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

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

Evaluation of left atrial systolic function in noncompaction cardiomyopathy by real-time three-dimensional echocardiography

Background Noncompaction cardiomyopathy (NCCM) is a rare disorder with persistance of the embryonic pattern of myoarchitecture. NCCM is characterized by loosened, spongy myocardium associated with a high incidence of systolic and diastolic left ventricular (LV) dysfunction and heart failure (HF). It is known that LV dysfunction contributes to elevated left atrial (LA) and pulmonary vascular pressures, however atrial function has not been examined in NCCM. The objective of the present study was to assess LA systolic function characterized by LA ejection force (LAEF) in NCCM patients using real-time three-dimensional echocardiography (RT3DE) and to compare to control subjects. Methods The study comprised 17 patients with an established diagnosis of NCCM and their results were compared to 17 healthy age-matched controls with no evidence of cardiovascular disease. Forty-one percent of NCCM patients were in NYHA functional class II / III HF. Previously proposed echocardiographic diagnostic criteria for NCCM were used. All patients underwent conventional two-dimensional echocardiography and RT3DE. LAEF was measured based on MA annulus diameter (LAEF3D-MAD) and area (LAEF3D-MAA) using RT3DE. Results The presence and severity of mitral regurgitation were more frequent in NCCM patients than in control subjects. LV diameters and mitral annulus were significantly increased in NCCM patients. Compared with control subjects, both LAEF3D-MAD (3.8 ± 2.2 vs 2.3 ± 1.0 kdyne, P < 0.05) and LAEF3D-MAA (12.7 ± 7.6 vs 4.9 ± 2.1 kdyne, P < 0.01) were significantly increased in NCCM patients. Conclusions LAEF as a characteristic of LA systolic function is increased in NCCM patients compared to normal individuals. These results can suggest compensating left atrial work against the dysfunctional LV in NCCM patients

An integrated approach to determine left atrial volume, mass and function in hypertrophic cardiomyopathy by two-dimensional echocardiography

Methods: The study included 25 hypertrophic cardiomyopathy (HCM) patients (15 non-obstructive and 10 obstructive) and 25 controls for assessment of left atrial (LA) volume, mass and function by two-dimensional echocardiography. Measurement included mean LA diameter (LAD), LA mass = {(mean LAD + anterior LA wall + posterior LA wall)3- mean LAD3} × 0.8 + 0.6, LA volume = [(8/3 φ L ̇ A1 ̇ A2), where L is LA length, A1 and A2 are LA area in 4-chambers and 2-chambers, respectively] including maximum (Vmax), minimum (Vmin), and pre-atrial contraction (Vpre-A), total atrial stroke volume (TA-SV), TA emptying fraction (TA-EF), active atrial SV (AA-SV), AA-EF, passive atrial SV (PA-SV), PA-EF, atrial expansion index (AEI), and LA kinetic energy (LA-KE) = 1/2 × AA-SV × P × V2. Results: LAD, LA mass, Vmax, Vmin, and Vpre-Awere significantly higher in HCM than controls. TA-SV and TA-EF were comparable in both HCM subgroups and controls. AA-SV and LA-KE were significantly higher in both HCM subgroups than controls. LA-KE was significantly higher in obstructive HCM than non-obstructive (P < 0.001). PA-EF and AEI were significantly lower in obstructive HCM than controls (P < 0.05). Conclusion: HCM is associated with increased LA size and augmented LA pump function especially obstructive type. LA conduit and reservoir functions are impaired in obstructive HCM

Assessment of normal tricuspid valve anatomy in adults by real-time three-dimensional echocardiography

Background: The tricuspid valve (TV) is a complex structure. Unlike the aortic and mitral valve it is not possible to visualize all TV leaflets simultaneously in one cross-sectional view by standard two-dimensional echocardiography (2DE) either transthoracic or transesophageal due to the position of TV in the far field. Aim: Quantitative and qualitative assessment of the normal TV using real-time 3-dimensional echocardiography (RT3DE). Methods: RT3DE was performed for 100 normal adults (mean age 30 ± 9 years, 65% males). RT3DE visualization was evaluated by 4-point score (1: not visualized, 2: inadequate, 3: sufficient, and 4: excellent). Measurements included TV annulus diameters (TAD), TV area (TVA), and commissural width. Results: In 90% of patients with good 2DE image quality, it was possible to analyse TV anatomy by RT3DE. A detailed anatomical structure including unique description and measurement of tricuspid annulus shape and size, TV leaflets shape, and mobility, and TV commissural width were obtained in majority of patients. Identification of each TV leaflet as seen in the routine 2DE views was obtained. Conclusion: RT3DE of the TVis feasible in a large number of patients. RT3DE may add to functional 2DE data in description of TV anatomy and providing highly reproducible and actual reality (anatomical and functional) measurements

ARHGDIA mutations cause nephrotic syndrome via defective RHO GTPase signaling

Nephrotic syndrome (NS) is divided into steroid-sensitive (SSNS) and -resistant (SRNS) variants. SRNS causes end-stage kidney disease, which cannot be cured. While the disease mechanisms of NS are not well understood, genetic mapping studies suggest a multitude of unknown single-gene causes. We combined homozygosity mapping with whole-exome resequencing and identified an ARHGDIA mutation that causes SRNS. We demonstrated that ARHGDIA is in a complex with RHO GTPases and is prominently expressed in podocytes of rat glomeruli. ARHGDIA mutations (R120X and G173V) from individuals with SRNS abrogated interaction with RHO GTPases and increased active GTP-bound RAC1 and CDC42, but not RHOA, indicating that RAC1 and CDC42 are more relevant to the pathogenesis of this SRNS variant than RHOA. Moreover, the mutations enhanced migration of cultured human podocytes; however, enhanced migration was reversed by treatment with RAC1 inhibitors. The nephrotic phenotype was recapitulated in arhgdia-deficient zebrafish. RAC1 inhibitors were partially effective in ameliorating arhgdia-associated defects. These findings identify a single-gene cause of NS and reveal that RHO GTPase signaling is a pathogenic mediator of SRNS.ope

Whole Exome Sequencing of Patients with Steroid-Resistant Nephrotic Syndrome

BACKGROUND AND OBJECTIVES: Steroid-resistant nephrotic syndrome overwhelmingly progresses to ESRD. More than 30 monogenic genes have been identified to cause steroid-resistant nephrotic syndrome. We previously detected causative mutations using targeted panel sequencing in 30% of patients with steroid-resistant nephrotic syndrome. Panel sequencing has a number of limitations when compared with whole exome sequencing. We employed whole exome sequencing to detect monogenic causes of steroid-resistant nephrotic syndrome in an international cohort of 300 families. DESIGN, SETTING, PARTIIPANTS AND MEASUREMENTS: Three hundred thirty-five individuals with steroid-resistant nephrotic syndrome from 300 families were recruited from April of 1998 to June of 2016. Age of onset was restricted to <25 years of age. Exome data were evaluated for 33 known monogenic steroid-resistant nephrotic syndrome genes. RESULTS: In 74 of 300 families (25%), we identified a causative mutation in one of 20 genes known to cause steroid-resistant nephrotic syndrome. In 11 families (3.7%), we detected a mutation in a gene that causes a phenocopy of steroid-resistant nephrotic syndrome. This is consistent with our previously published identification of mutations using a panel approach. We detected a causative mutation in a known steroid-resistant nephrotic syndrome gene in 38% of consanguineous families and in 13% of nonconsanguineous families, and 48% of children with congenital nephrotic syndrome. A total of 68 different mutations were detected in 20 of 33 steroid-resistant nephrotic syndrome genes. Fifteen of these mutations were novel. NPHS1, PLCE1, NPHS2, and SMARCAL1 were the most common genes in which we detected a mutation. In another 28% of families, we detected mutations in one or more candidate genes for steroid-resistant nephrotic syndrome. CONCLUSIONS: Whole exome sequencing is a sensitive approach toward diagnosis of monogenic causes of steroid-resistant nephrotic syndrome. A molecular genetic diagnosis of steroid-resistant nephrotic syndrome may have important consequences for the management of treatment and kidney transplantation in steroid-resistant nephrotic syndrome

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London