Pseudo-dissection of ascending aorta in inferior myocardial infarction

Acute aortic dissection is a cardiac emergency which can present as inferior myocardial infarction. It has high morbidity and mortality requiring prompt diagnosis and treatment. Rapid advances in non-invasive imaging have facilitated the early diagnosis of this condition and in ruling out this potentially catastrophic illness. We report an interesting case of a 57-year old man who presented with inferior myocardial infarction requiring thrombolysis and temporary pacing wire for complete heart block. An echocardiogram was highly suspicious of aortic dissection. CT scan confirmed that the malposition of the temporary pacing wire through the aorta mimicked aortic dissection

Molecular Role of Nitric Oxide in Secondary Products Production in Ginkgo biloba Cell Suspension Culture

Effects of sodium nitroprusside (SNP; nitric oxide donor) treatment on the enhancement of secondary metabolites production, oxidative stress mediators (O2-.) accumulation and antioxidant defense enzymes of Ginkgo biloba callus culture was investigated. On one hand, the obtained data showed a highly metabolic modification of chemical constituents, PAL activity and various antioxidant defense enzymes (APX, SOD), which gradually increased in response to SNP treatments. On the other hands the high NO levels significantly increased the accumulation of various oxidative burst of O2-.. MS basal medium supplemented with casein hydrolase (500 mg/L), NAA and BA at equal concentration (0.5 mg/L) recorded the highest number of regenerated shoots (4.81 cm) and shoot height (4.96 cm) as well as root number (2.25 cm) and root length (4.5 cm). The highest survival (40 %) was shown in acclimatization on the mixture containing sand, peat moss and vermiculite (1: 1: 1, v/v/v), which significantly confirmed and reflected the variation in survival percentage. Meanwhile, higher treatment (500 μM) of NO positively enhanced secondary products accumulation of total tannins, saponins, phenols and total flavonoids in G. biloba callus culture

Comparison of cognitive function, socioeconomic level, and the health-related quality of life between epileptic patients with attention deficit hyperactivity disorder and without

Background Epilepsy is one of the most common neurological conditions. Attention deficit hyperactivity disorder (ADHD) in children with epilepsy proves to be very common. Both epilepsy and ADHD impair quality of life. We aimed to evaluate cognitive function, socioeconomic level, and quality of life (QOL) among children with ADHD and epilepsy. A total of 100 children were divided into 5 groups (20 children/group) as (I) epilepsy, (II) ADHD with epilepsy, (III) ADHD with EEG changes, (IV) ADHD without EEG changes, and (V) control. Children aged between 6 and 11 years were recruited for this study. Early Childhood Epilepsy Severity Scale (E-Chess), Conners’ Parent Rating Scale (CPRS), Wechsler Intelligence Scale for Children-3rd edition (WISC-III), socioeconomic scale for assessment of social burden and socioeconomic classes, and PedsQL (quality of life measure) assessed. Results Children with ADHD and epilepsy had the lowest PedsQL total scores and lower scores than other groups especially in performance IQ score. The highest percentage of low socioeconomic class (25%) was observed in the group of ADHD with epilepsy and the group of epilepsy. Conclusion ADHD with epilepsy is associated with low performance IQ, poor socioeconomic level, and quality of life. Pediatric Quality of Life Inventory scores show significant correlation with total IQ score in the group of ADHD with epilepsy

FSS-2019-nCov:A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection

The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques allow extremely precise image segmentation; yet, they necessitate huge volumes of manually labeled data to be trained in a supervised manner. Few-Shot Learning (FSL) paradigms tackle this issue by learning a novel category from a small number of annotated instances. We present an innovative semi-supervised few-shot segmentation (FSS) approach for efficient segmentation of 2019-nCov infection (FSS-2019-nCov) from only a few amounts of annotated lung CT scans. The key challenge of this study is to provide accurate segmentation of COVID-19 infection from a limited number of annotated instances. For that purpose, we propose a novel dual-path deep-learning architecture for FSS. Every path contains encoder–decoder (E-D) architecture to extract high-level information while maintaining the channel information of COVID-19 CT slices. The E-D architecture primarily consists of three main modules: a feature encoder module, a context enrichment (CE) module, and a feature decoder module. We utilize the pre-trained ResNet34 as an encoder backbone for feature extraction. The CE module is designated by a newly introduced proposed Smoothed Atrous Convolution (SAC) block and Multi-scale Pyramid Pooling (MPP) block. The conditioner path takes the pairs of CT images and their labels as input and produces a relevant knowledge representation that is transferred to the segmentation path to be used to segment the new images. To enable effective collaboration between both paths, we propose an adaptive recombination and recalibration (RR) module that permits intensive knowledge exchange between paths with a trivial increase in computational complexity. The model is extended to multi-class labeling for various types of lung infections. This contribution overcomes the limitation of the lack of large numbers of COVID-19 CT scans. It also provides a general framework for lung disease diagnosis in limited data situations

Deep-IFS:Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment

The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures

Neonatal adrenal hemorrhage presenting as acute scrotum

Neonatal adrenal hemorrhage may rarely present as acute scrotum,  mimicking the conditions that require an immediate operative intervention. The authors report one such case and discuss the importance of clinical examination and ultrasonography to avoid an unnecessary surgical  exploration.Keywords: neonatal acute scrotum, neonatal adrenal hemorrhage, scrotal  hematoma, testicular torsio

Prognostic impact of Additional Chromosomal Abnormalities in Egyptian Chronic Myeloid Leukemia Patients

BACKGROUND: Emergence of additional chromosomal abnormalities (ACAs) in chronic myeloid leukemia (CML) is associated with disease progression to advanced phases and reflects the genetic instability of CML. AIM: Is to evaluate the frequency of ACAs in chronic phase (CP) and advanced disease (AP) CML patients and study their impact on patient’s outcome, overall survival (OS) and event-free survival (EFS). RESULTS: The studied group (n = 73) included 31 males (43%) and 42 females (57%). Median age of patients at diagnosis was 37 years (17–76). Median TLC was 208×109/L (2.1–784.2), median Hb was 9.4 g/dL (5.7–13), and median platelets count was 290.5×109/L (13–1271). We identified 32 patients (44%) with ACAs. ACAs emergence was significantly associated with advanced phases of CML (13/21, 62%) compared to CP (19/52, 36%) (p = 0.048). ACAs were associated with lower median OS and EFS in CP compared to AP (38 vs. 120 ms) and (58.3 vs. 77 ms) (p = 0.026 and p = 0.065, respectively). Early molecular responders (6/17, 35%) at 3 months, and 6 months (10/26, 38%) developed ACAs less than nonoptimal responders. Disease phase, hepatomegaly and bone marrow eosinophilia were significant predictors of OS (p < 0.001, p = 0.02, p = 0.04, respectively). CONCLUSION: Early identification of ACAs in Ph+ metaphases at diagnosis and during therapy predicts CML outcome. ACAs emergence occurred at a higher frequency and at a younger age in our CML patients and are related to inferior EFS and OS

The DFT+U: Approaches, Accuracy, and Applications

This chapter introduces the Hubbard model and its applicability as a corrective tool for accurate modeling of the electronic properties of various classes of systems. The attainment of a correct description of electronic structure is critical for predicting further electronic-related properties, including intermolecular interactions and formation energies. The chapter begins with an introduction to the formulation of density functional theory (DFT) functionals, while addressing the origin of bandgap problem with correlated materials. Then, the corrective approaches proposed to solve the DFT bandgap problem are reviewed, while comparing them in terms of accuracy and computational cost. The Hubbard model will then offer a simple approach to correctly describe the behavior of highly correlated materials, known as the Mott insulators. Based on Hubbard model, DFT+U scheme is built, which is computationally convenient for accurate calculations of electronic structures. Later in this chapter, the computational and semiempirical methods of optimizing the value of the Coulomb interaction potential (U) are discussed, while evaluating the conditions under which it can be most predictive. The chapter focuses on highlighting the use of U to correct the description of the physical properties, by reviewing the results of case studies presented in literature for various classes of materials

Experimental and Numerical Studies on Flexural Behavior of GGBS-Based Geopolymer Ferrocement Beams

The ferrocement structural concept has been shown to offer exceptional mechanical properties in terms of toughness, fracture control, and impact resistance, which are achieved by tight spacing and homogeneous reinforcement dispersion within the matrix. The flexure behavior of geopolymer ferrocement beams under axial flexural stress is being explored experimentally and computationally in this present work. Under flexural loads, nine samples of geopolymer ferrocement beams 150 mm thick, 75 mm wide, and 1700 mm long were tested to failure. The reinforcing steel bars and wire meshes, as well as the quantity of wire mesh layers, were the key factors studied. The initial crack load, ultimate failure load, and mid-span deflection with various loading phases, cracking patterns, energy absorption, and ductility index were all studied in relation to the behavior. In terms of carrying capacity, absorbing energy, and ductility, welded steel wire mesh beams fared better than other materials. Using ANSYS-19 software, nonlinear finite element analysis (NLFEA) was carried out to demonstrate the behavior of composite ferrocement geopolymer beams. The ensuing experimental and numerical data demonstrated that the degree of experimental value estimation supplied by the FE simulations was sufficient. It is crucial to demonstrate that, in comparison to control specimens, the increase in strength of specimens reinforced with tensar meshes was reduced by around 15%. Doi: 10.28991/CEJ-2023-09-03-010 Full Text: PD

Contribution of coagulation factor VII R353Q polymorphism to the risk of thrombotic disorders development (venous and arterial): A case-control study

Background: Elevated factor VII (FVII) level is a risk factor for thromboembolic disorders. It was reported that the FVII R353Q polymorphism is associated with variation in plasma FVII levels, where Q allele carriers were more associated with lower levels of FVII than R allele carriers. However, the association between coagulation FVII R353 Q polymorphisms and the risk of thrombosis is uncertain.Aim of the study: Is to investigate the contribution of factor VII R353Q gene polymorphism to the risk of thrombotic disorders development (venous and arterial) in a group of Egyptian patients.Subjects and methods: This study was conducted on 310 subjects: 110 acute myocardial infarction (AMI) patients, 108 deep venous thrombosis (DVT) patients and 92 healthy controls. FVII R353Q genotypes were assessed using restriction fragment length polymorphism analysis.Results: There were no statistically significant differences in the frequency of FVII R353Q polymorphism between each of the AMI and DVT patients and the control group (P = 0.9, 0.1). However the Q allele showed a significantly higher frequency in the AMI group (15.4%) vs. controls (8.7%) (OR: 1.92; 95% CI: 0.98–3.7). Bivariate analysis demonstrated no significant association between FVII R353Q genotypes and different studied risk factors, neither in arterial nor venous thrombosis.Conclusion: FVII R353Q polymorphism did not contribute to an increased risk of thrombosis (arterial and venous); also carrying the Q allele (of R353Q) did not confer protection against acute thrombotic events