Cerebro-costo-mandibular syndrome: Report of two cases

AbstractCerebro-costo-mandibular syndrome (CCMS) is a rare syndrome that includes a constellation of mandibular hypoplasia and posterior rib defects as its basic features. Additional features can include hearing loss, tracheal cartilage abnormalities, scoliosis, elbow hypoplasia, and spina bifida. Here we report two cases of CCMS and discuss the reported long-term outcome of the disease

Rourkela steel plant automation:A case study

n order to improve product quality, reduce cost, increase customer satisfaction and to sustain the global competition, automation in the existing infrastructure of the steel industry is essential. After liberalization started in the steel sector in India, a large number of steel plants have come up with most advanced technology. In this paper automation process in Integrated material management system (IMMS), Electronic Procurement system (EPS), Product Planning and Control System (PPCS) are discussed and possibility of implementation of ERP and GPS based transportation system is discussed for automation

Association of Cerebrovascular Stability Index and Head Circumference Between Infants With and Without Congenital Heart Disease

Congenital heart disease (CHD) is a common birth defect in the United States. CHD infants are more likely to have smaller head circumference and neurodevelopmental delays; however, the cause is unknown. Altered cerebrovascular hemodynamics may contribute to neurologic abnormalities, such as smaller head circumference, thus we created a novel Cerebrovascular Stability Index (CSI), as a surrogate for cerebral autoregulation. We hypothesized that CHD infants would have an association between CSI and head circumference. We performed a prospective, longitudinal study in CHD infants and healthy controls. We measured CSI and head circumference at 4 time points (newborn, 3, 6, 9 months). We calculated CSI by subtracting the average 2-min sitting from supine cerebral oxygenation (rcS

Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging

We propose to create a medical imaging artificial intelligence (AI) center (name: Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging). AI is the new revolutionary technique for medical research and is reshaping tomorrow’s clinical practice in medical imaging (radiology and pathology). Our long-term vision is to build a center for innovative AI in clinical translational medical imaging by combining computational expertise and clinical resources across Pitt, UPMC, and CMU. The Center concept is a formalization of a group of researchers and clinicians that are united by the common theme: “building advanced and trustworthy imaging AI for clinical applications.” Our short-term plan is to assemble dedicated members from the School of Medicine, the School of Engineering, and the School of Computing and Information. We seek a Scaling grant from the Momentum Funds to foster collaborative activities of the Center between these three Pitt schools to provide the essential components of a competitive P41 (Biomedical Technology Resource Centers) center grant in 2 years. The National Institute of Biomedical Imaging and Bioengineering (NIBIB) P41 mechanism aligns with the overall vision of this initiative to develop specific AI imaging tools and to support the dissemination and commercialization pathways that are essential to bringing AI imaging tools to clinical practice. These projects will include key components: 1) Clinical need-driven medical imaging AI development and evaluation of tools, models, systems, and informatics, 2) Core imaging AI theory, methodology, and algorithm investigation, and 3) Linking imaging phenotypes to the biological (genomics and proteomics) underpinnings. To date, we have already 35 members for the Center. The Pitt Momentum Funds will provide critical scaling support to promote communication between the three Pitt schools to develop a competitive P41 grant application and a sustainable framework to ensure the clinical impact of these AI imaging tools

Genetic link between renal birth defects and congenital heart disease

Structural birth defects in the kidney and urinary tract are observed in 0.5% of live births and are a major cause of end-stage renal disease, but their genetic aetiology is not well understood. Here we analyse 135 lines of mice identified in large-scale mouse mutagenesis screen and show that 29% of mutations causing congenital heart disease (CHD) also cause renal anomalies. The renal anomalies included duplex and multiplex kidneys, renal agenesis, hydronephrosis and cystic kidney disease. To assess the clinical relevance of these findings, we examined patients with CHD and observed a 30% co-occurrence of renal anomalies of a similar spectrum. Together, these findings demonstrate a common shared genetic aetiology for CHD and renal anomalies, indicating that CHD patients are at increased risk for complications from renal anomalies. This collection of mutant mouse models provides a resource for further studies to elucidate the developmental link between renal anomalies and CHD

The Impact of Caregiving on the Association Between Infant Emotional Behavior and Resting State Neural Network Functional Topology

The extent to which neural networks underlying emotional behavior in infancy serve as precursors of later behavioral and emotional problems is unclear. Even less is known about caregiving influences on these early brain-behavior relationships. To study brain-emotional behavior relationships in infants, we examined resting-state functional network metrics and infant emotional behavior in the context of early maternal caregiving. We assessed 46 3-month-old infants and their mothers from a community sample. Infants underwent functional MRI during sleep. Resting-state data were processed using graph theory techniques to examine specific nodal metrics as indicators of network functionality. Infant positive and negative emotional behaviors, and positive, negative and mental-state talk (MST) indices of maternal caregiving were coded independently from filmed interactions. Regression analyses tested associations among nodal metrics and infant emotionality, and the moderating effects of maternal behavior on these relationships. All results were FDR corrected at alpha = 0.05. While relationships between infant emotional behavior or maternal caregiving, and nodal metrics were weak, higher levels of maternal MST strengthened associations between infant positive emotionality and nodal metrics within prefrontal (p < 0.0001), and occipital (p < 0.0001) cortices more generally. Positive and negative aspects of maternal caregiving had little effect. Our findings suggest that maternal MST may play an important role in strengthening links between emotion regulation neural circuitry and early infant positive behavior. They also provide objective neural markers that could inform and monitor caregiving-based interventions designed to improve the health and well-being of vulnerable infants at-risk for behavioral and emotional problems

Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat

Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher\u27s ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n= 398 subjects recruited across 4 centers, with a total of n=763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure

Postnatal Brain Trajectories and Maternal Intelligence Predict Childhood Outcomes in Complex CHD

Objective: To determine whether early structural brain trajectories predict early childhood neurodevelopmental deficits in complex CHD patients and to assess relative cumulative risk profiles of clinical, genetic, and demographic risk factors across early development. Study Design: Term neonates with complex CHDs were recruited at Texas Children’s Hospital from 2005–2011. Ninety-five participants underwent three structural MRI scans and three neurodevelopmental assessments. Brain region volumes and white matter tract fractional anisotropy and radial diffusivity were used to calculate trajectories: perioperative, postsurgical, and overall. Gross cognitive, language, and visuo-motor outcomes were assessed with the Bayley Scales of Infant and Toddler Development and with the Wechsler Preschool and Primary Scale of Intelligence and Beery–Buktenica Developmental Test of Visual–Motor Integration. Multi-variable models incorporated risk factors. Results: Reduced overall period volumetric trajectories predicted poor language outcomes: brainstem ((β, 95% CI) 0.0977, 0.0382–0.1571; p = 0.0022) and white matter (0.0023, 0.0001–0.0046; p = 0.0397) at 5 years; brainstem (0.0711, 0.0157–0.1265; p = 0.0134) and deep grey matter (0.0085, 0.0011–0.0160; p = 0.0258) at 3 years. Maternal IQ was the strongest contributor to language variance, increasing from 37% at 1 year, 62% at 3 years, and 81% at 5 years. Genetic abnormality’s contribution to variance decreased from 41% at 1 year to 25% at 3 years and was insignificant at 5 years. Conclusion: Reduced postnatal subcortical–cerebral white matter trajectories predicted poor early childhood neurodevelopmental outcomes, despite high contribution of maternal IQ. Maternal IQ was cumulative over time, exceeding the influence of known cardiac and genetic factors in complex CHD, underscoring the importance of heritable and parent-based environmental factors