEs and endpoint RTER measurements were greater for 23.7-35-58.1 dyne per cm2 than 17.6 dyne per cm2 shear conditions.

Adsorption Properties and Electron-transfer Rates of a Redox Probe at Different Interfaces of an Immunoassay Assembled on an Electro-active Photonic Platform.

Physical and chemical properties of a redox protein adsorbed to different interfaces of a multilayer immunoassay assembly were studied using a single-mode, electro-active, integrated optical waveguide (SM-EA-IOW) platform. For each interface of the immunoassay assembly (indium tin oxide, 3-aminopropyl triethoxysilane, recombinant protein G, antibody, and bovine serum albumin) the surface density, the adsorption kinetics, and the electron-transfer rate of bound species of the redox-active cytochrome c (Cyt-C) protein were accurately quantified at very low surface concentrations of redox species (from 0.4 to 4% of a full monolayer) using a highly sensitive optical impedance spectroscopy (OIS) technique based on measurements obtained with the SM-EA-IOW platform. The technique is shown here to provide quantitative insights into an important immunoassay assembly for characterization and understanding of the mechanisms of electron transfer rate, the affinity strength of molecular binding, and the associated bio-selectivity. Such methodology and acquired knowledge are crucial for the development of novel and advanced immuno-biosensors.

A novel 3D segmentation approach for extracting retinal layers from optical coherence tomography images.

PURPOSE: Accurate segmentation of retinal layers of the eye in 3D Optical Coherence Tomography (OCT) data provides relevant information for clinical diagnosis. This manuscript describes a 3D segmentation approach that uses an adaptive patient-specific retinal atlas, as well as an appearance model for 3D OCT data. METHODS: To reconstruct the atlas of 3D retinal scan, the central area of the macula (macula mid-area) where the fovea could be clearly identified, was segmented initially. Markov Gibbs Random Field (MGRF) including intensity, spatial information, and shape of 12 retinal layers were used to segment the selected area of retinal fovea. A set of coregistered OCT scans that were gathered from 200 different individuals were used to build a 2D shape prior. This shape prior was adapted subsequently to the first order appearance and second order spatial interaction MGRF model. After segmenting the center of the macula "foveal area", the labels and appearances of the layers that were segmented were utilized to segment the adjacent slices. The final step was repeated recursively until a 3D OCT scan of the patient was segmented. RESULTS: This approach was tested in 50 patients with normal and with ocular pathological conditions. The segmentation was compared to a manually segmented ground truth. The results were verified by clinical retinal experts. Dice Similarity Coefficient (DSC), 95% bidirectional modified Hausdorff Distance (HD), Unsigned Mean Surface Position Error (MSPE), and Average Volume Difference (AVD) metrics were used to quantify the performance of the proposed approach. The proposed approach was proved to be more accurate than the current state-of-the-art 3D OCT approaches. CONCLUSIONS: The proposed approach has the advantage of segmenting all the 12 retinal layers rapidly and more accurately than current state-of-the-art 3D OCT approaches.

Long term storage of miRNA at room and elevated temperatures in a silica sol-gel matrix.

Storage of biospecimens in their near native environment at room temperature can have a transformative global impact, however, this remains an arduous challenge to date due to the rapid degradation of biospecimens over time. Currently, most isolated biospecimens are refrigerated for short-term storage and frozen (-20 °C, -80 °C, liquid nitrogen) for long-term storage. Recent advances in room temperature storage of purified biomolecules utilize anhydrobiosis. However, a near aqueous storage solution that can preserve the biospecimen nearly "as is" has not yet been achieved by any current technology. Here, we demonstrate an aqueous silica sol-gel matrix for optimized storage of biospecimens. Our technique is facile, reproducible, and has previously demonstrated stabilization of DNA and proteins, within a few minutes using a standard benchtop microwave. Herein, we demonstrate complete integrity of miRNA 21, a highly sensitive molecule at 4, 25, and 40 °C over a period of ∼3 months. In contrast, the control samples completely degrade in less than 1 week. We attribute excellent stability to entrapment of miRNA within silica-gel matrices.

Impact of stress and hypertension on the cerebrovasculature.

OBJECTIVES: Both stress and hypertension (HTN) are considered major health problems that negatively impact the cerebral vasculature. In this article we summarize the possible relationship between stress and HTN. METHODS: We conducted a systematic review of the literature using a database search of MEDLINE, PubMed, Scopus, and Web of Science. RESULTS: Psychological stress is known to be an important risk factor for essential hypertension. Acute stress can induce transient elevations of blood pressure in the context of the fight-or-flight response. With increased intensity and duration of a perceived harmful event, the normal physiological response is altered, resulting in a failure to return to the resting levels. These changes are responsible for the development of HTN. Genetic and behavioral factors are also very important for the pathogenesis of hypertension under chronic stress situation. In addition, HTN and chronic stress may lead to impaired auto-regulation, regional vascular remodeling, and breakdown of the blood brain barrier (BBB). The effects of both HTN and chronic stress on the cerebral blood vessels shows that both have common structural and functional effects including endothelial damage with subsequent increased wall thickness, vessel resistance, stiffness, arterial atherosclerosis, and altered hemodynamics. CONCLUSION: Most of the above mentioned vascular effects of stress were primarily reported in animal models. Further in-vivo standardization of pathological vascular indices and imaging modalities is warranted. Radiological quantification of these cerebrovascular changes is therefore essential for in depth understanding of the healthy and diseased cerebral arteries functions, identification and stratification of patients at risk of cardiovascular and neurological adverse events, enactment of preventive measures prior to the onset of systemic HTN, and the initiation of personalized medical management.

Bio-CaRGOS: capture and release gels for optimized storage of hemoglobin.

Room temperature biospecimen storage for prolonged periods is essential to eliminate energy consumption by ultra-low freezing or refrigeration-based storage techniques. State of the art practices that sufficiently minimize the direct or hidden costs associated with cold-chain logistics include ambient temperature storage of biospecimens (i.e., DNA, RNA, proteins, lipids) in the dry state. However, the biospecimens are still well-exposed to the stress associated with drying and reconstitution cycles, which augments the pre-analytical degradation of biospecimens prior to their downstream processing. An aqueous storage solution that can eliminate these stresses which are correlated to several cycles of drying/rehydration or freezing of biospecimens, is yet to be achieved by any current technology. In our study, we have addressed this room temperature biospecimen-protection challenge using aqueous capture and release gels for optimized storage (Bio-CaRGOS) of biospecimens. Herein, we have demonstrated a single-step ∼95% recovery of a metalloprotein hemoglobin at room temperature using a cost-effective standard microwave-based aqueous formulation of Bio-CaRGOS. Although hemoglobin samples are currently stored at sub-zero or under refrigeration (4 °C) conditions to avoid loss of integrity and an unpredictable diagnosis during their downstream assays, our results have displayed an unprecedented room temperature integrity preservation of hemoglobin. Bio-CaRGOS formulations efficiently preserve hemoglobin in its native state, with single-step protein recovery of ∼95% at ambient conditions (1 month) and ∼96% (7 months) under refrigeration conditions. In contrast, two-thirds of the control samples degrade under ambient (1 month) and refrigeration (7 months) settings.

A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI.

Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.

A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results.

PURPOSE: Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS: The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS: Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.

A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.

Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.

Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography.

PURPOSE: To determine if combining clinical, demographic, and imaging data improves automated diagnosis of nonproliferative diabetic retinopathy (NPDR). DESIGN: Cross-sectional imaging and machine learning study. METHODS: This was a retrospective study performed at a single academic medical center in the United States. Inclusion criteria were age >18 years and a diagnosis of diabetes mellitus (DM). Exclusion criteria were non-DR retinal disease and inability to image the macula. Optical coherence tomography (OCT) and OCT angiography (OCTA) were performed, and data on age, sex, hypertension, hyperlipidemia, and hemoglobin A1c were collected. Machine learning techniques were then applied. Multiple pathophysiologically important features were automatically extracted from each layer on OCT and each OCTA plexus and combined with clinical data in a random forest classifier to develop the system, whose results were compared to the clinical grading of NPDR, the gold standard. RESULTS: A total of 111 patients with DM II were included in the study, 36 with DM without DR, 53 with mild NPDR, and 22 with moderate NPDR. When OCT images alone were analyzed by the system, accuracy of diagnosis was 76%, sensitivity 85%, specificity 87%, and area under the curve (AUC) was 0.78. When OCT and OCTA data together were analyzed, accuracy was 92%, sensitivity 95%, specificity 98%, and AUC 0.92. When all data modalities were combined, the system achieved an accuracy of 96%, sensitivity 100%, specificity 94%, and AUC 0.96. CONCLUSIONS: Combining common clinical data points with OCT and OCTA data enhances the power of computer-aided diagnosis of NPDR.

Radiation-induced curcumin release from curcumin-chitosan polymer films.

The probability of human exposure to damaging radiation is increased in activities associated with long-term space flight, medical radiation therapies, and responses to nuclear accidents. However, the development of responsive countermeasures to combat radiation damage to biological tissue is lagging behind rates of human exposure. Herein, we report a radiation-responsive drug delivery system that releases doses of curcumin from a chitosan polymer/film in response to low level gamma radiation exposure. As a fibrous chitosan-curcumin polymer, 1 Gy gamma irradiation (137Cs) released 5 ± 1% of conjugated curcumin, while 6 Gy exposure releases 98 ± 1% of conjugated curcumin. The same polymer was formed into a film through solvent casting. The films showed similar, albeit attenuated behavior in water (100% released) and isopropyl alcohol (32% released) with statistically significant drug release following 2 Gy irradiation. ATR FT-IR studies confirmed glycosidic bond cleavage in the chitosan-curcumin polymer in response to gamma radiation exposure. Similar behavior was noted upon exposure of the polymer to 20 cGy (1 GeV amu-1, at 20 cGy min-1) high linear energy transfer (LET) 56Fe radiation based on FTIR studies. Density Functional Theory calculations indicate homolytic bond scission as the primary mechanism for polymer disintegration upon radiation exposure. Films did not change in thickness during the course of radiation exposure. The successful demonstration of radiation-triggered drug release may lead to new classes of radio-protective platforms for developing countermeasures to biological damage from ionizing radiation.

A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS.

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, …, b1000 s/mm2), while the R2* values were extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (2ms, 7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N = 30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations.

Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains' cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events.

Dataset for dose and time-dependent transcriptional response to ionizing radiation exposure.

Exposure to ionizing radiation associated with highly energetic and charged heavy particles is an inherent risk astronauts face in long duration space missions. We have previously considered the transcriptional effects that three levels of radiation (0.3 Gy, 1.5 Gy, and 3.0 Gy) have at an immediate time point (1 hr) post-exposure [1]. Our analysis of these results suggest effects on transcript levels that could be modulated at lower radiation doses [2]. In addition, a time dependent effect is likely to be present. Therefore, in order to develop a lab-on-a-chip approach for detection of radiation exposure in terms of both radiation level and time since exposure, we developed a time- and dose-course study to determine appropriate sensitive and specific transcript biomarkers that are detectable in blood samples. The data described herein was developed from a study measuring exposure to 0.15 Gy, 0.30 Gy, and 1.5 Gy of radiation at 1 hr, 2 hr, and 6 hr post-exposure using Affymetrix® GeneChip® PrimeView™ microarrays. This report includes raw gene expression data files from the resulting microarray experiments representing typical radiation exposure levels an astronaut may experience as part of a long duration space mission. The data described here is available in NCBI's Gene Expression Omnibus (GEO), accession GSE63952.

A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography.

Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels' diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.

A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data.

Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

Targeted Gold Nanoparticle⁻Oligonucleotide Contrast Agents in Combination with a New Local Voxel-Wise MRI Analysis Algorithm for In Vitro Imaging of Triple-Negative Breast Cancer.

Gold nanoparticles (GNPs) have tremendous potential as cancer-targeted contrast agents for diagnostic imaging. The ability to modify the particle surface with both disease-targeting molecules (such as the cancer-specific aptamer AS1411) and contrast agents (such as the gadolinium chelate Gd(III)-DO3A-SH) enables tailoring the particles for specific cancer-imaging and diagnosis. While the amount of image contrast generated by nanoparticle contrast agents is often low, it can be augmented with the assistance of computer image analysis algorithms. In this work, the ability of cancer-targeted gold nanoparticle-oligonucleotide conjugates to distinguish between malignant (MDA-MB-231) and healthy cells (MCF-10A) is tested using a T1-weighted image analysis algorithm based on three-dimensional, deformable model-based segmentation to extract the Volume of Interest (VOI). The gold nanoparticle/algorithm tandem was tested using contrast agent GNP-Gd(III)-DO3A-SH-AS1411) and nontargeted c-rich oligonucleotide (CRO) analogs and control (CTR) counterparts (GNP-Gd(III)-DO3A-SH-CRO/CTR) via in vitro studies. Remarkably, the cancer cells were notably distinguished from the nonmalignant cells, especially at nanomolar contrast agent concentrations. The T1-weighted image analysis algorithm provided similar results to the industry standard Varian software interface (VNMRJ) analysis of T1 maps at micromolar contrast agent concentrations, in which the VNMRJ produced a 19.5% better MRI contrast enhancement. However, our algorithm provided more sensitive and consistent results at nanomolar contrast agent concentrations, where our algorithm produced ~500% better MRI contrast enhancement.

Detection of influenza virus by electrochemical surface plasmon resonance under potential modulation.

In this study we report the development of a novel viral pathogen immunosensor technology based on the electrochemical modulation of the optical signal from a surface plasmon wave interacting with a redox dye reporter. The device is formed by incorporating a sandwich immunoassay onto the surface of a plasmonic device mounted in a micro-electrochemical flow cell, where it is functionalized with a monoclonal antibody aimed to a specific target pathogen antigen. Once the target antigen is bound to the surface, it promotes the capturing of a secondary polyclonal antibody that has been conjugated with a redox-active methylene blue dye. The methylene blue displays a reversible change in the complex refractive index throughout a reduction-oxidation transition, which generates an optical signal that can be electrochemically modulated and detected at high sensitivity. For proof-of-principle measurements, we have targeted the hemagglutinin protein from the H5N1 avian influenza A virus to demonstrate the capabilities of our device for detection and quantification of a critical influenza antigen. Our experimental results of the EC-SPR-based immunosensor under potential modulation showed a 300 pM limit of detection for the H5N1 antigen.

A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.