Echocardiographic Study of Ventricular Septal Defect in 1- to 12-Year-Old Children Visiting a Tertiary Care Center in Patna, India.

Background Symptoms of serious heart problems present at birth often appear during the first few days, weeks, or months of a child's life. Shunt formation between the left and right ventricle is a crucial component of the pathophysiology of ventral septal defects. Objectives We aim to determine the most frequent types of ventricular septal defect (VSD) by echocardiography and whether there is any gender variation in the type of ventricular septal defect. Material and methods A total of 100 children who were clinically suspected of or diagnosed with VSD at the age of 1-12 years were enrolled in this study. The septum of the atrioventricular (AV) canal, the muscular septum, and the parietal band of the distal conal septum were evaluated by color Doppler. Ventricular septal defect (VSD) size and kind are similarly impacted by the 2D echo mode. The size and site of the VSD, associated congenital anomaly, and significant morphological changes in ventricular cavities, gender discrimination, and relation-specific types of ventricular septal defect were observed. Results A total of 100 VSD children presented with clinical symptoms of fast breathing, retraction of the chest, cough, cyanosis, fever, difficulty during feeding, cyanotic spell, chest pain, and edema at 65%, 62%, 54%, 52%, 54%, 29%, 9%, 11%, and 4%, respectively. Conclusion Early diagnosis is essential for effective medical care of diseases such as infective endocarditis (IE), which is present in some cases of VSD, and the avoidance of persistent pulmonary veno-occlusive disease (PVOD).

A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images.

BACKGROUND AND OBJECTIVES: This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network. METHODS: The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information. RESULTS: The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD). CONCLUSIONS: Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.

Shared ancestry of core-histone subunits and non-histone plant proteins containing the Histone Fold Motif (HFM).

The three helical Histone Fold Motif (HFM) of core histone proteins provides an evolutionarily favored site for the protein-DNA interface. Despite significant variation in sequence, the HFM retains a distinctive structural fold that has diversified into several non-histone protein families. In this work, we explore the ancestry of non-histone HFM containing families in the plant kingdom. A sequence search algorithm was developed using iterative profile Hidden Markov Models to identify remote homologs of core-histone proteins. The resulting hits were functionally annotated, classified into families, and subjected to comprehensive phylogenetic analyses via Maximum likelihood and Bayesian methods. We have identified 4390 HFM containing proteins in the plant kingdom that are not histones, mostly existing as diverse transcription factor families, distributed widely within and across taxonomic groups. Patterns of homology suggest that core histone subunit H2A has evolved into newer families like NF-YC and DRAP1, whereas the H2B subunit of core histones shares a common ancestry with NF-YB and DR1 class of TFs. Core histone subunits H3 and H4 were found to have evolved into DPE and TAF proteins, respectively. Taken together these results provide insights into diversification events during the evolution of the HFM, including sub-functionalization and neo-functionalization of the HFM.

MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images.

BACKGROUND AND OBJECTIVES: Accurate segmentation of critical tissues from a brain MRI is pivotal for characterization and quantitative pattern analysis of the human brain and thereby, identifies the earliest signs of various neurodegenerative diseases. To date, in most cases, it is done manually by the radiologists. The overwhelming workload in some of the thickly populated nations may cause exhaustion leading to interruption for the doctors, which may pose a continuing threat to patient safety. A novel fusion method called U-Net inception based on 3D convolutions and transition layers is proposed to address this issue. METHODS: A 3D deep learning method called Multi headed U-Net with Residual Inception (MhURI) accompanied by Morphological Gradient channel for brain tissue segmentation is proposed, which incorporates Residual Inception 2-Residual (RI2R) module as the basic building block. The model exploits the benefits of morphological pre-processing for structural enhancement of MR images. A multi-path data encoding pipeline is introduced on top of the U-Net backbone, which encapsulates initial global features and captures the information from each MRI modality. RESULTS: The proposed model has accomplished encouraging outcomes, which appreciates the adequacy in terms of some of the established quality metrices when compared with some of the state-of-the-art methods while evaluating with respect to two popular publicly available data sets. CONCLUSION: The model is entirely automatic and able to segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from brain MRI effectively with sufficient accuracy. Hence, it may be considered to be a potential computer-aided diagnostic (CAD) tool for radiologists and other medical practitioners in their clinical diagnosis workflow.

Assessment of Anatomical Variations of Mandibular Canal Depicted in Panoramic Radiography.

BACKGROUND: Anatomical variations of the mandibular canal play a vital role while performing surgical procedures affecting an area with mandibular canal course in the mandible. The neurovascular bundle may be severed during surgical procedures carried out mandible. AIMS AND OBJECTIVES: The present retrospective study was aimed to assess and evaluate the mandibular canal and its variations on the panoramic radiography. MATERIALS AND METHODS: The study included 88 randomly selected panoramic radiographs with complete mandible and associated bone. On a panoramic radiograph, the following parameters were assessed including bifid mandibular canal and location of bifurcation, the diameter of the mandibular canal as recorded in the first molar region, trabeculation in submandibular gland fossa, anterior loop measurement, direction and diameter of the bifid mandibular canal were all evaluated. Statistical analysis was done. RESULTS: In 51 hemimandibles, the mandibular canal was found to be corticalized, whereas in 21.59% (n = 19), the mandibular canal was visible. In the remaining 20.45% (n = 18) of the study participants, the mandibular canal was not visualized. In the submandibular gland fossa region, diminished trabeculation was seen in 55.68% of the evaluated radiographs, whereas trabeculation was not seen at all in the remaining 23.86% of the subjects. A significant correlation was seen in decreased trabeculation of submandibular gland fossa and absence of the mandibular canal (P value < 0.001). The bifid mandibular canal was seen in 19.31% of the study participants (n = 17) with a mean width of 3.12 ± 1.1 mm. Extension of the anterior loop of the mental nerve was seen as up to 2 mm in majority participants in 67.04% individuals (n = 59). CONCLUSION: The present study suggests that panoramic radiographs are a reliable tool for assessment of the mandibular canal and associated anatomical variations associated with it.

CSNet: A new DeepNet framework for ischemic stroke lesion segmentation.

BACKGROUND AND OBJECTIVES: Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners. METHODS: We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy. RESULTS: The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision. CONCLUSIONS: We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.

Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis.

Abiotic stress tolerance is a complex trait regulated by multiple genes and gene networks in plants. A range of abiotic stresses are known to limit rice productivity. Meta-transcriptomics has emerged as a powerful approach to decipher stress-associated molecular network in model crops. However, retaining specificity of gene expression in tolerant and susceptible genotypes during meta-transcriptome analysis is important for understanding genotype-dependent stress tolerance mechanisms. Addressing this aspect, we describe here "abiotic stress tolerant" (ASTR) genes and networks specifically and differentially expressing in tolerant rice genotypes in response to different abiotic stress conditions. We identified 6,956 ASTR genes, key hub regulatory genes, transcription factors, and functional modules having significant association with abiotic stress-related ontologies and cis-motifs. Out of the 6956 ASTR genes, 73 were co-located within the boundary of previously identified abiotic stress trait-related quantitative trait loci. Functional annotation of 14 uncharacterized ASTR genes is proposed using multiple computational methods. Around 65% of the top ASTR genes were found to be differentially expressed in at least one of the tolerant genotypes under different stress conditions (cold, salt, drought, or heat) from publicly available RNAseq data comparison. The candidate ASTR genes specifically associated with tolerance could be utilized for engineering rice and possibly other crops for broad-spectrum tolerance to abiotic stresses.

Efficacy, Safety and Treatment Satisfaction of Glimepiride vs Sitagliptin in Combination with Metformin in Type 2 Diabetes Mellitus.

INTRODUCTION: Metformin is a preferred drug for starting treatment in type 2 diabetes mellitus. But, eventually most of the patients need additional drug to control blood sugar level. The choice of drug depends upon several factors including patient specific criteria, economical factors and treatment satisfaction. AIM: The aim of the present study is to investigate the effects of adding sitagliptin or glimepiride on efficacy, safety and treatment satisfaction in patients with type 2 diabetes mellitus. MATERIALS AND METHODS: It was a retrospective observational study on 50 patients each in sitagliptin and glimepiride group, who are receiving treatment for at least 12 weeks and are stable on respective treatment regimen. Glycated haemoglobin (HBA1c) was the primary measure of efficacy. Safety was assessed by checking weight gain/loss, hypoglycaemia episodes and other laboratory investigations. Patient satisfaction was assessed by Diabetes Treatment Satisfaction Questionnaire. RESULTS: The HbA1c level after 12-24 weeks of treatment was not found to be significant compared to each other or from baseline. Compared to baseline fasting plasma glucose & postprandial plasma glucose were lower in glimepiride group. Sitagliptin was associated with less episodes of hypoglycaemia. Weight gain was associated with glimepiride but it was non-significant (p=0.08). Overall treatment satisfaction score were better for sitagliptin but were not statistically significant. CONCLUSION: The efficacy of sitagliptin was comparable. Sitagliptin had superior adverse effect profile with less chances of hypoglycaemia and weight gain. Questionnaire scores were higher for sitagliptin indicating better treatment satisfaction compared to glimepiride.