Iatrogenic dissection in paediatric cardiac surgery.

Iatrogenic dissection due to aortic cannulation is extremely rare. We are reporting here a case of 11-month-old child who developed an iatrogenic aortic dissection during repair of supracardiac total anomalous pulmonary venous connection (TAPVC).

Concomitant pulmonary valve replacement with intracardiac repair for adult tetralogy of Fallot.

OBJECTIVES: Adult patients undergoing tetralogy of Fallot (TOF) repair have a higher risk of mortality compared to pediatric patients. Pulmonary regurgitation (PR) further predisposes these patients to heart failure, arrhythmias, and sudden death. Pulmonary valve replacement (PVR) may improve the symptoms in these patients but, fails to reverse the other deleterious effects. Aim of our study was to evaluate the effect of concomitant PVR with TOF repair on right ventricular (RV) parameters, cardiopulmonary exercise capacity, and bioprosthetic valve durability at mid-term. MATERIALS AND METHODS: Between January 2013 and August 2018, 37 adolescents and adults with TOF who had hypoplastic pulmonary annulus underwent concomitant TOF repair with PVR at our institute. We retrospectively collected the data from the hospital records including follow-up. RESULTS: Mean age of the patients was 18.48 ± 7.53 years. Bioprosthetic valve size ranged from 19 mm to 25 mm. There was no early or late mortality. No patient had developed significant perioperative complications. At a mean follow-up of 53.3 ± 16.4 months, there was no significant change in mean QRS duration, RV function, RV end-systolic and end-diastolic dimensions, RV myocardial performance index, and functional status (including NYHA class and 6-min walk test) compared to at-discharge values. Four patients developed prosthetic valve degeneration with mild PR and without significant increase in gradient. CONCLUSION: Concomitant PVR with TOF repair in adult provides excellent mid-term outcome, with a minimal rate of pulmonary valve degeneration. It not only eases the early postoperative course but also preserves the RV function as well as functional status at mid-term.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD.

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

A Heterogeneous Ensemble Forecasting Model for Disease Prediction.

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government site of Hong Kong from the year 2010 to 2019. The preprocessing is done using log transformation and z score transformation. The proposed ensemble model is applied, and its applicability to a specific disease dataset is presented. The proposed ensemble model is compared against the ensemble models, namely dynamic ensemble for time series, arbitrated dynamic ensemble, and random forest using different error metrics. The proposed model shows the reduced value of MAE (mean average error) by 27.18%, 3.07%, 11.58%, 13.46% for tuberculosis, dengue, food poisoning, and chickenpox, respectively. The comparison drawn between the proposed model and the existing models shows that the proposed ensemble model gives better accuracy in the case of all the four-disease datasets.

Levoatriocardinal Vein With Multiple Ventricular Septal Defects and Without Left-Sided Valvular Atresia.

Levoatriocardinal vein without left-sided valvular atresia is rare. We hereby present an image of the levoatriocardinal vein in a patient with multiple muscular ventricular septal defect with small atrial septal defect and mitral regurgitation.

Conditional entropy approach to analyze cognitive dynamics in autism spectrum disorder.

OBJECTIVE: Preliminary evidence has documented functional connectivity during the cognitive task in Autism Spectrum Disorder (ASD). However, evidence of effective neural connectivity with respect to information flow between different brain regions during complex tasks is missing. The present paper aims to provide insights into the cognition-based neural dynamics reflecting information exchange in brain network under cognitive load in ASD. METHODS: Twenty-two individuals with ASD (8-18 years) and 18 Typically Developing (TD; 6-17 years) individuals participated in the cognitive task of differentiating risky from neutral stimuli. The Conditional Entropy (CE) technique is applied upon task-activated Electroencephalogram (EEG) to measure the causal influence of the activity of brain's one Region of interest (ROI) over another. RESULTS: A higher CE in frontal ROI and left hemisphere reflected atypical brain complexity in ASD. The absence of causal effect, poor Coupling Strength (CS; measured using CE) and hemisphere lateralization is responsible for lower cognition in ASD. However, the persistent information exchange during the task reflects the existence of certain alternative paths when other direct paths remained disconnected due to cognitive impairment. The Support Vector Machine (SVM) classifier showed that CE can identify the atypical information exchange with an accuracy of 96.89% and area under curve = 0.987. DISCUSSION: The statistical results reflect a significant change in the information flow between different ROIs in ASD. A correlation of CS and behavioral domain suggests that the cognitive decline could be predicted from the connectivity patterns. Thus, CS could be a potential biomarker to identify cognitive status at a higher discrimination rate in ASD.

Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in Autism Spectrum Disorder.

Methods: Thirty individuals with ASD (11-18years) and thirty Typically Developing (TD) individuals (11-16years) were recruited to perform the mental task. The participants were instructed to flip the shown geometric images mentally and mark their response on a scale. The task-related multivariate EEG activations were analyzed using multiplex temporal Visibility Graphs (VGs) to compute local and global brain network functional connectivity dynamics.Results: With cognitive load (0-back to the 2-back task), the behavioral performance (d' index and Reaction Time) has reduced in ASD. The brain network has become more segregated and less integrated, reflecting more involvement of intra-regions over inter-regions in ASD. The frequent rerouting in hubs measured by Eigenvector Centrality (EC) indicated the progression of brain trajectory towards ASD. Overall, the neural mechanisms involving hyperactive response in frontal regions, frequent rewiring, and strengthened brain connectivity as a result of learning-induced performance reflected the adaptation to cognitive demands in ASD.Discussion: The correlation between complex graph measures and behavioral domain further reflected that neural metrics could predict the behavioral performance of the individuals in the task. In the future, modeling functional connectivity-based markers have the potential to reflect the brain trajectory alterations, which can detect ASD even before the behavioral manifestations become apparent.