Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

© 1964-2012 IEEE. Objective: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. Methods: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. Results: In our initial \u27leave-one-subject-out\u27 experiment on 100 subjects, 97.0% of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of 96.0% and 94.0%, respectively. Conclusion: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. Significance: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy

Computer Aided Autism Diagnosis Using Diffusion Tensor Imaging

© 2013 IEEE. Autism Spectrum Disorder (ASD), commonly known as autism, is a lifelong developmental disorder associated with a broad range of symptoms including difficulties in social interaction, communication skills, and restricted and repetitive behaviors. In autism spectrum disorder, numerous studies suggest abnormal development of neural networks that manifest itself as abnormalities of brain shape, functionality, and/ or connectivity. The aim of this work is to present our automated computer aided diagnostic (CAD) system for accurate identification of autism spectrum disorder based on the connectivity of the white matter (WM) tracts. To achieve this goal, two levels of analysis are provided for local and global scores using diffusion tensor imaging (DTI) data. A local analysis using the Johns Hopkins WM atlas is exploited for DTI atlas-based segmentation. Furthermore, WM integrity is examined by extracting the most notable features representing WM connectivity from DTI. Interactions of WM features between different areas in the brain, demonstrating correlations between WM areas were used, and feature selection among those associations were made. Finally, a leave-one-subject-out classifier is employed to yield a final per-subject decision. The proposed system was tested on a large dataset of 263 subjects from the National Database of Autism Research (NDAR) with their Autism Diagnostic Observation Schedule (ADOS) scores and diagnosis (139 typically developed: 66 males, and 73 females, and 124 autistics: 66 males, and 58 females), with ages ranging from 96 to 215 months, achieving an overall accuracy of 73%. In addition to this achieved global accuracy, diagnostically-important brain areas were identified, allowing for a better understanding of ASD-related brain abnormalities, which is considered as an essential step towards developing early personalized treatment plans for children with autism spectrum disorder

Gender Differences in Presentation, Management, and In-Hospital Outcomes for Patients with AMI in a Lower-Middle Income Country: Evidence from Egypt

BACKGROUND: Many studies in high-income countries have investigated gender differences in the care and outcomes of patients hospitalized with acute myocardial infarction (AMI). However, little evidence exists on gender differences among patients with AMI in lower-middle-income countries, where the proportion deaths stemming from cardiovascular disease is projected to increase dramatically. This study examines gender differences in patients in the lower-middle-income country of Egypt to determine if female patients with AMI have a different presentation, management, or outcome compared with men. METHODS AND FINDINGS: Using registry data collected over 18 months from 5 Egyptian hospitals, we considered 1204 patients (253 females, 951 males) with a confirmed diagnosis of AMI. We examined gender differences in initial presentation, clinical management, and in-hospital outcomes using t-tests and χ(2) tests. Additionally, we explored gender differences in in-hospital death using multivariate logistic regression to adjust for age and other differences in initial presentation. We found that women were older than men, had higher BMI, and were more likely to have hypertension, diabetes mellitus, dyslipidemia, heart failure, and atrial fibrillation. Women were less likely to receive aspirin upon admission (p<0.01) or aspirin or statins at discharge (p = 0.001 and p<0.05, respectively), although the magnitude of these differences was small. While unadjusted in-hospital mortality was significantly higher for women (OR: 2.10; 95% CI: 1.54 to 2.87), this difference did not persist in the fully adjusted model (OR: 1.18; 95% CI: 0.55 to 2.55). CONCLUSIONS: We found that female patients had a different profile than men at the time of presentation. Clinical management of men and women with AMI was similar, though there are small but significant differences in some areas. These gender differences did not translate into differences in in-hospital outcome, but highlight differences in quality of care and represent important opportunities for improvement

A biochemical, theoretical and immunohistochemical study comparing the therapeutic efficacy of curcumin and taurine on T-2 toxin induced hepatotoxicity in rats

Introduction: Foodborne trichothecene T-2 Toxin, is a highly toxic metabolite produced by Fusarium species contaminating animal and human food, causing multiple organ failure and health hazards. T-2 toxins induce hepatotoxicity via oxidative stress causing hepatocytes cytotoxicity and genotoxicity. In this study, curcumin and taurine were investigated and compared as antioxidants against T-2-provoked hepatotoxicity.Methods: Wistar rats were administrated T-2 toxin sublethal oral dose (0.1 mg/kg) for 2 months, followed by curcumin (80 mg/kg) and taurine (50 mg/kg) for 3 weeks. Biochemical assessment of liver enzymes, lipid profiles, thiobarbituric acid reactive substances (TBARs), AFU, TNF-α, total glutathione, molecular docking, histological and immunohistochemical markers for anti-transforming growth factor-β1 (TGFβ1), double-strand DNA damage (H2AX), regeneration (KI67) and apoptosis (Active caspase3) were done.Results and Discussion: Compared to T-2 toxin, curcumin and taurine treatment significantly ameliorated hepatoxicity as; hemoglobin, hematocrit and glutathione, hepatic glycogen, and KI-67 immune-reactive hepatocytes were significantly increased. Although, liver enzymes, inflammation, fibrosis, TGFβ1 immunoexpressing and H2AX and active caspase 3 positive hepatocytes were significantly decreased. Noteworthy, curcumin’s therapeutic effect was superior to taurine by histomorphometry parameters. Furthermore, molecular docking of the structural influence of curcumin and taurine on the DNA sequence showed curcumin’s higher binding affinity than taurine.Conclusion: Both curcumin and taurine ameliorated T-2 induced hepatotoxicity as strong antioxidative agents with more effectiveness for curcumin

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency

Level-Set-Based Kidney Segmentation from DCE-MRI Using Fuzzy Clustering with Population-Based and Subject-Specific Shape Statistics

The segmentation of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of the kidney is a fundamental step in the early and noninvasive detection of acute renal allograft rejection. In this paper, a new and accurate DCE-MRI kidney segmentation method is proposed. In this method, fuzzy c-means (FCM) clustering is embedded into a level set method, with the fuzzy memberships being iteratively updated during the level set contour evolution. Moreover, population-based shape (PB-shape) and subject-specific shape (SS-shape) statistics are both exploited. The PB-shape model is trained offline from ground-truth kidney segmentations of various subjects, whereas the SS-shape model is trained on the fly using the segmentation results that are obtained for a specific subject. The proposed method was evaluated on the real medical datasets of 45 subjects and reports a Dice similarity coefficient (DSC) of 0.953 &plusmn; 0.018, an intersection-over-union (IoU) of 0.91 &plusmn; 0.033, and 1.10 &plusmn; 1.4 in the 95-percentile of Hausdorff distance (HD95). Extensive experiments confirm the superiority of the proposed method over several state-of-the-art level set methods, with an average improvement of 0.7 in terms of HD95. It also offers an HD95 improvement of 9.5 and 3.8 over two deep neural networks based on the U-Net architecture. The accuracy improvements have been experimentally found to be more prominent on low-contrast and noisy images

Exosomes in Parkinson: Revisiting Their Pathologic Role and Potential Applications

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, and tremor. Considerable progress has been made to understand the exact mechanism leading to this disease. Most of what is known comes from the evidence of PD brains’ autopsies showing a deposition of Lewy bodies—containing a protein called α-synuclein (α-syn)—as the pathological determinant of PD. α-syn predisposes neurons to neurotoxicity and cell death, while the other associated mechanisms are mitochondrial dysfunction and oxidative stress, which are underlying precursors to the death of dopaminergic neurons at the substantia nigra pars compacta leading to disease progression. Several mechanisms have been proposed to unravel the pathological cascade of these diseases; most of them share a particular similarity: cell-to-cell communication through exosomes (EXOs). EXOs are intracellular membrane-based vesicles with diverse compositions involved in biological and pathological processes, which their secretion is driven by the NLR family pyrin domain-containing three proteins (NLRP3) inflammasome. Toxic biological fibrils are transferred to recipient cells, and the disposal of damaged organelles through generating mitochondrial-derived vesicles are suggested mechanisms for developing PD. EXOs carry various biomarkers; thus, they are promising to diagnose different neurological disorders, including neurodegenerative diseases (NDDs). As nanovesicles, the applications of EXOs are not only restricted as diagnostics but also expanded to treat NDDs as therapeutic carriers and nano-scavengers. Herein, the aim is to highlight the potential incrimination of EXOs in the pathological cascade and progression of PD and their role as biomarkers and therapeutic carriers for diagnosing and treating this neuro-debilitating disorder

Cloud-Based Monitoring of Thermal Anomalies in Industrial Environments Using AI and the Internet of Robotic Things

Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates

Serum Survivin and TP53 Gene Expression in Children with Acute Lym-phoblastic Leukemia

&quot;nBackground: The aim of this study was to detect the prognostic significance of survivin level and the expression of total p53 in acute lymphoblastic leukemia (ALL) and its correlation to patients&apos; outcome.&quot;nMethods: Sixty two children newly diagnosed with acute lymphoblastic leukemia were treated with chemotherapy and followed up for 2 years or until death. Twenty apparently healthy volunteers with matched age and sex were taken as control. Survivin protein was measured by quantitative sandwich enzyme immunoassay and total human p53 was measured by Flow cytometry in peripheral blood at diagnosis and at complete remission.&quot;nResults: A highly significant elevation (P&amp;lt;0.0001) was found in survivin protein and total p53 levels in acute lymphoblastic leukemia children patients at diagnosis compared to controls. At complete remission a significant decrease of the two indices were found in ALL patients compared to those at diagnosis (P&amp;lt;0.0001).&amp;nbsp; Survivin protein and total p53 was significantly higher in non-survived compared to survived group (P&amp;lt;0.0001 &amp;amp; P=0.016, respectively). A positive correlation was found between survivin level and total human p53 level in children with ALL (r=0.501 &amp;amp; P&amp;lt;0.0001).&quot;nConclusion: survivin protein is related to anti-apoptotic proteins and its high expression lead to unsuccessful treatment of ALL. Survivin and TP53 are new prognostic tools in ALL, independent of age and sex